NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

PubMed

Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

2017-11-01

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes. Copyright © 2017 by The American Association of Immunologists, Inc.

Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

PubMed Central

Mooney, Catherine; Haslam, Niall J.; Pollastri, Gianluca; Shields, Denis C.

2012-01-01

The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides. We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure. We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive. PMID:23056189

BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity.

PubMed

Wang, Lian; Pan, Danling; Hu, Xihao; Xiao, Jinyu; Gao, Yangyang; Zhang, Huifang; Zhang, Yan; Liu, Juan; Zhu, Shanfeng

2009-05-01

Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class II MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http://biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class II MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.

Methods and kits for predicting a response to an erythropoietic agent

DOEpatents

Merchant, Michael L.; Klein, Jon B.; Brier, Michael E.; Gaweda, Adam E.

2015-06-16

Methods for predicting a response to an erythropoietic agent in a subject include providing a biological sample from the subject, and determining an amount in the sample of at least one peptide selected from the group consisting of SEQ ID NOS: 1-17. If there is a measurable difference in the amount of the at least one peptide in the sample, when compared to a control level of the same peptide, the subject is then predicted to have a good response or a poor response to the erythropoietic agent. Kits for predicting a response to an erythropoietic agent are further provided and include one or more antibodies, or fragments thereof, that specifically recognize a peptide of SEQ ID NOS: 1-17.

Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models.

PubMed

Tian, Feifei; Tan, Rui; Guo, Tailin; Zhou, Peng; Yang, Li

2013-07-01

Domain-peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain-peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain-peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain-peptide binding at three-dimensional structure level. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Toward structure prediction of cyclic peptides.

PubMed

Yu, Hongtao; Lin, Yu-Shan

2015-02-14

Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity

PubMed Central

Liu, Geng; Li, Dongli; Li, Zhang; Qiu, Si; Li, Wenhui; Chao, Cheng-chi; Yang, Naibo; Li, Handong; Cheng, Zhen; Song, Xin; Cheng, Le; Zhang, Xiuqing; Wang, Jian; Yang, Huanming

2017-01-01

Abstract Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles. PMID:28327987

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

PubMed Central

Nielsen, Morten; Lundegaard, Claus; Blicher, Thomas; Lamberth, Kasper; Harndahl, Mikkel; Justesen, Sune; Røder, Gustav; Peters, Bjoern; Sette, Alessandro; Lund, Ole; Buus, Søren

2007-01-01

Background Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking. Principal Findings Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis. Conclusions Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan. PMID:17726526

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

PubMed

Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

2007-01-01

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Identification and the molecular mechanism of a novel myosin-derived ACE inhibitory peptide.

PubMed

Yu, Zhipeng; Wu, Sijia; Zhao, Wenzhu; Ding, Long; Shiuan, David; Chen, Feng; Li, Jianrong; Liu, Jingbo

2018-01-24

The objective of this work was to identify a novel ACE inhibitory peptide from myosin using a number of in silico methods. Myosin was evaluated as a substrate for use in the generation of ACE inhibitory peptides using BIOPEP and ExPASy PeptideCutter. Then the ACE inhibitory activity prediction of peptides in silico was evaluated using the program peptide ranker, following the database search of known and unknown peptides using the program BIOPEP. In addition, the interaction mechanisms of the peptide and ACE were evaluated by DS. All of the tripeptides were predicted to be nontoxic. Results suggested that the tripeptide NCW exerted potent ACE inhibitory activity with an IC 50 value of 35.5 μM. Furthermore, the results suggested that the peptide NCW comes into contact with Zn 701, Tyr 523, His 383, Glu 384, Glu 411, and His 387. The potential molecular mechanism of the NCW/ACE interaction was investigated. Results confirmed that the higher inhibitory potency of NCW might be attributed to the formation of more hydrogen bonds with the ACE's active site. Therefore, the in silico method is effective to predict and identify novel ACE inhibitory peptides from protein hydrolysates.

Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity.

PubMed

Dhanda, Sandeep Kumar; Grifoni, Alba; Pham, John; Vaughan, Kerrie; Sidney, John; Peters, Bjoern; Sette, Alessandro

2018-01-01

Unwanted immune responses against protein therapeutics can reduce efficacy or lead to adverse reactions. T-cell responses are key in the development of such responses, and are directed against immunodominant regions within the protein sequence, often associated with binding to several allelic variants of HLA class II molecules (promiscuous binders). Herein, we report a novel computational strategy to predict 'de-immunized' peptides, based on previous studies of erythropoietin protein immunogenicity. This algorithm (or method) first predicts promiscuous binding regions within the target protein sequence and then identifies residue substitutions predicted to reduce HLA binding. Further, this method anticipates the effect of any given substitution on flanking peptides, thereby circumventing the creation of nascent HLA-binding regions. As a proof-of-principle, the algorithm was applied to Vatreptacog α, an engineered Factor VII molecule associated with unintended immunogenicity. The algorithm correctly predicted the two immunogenic peptides containing the engineered residues. As a further validation, we selected and evaluated the immunogenicity of seven substitutions predicted to simultaneously reduce HLA binding for both peptides, five control substitutions with no predicted reduction in HLA-binding capacity, and additional flanking region controls. In vitro immunogenicity was detected in 21·4% of the cultures of peptides predicted to have reduced HLA binding and 11·4% of the flanking regions, compared with 46% for the cultures of the peptides predicted to be immunogenic. This method has been implemented as an interactive application, freely available online at http://tools.iedb.org/deimmunization/. © 2017 John Wiley & Sons Ltd.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

PubMed

Mukherjee, Sumanta; Bhattacharyya, Chiranjib; Chandra, Nagasuma

2016-08-01

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy : nchandra@biochem.iisc.ernet.in Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.

PubMed

Busk, P K; Pilgaard, B; Lezyk, M J; Meyer, A S; Lange, L

2017-04-12

Carbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy. We identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows. Hotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.

Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential

PubMed Central

Nagpal, Gandharva; Usmani, Salman Sadullah; Dhanda, Sandeep Kumar; Kaur, Harpreet; Singh, Sandeep; Sharma, Meenu; Raghava, Gajendra P. S.

2017-01-01

In the past, numerous methods have been developed to predict MHC class II binders or T-helper epitopes for designing the epitope-based vaccines against pathogens. In contrast, limited attempts have been made to develop methods for predicting T-helper epitopes/peptides that can induce a specific type of cytokine. This paper describes a method, developed for predicting interleukin-10 (IL-10) inducing peptides, a cytokine responsible for suppressing the immune system. All models were trained and tested on experimentally validated 394 IL-10 inducing and 848 non-inducing peptides. It was observed that certain types of residues and motifs are more frequent in IL-10 inducing peptides than in non-inducing peptides. Based on this analysis, we developed composition-based models using various machine-learning techniques. Random Forest-based model achieved the maximum Matthews’s Correlation Coefficient (MCC) value of 0.59 with an accuracy of 81.24% developed using dipeptide composition. In order to facilitate the community, we developed a web server “IL-10pred”, standalone packages and a mobile app for designing IL-10 inducing peptides (http://crdd.osdd.net/raghava/IL-10pred/). PMID:28211521

Structural prediction and analysis of VIH-related peptides from selected crustacean species.

PubMed

Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

2009-08-17

The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family.

Prediction of cell penetrating peptides by support vector machines.

PubMed

Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

2011-07-01

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

POPISK: T-cell reactivity prediction using support vector machines and string kernels

PubMed Central

2011-01-01

Background Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. Results This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. Conclusions A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK. PMID:22085524

POPISK: T-cell reactivity prediction using support vector machines and string kernels.

PubMed

Tung, Chun-Wei; Ziehm, Matthias; Kämper, Andreas; Kohlbacher, Oliver; Ho, Shinn-Ying

2011-11-15

Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

SPEPlip: the detection of signal peptide and lipoprotein cleavage sites.

PubMed

Fariselli, Piero; Finocchiaro, Giacomo; Casadio, Rita

2003-12-12

SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins. It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/

Structural prediction and analysis of VIH-related peptides from selected crustacean species

PubMed Central

Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

2009-01-01

The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family. PMID:20011146

Recovery of known T-cell epitopes by computational scanning of a viral genome

NASA Astrophysics Data System (ADS)

Logean, Antoine; Rognan, Didier

2002-04-01

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

Detection of trans–cis flips and peptide-plane flips in protein structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Touw, Wouter G., E-mail: wouter.touw@radboudumc.nl; Joosten, Robbie P.; Vriend, Gert, E-mail: wouter.touw@radboudumc.nl

A method is presented to detect peptide bonds that need either a trans–cis flip or a peptide-plane flip. A coordinate-based method is presented to detect peptide bonds that need correction either by a peptide-plane flip or by a trans–cis inversion of the peptide bond. When applied to the whole Protein Data Bank, the method predicts 4617 trans–cis flips and many thousands of hitherto unknown peptide-plane flips. A few examples are highlighted for which a correction of the peptide-plane geometry leads to a correction of the understanding of the structure–function relation. All data, including 1088 manually validated cases, are freely availablemore » and the method is available from a web server, a web-service interface and through WHAT-CHECK.« less

In Vitro and In Vivo Activities of Antimicrobial Peptides Developed Using an Amino Acid-Based Activity Prediction Method

PubMed Central

Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian

2014-01-01

To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections. PMID:24982064

A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay

PubMed Central

Lin, Jing; Bruni, Francesca M.; Fu, Zhiyan; Maloney, Jennifer; Bardina, Ludmilla; Boner, Attilio L.; Gimenez, Gustavo; Sampson, Hugh A.

2013-01-01

Background Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy–related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice. Objective We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods. Methods Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG4 binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis. Results Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG4 binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers. Conclusions In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice. PMID:22444503

NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

PubMed

Lundegaard, Claus; Lamberth, Kasper; Harndahl, Mikkel; Buus, Søren; Lund, Ole; Nielsen, Morten

2008-07-01

NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8-11 for all 122 alleles. artificial neural network predictions are given as actual IC(50) values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75-80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach.

PubMed

Kashyap, Manju; Jaiswal, Varun; Farooq, Umar

2017-09-01

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated benchmarking of peptide-MHC class I binding predictions.

PubMed

Trolle, Thomas; Metushi, Imir G; Greenbaum, Jason A; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

2015-07-01

Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. mniel@cbs.dtu.dk or bpeters@liai.org Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Automated benchmarking of peptide-MHC class I binding predictions

PubMed Central

Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

2015-01-01

Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. Contact: mniel@cbs.dtu.dk or bpeters@liai.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25717196

Enriching peptide libraries for binding affinity and specificity through computationally directed library design

PubMed Central

Foight, Glenna Wink; Chen, T. Scott; Richman, Daniel; Keating, Amy E.

2017-01-01

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model. PMID:28236241

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.

PubMed

Foight, Glenna Wink; Chen, T Scott; Richman, Daniel; Keating, Amy E

2017-01-01

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

The critical role of peptide chemistry in the life sciences.

PubMed

Kent, Stephen B H

2015-03-01

Peptide chemistry plays a key role in the synthesis and study of protein molecules and their functions. Modern ligation methods enable the total synthesis of enzymes and the systematic dissection of the chemical basis of enzyme catalysis. Predicted developments in peptide science are described. Copyright © 2015 European Peptide Society and John Wiley & Sons, Ltd.

Prediction of Surface and pH-Specific Binding of Peptides to Metal and Oxide Nanoparticles

NASA Astrophysics Data System (ADS)

Heinz, Hendrik; Lin, Tzu-Jen; Emami, Fateme Sadat; Ramezani-Dakhel, Hadi; Naik, Rajesh; Knecht, Marc; Perry, Carole C.; Huang, Yu

2015-03-01

The mechanism of specific peptide adsorption onto metallic and oxidic nanostructures has been elucidated in atomic resolution using novel force fields and surface models in comparison to measurements. As an example, variations in peptide adsorption on Pd and Pt nanoparticles depending on shape, size, and location of peptides on specific bounding facets are explained. Accurate computational predictions of reaction rates in C-C coupling reactions using particle models derived from HE-XRD and PDF data illustrate the utility of computational methods for the rational design of new catalysts. On oxidic nanoparticles such as silica and apatites, it is revealed how changes in pH lead to similarity scores of attracted peptides lower than 20%, supported by appropriate model surfaces and data from adsorption isotherms. The results demonstrate how new computational methods can support the design of nanoparticle carriers for drug release and the understanding of calcification mechanisms in the human body.

Using iRT, a normalized retention time for more targeted measurement of peptides

PubMed Central

Escher, Claudia; Reiter, Lukas; MacLean, Brendan; Ossola, Reto; Herzog, Franz; Chilton, John; MacCoss, Michael J.; Rinner, Oliver

2014-01-01

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than 4 times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments. PMID:22577012

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

PubMed

Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

2015-11-01

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme.

PubMed

Bi, Jianjun; Song, Rengang; Yang, Huilan; Li, Bingling; Fan, Jianyong; Liu, Zhongrong; Long, Chaoqin

2011-01-01

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods

PubMed Central

Wang, Ping; Hu, Lele; Liu, Guiyou; Jiang, Nan; Chen, Xiaoyun; Xu, Jianyong; Zheng, Wen; Li, Li; Tan, Ming; Chen, Zugen; Song, Hui; Cai, Yu-Dong; Chou, Kuo-Chen

2011-01-01

Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/. PMID:21533231

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

PubMed

Carrasco Pro, S; Zimic, M; Nielsen, M

2014-02-01

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins.

PubMed

Sarkar, Debasree; Patra, Piya; Ghosh, Abhirupa; Saha, Sudipto

2016-01-01

A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators.

Effective Design of Multifunctional Peptides by Combining Compatible Functions

PubMed Central

Diener, Christian; Garza Ramos Martínez, Georgina; Moreno Blas, Daniel; Castillo González, David A.; Corzo, Gerardo; Castro-Obregon, Susana; Del Rio, Gabriel

2016-01-01

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf. PMID:27096600

Designing of interferon-gamma inducing MHC class-II binders

PubMed Central

2013-01-01

Background The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4+ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides. Results It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC. Conclusion Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/). Reviewers This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai. PMID:24304645

PepMapper: a collaborative web tool for mapping epitopes from affinity-selected peptides.

PubMed

Chen, Wenhan; Guo, William W; Huang, Yanxin; Ma, Zhiqiang

2012-01-01

Epitope mapping from affinity-selected peptides has become popular in epitope prediction, and correspondingly many Web-based tools have been developed in recent years. However, the performance of these tools varies in different circumstances. To address this problem, we employed an ensemble approach to incorporate two popular Web tools, MimoPro and Pep-3D-Search, together for taking advantages offered by both methods so as to give users more options for their specific purposes of epitope-peptide mapping. The combined operation of Union finds as many associated peptides as possible from both methods, which increases sensitivity in finding potential epitopic regions on a given antigen surface. The combined operation of Intersection achieves to some extent the mutual verification by the two methods and hence increases the likelihood of locating the genuine epitopic region on a given antigen in relation to the interacting peptides. The Consistency between Intersection and Union is an indirect sufficient condition to assess the likelihood of successful peptide-epitope mapping. On average from 27 tests, the combined operations of PepMapper outperformed either MimoPro or Pep-3D-Search alone. Therefore, PepMapper is another multipurpose mapping tool for epitope prediction from affinity-selected peptides. The Web server can be freely accessed at: http://informatics.nenu.edu.cn/PepMapper/

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms. Copyright © 2014 Elsevier B.V. All rights reserved.

Gastrointestinal Endogenous Proteins as a Source of Bioactive Peptides - An In Silico Study

PubMed Central

Dave, Lakshmi A.; Montoya, Carlos A.; Rutherfurd, Shane M.; Moughan, Paul J.

2014-01-01

Dietary proteins are known to contain bioactive peptides that are released during digestion. Endogenous proteins secreted into the gastrointestinal tract represent a quantitatively greater supply of protein to the gut lumen than those of dietary origin. Many of these endogenous proteins are digested in the gastrointestinal tract but the possibility that these are also a source of bioactive peptides has not been considered. An in silico prediction method was used to test if bioactive peptides could be derived from the gastrointestinal digestion of gut endogenous proteins. Twenty six gut endogenous proteins and seven dietary proteins were evaluated. The peptides present after gastric and intestinal digestion were predicted based on the amino acid sequence of the proteins and the known specificities of the major gastrointestinal proteases. The predicted resultant peptides possessing amino acid sequences identical to those of known bioactive peptides were identified. After gastrointestinal digestion (based on the in silico simulation), the total number of bioactive peptides predicted to be released ranged from 1 (gliadin) to 55 (myosin) for the selected dietary proteins and from 1 (secretin) to 39 (mucin-5AC) for the selected gut endogenous proteins. Within the intact proteins and after simulated gastrointestinal digestion, angiotensin converting enzyme (ACE)-inhibitory peptide sequences were the most frequently observed in both the dietary and endogenous proteins. Among the dietary proteins, after in silico simulated gastrointestinal digestion, myosin was found to have the highest number of ACE-inhibitory peptide sequences (49 peptides), while for the gut endogenous proteins, mucin-5AC had the greatest number of ACE-inhibitory peptide sequences (38 peptides). Gut endogenous proteins may be an important source of bioactive peptides in the gut particularly since gut endogenous proteins represent a quantitatively large and consistent source of protein. PMID:24901416

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

PubMed

Du, Q S; Ma, Y; Xie, N Z; Huang, R B

2014-01-01

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

Predicted MHC peptide binding promiscuity explains MHC class I 'hotspots' of antigen presentation defined by mass spectrometry eluted ligand data.

PubMed

Jappe, Emma Christine; Kringelum, Jens; Trolle, Thomas; Nielsen, Morten

2018-02-15

Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots. © 2018 John Wiley & Sons Ltd.

Using iRT, a normalized retention time for more targeted measurement of peptides.

PubMed

Escher, Claudia; Reiter, Lukas; MacLean, Brendan; Ossola, Reto; Herzog, Franz; Chilton, John; MacCoss, Michael J; Rinner, Oliver

2012-04-01

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

NNAlign: A Web-Based Prediction Method Allowing Non-Expert End-User Discovery of Sequence Motifs in Quantitative Peptide Data

PubMed Central

Andreatta, Massimo; Schafer-Nielsen, Claus; Lund, Ole; Buus, Søren; Nielsen, Morten

2011-01-01

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points. NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign. PMID:22073191

NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data.

PubMed

Andreatta, Massimo; Schafer-Nielsen, Claus; Lund, Ole; Buus, Søren; Nielsen, Morten

2011-01-01

Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new "omics"-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points. NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.

[ProteoСat: a tool for planning of proteomic experiments].

PubMed

Skvortsov, V S; Alekseychuk, N N; Khudyakov, D V; Mikurova, A V; Rybina, A V; Novikova, S E; Tikhonova, O V

2015-01-01

ProteoCat is a computer program has been designed to help researchers in the planning of large-scale proteomic experiments. The central part of this program is the subprogram of hydrolysis simulation that supports 4 proteases (trypsin, lysine C, endoproteinases AspN and GluC). For the peptides obtained after virtual hydrolysis or loaded from data file a number of properties important in mass-spectrometric experiments can be calculated or predicted. The data can be analyzed or filtered to reduce a set of peptides. The program is using new and improved modification of our methods developed to predict pI and probability of peptide detection; pI can also be predicted for a number of popular pKa's scales, proposed by other investigators. The algorithm for prediction of peptide retention time was realized similar to the algorithm used in the program SSRCalc. ProteoCat can estimate the coverage of amino acid sequences of proteins under defined limitation on peptides detection, as well as the possibility of assembly of peptide fragments with user-defined size of "sticky" ends. The program has a graphical user interface, written on JAVA and available at http://www.ibmc.msk.ru/LPCIT/ProteoCat.

Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography.

PubMed

Gussakovsky, Daniel; Neustaeter, Haley; Spicer, Victor; Krokhin, Oleg V

2017-11-07

The development of a peptide retention prediction model for strong cation exchange (SCX) separation on a Polysulfoethyl A column is reported. Off-line 2D LC-MS/MS analysis (SCX-RPLC) of S. cerevisiae whole cell lysate was used to generate a retention dataset of ∼30 000 peptides, sufficient for identifying the major sequence-specific features of peptide retention mechanisms in SCX. In contrast to RPLC/hydrophilic interaction liquid chromatography (HILIC) separation modes, where retention is driven by hydrophobic/hydrophilic contributions of all individual residues, SCX interactions depend mainly on peptide charge (number of basic residues at acidic pH) and size. An additive model (incorporating the contributions of all 20 residues into the peptide retention) combined with a peptide length correction produces a 0.976 R 2 value prediction accuracy, significantly higher than the additive models for either HILIC or RPLC. Position-dependent effects on peptide retention for different residues were driven by the spatial orientation of tryptic peptides upon interaction with the negatively charged surface functional groups. The positively charged N-termini serve as a primary point of interaction. For example, basic residues (Arg, His, Lys) increase peptide retention when located closer to the N-terminus. We also found that hydrophobic interactions, which could lead to a mixed-mode separation mechanism, are largely suppressed at 20-30% of acetonitrile in the eluent. The accuracy of the final Sequence-Specific Retention Calculator (SSRCalc) SCX model (∼0.99 R 2 value) exceeds all previously reported predictors for peptide LC separations. This also provides a solid platform for method development in 2D LC-MS protocols in proteomics and peptide retention prediction filtering of false positive identifications.

The utility and limitations of current web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection #

PubMed Central

Chaves, Francisco A.; Lee, Alvin H.; Nayak, Jennifer; Richards, Katherine A.; Sant, Andrea J.

2012-01-01

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding their success in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple web-available algorithms by comparing their predictions and our results using purely empirical methods for epitope discovery in influenza that utilized overlapping peptides and cytokine Elispots, for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false positive and false negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide:MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules. PMID:22467652

Diagnostic Peptide Discovery: Prioritization of Pathogen Diagnostic Markers Using Multiple Features

PubMed Central

Carmona, Santiago J.; Sartor, Paula A.; Leguizamón, María S.; Campetella, Oscar E.; Agüero, Fernán

2012-01-01

The availability of complete pathogen genomes has renewed interest in the development of diagnostics for infectious diseases. Synthetic peptide microarrays provide a rapid, high-throughput platform for immunological testing of potential B-cell epitopes. However, their current capacity prevent the experimental screening of complete “peptidomes”. Therefore, computational approaches for prediction and/or prioritization of diagnostically relevant peptides are required. In this work we describe a computational method to assess a defined set of molecular properties for each potential diagnostic target in a reference genome. Properties such as sub-cellular localization or expression level were evaluated for the whole protein. At a higher resolution (short peptides), we assessed a set of local properties, such as repetitive motifs, disorder (structured vs natively unstructured regions), trans-membrane spans, genetic polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, and sequence similarity against human proteins and other potential cross-reacting species (e.g. other pathogens endemic in overlapping geographical locations). A scoring function based on these different features was developed, and used to rank all peptides from a large eukaryotic pathogen proteome. We applied this method to the identification of candidate diagnostic peptides in the protozoan Trypanosoma cruzi, the causative agent of Chagas disease. We measured the performance of the method by analyzing the enrichment of validated antigens in the high-scoring top of the ranking. Based on this measure, our integrative method outperformed alternative prioritizations based on individual properties (such as B-cell epitope predictors alone). Using this method we ranked 10 million 12-mer overlapping peptides derived from the complete T. cruzi proteome. Experimental screening of 190 high-scoring peptides allowed the identification of 37 novel epitopes with diagnostic potential, while none of the low scoring peptides showed significant reactivity. Many of the metrics employed are dependent on standard bioinformatic tools and data, so the method can be easily extended to other pathogen genomes. PMID:23272069

Virtual screening using combinatorial cyclic peptide libraries reveals protein interfaces readily targetable by cyclic peptides.

PubMed

Duffy, Fergal J; O'Donovan, Darragh; Devocelle, Marc; Moran, Niamh; O'Connell, David J; Shields, Denis C

2015-03-23

Protein-protein and protein-peptide interactions are responsible for the vast majority of biological functions in vivo, but targeting these interactions with small molecules has historically been difficult. What is required are efficient combined computational and experimental screening methods to choose among a number of potential protein interfaces worthy of targeting lead macrocyclic compounds for further investigation. To achieve this, we have generated combinatorial 3D virtual libraries of short disulfide-bonded peptides and compared them to pharmacophore models of important protein-protein and protein-peptide structures, including short linear motifs (SLiMs), protein-binding peptides, and turn structures at protein-protein interfaces, built from 3D models available in the Protein Data Bank. We prepared a total of 372 reference pharmacophores, which were matched against 108,659 multiconformer cyclic peptides. After normalization to exclude nonspecific cyclic peptides, the top hits notably are enriched for mimetics of turn structures, including a turn at the interaction surface of human α thrombin, and also feature several protein-binding peptides. The top cyclic peptide hits also cover the critical "hot spot" interaction sites predicted from the interaction crystal structure. We have validated our method by testing cyclic peptides predicted to inhibit thrombin, a key protein in the blood coagulation pathway of important therapeutic interest, identifying a cyclic peptide inhibitor with lead-like activity. We conclude that protein interfaces most readily targetable by cyclic peptides and related macrocyclic drugs may be identified computationally among a set of candidate interfaces, accelerating the choice of interfaces against which lead compounds may be screened.

Designing of peptides with desired half-life in intestine-like environment.

PubMed

Sharma, Arun; Singla, Deepak; Rashid, Mamoon; Raghava, Gajendra Pal Singh

2014-08-20

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life. In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides. In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

PubMed

Nielsen, Morten; Lundegaard, Claus; Worning, Peder; Hvid, Christina Sylvester; Lamberth, Kasper; Buus, Søren; Brunak, Søren; Lund, Ole

2004-06-12

Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.

PubMed

Karosiene, Edita; Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2012-03-01

A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC-peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at www.cbs.dtu.dk/services/NetMHCcons , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule.

Variable context Markov chains for HIV protease cleavage site prediction.

PubMed

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

AntiAngioPred: A Server for Prediction of Anti-Angiogenic Peptides.

PubMed

Ettayapuram Ramaprasad, Azhagiya Singam; Singh, Sandeep; Gajendra P S, Raghava; Venkatesan, Subramanian

2015-01-01

The process of angiogenesis is a vital step towards the formation of malignant tumors. Anti-angiogenic peptides are therefore promising candidates in the treatment of cancer. In this study, we have collected anti-angiogenic peptides from the literature and analyzed the residue preference in these peptides. Residues like Cys, Pro, Ser, Arg, Trp, Thr and Gly are preferred while Ala, Asp, Ile, Leu, Val and Phe are not preferred in these peptides. There is a positional preference of Ser, Pro, Trp and Cys in the N terminal region and Cys, Gly and Arg in the C terminal region of anti-angiogenic peptides. Motif analysis suggests the motifs "CG-G", "TC", "SC", "SP-S", etc., which are highly prominent in anti-angiogenic peptides. Based on the primary analysis, we developed prediction models using different machine learning based methods. The maximum accuracy and MCC for amino acid composition based model is 80.9% and 0.62 respectively. The performance of the models on independent dataset is also reasonable. Based on the above study, we have developed a user-friendly web server named "AntiAngioPred" for the prediction of anti-angiogenic peptides. AntiAngioPred web server is freely accessible at http://clri.res.in/subramanian/tools/antiangiopred/index.html (mirror site: http://crdd.osdd.net/raghava/antiangiopred/).

Computer-based prediction of mitochondria-targeting peptides.

PubMed

Martelli, Pier Luigi; Savojardo, Castrense; Fariselli, Piero; Tasco, Gianluca; Casadio, Rita

2015-01-01

Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide.

A new genome-mining tool redefines the lasso peptide biosynthetic landscape

PubMed Central

Tietz, Jonathan I.; Schwalen, Christopher J.; Patel, Parth S.; Maxson, Tucker; Blair, Patricia M.; Tai, Hua-Chia; Zakai, Uzma I.; Mitchell, Douglas A.

2016-01-01

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts. PMID:28244986

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins.

PubMed

Concu, Riccardo; Dea-Ayuela, Maria A; Perez-Montoto, Lazaro G; Bolas-Fernández, Francisco; Prado-Prado, Francisco J; Podda, Gianni; Uriarte, Eugenio; Ubeira, Florencio M; González-Díaz, Humberto

2009-09-01

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches.

PubMed

Zhang, Yiming; Jin, Quan; Wang, Shuting; Ren, Ren

2011-05-01

The mobile behavior of 1481 peptides in ion mobility spectrometry (IMS), which are generated by protease digestion of the Drosophila melanogaster proteome, is modeled and predicted based on two different types of characterization methods, i.e. sequence-based approach and structure-based approach. In this procedure, the sequence-based approach considers both the amino acid composition of a peptide and the local environment profile of each amino acid in the peptide; the structure-based approach is performed with the CODESSA protocol, which regards a peptide as a common organic compound and generates more than 200 statistically significant variables to characterize the whole structure profile of a peptide molecule. Subsequently, the nonlinear support vector machine (SVM) and Gaussian process (GP) as well as linear partial least squares (PLS) regression is employed to correlate the structural parameters of the characterizations with the IMS drift times of these peptides. The obtained quantitative structure-spectrum relationship (QSSR) models are evaluated rigorously and investigated systematically via both one-deep and two-deep cross-validations as well as the rigorous Monte Carlo cross-validation (MCCV). We also give a comprehensive comparison on the resulting statistics arising from the different combinations of variable types with modeling methods and find that the sequence-based approach can give the QSSR models with better fitting ability and predictive power but worse interpretability than the structure-based approach. In addition, though the QSSR modeling using sequence-based approach is not needed for the preparation of the minimization structures of peptides before the modeling, it would be considerably efficient as compared to that using structure-based approach. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

NASA Astrophysics Data System (ADS)

Basyuni, M.; Wati, R.

2018-03-01

The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) for Conformational Space Search of Peptide and Miniprotein

PubMed Central

Hao, Ge-Fei; Xu, Wei-Fang; Yang, Sheng-Gang; Yang, Guang-Fu

2015-01-01

Protein and peptide structure predictions are of paramount importance for understanding their functions, as well as the interactions with other molecules. However, the use of molecular simulation techniques to directly predict the peptide structure from the primary amino acid sequence is always hindered by the rough topology of the conformational space and the limited simulation time scale. We developed here a new strategy, named Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) to identify the native states of a peptide and miniprotein. A cluster of near native structures could be obtained by using the MSA-MD method, which turned out to be significantly more efficient in reaching the native structure compared to continuous MD and conventional SA-MD simulation. PMID:26492886

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

PubMed

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013

A systematic molecular dynamics approach to the study of peptide Keap1-Nrf2 protein-protein interaction inhibitors and its application to p62 peptides.

PubMed

Lu, Meng-Chen; Yuan, Zhen-Wei; Jiang, Yong-Lin; Chen, Zhi-Yun; You, Qi-Dong; Jiang, Zheng-Yu

2016-04-01

Protein-protein interactions (PPIs) as drug targets have been gaining growing interest, though developing drug-like small molecule PPI inhibitors remains challenging. Peptide PPI inhibitors, which can provide informative data on the PPI interface, are good starting points to develop small molecule modulators. Computational methods combining molecular dynamics simulations and binding energy calculations could give both the structural and the energetic perspective of peptide PPI inhibitors. Herein, we set up a computational workflow to investigate Keap1-Nrf2 peptide PPI inhibitors and predict the activity of novel sequences. Furthermore, we applied this method to investigate p62 peptides as PPI inhibitors of Keap1-Nrf2 and explored the activity change induced by the phosphorylation of serine. Our results showed that because of the unfavorable solvation effects, the binding affinity of the phosphorylated p62 peptide is lower than the Nrf2 ETGE peptide. Our research results not only provide a useful method to investigate the Keap1-Nrf2 peptide inhibitors, but also give a good example to show how to incorporate computational methods into the study of peptide PPI inhibitors. Besides, applying this method to p62 peptides provides a detailed explanation for the expression of cytoprotective Nrf2 targets induced by p62 phosphorylation, which may benefit the further study of the crosstalk between the Keap1-Nrf2 pathway and p62-mediated selective autophagy.

Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins.

PubMed

Usman Mirza, Muhammad; Rafique, Shazia; Ali, Amjad; Munir, Mobeen; Ikram, Nazia; Manan, Abdul; Salo-Ahen, Outi M H; Idrees, Muhammad

2016-12-09

The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV.

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

PubMed

Kundu, Kousik; Backofen, Rolf

2017-01-01

Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins

PubMed Central

Tsolis, Antonios C.; Papandreou, Nikos C.; Iconomidou, Vassiliki A.; Hamodrakas, Stavros J.

2013-01-01

The purpose of this work was to construct a consensus prediction algorithm of ‘aggregation-prone’ peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/‘aggregation-prone’ peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins). PMID:23326595

Prediction of lipoprotein signal peptides in Gram-negative bacteria.

PubMed

Juncker, Agnieszka S; Willenbrock, Hanni; Von Heijne, Gunnar; Brunak, Søren; Nielsen, Henrik; Krogh, Anders

2003-08-01

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

Prediction of lipoprotein signal peptides in Gram-negative bacteria

PubMed Central

Juncker, Agnieszka S.; Willenbrock, Hanni; von Heijne, Gunnar; Brunak, Søren; Nielsen, Henrik; Krogh, Anders

2003-01-01

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/. PMID:12876315

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments*

PubMed Central

Searle, Brian C.; Egertson, Jarrett D.; Bollinger, James G.; Stergachis, Andrew B.; MacCoss, Michael J.

2015-01-01

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40–85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature. PMID:26100116

Method for detecting the reactivity of chemicals towards peptides as an alternative test method for assessing skin sensitization potential.

PubMed

Cho, Sun-A; Jeong, Yun Hyeok; Kim, Ji Hoon; Kim, Seoyoung; Cho, Jun-Cheol; Heo, Yong; Heo, Young; Suh, Kyung-Do; Shin, Kyeho; An, Susun

2014-02-10

Cosmetics are normally composed of various ingredients. Some cosmetic ingredients can act as chemical haptens reacting toward proteins or peptides of human skin and they can provoke an immunologic reaction, called as skin sensitization. This haptenation process is very important step of inducing skin sensitization and evaluating the sensitizing potentials of cosmetic ingredients is very important for consumer safety. Therefore, animal alternative methods focusing on monitoring haptenation potential are undergoing vigorous research. To examine the further usefulness of spectrophotometric methods to monitor reactivity of chemicals toward peptides for cosmetic ingredients. Forty chemicals (25 sensitizers and 15 non-sensitizers) were reacted with 2 synthetic peptides, e.g., the cysteine peptides (Ac-RFAACAA-COOH) with free thiol group and the lysine peptides (Ac-RFAAKAA-COOH) with free amine group. Unreacted peptides can be detected after incubating with 5,5'-dithiobis-2-nitrobenzoic acid or fluorescamine™ as detection reagents for free thiol and amine group, respectively. Chemicals were categorized as sensitizers when they induced more than 10% depletion of cysteine peptides or more than 30% depletion of lysine peptides. The sensitivity, specificity, and accuracy were 80.0%, 86.7% and 82.5%, respectively. These results demonstrate that spectrophotometric methods can be an easy, fast, and high-throughput screening tools predicting the skin sensitization potential of chemical including cosmetic ingredient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Prediction of Antibacterial Activity from Physicochemical Properties of Antimicrobial Peptides

PubMed Central

Melo, Manuel N.; Ferre, Rafael; Feliu, Lídia; Bardají, Eduard; Planas, Marta; Castanho, Miguel A. R. B.

2011-01-01

Consensus is gathering that antimicrobial peptides that exert their antibacterial action at the membrane level must reach a local concentration threshold to become active. Studies of peptide interaction with model membranes do identify such disruptive thresholds but demonstrations of the possible correlation of these with the in vivo onset of activity have only recently been proposed. In addition, such thresholds observed in model membranes occur at local peptide concentrations close to full membrane coverage. In this work we fully develop an interaction model of antimicrobial peptides with biological membranes; by exploring the consequences of the underlying partition formalism we arrive at a relationship that provides antibacterial activity prediction from two biophysical parameters: the affinity of the peptide to the membrane and the critical bound peptide to lipid ratio. A straightforward and robust method to implement this relationship, with potential application to high-throughput screening approaches, is presented and tested. In addition, disruptive thresholds in model membranes and the onset of antibacterial peptide activity are shown to occur over the same range of locally bound peptide concentrations (10 to 100 mM), which conciliates the two types of observations. PMID:22194847

Quantitative modeling of peptide binding to TAP using support vector machine.

PubMed

Diez-Rivero, Carmen M; Chenlo, Bernardo; Zuluaga, Pilar; Reche, Pedro A

2010-01-01

The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. In this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer peptides of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10-fold cross-validation experiments and measured using Pearson's correlation coefficients (R(p)). Our results show that every peptide position (P1-P9) contributes to TAP binding (minimum R(p) of 0.26 +/- 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C-terminal end (R(p) = 0.68 +/- 0.06) and the P1 (R(p) = 0.51 +/- 0.09) and P2 (0.57 +/- 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (R(p) = 0.89 +/- 0.03) was achieved by a model trained on the full-length sequences or a residue selection consisting of the first 5 N- and last 3 C-terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/. (c) 2009 Wiley-Liss, Inc.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

PubMed

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-02-04

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

MLACP: machine-learning-based prediction of anticancer peptides

PubMed Central

Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan; Choi, Sun; Kim, Myeong Ok; Lee, Gwang

2017-01-01

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. PMID:29100375

Identification of B cell epitopes of alcohol dehydrogenase allergen of Curvularia lunata.

PubMed

Nair, Smitha; Kukreja, Neetu; Singh, Bhanu Pratap; Arora, Naveen

2011-01-01

Epitope identification assists in developing molecules for clinical applications and is useful in defining molecular features of allergens for understanding structure/function relationship. The present study was aimed to identify the B cell epitopes of alcohol dehydrogenase (ADH) allergen from Curvularia lunata using in-silico methods and immunoassay. B cell epitopes of ADH were predicted by sequence and structure based methods and protein-protein interaction tools while T cell epitopes by inhibitory concentration and binding score methods. The epitopes were superimposed on a three dimensional model of ADH generated by homology modeling and analyzed for antigenic characteristics. Peptides corresponding to predicted epitopes were synthesized and immunoreactivity assessed by ELISA using individual and pooled patients' sera. The homology model showed GroES like catalytic domain joined to Rossmann superfamily domain by an alpha helix. Stereochemical quality was confirmed by Procheck which showed 90% residues in most favorable region of Ramachandran plot while Errat gave a quality score of 92.733%. Six B cell (P1-P6) and four T cell (P7-P10) epitopes were predicted by a combination of methods. Peptide P2 (epitope P2) showed E(X)(2)GGP(X)(3)KKI conserved pattern among allergens of pathogenesis related family. It was predicted as high affinity binder based on electronegativity and low hydrophobicity. The computational methods employed were validated using Bet v 1 and Der p 2 allergens where 67% and 60% of the epitope residues were predicted correctly. Among B cell epitopes, Peptide P2 showed maximum IgE binding with individual and pooled patients' sera (mean OD 0.604±0.059 and 0.506±0.0035, respectively) followed by P1, P4 and P3 epitopes. All T cell epitopes showed lower IgE binding. Four B cell epitopes of C. lunata ADH were identified. Peptide P2 can serve as a potential candidate for diagnosis of allergic diseases.

Prediction and identification of potential immunodominant epitopes in glycoproteins B, C, E, G, and I of herpes simplex virus type 2.

PubMed

Pan, Mingjie; Wang, Xingsheng; Liao, Jianmin; Yin, Dengke; Li, Suqin; Pan, Ying; Wang, Yao; Xie, Guangyan; Zhang, Shumin; Li, Yuexi

2012-01-01

Twenty B candidate epitopes of glycoproteins B (gB2), C (gC2), E (gE2), G (gG2), and I (gI2) of herpes simplex virus type 2 (HSV-2) were predicted using DNAstar, Biosun, and Antheprot methods combined with the polynomial method. Subsequently, the biological functions of the peptides were tested via experiments in vitro. Among the 20 epitope peptides, 17 could react with the antisera to the corresponding parent proteins in the EIA tests. In particular, five peptides, namely, gB2(466-473) (EQDRKPRN), gC2(216-223) (GRTDRPSA), gE2(483-491) (DPPERPDSP), gG2(572-579) (EPPDDDDS), and gI2(286-295) (CRRRYRRPRG) had strong reaction with the antisera. All conjugates of the five peptides with the carrier protein BSA could stimulate mice into producing antibodies. The antisera to these peptides reacted strongly with the corresponding parent glycoproteins during the Western Blot tests, and the peptides reacted strongly with the antibodies against the parent glycoproteins during the EIA tests. The antisera against the five peptides could neutralize HSV-2 infection in vitro, which has not been reported until now. These results suggest that the immunodominant epitopes screened using software algorithms may be used for virus diagnosis and vaccine design against HSV-2.

Trimethylation enhancement using diazomethane (TrEnDi): rapid on-column quaternization of peptide amino groups via reaction with diazomethane significantly enhances sensitivity in mass spectrometry analyses via a fixed, permanent positive charge.

PubMed

Wasslen, Karl V; Tan, Le Hoa; Manthorpe, Jeffrey M; Smith, Jeffrey C

2014-04-01

Defining cellular processes relies heavily on elucidating the temporal dynamics of proteins. To this end, mass spectrometry (MS) is an extremely valuable tool; different MS-based quantitative proteomics strategies have emerged to map protein dynamics over the course of stimuli. Herein, we disclose our novel MS-based quantitative proteomics strategy with unique analytical characteristics. By passing ethereal diazomethane over peptides on strong cation exchange resin within a microfluidic device, peptides react to contain fixed, permanent positive charges. Modified peptides display improved ionization characteristics and dissociate via tandem mass spectrometry (MS(2)) to form strong a2 fragment ion peaks. Process optimization and determination of reactive functional groups enabled a priori prediction of MS(2) fragmentation patterns for modified peptides. The strategy was tested on digested bovine serum albumin (BSA) and successfully quantified a peptide that was not observable prior to modification. Our method ionizes peptides regardless of proton affinity, thus decreasing ion suppression and permitting predictable multiple reaction monitoring (MRM)-based quantitation with improved sensitivity.

Virtual screening of a milk peptide database for the identification of food-derived antimicrobial peptides.

PubMed

Liu, Yufang; Eichler, Jutta; Pischetsrieder, Monika

2015-11-01

Milk provides a wide range of bioactive substances, such as antimicrobial peptides and proteins. Our study aimed to identify novel antimicrobial peptides naturally present in milk. The components of an endogenous bovine milk peptide database were virtually screened for charge, amphipathy, and predicted secondary structure. Thus, 23 of 248 screened peptides were identified as candidates for antimicrobial effects. After commercial synthesis, their antimicrobial activities were determined against Escherichia coli NEB5α, E. coli ATCC25922, and Bacillus subtilis ATCC6051. In the tested concentration range (<2 mM), bacteriostatic activity of 14 peptides was detected including nine peptides inhibiting both Gram-positive and Gram-negative bacteria. The most effective fragment was TKLTEEEKNRLNFLKKISQRYQKFΑLPQYLK corresponding to αS2 -casein151-181 , with minimum inhibitory concentration (MIC) of 4.0 μM against B. subtilis ATCC6051, and minimum inhibitory concentrations of 16.2 μM against both E. coli strains. Circular dichroism spectroscopy revealed conformational changes of most active peptides in a membrane-mimic environment, transitioning from an unordered to α-helical structure. Screening of food peptide databases by prediction tools is an efficient method to identify novel antimicrobial food-derived peptides. Milk-derived antimicrobial peptides may have potential use as functional food ingredients and help to understand the molecular mechanisms of anti-infective milk effects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays.

PubMed

Williams, Stephanie; Stafford, Phillip; Hoffman, Steven A

2014-06-07

An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans. In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets. These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

PubMed Central

Porto, William F.; Pires, Állan S.; Franco, Octavio L.

2012-01-01

The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

PubMed Central

Tsirigos, Konstantinos D.; Peters, Christoph; Shu, Nanjiang; Käll, Lukas; Elofsson, Arne

2015-01-01

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions. PMID:25969446

Identification and application of self-binding zipper-like sequences in SARS-CoV spike protein.

PubMed

Zhang, Si Min; Liao, Ying; Neo, Tuan Ling; Lu, Yanning; Liu, Ding Xiang; Vahlne, Anders; Tam, James P

2018-05-22

Self-binding peptides containing zipper-like sequences, such as the Leu/Ile zipper sequence within the coiled coil regions of proteins and the cross-β spine steric zippers within the amyloid-like fibrils, could bind to the protein-of-origin through homophilic sequence-specific zipper motifs. These self-binding sequences represent opportunities for the development of biochemical tools and/or therapeutics. Here, we report on the identification of a putative self-binding β-zipper-forming peptide within the severe acute respiratory syndrome-associated coronavirus spike (S) protein and its application in viral detection. Peptide array scanning of overlapping peptides covering the entire length of S protein identified 34 putative self-binding peptides of six clusters, five of which contained octapeptide core consensus sequences. The Cluster I consensus octapeptide sequence GINITNFR was predicted by the Eisenberg's 3D profile method to have high amyloid-like fibrillation potential through steric β-zipper formation. Peptide C6 containing the Cluster I consensus sequence was shown to oligomerize and form amyloid-like fibrils. Taking advantage of this, C6 was further applied to detect the S protein expression in vitro by fluorescence staining. Meanwhile, the coiled-coil-forming Leu/Ile heptad repeat sequences within the S protein were under-represented during peptide array scanning, in agreement with that long peptide lengths were required to attain high helix-mediated interaction avidity. The data suggest that short β-zipper-like self-binding peptides within the S protein could be identified through combining the peptide scanning and predictive methods, and could be exploited as biochemical detection reagents for viral infection. Copyright © 2018. Published by Elsevier Ltd.

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

PubMed Central

Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

2014-01-01

Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains.

PubMed

Kundu, Kousik; Costa, Fabrizio; Backofen, Rolf

2013-07-01

State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains. Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs). The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz. Supplementary data are available at Bioinformatics online.

A graph kernel approach for alignment-free domain–peptide interaction prediction with an application to human SH3 domains

PubMed Central

Kundu, Kousik; Costa, Fabrizio; Backofen, Rolf

2013-01-01

Motivation: State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains. Results: Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs). Availability: The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813002

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

PubMed Central

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-01-01

Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method. PMID:19193216

Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures.

PubMed

Le Bihan, Thierry; Robinson, Mark D; Stewart, Ian I; Figeys, Daniel

2004-01-01

Although HPLC-ESI-MS/MS is rapidly becoming an indispensable tool for the analysis of peptides in complex mixtures, the sequence coverage it affords is often quite poor. Low protein expression resulting in peptide signal intensities that fall below the limit of detection of the MS system in combination with differences in peptide ionization efficiency plays a significant role in this. A second important factor stems from differences in physicochemical properties of each peptide and how these properties relate to chromatographic retention and ultimate detection. To identify and understand those properties, we compared data from experimentally identified peptides with data from peptides predicted by in silico digest of all corresponding proteins in the experimental set. Three different complex protein mixtures extracted were used to define a training set to evaluate the amino acid retention coefficients based on linear regression analysis. The retention coefficients were also compared with other previous hydrophobic and retention scale. From this, we have constructed an empirical model that can be readily used to predict peptides that are likely to be observed on our HPLC-ESI-MS/MS system based on their physicochemical properties. Finally, we demonstrated that in silico prediction of peptides and their retention coefficients can be used to generate an inclusion list for a targeted mass spectrometric identification of low abundance proteins in complex protein samples. This approach is based on experimentally derived data to calibrate the method and therefore may theoretically be applied to any HPLC-MS/MS system on which data are being generated.

Prediction of Brugia malayi antigenic peptides: candidates for synthetic vaccine design against lymphatic filariasis.

PubMed

Gomase, Virendra S; Chitlange, Nikhilkumar R; Changbhale, Smruti S; Kale, Karbhari V

2013-08-01

Brugia malayi is a threadlike nematode cause's swelling of lymphatic organs, condition well known as lymphatic filariasis; till date no invention made to effectively address lymphatic filariasis. In this analysis we a have predicted suitable antigenic peptides from Brugia malayi antigen protein for peptide vaccine design against lymphatic filariasis based on cross protection phenomenon as, an ample immune response can be generated with a single protein subunit. We found MHC class II binding peptides of Brugia malayi antigen protein are important determinant against the diseased condition. The analysis shows Brugia malayi antigen protein having 505 amino acids, which shows 497 nonamers. In this assay, we have predicted MHC-I binding peptides for 8mer_H2_Db (optimal score- 15.966), 9mer_H2_Db (optimal score- 15.595), 10mer_H2_Db (optimal score- 19.405), 11mer_H2_Dballeles (optimal score- 23.801). We also predicted the SVM based MHCII-IAb nonamers, 51-FQQIDPLDA, 442-FAAIACLVH, 206-YLNPFGHQF, 167-WYVIMAACY, 367-YAMIVIRLL, 434- LVITTAANF, 176-LDSYCLWKP, 435-VITTAANFA, 364-WPGYAMIVI (optimal score- 13.963); MHCII-IAd nonamers, 52-QQIDPLDAE, 171-MAACYLDSY, 239-QWRSVILCN, 168-YVIMAACYL, 3-QYLSVHSLS, 322-EILLHAKVV, 417- LGIIASFVS, 396-KAIFLAHFG, 167-WYVIMAACY, 269-LALHCINVI, 93-FINKAAPKQ, 259-NCIIVLKAF, 79- QGVLLIIPR, 22-TILQRSQAI, 63-RGFVYGNVS, 109-NISSLAFET,(optimal score- 16.748); and MHCII-IAg7 nonamers 171-MAACYLDSY, 73-KIVNGAQGV, 259-NCIIVLKAF, 209-PFGHQFSFE, 102-SCDTLLKNI, 25-QRSQAIRIV, 444- AIACLVHLF, 88-SLVNGFINK, 252-FPRHQLLNC, 471-RFVLANDNE, 52-QQIDPLDAE, 469-HRRFVLAND, 457- SNRHYFLAD, 362-KSWPGYAMI, 476-NDNEGEDFE, 370-IVIRLLQAL (optimal score- 19.847) which represents potential binders from Brugia malayi antigen protein. The method integrates prediction of MHC class I binding proteasomal C-terminal cleavage peptides and Eighteen potential antigenic peptides at average propensity 1.063 having highest local hydrophilicity. Thus a small antigen fragment can induce immune response against whole antigen. This approach can be applied for designing subunit and synthetic peptide vaccines.

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides.

PubMed

Sumowski, Chris Vanessa; Hanni, Matti; Schweizer, Sabine; Ochsenfeld, Christian

2014-01-14

The structural sensitivity of NMR chemical shifts as computed by quantum chemical methods is compared to a variety of empirical approaches for the example of a prototypical peptide, the 38-residue kaliotoxin KTX comprising 573 atoms. Despite the simplicity of empirical chemical shift prediction programs, the agreement with experimental results is rather good, underlining their usefulness. However, we show in our present work that they are highly insensitive to structural changes, which renders their use for validating predicted structures questionable. In contrast, quantum chemical methods show the expected high sensitivity to structural and electronic changes. This appears to be independent of the quantum chemical approach or the inclusion of solvent effects. For the latter, explicit solvent simulations with increasing number of snapshots were performed for two conformers of an eight amino acid sequence. In conclusion, the empirical approaches neither provide the expected magnitude nor the patterns of NMR chemical shifts determined by the clearly more costly ab initio methods upon structural changes. This restricts the use of empirical prediction programs in studies where peptide and protein structures are utilized for the NMR chemical shift evaluation such as in NMR refinement processes, structural model verifications, or calculations of NMR nuclear spin relaxation rates.

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.

PubMed

Nielsen, Morten; Andreatta, Massimo

2016-03-30

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .

Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

PubMed Central

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Background Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. Methodology/Principal Findings A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. Conclusions/Significance MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS. PMID:21991318

Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

PubMed

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

PubMed Central

Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D.; Keasling, Jay D.; Mukhopadhyay, Aindrila; Petzold, Christopher J.; Martín, Héctor García

2014-01-01

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. PMID:25188426

PaFlexPepDock: parallel ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta.

PubMed

Li, Haiou; Lu, Liyao; Chen, Rong; Quan, Lijun; Xia, Xiaoyan; Lü, Qiang

2014-01-01

Structural information related to protein-peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein-peptide interactions explored by experimental ways. Protein-peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein-peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein-peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions.

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides.

PubMed

Porto, William F; Pires, Állan S; Franco, Octavio L

2017-08-07

The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations. Copyright © 2017 Elsevier Ltd. All rights reserved.

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides.

PubMed

Tsirigos, Konstantinos D; Peters, Christoph; Shu, Nanjiang; Käll, Lukas; Elofsson, Arne

2015-07-01

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Transmembrane protein topology prediction using support vector machines.

PubMed

Nugent, Timothy; Jones, David T

2009-05-26

Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/. The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

An automated benchmarking platform for MHC class II binding prediction methods.

PubMed

Andreatta, Massimo; Trolle, Thomas; Yan, Zhen; Greenbaum, Jason A; Peters, Bjoern; Nielsen, Morten

2018-05-01

Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method. Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/. mniel@bioinformatics.dtu.dk. Supplementary data are available at Bioinformatics online.

Toward a standard in structural genome annotation for prokaryotes

DOE PAGES

Tripp, H. James; Sutton, Granger; White, Owen; ...

2015-07-25

In an effort to identify the best practice for finding genes in prokaryotic genomes and propose it as a standard for automated annotation pipelines, we collected 1,004,576 peptides from various publicly available resources, and these were used as a basis to evaluate various gene-calling methods. The peptides came from 45 bacterial replicons with an average GC content from 31 % to 74 %, biased toward higher GC content genomes. Automated, manual, and semi-manual methods were used to tally errors in three widely used gene calling methods, as evidenced by peptides mapped outside the boundaries of called genes. We found thatmore » the consensus set of identical genes predicted by the three methods constitutes only about 70 % of the genes predicted by each individual method (with start and stop required to coincide). Peptide data was useful for evaluating some of the differences between gene callers, but not reliable enough to make the results conclusive, due to limitations inherent in any proteogenomic study. A single, unambiguous, unanimous best practice did not emerge from this analysis, since the available proteomics data were not adequate to provide an objective measurement of differences in the accuracy between these methods. However, as a result of this study, software, reference data, and procedures have been better matched among participants, representing a step toward a much-needed standard. In the absence of sufficient amount of experimental data to achieve a universal standard, our recommendation is that any of these methods can be used by the community, as long as a single method is employed across all datasets to be compared.« less

De novo design and engineering of non-ribosomal peptide synthetases

NASA Astrophysics Data System (ADS)

Bozhüyük, Kenan A. J.; Fleischhacker, Florian; Linck, Annabell; Wesche, Frank; Tietze, Andreas; Niesert, Claus-Peter; Bode, Helge B.

2018-03-01

Peptides derived from non-ribosomal peptide synthetases (NRPSs) represent an important class of pharmaceutically relevant drugs. Methods to generate novel non-ribosomal peptides or to modify peptide natural products in an easy and predictable way are therefore of great interest. However, although the overall modular structure of NRPSs suggests the possibility of adjusting domain specificity and selectivity, only a few examples have been reported and these usually show a severe drop in production titre. Here we report a new strategy for the modification of NRPSs that uses defined exchange units (XUs) and not modules as functional units. XUs are fused at specific positions that connect the condensation and adenylation domains and respect the original specificity of the downstream module to enable the production of the desired peptides. We also present the use of internal condensation domains as an alternative to other peptide-chain-releasing domains for the production of cyclic peptides.

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

NASA Astrophysics Data System (ADS)

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane.

PubMed

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S; Gray, C G

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05+/-0.39 kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

PubMed

Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert

2012-01-03

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Identification of potential serum peptide biomarkers of biliary tract cancer using MALDI MS profiling

PubMed Central

2014-01-01

Background The aim of this discovery study was the identification of peptide serum biomarkers for detecting biliary tract cancer (BTC) using samples from healthy volunteers and benign cases of biliary disease as control groups. This work was based on the hypothesis that cancer-specific exopeptidases exist and that their activities in serum can generate cancer-predictive peptide fragments from circulating proteins during coagulation. Methods This case control study used a semi-automated platform incorporating polypeptide extraction linked to matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to profile 92 patient serum samples. Predictive models were generated to test a validation serum set from BTC cases and healthy volunteers. Results Several peptide peaks were found that could significantly differentiate BTC patients from healthy controls and benign biliary disease. A predictive model resulted in a sensitivity of 100% and a specificity of 93.8% in detecting BTC in the validation set, whilst another model gave a sensitivity of 79.5% and a specificity of 83.9% in discriminating BTC from benign biliary disease samples in the training set. Discriminatory peaks were identified by tandem MS as fragments of abundant clotting proteins. Conclusions Serum MALDI MS peptide signatures can accurately discriminate patients with BTC from healthy volunteers. PMID:24495412

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

PubMed

Das, Sreetama; Bhadra, Pratiti; Ramakumar, Suryanarayanarao; Pal, Debnath

2017-08-04

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

SATPdb: a database of structurally annotated therapeutic peptides

PubMed Central

Singh, Sandeep; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Bhalla, Sherry; Usmani, Salman Sadullah; Gautam, Ankur; Tuknait, Abhishek; Agrawal, Piyush; Mathur, Deepika; Raghava, Gajendra P.S.

2016-01-01

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics. PMID:26527728

Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence

PubMed Central

Raghava, Gajendra P. S.

2013-01-01

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/). PMID:23667458

FSPP: A Tool for Genome-Wide Prediction of smORF-Encoded Peptides and Their Functions

PubMed Central

Li, Hui; Xiao, Li; Zhang, Lili; Wu, Jiarui; Wei, Bin; Sun, Ninghui; Zhao, Yi

2018-01-01

smORFs are small open reading frames of less than 100 codons. Recent low throughput experiments showed a lot of smORF-encoded peptides (SEPs) played crucial rule in processes such as regulation of transcription or translation, transportation through membranes and the antimicrobial activity. In order to gather more functional SEPs, it is necessary to have access to genome-wide prediction tools to give profound directions for low throughput experiments. In this study, we put forward a functional smORF-encoded peptides predictor (FSPP) which tended to predict authentic SEPs and their functions in a high throughput method. FSPP used the overlap of detected SEPs from Ribo-seq and mass spectrometry as target objects. With the expression data on transcription and translation levels, FSPP built two co-expression networks. Combing co-location relations, FSPP constructed a compound network and then annotated SEPs with functions of adjacent nodes. Tested on 38 sequenced samples of 5 human cell lines, FSPP successfully predicted 856 out of 960 annotated proteins. Interestingly, FSPP also highlighted 568 functional SEPs from these samples. After comparison, the roles predicted by FSPP were consistent with known functions. These results suggest that FSPP is a reliable tool for the identification of functional small peptides. FSPP source code can be acquired at https://www.bioinfo.org/FSPP. PMID:29675032

Optimization of culturing conditions of recombined Escherichia coli to produce umami octopeptide-containing protein.

PubMed

Zhang, Yin; Wei, Xiong; Lu, Zhou; Pan, Zhongli; Gou, Xinhua; Venkitasamy, Chandrasekar; Guo, Siya; Zhao, Liming

2017-07-15

Using synthesized peptides to verify the taste of natural peptides was probably the leading cause for tasting disputes regarding umami peptides. A novel method was developed to prepare the natural peptide which could be used to verify the taste of umami peptide. A controversial octopeptide was selected and gene engineering was used to structure its Escherichia coli. expressing vector. A response surface method was adopted to optimize the expression conditions of the recombinant protein. The results of SDS-PAGE for the recombinant protein indicated that the recombinant expression system was successfully structured. The fitting results of the response surface experiment showed that the OD 600 value was the key factor which influenced the expression of the recombinant protein. The optimal culturing process conditions predicted with the fitting model were an OD 600 value of 0.5, an IPTG concentration of 0.6mM, a culturing temperature of 28.75°C and a culturing time of 5h. Copyright © 2017 Elsevier Ltd. All rights reserved.

MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation.

PubMed

Degroeve, Sven; Maddelein, Davy; Martens, Lennart

2015-07-01

We present an MS(2) peak intensity prediction server that computes MS(2) charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published MS(2)PIP model for Orbitrap-LTQ CID spectra. Predicted MS(2) spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method. The MS(2)PIP prediction server is accessible from http://iomics.ugent.be/ms2pip. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

PubMed

Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

2016-10-01

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Database-Guided Discovery of Potent Peptides to Combat HIV-1 or Superbugs

PubMed Central

Wang, Guangshun

2013-01-01

Antimicrobial peptides (AMPs), small host defense proteins, are indispensable for the protection of multicellular organisms such as plants and animals from infection. The number of AMPs discovered per year increased steadily since the 1980s. Over 2,000 natural AMPs from bacteria, protozoa, fungi, plants, and animals have been registered into the antimicrobial peptide database (APD). The majority of these AMPs (>86%) possess 11–50 amino acids with a net charge from 0 to +7 and hydrophobic percentages between 31–70%. This article summarizes peptide discovery on the basis of the APD. The major methods are the linguistic model, database screening, de novo design, and template-based design. Using these methods, we identified various potent peptides against human immunodeficiency virus type 1 (HIV-1) or methicillin-resistant Staphylococcus aureus (MRSA). While the stepwise designed anti-HIV peptide is disulfide-linked and rich in arginines, the ab initio designed anti-MRSA peptide is linear and rich in leucines. Thus, there are different requirements for antiviral and antibacterial peptides, which could kill pathogens via different molecular targets. The biased amino acid composition in the database-designed peptides, or natural peptides such as θ-defensins, requires the use of the improved two-dimensional NMR method for structural determination to avoid the publication of misleading structure and dynamics. In the case of human cathelicidin LL-37, structural determination requires 3D NMR techniques. The high-quality structure of LL-37 provides a solid basis for understanding its interactions with membranes of bacteria and other pathogens. In conclusion, the APD database is a comprehensive platform for storing, classifying, searching, predicting, and designing potent peptides against pathogenic bacteria, viruses, fungi, parasites, and cancer cells. PMID:24276259

Signal-3L: A 3-layer approach for predicting signal peptides.

PubMed

Shen, Hong-Bin; Chou, Kuo-Chen

2007-11-16

Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide annotations or are annotated with uncertain terms but are classified by Signal-3L as secretory proteins are provided in a downloadable file. The large-scale file is prepared with Microsoft Excel and named "Tab-Signal-3L.xls", and will be updated once a year to include new protein entries and reflect the continuous development of Signal-3L.

A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics.

PubMed

Marx, Harald; Lemeer, Simone; Schliep, Jan Erik; Matheron, Lucrece; Mohammed, Shabaz; Cox, Jürgen; Mann, Matthias; Heck, Albert J R; Kuster, Bernhard

2013-06-01

We present a peptide library and data resource of >100,000 synthetic, unmodified peptides and their phosphorylated counterparts with known sequences and phosphorylation sites. Analysis of the library by mass spectrometry yielded a data set that we used to evaluate the merits of different search engines (Mascot and Andromeda) and fragmentation methods (beam-type collision-induced dissociation (HCD) and electron transfer dissociation (ETD)) for peptide identification. We also compared the sensitivities and accuracies of phosphorylation-site localization tools (Mascot Delta Score, PTM score and phosphoRS), and we characterized the chromatographic behavior of peptides in the library. We found that HCD identified more peptides and phosphopeptides than did ETD, that phosphopeptides generally eluted later from reversed-phase columns and were easier to identify than unmodified peptides and that current computational tools for proteomics can still be substantially improved. These peptides and spectra will facilitate the development, evaluation and improvement of experimental and computational proteomic strategies, such as separation techniques and the prediction of retention times and fragmentation patterns.

Design and synthesis of a new peptide derived from Fasciola gigantica cathepsin L1 with potential application in serodiagnosis of fascioliasis.

PubMed

Meshgi, Behnam; Jalousian, Fatemeh; Fathi, Saeid; Jahani, Zahra

2018-06-01

Fascioliasis is a global parasitic disease that affects domestic animals and causes considerable economic losses in the process of domestic animal breeding in endemic regions. The cause of the disease involves a liver trematode of the genus Fasciola, which secretes materials into a host's body (mainly proteins) in order to protect it from the host's immune system. These materials can be involved in the migration, growth, and nutrition of the parasite. Among the expressive proteins of Fasciola, proteases have been introduced as the appropriate targets for diagnosis, treatment, and vaccination against parasites. Cathepsin L (CL) is a member of cysteine proteases; it is widely expressed in the Fasciola species. The aim of this study was to evaluate two synthetic peptides from F. gigantica CL1 for improving serological diagnosis of the Fasciola infection. Therefore, the potential diagnostic value of the surface epitopes of CL1 was assessed using ELISA. In the current study, bioinformatics tools were applied to select two appropriate epitopes of Fasciola Cathepsin L1 as synthetic antigens. Their diagnostic values were evaluated by two methods of indirect ELISA and dot blot analysis. The findings revealed that the first peptide at a dilution ratio of 1:400 and the second peptide at a dilution ratio of 1:100 had the best results and the best concentration of antigens was introduced at 4 μg/ml. Moreover, 191 sera samples were analyzed by both peptides by using the ELISA method, including fascioliasis sera, other parasitic sera and negative sera. The sensitivity of the peptides 1-ELISA and peptide 2-ELISA for the diagnosis of the various cases was 100%. The specificity of the first peptide was 87.3% and its efficacy was determined to be 93.65%. The specificity and the efficacy of the second peptide were 79% and 89.5%, respectively. The positive predictive values of the first and second peptides were obtained to be 86.27% and 79.27% respectively, and the negative predictive values of both peptides was calculated as 100%. In conclusion, the results of this study indicated that the peptide 1 from CL1 may be used as an appropriate antigen for the diagnosis of fascioliasis if the findings are backed up by using other serodiagnostic methods for checking serological cross-reactivity linked to other parasites. Copyright © 2018 Elsevier Inc. All rights reserved.

Comprehensive computational design of ordered peptide macrocycles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hosseinzadeh, Parisa; Bhardwaj, Gaurav; Mulligan, Vikram Khipple

Mixed chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to-date, but there is currently no way to systematically search through the structural space spanned by such compounds for new drug candidates. Natural proteins do not provide a useful guide: peptide macrocycles lack regular secondary structures and hydrophobic cores and have different backbone torsional constraints. Hence the development of new peptide macrocycles has been approached by modifying natural products or using library selection methods; the former is limited by the small number of known structures, and the latter by the limited size and diversity accessible throughmore » library-based methods. To overcome these limitations, here we enumerate the stable structures that can be adopted by macrocyclic peptides composed of L and D amino acids. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. We synthesize and characterize by NMR twelve 7-10 residue macrocycles, 9 of which have structures very close to the design models in solution. NMR structures of three 11-14 residue bicyclic designs are also very close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide based macrocycles unparalleled for other molecular systems, and vastly increase the available starting scaffolds for both rational drug design and library selection methods.« less

MHC class I antigen presentation and implications for developing a new generation of therapeutic vaccines.

PubMed

Comber, Joseph D; Philip, Ramila

2014-05-01

Major histocompatibility complex class I (MHC-I) presented peptide epitopes provide a 'window' into the changes occurring in a cell. Conventionally, these peptides are generated by proteolysis of endogenously synthesized proteins in the cytosol, loaded onto MHC-I molecules, and presented on the cell surface for surveillance by CD8(+) T cells. MHC-I restricted processing and presentation alerts the immune system to any infectious or tumorigenic processes unfolding intracellularly and provides potential targets for a cytotoxic T cell response. Therefore, therapeutic vaccines based on MHC-I presented peptide epitopes could, theoretically, induce CD8(+) T cell responses that have tangible clinical impacts on tumor eradication and patient survival. Three major methods have been used to identify MHC-I restricted epitopes for inclusion in peptide-based vaccines for cancer: genetic, motif prediction and, more recently, immunoproteomic analysis. Although the first two methods are capable of identifying T cell stimulatory epitopes, these have significant disadvantages and may not accurately represent epitopes presented by a tumor cell. In contrast, immunoproteomic methods can overcome these disadvantages and identify naturally processed and presented tumor associated epitopes that induce more clinically relevant tumor specific cytotoxic T cell responses. In this review, we discuss the importance of using the naturally presented MHC-I peptide repertoire in formulating peptide vaccines, the recent application of peptide-based vaccines in a variety of cancers, and highlight the pros and cons of the current state of peptide vaccines.

Discovery of Neuropeptides in the Nematode Ascaris suum by Database Mining and Tandem Mass Spectrometry

PubMed Central

Jarecki, Jessica L.; Frey, Brian L.; Smith, Lloyd M.; Stretton, Antony O.

2011-01-01

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was used to discover peptides in extracts of the large parasitic nematode Ascaris suum. This required the assembly of a new database of known and predicted peptides. In addition to those already sequenced, peptides were either previously predicted to be processed from precursor proteins identified in an A. suum library of expressed sequence tags (ESTs), or newly predicted from a library of A. suum genome survey sequences (GSSs). The predicted MS/MS fragmentation patterns of this collection of real and putative peptides were compared with the actual fragmentation patterns found in the MS/MS spectra of peptides fractionated by MS; this enabled individual peptides to be sequenced. Many previously identified peptides were found, and 21 novel peptides were discovered. Thus, this approach is very useful, despite the fact that the available GSS database is still preliminary, having only 1X coverage. PMID:21524146

Artificial intelligence systems based on texture descriptors for vaccine development.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Meat Authentication via Multiple Reaction Monitoring Mass Spectrometry of Myoglobin Peptides.

PubMed

Watson, Andrew D; Gunning, Yvonne; Rigby, Neil M; Philo, Mark; Kemsley, E Kate

2015-10-20

A rapid multiple reaction monitoring (MRM) mass spectrometric method for the detection and relative quantitation of the adulteration of meat with that of an undeclared species is presented. Our approach uses corresponding proteins from the different species under investigation and corresponding peptides from those proteins, or CPCP. Selected peptide markers can be used for species detection. The use of ratios of MRM transition peak areas for corresponding peptides is proposed for relative quantitation. The approach is introduced by use of myoglobin from four meats: beef, pork, horse and lamb. Focusing in the present work on species identification, by use of predictive tools, we determine peptide markers that allow the identification of all four meats and detection of one meat added to another at levels of 1% (w/w). Candidate corresponding peptide pairs to be used for the relative quantification of one meat added to another have been observed. Preliminary quantitation data presented here are encouraging.

Empirical parameterization of a model for predicting peptide helix/coil equilibrium populations.

PubMed Central

Andersen, N. H.; Tong, H.

1997-01-01

A modification of the Lifson-Roig formulation of helix/coil transitions is presented; it (1) incorporates end-capping and coulombic (salt bridges, hydrogen bonding, and side-chain interactions with charged termini and the helix dipole) effects, (2) helix-stabilizing hydrophobic clustering, (3) allows for different inherent termination probabilities of individual residues, and (4) differentiates helix elongation in the first versus subsequent turns of a helix. Each residue is characterized by six parameters governing helix formation. The formulation of the conditional probability of helix initiation and termination that we developed is essentially the same as one presented previously (Shalongo W, Stellwagen, E. 1995. Protein Sci 4:1161-1166) and nearly the mathematical equivalent of the new capping formulation incorporated in the model presented by Rohl et al. (1996. Protein Sci 5:2623-2637). Side-chain/side-chain interactions are, in most cases, incorporated as context dependent modifications of propagation rather than nucleation parameters. An alternative procedure for converting [theta]221 values to experimental fractional helicities () is presented. Tests of the program predictions suggest this method may have some advantages both for designed peptides and for the analysis of secondary structure preferences that could drive the formation of molten-globule intermediates on protein folding pathways. The model predicts the fractional helicity of 385 peptides with a root-mean-square deviation (RMSD) of 0.050 and locates (with precise definition of the termini in many cases) helices in proteins as well as competing methods. The propagation and nucleation parameters were derived from NMR data and from the CD data for a 79 peptide "learning set" for which an excellent fit resulted (RMSD = 0.0295). The current set of parameter corrections for capping boxes, helix dipole interactions, and side-chain/side-chain interactions (coulombic, hydrogen bonding and hydrophobic clustering), although still under development provide a significant improvement in both helix/coil equilibrium prediction for peptides and helix location in protein sequences. This is clearly evident in the rms deviations between CD measures and calculated values of fractional helicity for different classes of peptides before and after applying the corrections: for peptides lacking capping boxes and i/i + 3 and i/i + 4 side-chain/side-chain interactions RMSD = 0.044 (n = 164) versus RMSD = 0.054 (0.172 without the corrections, n = 221) for peptides that required context-dependent corrections of the parameters. If we restrict the analysis to N-acylated peptides with helix stabilizing side-chain/side-chain interactions (including N-capping boxes), the degree to which our corrections account for the stabilizing interaction can be judged from the change in helicity underestimation, (calc-CD): -0.15 +/- 0.10, which is reduced to -0.018 +/- 0.048 (n = 191) upon applying the corrections. PMID:9300492

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

NASA Astrophysics Data System (ADS)

Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

2013-12-01

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with R osetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several R osetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Computational design and experimental study of tighter binding peptides to an inactivated mutant of HIV-1 protease

PubMed Central

Altman, Michael D.; Nalivaika, Ellen A.; Prabu-Jeyabalan, Moses; Schiffer, Celia A.; Tidor, Bruce

2009-01-01

Drug resistance in HIV-1 protease, a barrier to effective treatment, is generally caused by mutations in the enzyme that disrupt inhibitor binding but still allow for substrate processing. Structural studies with mutant, inactive enzyme, have provided detailed information regarding how the substrates bind to the protease yet avoid resistance mutations; insights obtained inform the development of next generation therapeutics. Although structures have been obtained of complexes between substrate peptide and inactivated (D25N) protease, thermodynamic studies of peptide binding have been challenging due to low affinity. Peptides that bind tighter to the inactivated protease than the natural substrates would be valuable for thermodynamic studies as well as to explore whether the structural envelope observed for substrate peptides is a function of weak binding. Here, two computational methods — namely, charge optimization and protein design — were applied to identify peptide sequences predicted to have higher binding affinity to the inactivated protease, starting from an RT–RH derived substrate peptide. Of the candidate designed peptides, three were tested for binding with isothermal titration calorimetry, with one, containing a single threonine to valine substitution, measured to have more than a ten-fold improvement over the tightest binding natural substrate. Crystal structures were also obtained for the same three designed peptide complexes; they show good agreement with computational prediction. Thermodynamic studies show that binding is entropically driven, more so for designed affinity enhanced variants than for the starting substrate. Structural studies show strong similarities between natural and tighter-binding designed peptide complexes, which may have implications in understanding the molecular mechanisms of drug resistance in HIV-1 protease. PMID:17729291

AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking.

PubMed

Ben-Shimon, Avraham; Niv, Masha Y

2015-05-05

The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements. Copyright © 2015 Elsevier Ltd. All rights reserved.

Highly Reproducible Label Free Quantitative Proteomic Analysis of RNA Polymerase Complexes*

PubMed Central

Mosley, Amber L.; Sardiu, Mihaela E.; Pattenden, Samantha G.; Workman, Jerry L.; Florens, Laurence; Washburn, Michael P.

2011-01-01

The use of quantitative proteomics methods to study protein complexes has the potential to provide in-depth information on the abundance of different protein components as well as their modification state in various cellular conditions. To interrogate protein complex quantitation using shotgun proteomic methods, we have focused on the analysis of protein complexes using label-free multidimensional protein identification technology and studied the reproducibility of biological replicates. For these studies, we focused on three highly related and essential multi-protein enzymes, RNA polymerase I, II, and III from Saccharomyces cerevisiae. We found that label-free quantitation using spectral counting is highly reproducible at the protein and peptide level when analyzing RNA polymerase I, II, and III. In addition, we show that peptide sampling does not follow a random sampling model, and we show the need for advanced computational models to predict peptide detection probabilities. In order to address these issues, we used the APEX protocol to model the expected peptide detectability based on whole cell lysate acquired using the same multidimensional protein identification technology analysis used for the protein complexes. Neither method was able to predict the peptide sampling levels that we observed using replicate multidimensional protein identification technology analyses. In addition to the analysis of the RNA polymerase complexes, our analysis provides quantitative information about several RNAP associated proteins including the RNAPII elongation factor complexes DSIF and TFIIF. Our data shows that DSIF and TFIIF are the most highly enriched RNAP accessory factors in Rpb3-TAP purifications and demonstrate our ability to measure low level associated protein abundance across biological replicates. In addition, our quantitative data supports a model in which DSIF and TFIIF interact with RNAPII in a dynamic fashion in agreement with previously published reports. PMID:21048197

Density-based clustering of small peptide conformations sampled from a molecular dynamics simulation.

PubMed

Kim, Minkyoung; Choi, Seung-Hoon; Kim, Junhyoung; Choi, Kihang; Shin, Jae-Min; Kang, Sang-Kee; Choi, Yun-Jaie; Jung, Dong Hyun

2009-11-01

This study describes the application of a density-based algorithm to clustering small peptide conformations after a molecular dynamics simulation. We propose a clustering method for small peptide conformations that enables adjacent clusters to be separated more clearly on the basis of neighbor density. Neighbor density means the number of neighboring conformations, so if a conformation has too few neighboring conformations, then it is considered as noise or an outlier and is excluded from the list of cluster members. With this approach, we can easily identify clusters in which the members are densely crowded in the conformational space, and we can safely avoid misclustering individual clusters linked by noise or outliers. Consideration of neighbor density significantly improves the efficiency of clustering of small peptide conformations sampled from molecular dynamics simulations and can be used for predicting peptide structures.

Prediction of proprotein convertase cleavage sites.

PubMed

Duckert, Peter; Brunak, Søren; Blom, Nikolaj

2004-01-01

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

PubMed

Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

2017-12-05

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

Evolving serodiagnostics by rationally designed peptide arrays: the Burkholderia paradigm in Cystic Fibrosis

NASA Astrophysics Data System (ADS)

Peri, Claudio; Gori, Alessandro; Gagni, Paola; Sola, Laura; Girelli, Daniela; Sottotetti, Samantha; Cariani, Lisa; Chiari, Marcella; Cretich, Marina; Colombo, Giorgio

2016-09-01

Efficient diagnosis of emerging and novel bacterial infections is fundamental to guide decisions on therapeutic treatments. Here, we engineered a novel rational strategy to design peptide microarray platforms, which combines structural and genomic analyses to predict the binding interfaces between diverse protein antigens and antibodies against Burkholderia cepacia complex infections present in the sera of Cystic Fibrosis (CF) patients. The predicted binding interfaces on the antigens are synthesized in the form of isolated peptides and chemically optimized for controlled orientation on the surface. Our platform displays multiple Burkholderia-related epitopes and is shown to diagnose infected individuals even in presence of superinfections caused by other prevalent CF pathogens, with limited cost and time requirements. Moreover, our data point out that the specific patterns determined by combined probe responses might provide a characterization of Burkholderia infections even at the subtype level (genomovars). The method is general and immediately applicable to other bacteria.

Changes in beta cell function during the proximate post-diagnosis period in persons with type 1 diabetes.

PubMed

DiMeglio, Linda A; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Slover, Robert; Aye, Tandy; Weinzimer, Stuart A; Bremer, Andrew A; Buckingham, Bruce

2016-06-01

Prior studies examining beta-cell preservation in type 1 diabetes have predominantly assessed stimulated C-peptide concentrations approximately 10 wk after diagnosis. We examined whether earlier assessments might aid in prediction of beta cell function over time. Using data from a multi-center randomized trial assessing the effect of intensive diabetes management initiated within 1 wk of diagnosis, we assessed which clinical factors predicted 90-min mixed-meal tolerance test (MMTT) stimulated C-peptide values obtained 2 and 6 wk after diagnosis. We also studied associations of these factors with C-peptide values at 1- and 2-year post-diagnosis. Data from intervention and control groups were pooled. Among 67 study participants (mean age 13.3 ± 5.7 yr, range 7.8-45.7 yr) in multivariable analyses, C-peptide increased from baseline to 2 wks and then 6 wk. C-peptide levels at these times were significantly correlated with 1- and 2-yr C-peptide concentrations (all p < 0.001), with the strongest observed associations between 6-wk C-peptide and the 1- and 2-yr values (r = 0.66 and r = 0.61, respectively). In multivariable analyses, greater baseline and 6-wk C-peptide, and older age independently predicted greater 1- and 2-yr C-peptide concentrations. C-peptide assessments close to diagnosis were predictive of subsequent C-peptide production. Our data demonstrate a clear increase in C-peptide over the initial 6 wk after diabetes diagnosis followed by a plateau. Our data do not suggest that MMTT assessments performed closer to diagnosis than 6 wk would improve prediction of subsequent residual beta cell function. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

UVnovo: A De Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry

PubMed Central

Robotham, Scott A.; Horton, Andrew P.; Cannon, Joe R.; Cotham, Victoria C.; Marcotte, Edward M.; Brodbelt, Jennifer S.

2016-01-01

De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel peptides and proteins, in which a peptide’s amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum (MS/MS or MS3) fragment ions, without comparison to a reference proteome. This method is ideal for organisms or samples lacking a complete or well-annotated reference sequence set. One of the major barriers to de novo spectral interpretation arises from confusion of N- and C-terminal ion series due to the symmetry between b and y ion pairs created by collisional activation methods (or c, z ions for electron-based activation methods). This is known as the ‘antisymmetric path problem’ and leads to inverted amino acid subsequences within a de novo reconstruction. Here, we combine several key strategies for de novo peptide sequencing into a single high-throughput pipeline: high efficiency carbamylation blocks lysine side chains, and subsequent tryptic digestion and N-terminal peptide derivatization with the ultraviolet chromophore AMCA yields peptides susceptible to 351 nm ultraviolet photodissociation (UVPD). UVPD-MS/MS of the AMCA-modified peptides then predominantly produces y ions in the MS/MS spectra, specifically addressing the antisymmetric path problem. Finally, the program UVnovo applies a random forest algorithm to automatically learn from and then interpret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an E. coli lysate at high confidence. PMID:26938041

Using 1H and 13C NMR chemical shifts to determine cyclic peptide conformations: a combined molecular dynamics and quantum mechanics approach.

PubMed

Nguyen, Q Nhu N; Schwochert, Joshua; Tantillo, Dean J; Lokey, R Scott

2018-05-10

Solving conformations of cyclic peptides can provide insight into structure-activity and structure-property relationships, which can help in the design of compounds with improved bioactivity and/or ADME characteristics. The most common approaches for determining the structures of cyclic peptides are based on NMR-derived distance restraints obtained from NOESY or ROESY cross-peak intensities, and 3J-based dihedral restraints using the Karplus relationship. Unfortunately, these observables are often too weak, sparse, or degenerate to provide unequivocal, high-confidence solution structures, prompting us to investigate an alternative approach that relies only on 1H and 13C chemical shifts as experimental observables. This method, which we call conformational analysis from NMR and density-functional prediction of low-energy ensembles (CANDLE), uses molecular dynamics (MD) simulations to generate conformer families and density functional theory (DFT) calculations to predict their 1H and 13C chemical shifts. Iterative conformer searches and DFT energy calculations on a cyclic peptide-peptoid hybrid yielded Boltzmann ensembles whose predicted chemical shifts matched the experimental values better than any single conformer. For these compounds, CANDLE outperformed the classic NOE- and 3J-coupling-based approach by disambiguating similar β-turn types and also enabled the structural elucidation of the minor conformer. Through the use of chemical shifts, in conjunction with DFT and MD calculations, CANDLE can help illuminate conformational ensembles of cyclic peptides in solution.

NetCTLpan: pan-specific MHC class I pathway epitope predictions

PubMed Central

Larsen, Mette Voldby; Lundegaard, Claus; Nielsen, Morten

2010-01-01

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/. Electronic supplementary material The online version of this article (doi:10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users. PMID:20379710

Methods for determining microcystins (peptide hepatotoxins) and microcystin-producing cyanobacteria.

PubMed

Sangolkar, Lalita N; Maske, Sarika S; Chakrabarti, Tapan

2006-11-01

Episodes of cyanobacterial toxic blooms and fatalities to animals and humans due to cyanobacterial toxins (CBT) are known worldwide. The hepatotoxins and neurotoxins (cyanotoxins) produced by bloom-forming cyanobacteria have been the cause of human and animal health hazards and even death. Prevailing concentration of cell bound endotoxin, exotoxin and the toxin variants depend on developmental stages of the bloom and the cyanobacterial (CB) species involved. Toxic and non-toxic strains do not show any predictable morphological difference. The current instrumental, immunological and molecular methods applied for determining microcystins (peptide hepatotoxins) and microcystin-producing cyanobacteria are reviewed.

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm.

PubMed

Zhou, Pei; Jin, Bowen; Li, Hao; Huang, Sheng-You

2018-05-09

Protein-peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein-peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein-peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain.

PubMed

Hou, Tingjun; Zhang, Wei; Case, David A; Wang, Wei

2008-02-29

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

PubMed

Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

2015-07-05

Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

LC-MS-based characterization of the peptide reactivity of chemicals to improve the in vitro prediction of the skin sensitization potential.

PubMed

Natsch, Andreas; Gfeller, Hans

2008-12-01

A key step in the skin sensitization process is the formation of a covalent adduct between skin sensitizers and endogenous proteins and/or peptides in the skin. Based on this mechanistic understanding, there is a renewed interest in in vitro assays to determine the reactivity of chemicals toward peptides in order to predict their sensitization potential. A standardized peptide reactivity assay yielded a promising predictivity. This published assay is based on high-performance liquid chromatography with ultraviolet detection to quantify peptide depletion after incubation with test chemicals. We had observed that peptide depletion may be due to either adduct formation or peptide oxidation. Here we report a modified assay based on both liquid chromatography-mass spectrometry (LC-MS) analysis and detection of free thiol groups. This approach allows simultaneous determination of (1) peptide depletion, (2) peptide oxidation (dimerization), (3) adduct formation, and (4) thiol reactivity and thus generates a more detailed characterization of the reactivity of a molecule. Highly reactive molecules are further discriminated with a kinetic measure. The assay was validated on 80 chemicals. Peptide depletion could accurately be quantified both with LC-MS detection and depletion of thiol groups. The majority of the moderate/strong/extreme sensitizers formed detectable peptide adducts, but many sensitizers were also able to catalyze peptide oxidation. Whereas adduct formation was only observed for sensitizers, this oxidation reaction was also observed for two nonsensitizing fragrance aldehydes, indicating that peptide depletion might not always be regarded as sufficient evidence for rating a chemical as a sensitizer. Thus, this modified assay gives a more informed view of the peptide reactivity of chemicals to better predict their sensitization potential.

BIOPEP database and other programs for processing bioactive peptide sequences.

PubMed

Minkiewicz, Piotr; Dziuba, Jerzy; Iwaniak, Anna; Dziuba, Marta; Darewicz, Małgorzata

2008-01-01

This review presents the potential for application of computational tools in peptide science based on a sample BIOPEP database and program as well as other programs and databases available via the World Wide Web. The BIOPEP application contains a database of biologically active peptide sequences and a program enabling construction of profiles of the potential biological activity of protein fragments, calculation of quantitative descriptors as measures of the value of proteins as potential precursors of bioactive peptides, and prediction of bonds susceptible to hydrolysis by endopeptidases in a protein chain. Other bioactive and allergenic peptide sequence databases are also presented. Programs enabling the construction of binary and multiple alignments between peptide sequences, the construction of sequence motifs attributed to a given type of bioactivity, searching for potential precursors of bioactive peptides, and the prediction of sites susceptible to proteolytic cleavage in protein chains are available via the Internet as are other approaches concerning secondary structure prediction and calculation of physicochemical features based on amino acid sequence. Programs for prediction of allergenic and toxic properties have also been developed. This review explores the possibilities of cooperation between various programs.

Prediction of anticancer peptides against MCF-7 breast cancer cells from the peptidomes of Achatina fulica mucus fractions.

PubMed

E-Kobon, Teerasak; Thongararm, Pennapa; Roytrakul, Sittiruk; Meesuk, Ladda; Chumnanpuen, Pramote

2016-01-01

Several reports have shown antimicrobial and anticancer activities of mucous glycoproteins extracted from the giant African snail Achatina fulica. Anticancer properties of the snail mucous peptides remain incompletely revealed. The aim of this study was to predict anticancer peptides from A. fulica mucus. Two of HPLC-separated mucous fractions (F2 and F5) showed in vitro cytotoxicity against the breast cancer cell line (MCF-7) and normal epithelium cell line (Vero). According to the mass spectrometric analysis, 404 and 424 peptides from the F2 and F5 fractions were identified. Our comprehensive bioinformatics workflow predicted 16 putative cationic and amphipathic anticancer peptides with diverse structures from these two peptidome data. These peptides would be promising molecules for new anti-breast cancer drug development.

MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions.

PubMed

Mead, Jennifer A; Bianco, Luca; Ottone, Vanessa; Barton, Chris; Kay, Richard G; Lilley, Kathryn S; Bond, Nicholas J; Bessant, Conrad

2009-04-01

Multiple reaction monitoring (MRM) of peptides uses tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential studies. Using this technique, the specificity of precursor to product transitions is harnessed for quantitative analysis of multiple proteins in a single sample. The design of transitions is critical for the success of MRM experiments, but predicting signal intensity of peptides and fragmentation patterns ab initio is challenging given existing methods. The tool presented here, MRMaid (pronounced "mermaid") offers a novel alternative for rapid design of MRM transitions for the proteomics researcher. The program uses a combination of knowledge of the properties of optimal MRM transitions taken from expert practitioners and literature with MS/MS evidence derived from interrogation of a database of peptide identifications and their associated mass spectra. The tool also predicts retention time using a published model, allowing ordering of transition candidates. By exploiting available knowledge and resources to generate the most reliable transitions, this approach negates the need for theoretical prediction of fragmentation and the need to undertake prior "discovery" MS studies. MRMaid is a modular tool built around the Genome Annotating Proteomic Pipeline framework, providing a web-based solution with both descriptive and graphical visualizations of transitions. Predicted transition candidates are ranked based on a novel transition scoring system, and users may filter the results by selecting optional stringency criteria, such as omitting frequently modified residues, constraining the length of peptides, or omitting missed cleavages. Comparison with published transitions showed that MRMaid successfully predicted the peptide and product ion pairs in the majority of cases with appropriate retention time estimates. As the data content of the Genome Annotating Proteomic Pipeline repository increases, the coverage and reliability of MRMaid are set to increase further. MRMaid is freely available over the internet as an executable web-based service at www.mrmaid.info.

MRMaid, the Web-based Tool for Designing Multiple Reaction Monitoring (MRM) Transitions*

PubMed Central

Mead, Jennifer A.; Bianco, Luca; Ottone, Vanessa; Barton, Chris; Kay, Richard G.; Lilley, Kathryn S.; Bond, Nicholas J.; Bessant, Conrad

2009-01-01

Multiple reaction monitoring (MRM) of peptides uses tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential studies. Using this technique, the specificity of precursor to product transitions is harnessed for quantitative analysis of multiple proteins in a single sample. The design of transitions is critical for the success of MRM experiments, but predicting signal intensity of peptides and fragmentation patterns ab initio is challenging given existing methods. The tool presented here, MRMaid (pronounced “mermaid”) offers a novel alternative for rapid design of MRM transitions for the proteomics researcher. The program uses a combination of knowledge of the properties of optimal MRM transitions taken from expert practitioners and literature with MS/MS evidence derived from interrogation of a database of peptide identifications and their associated mass spectra. The tool also predicts retention time using a published model, allowing ordering of transition candidates. By exploiting available knowledge and resources to generate the most reliable transitions, this approach negates the need for theoretical prediction of fragmentation and the need to undertake prior “discovery” MS studies. MRMaid is a modular tool built around the Genome Annotating Proteomic Pipeline framework, providing a web-based solution with both descriptive and graphical visualizations of transitions. Predicted transition candidates are ranked based on a novel transition scoring system, and users may filter the results by selecting optional stringency criteria, such as omitting frequently modified residues, constraining the length of peptides, or omitting missed cleavages. Comparison with published transitions showed that MRMaid successfully predicted the peptide and product ion pairs in the majority of cases with appropriate retention time estimates. As the data content of the Genome Annotating Proteomic Pipeline repository increases, the coverage and reliability of MRMaid are set to increase further. MRMaid is freely available over the internet as an executable web-based service at www.mrmaid.info. PMID:19011259

Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility.

PubMed

Kashyap, Manju; Farooq, Umar; Jaiswal, Varun

2016-10-01

Human leukocyte antigen (HLA) plays significant role via the regulation of immune system and contribute in the progression and protection of many diseases. HLA molecules bind and present peptides to T- cell receptors which generate the immune response. HLA peptide interaction and molecular function of HLA molecule is the key to predict peptide binding and understanding its role in different diseases. The availability of accurate three dimensional (3D) structures is the initial step towards this direction. In the present work, homology modelling of important and frequent HLA-DRB1 alleles (07:01, 11:01 and 09:01) was done and acceptable models were generated. These modelled alleles were further refined and cross validated by using several methods including Ramachandran plot, Z-score, ERRAT analysis and root mean square deviation (RMSD) calculations. It is known that numbers of allelic variants are related to the susceptibility or protection of various infectious diseases. Difference in amino acid sequences and structures of alleles were also studied to understand the association of HLA with disease susceptibility and protection. Susceptible alleles showed more amino acid variations than protective alleles in three selected diseases caused by different pathogens. Amino acid variations at binding site were found to be more than other part of alleles. RMSD values were also higher at variable positions within binding site. Higher RMSD values indicate that mutations occurring at peptide binding site alter protein structure more than rest of the protein. Hence, these findings and modelled structures can be used to design HLA-DRB1 binding peptides to overcome low prediction accuracy of HLA class II binding peptides. Furthermore, it may help to understand the allele specific molecular mechanisms involved in susceptibility/resistance against pathogenic diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of tissue-specific targeting peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Seung-Hoon; Kim, Daejin; Park, Kisoo; Choi, Kihang; Choi, Yun-Jaie; Jung, Dong Hyun

2012-11-01

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.

Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation.

PubMed

Klein, Julie; Eales, James; Zürbig, Petra; Vlahou, Antonia; Mischak, Harald; Stevens, Robert

2013-04-01

In this study, we have developed Proteasix, an open-source peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. We developed a curated cleavage site (CS) database, containing 3500 entries about human protease/CS combinations. On top of this database, we built a tool, Proteasix, which allows CS retrieval and protease associations from a list of peptides. To establish the proof of concept of the approach, we used a list of 1388 peptides identified from human urine samples, and compared the prediction to the analysis of 1003 randomly generated amino acid sequences. Metalloprotease activity was predominantly involved in urinary peptide generation, and more particularly to peptides associated with extracellular matrix remodelling, compared to proteins from other origins. In comparison, random sequences returned almost no results, highlighting the specificity of the prediction. This study provides a tool that can facilitate linking of identified protein fragments to predicted protease activity, and therefore into presumed mechanisms of disease. Experiments are needed to confirm the in silico hypotheses; nevertheless, this approach may be of great help to better understand molecular mechanisms of disease, and define new biomarkers, and therapeutic targets. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Elucidation of Peptide-Directed Palladium Surface Structure for Biologically Tunable Nanocatalysts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bedford, Nicholas M.; Ramezani-Dakhel, Hadi; Slocik, Joseph M.

Peptide-enabled synthesis of inorganic nanostructures represents an avenue to access catalytic materials with tunable and optimized properties. This is achieved via peptide complexity and programmability that is missing in traditional ligands for catalytic nanomaterials. Unfortunately, there is limited information available to correlate peptide sequence to particle structure and catalytic activity to date. As such, the application of peptide-enabled nanocatalysts remains limited to trial and error approaches. In this paper, a hybrid experimental and computational approach is introduced to systematically elucidate biomolecule-dependent structure/function relationships for peptide-capped Pd nanocatalysts. Synchrotron X-ray techniques were used to uncover substantial particle surface structural disorder, whichmore » was dependent upon the amino acid sequence of the peptide capping ligand. Nanocatalyst configurations were then determined directly from experimental data using reverse Monte Carlo methods and further refined using molecular dynamics simulation, obtaining thermodynamically stable peptide-Pd nanoparticle configurations. Sequence-dependent catalytic property differences for C-C coupling and olefin hydrogenation were then eluddated by identification of the catalytic active sites at the atomic level and quantitative prediction of relative reaction rates. This hybrid methodology provides a clear route to determine peptide-dependent structure/function relationships, enabling the generation of guidelines for catalyst design through rational tailoring of peptide sequences« less

Elucidation of peptide-directed palladium surface structure for biologically tunable nanocatalysts.

PubMed

Bedford, Nicholas M; Ramezani-Dakhel, Hadi; Slocik, Joseph M; Briggs, Beverly D; Ren, Yang; Frenkel, Anatoly I; Petkov, Valeri; Heinz, Hendrik; Naik, Rajesh R; Knecht, Marc R

2015-05-26

Peptide-enabled synthesis of inorganic nanostructures represents an avenue to access catalytic materials with tunable and optimized properties. This is achieved via peptide complexity and programmability that is missing in traditional ligands for catalytic nanomaterials. Unfortunately, there is limited information available to correlate peptide sequence to particle structure and catalytic activity to date. As such, the application of peptide-enabled nanocatalysts remains limited to trial and error approaches. In this paper, a hybrid experimental and computational approach is introduced to systematically elucidate biomolecule-dependent structure/function relationships for peptide-capped Pd nanocatalysts. Synchrotron X-ray techniques were used to uncover substantial particle surface structural disorder, which was dependent upon the amino acid sequence of the peptide capping ligand. Nanocatalyst configurations were then determined directly from experimental data using reverse Monte Carlo methods and further refined using molecular dynamics simulation, obtaining thermodynamically stable peptide-Pd nanoparticle configurations. Sequence-dependent catalytic property differences for C-C coupling and olefin hydrogenation were then elucidated by identification of the catalytic active sites at the atomic level and quantitative prediction of relative reaction rates. This hybrid methodology provides a clear route to determine peptide-dependent structure/function relationships, enabling the generation of guidelines for catalyst design through rational tailoring of peptide sequences.

A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces.

PubMed

Lubin, Joseph H; Pacella, Michael S; Gray, Jeffrey J

2018-05-08

Although structures have been determined for many soluble proteins and an increasing number of membrane proteins, experimental structure determination methods are limited for complexes of proteins and solid surfaces. An economical alternative or complement to experimental structure determination is molecular simulation. Rosetta is one software suite that models protein-surface interactions, but Rosetta is normally benchmarked on soluble proteins. For surface interactions, the validity of the energy function is uncertain because it is a combination of independent parameters from energy functions developed separately for solution proteins and mineral surfaces. Here, we assess the performance of the RosettaSurface algorithm and test the accuracy of its energy function by modeling the adsorption of leucine/lysine (LK)-repeat peptides on methyl- and carboxy-terminated self-assembled monolayers (SAMs). We investigated how RosettaSurface predictions for this system compare with the experimental results, which showed that on both surfaces, LK-α peptides folded into helices and LK-β peptides held extended structures. Utilizing this model system, we performed a parametric analysis of Rosetta's Talaris energy function and determined that adjusting solvation parameters offered improved predictive accuracy. Simultaneously increasing lysine carbon hydrophilicity and the hydrophobicity of the surface methyl head groups yielded computational predictions most closely matching the experimental results. De novo models still should be interpreted skeptically unless bolstered in an integrative approach with experimental data.

Urine Trefoil Factors as Prognostic Biomarkers in Chronic Kidney Disease

PubMed Central

Yamanari, Toshio; Tanaka, Keiko; Morinaga, Hiroshi; Kitagawa, Masashi; Onishi, Akifumi; Ogawa-Akiyama, Ayu; Kano, Yuzuki; Mise, Koki; Ohmoto, Yasukazu; Shikata, Kenichi

2018-01-01

Introduction Trefoil factor family (TFF) peptides are increased in serum and urine in patients with chronic kidney disease (CKD). However, whether the levels of TFF predict the progression of CKD remains to be elucidated. Methods We determined the TFF levels using peptide-specific ELISA in spot urine samples and performed a prospective cohort study. The association between the levels of urine TFFs and other urine biomarkers as well as the renal prognosis was analyzed in 216 CKD patients (mean age: 53.7 years, 47.7% female, 56.9% with chronic glomerulonephritis, and mean eGFR: 58.5 ml/min/1.73 m2). Results The urine TFF1 and TFF3 levels significantly increased with the progression of CKD stages, but not the urine TFF2 levels. The TFF1 and TFF3 peptide levels predicted the progression of CKD ≥ stage 3b by ROC analysis (AUC 0.750 and 0.879, resp.); however, TFF3 alone predicted CKD progression in a multivariate logistic regression analysis (odds ratio 3.854, 95% confidence interval 1.316–11.55). The Kaplan-Meier survival curves demonstrated that patients with a higher TFF1 and TFF3 alone, or in combination with macroalbuminuria, had a significantly worse renal prognosis. Conclusion The data suggested that urine TFF peptides are associated with renal progression and the outcomes in patients with CKD. PMID:29850501

Using genome-wide measurements for computational prediction of SH2–peptide interactions

PubMed Central

Wunderlich, Zeba; Mirny, Leonid A.

2009-01-01

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions. PMID:19502496

The role of anti-cyclic citrullinated peptide antibodies in predicting rheumatoid arthritis.

PubMed

Rexhepi, Sylejman; Rexhepi, Mjellma; Sahatçiu-Meka, Vjollca; Tafaj, Argjend; Izairi, Remzi; Rexhepi, Blerta

2011-01-01

The study presents the results of predicting role of anti-cyclic citrullinated peptide antibodies in rheumatoid arthritis, compared to rheumatoid factor. 32 patients with rheumatoid arthritis were identified from a retrospective chart review. The results of our study show that presence of the rheumatoid factor has less diagnostic and prognostic significance than the anti-cyclic citrullinated peptide, and suggests its superiority in predicting an erosive disease course.

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.

PubMed

Chevrette, Marc G; Aicheler, Fabian; Kohlbacher, Oliver; Currie, Cameron R; Medema, Marnix H

2017-10-15

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool. SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3). chevrette@wisc.edu or marnix.medema@wur.nl. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics.

PubMed

Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas

2017-02-15

Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Identification and classification of conopeptides using profile Hidden Markov Models.

PubMed

Laht, Silja; Koua, Dominique; Kaplinski, Lauris; Lisacek, Frédérique; Stöcklin, Reto; Remm, Maido

2012-03-01

Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families. Copyright © 2012 Elsevier B.V. All rights reserved.

A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics

PubMed Central

Tang, Haixu; Li, Sujun; Ye, Yuzhen

2016-01-01

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro. PMID:27918579

Epitope predictions indicate the presence of two distinct types of epitope-antibody-reactivities determined by epitope profiling of intravenous immunoglobulins.

PubMed

Luštrek, Mitja; Lorenz, Peter; Kreutzer, Michael; Qian, Zilliang; Steinbeck, Felix; Wu, Di; Born, Nadine; Ziems, Bjoern; Hecker, Michael; Blank, Miri; Shoenfeld, Yehuda; Cao, Zhiwei; Glocker, Michael O; Li, Yixue; Fuellen, Georg; Thiesen, Hans-Jürgen

2013-01-01

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR.

Expansion of the neuropeptidome of the globally invasive marine crab Carcinus maenas.

PubMed

Christie, Andrew E

2016-09-01

Carcinus maenas is widely recognized as one of the world's most successful marine invasive species; its success as an invader is due largely to its ability to thrive under varied environmental conditions. The physiological/behavioral control systems that allow C. maenas to adapt to new environments are undoubtedly under hormonal control, the largest single class of hormones being peptides. While numerous studies have focused on identifying native C. maenas peptides, none has taken advantage of mining transcriptome shotgun assembly (TSA) sequence data, a strategy proven highly successful for peptide discovery in other crustaceans. Here, a C. maenas peptidome was predicted via in silico transcriptome mining. Thirty-seven peptide families were searched for in the extant TSA database, with transcripts encoding precursors for 29 groups identified. The pre/preprohormones deduced from the identified sequences allowed for the prediction of 263 distinct mature peptides, 193 of which are new discoveries for C. maenas. The predicted peptides include isoforms of adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone, diuretic hormone 31, diuretic hormone 44, eclosion hormone, FMRFamide-like peptide, HIGSLYRamide, intocin, leucokinin, myosuppressin, neuroparsin, neuropeptide F, orcokinin, pigment dispersing hormone, proctolin, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide, and tachykinin-related peptide. This peptidome is the largest predicted from any single crustacean using the in silico approach, and provides a platform for investigating peptidergic signaling in C. maenas, including control of the processes that allow for its success as a global marine invader. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of Scylla olivacea (Crustacea; Brachyura) peptide hormones using publicly accessible transcriptome shotgun assembly (TSA) sequences.

PubMed

Christie, Andrew E

2016-05-01

The aquaculture of crabs from the genus Scylla is of increasing economic importance for many Southeast Asian countries. Expansion of Scylla farming has led to increased efforts to understand the physiology and behavior of these crabs, and as such, there are growing molecular resources for them. Here, publicly accessible Scylla olivacea transcriptomic data were mined for putative peptide-encoding transcripts; the proteins deduced from the identified sequences were then used to predict the structures of mature peptide hormones. Forty-nine pre/preprohormone-encoding transcripts were identified, allowing for the prediction of 187 distinct mature peptides. The identified peptides included isoforms of adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon β, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone/molt-inhibiting hormone, diuretic hormone 31, eclosion hormone, FMRFamide-like peptide, HIGSLYRamide, insulin-like peptide, intocin, leucokinin, myosuppressin, neuroparsin, neuropeptide F, orcokinin, pigment dispersing hormone, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide and tachykinin-related peptide, all well-known neuropeptide families. Surprisingly, the tissue used to generate the transcriptome mined here is reported to be testis. Whether or not the testis samples had neural contamination is unknown. However, if the peptides are truly produced by this reproductive organ, it could have far reaching consequences for the study of crustacean endocrinology, particularly in the area of reproductive control. Regardless, this peptidome is the largest thus far predicted for any brachyuran (true crab) species, and will serve as a foundation for future studies of peptidergic control in members of the commercially important genus Scylla. Copyright © 2016 Elsevier Inc. All rights reserved.

The Separation and Quantitation of Peptides with and without Oxidation of Methionine and Deamidation of Asparagine Using Hydrophilic Interaction Liquid Chromatography with Mass Spectrometry (HILIC-MS)

NASA Astrophysics Data System (ADS)

Badgett, Majors J.; Boyes, Barry; Orlando, Ron

2017-05-01

Peptides with deamidated asparagine residues and oxidized methionine residues are often not resolved sufficiently to allow quantitation of their native and modified forms using reversed phase (RP) chromatography. The accurate quantitation of these modifications is vital in protein biotherapeutic analysis because they can affect a protein's function, activity, and stability. We demonstrate here that hydrophilic interaction liquid chromatography (HILIC) adequately and predictably separates peptides with these modifications from their native counterparts. Furthermore, coefficients describing the extent of the hydrophilicity of these modifications have been derived and were incorporated into a previously made peptide retention prediction model that is capable of predicting the retention times of peptides with and without these modifications.

The Separation and Quantitation of Peptides with and without Oxidation of Methionine and Deamidation of Asparagine Using Hydrophilic Interaction Liquid Chromatography with Mass Spectrometry (HILIC-MS).

PubMed

Badgett, Majors J; Boyes, Barry; Orlando, Ron

2017-05-01

Peptides with deamidated asparagine residues and oxidized methionine residues are often not resolved sufficiently to allow quantitation of their native and modified forms using reversed phase (RP) chromatography. The accurate quantitation of these modifications is vital in protein biotherapeutic analysis because they can affect a protein's function, activity, and stability. We demonstrate here that hydrophilic interaction liquid chromatography (HILIC) adequately and predictably separates peptides with these modifications from their native counterparts. Furthermore, coefficients describing the extent of the hydrophilicity of these modifications have been derived and were incorporated into a previously made peptide retention prediction model that is capable of predicting the retention times of peptides with and without these modifications. Graphical Abstract ᅟ.

Probabilistic consensus scoring improves tandem mass spectrometry peptide identification.

PubMed

Nahnsen, Sven; Bertsch, Andreas; Rahnenführer, Jörg; Nordheim, Alfred; Kohlbacher, Oliver

2011-08-05

Database search is a standard technique for identifying peptides from their tandem mass spectra. To increase the number of correctly identified peptides, we suggest a probabilistic framework that allows the combination of scores from different search engines into a joint consensus score. Central to the approach is a novel method to estimate scores for peptides not found by an individual search engine. This approach allows the estimation of p-values for each candidate peptide and their combination across all search engines. The consensus approach works better than any single search engine across all different instrument types considered in this study. Improvements vary strongly from platform to platform and from search engine to search engine. Compared to the industry standard MASCOT, our approach can identify up to 60% more peptides. The software for consensus predictions is implemented in C++ as part of OpenMS, a software framework for mass spectrometry. The source code is available in the current development version of OpenMS and can easily be used as a command line application or via a graphical pipeline designer TOPPAS.

Comprehensive computational design of ordered peptide macrocycles

PubMed Central

Hosseinzadeh, Parisa; Bhardwaj, Gaurav; Mulligan, Vikram Khipple; Shortridge, Matthew D.; Craven, Timothy W.; Pardo-Avila, Fátima; Rettie, Stephen A.; Kim, David E.; Silva, Daniel-Adriano; Ibrahim, Yehia M.; Webb, Ian K.; Cort, John R.; Adkins, Joshua N.; Varani, Gabriele; Baker, David

2018-01-01

Mixed-chirality peptide macrocycles such as cyclosporine are among the most potent therapeutics identified to date, but there is currently no way to systematically search the structural space spanned by such compounds. Natural proteins do not provide a useful guide: Peptide macrocycles lack regular secondary structures and hydrophobic cores, and can contain local structures not accessible with L-amino acids. Here, we enumerate the stable structures that can be adopted by macrocyclic peptides composed of L- and D-amino acids by near-exhaustive backbone sampling followed by sequence design and energy landscape calculations. We identify more than 200 designs predicted to fold into single stable structures, many times more than the number of currently available unbound peptide macrocycle structures. Nuclear magnetic resonance structures of 9 of 12 designed 7- to 10-residue macrocycles, and three 11- to 14-residue bicyclic designs, are close to the computational models. Our results provide a nearly complete coverage of the rich space of structures possible for short peptide macrocycles and vastly increase the available starting scaffolds for both rational drug design and library selection methods. PMID:29242347

Structure-based characterization of the binding of peptide to the human endophilin-1 Src homology 3 domain using position-dependent noncovalent potential analysis.

PubMed

Fu, Chunjiang; Wu, Gang; Lv, Fenglin; Tian, Feifei

2012-05-01

Many protein-protein interactions are mediated by a peptide-recognizing domain, such as WW, PDZ, or SH3. In the present study, we describe a new method called position-dependent noncovalent potential analysis (PDNPA), which can accurately characterize the nonbonding profile between the human endophilin-1 Src homology 3 (hEndo1 SH3) domain and its peptide ligands and quantitatively predict the binding affinity of peptide to hEndo1 SH3. In this procedure, structure models of diverse peptides in complex with the hEndo1 SH3 domain are constructed by molecular dynamics simulation and a virtual mutagenesis protocol. Subsequently, three noncovalent interactions associated with each position of the peptide ligand in the complexed state are analyzed using empirical potential functions, and the resulting potential descriptors are then correlated with the experimentally measured affinity on the basis of 1997 hEndo1 SH3-binding peptides with known activities, using linear partial least squares regression (PLS) and the nonlinear support vector machine (SVM). The results suggest that: (i) the electrostatics appears to be more important than steric properties and hydrophobicity in the formation of the hEndo1 SH3-peptide complex; (ii) P(-4) of the core decapeptide ligand with the sequence pattern P(-6)P(-5)P(-4)P(-3)P(-2)P(-1)P(0)P(1)P(2)P(3) is the most important position in terms of determining both the stability and specificity of the architecture of the complex, and; (iii) nonlinear SVM appears to be more effective than linear PLS for accurately predicting the binding affinity of a peptide ligand to hEndo1 SH3, whereas PLS models are straightforward and easy to interpret as compared to those built by SVM.

A comprehensive analysis of the thermodynamic events involved in ligand receptor binding using CoRIA and its variants

NASA Astrophysics Data System (ADS)

Verma, Jitender; Khedkar, Vijay M.; Prabhu, Arati S.; Khedkar, Santosh A.; Malde, Alpeshkumar K.; Coutinho, Evans C.

2008-02-01

Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA ( rCoRIA) and mixed-CoRIA ( mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole ( rCoRIA) and with individual active site residues in the receptor ( mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r 2 (correlation coefficient) and r 2 pred (predictive r 2). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.

In Silico Analysis of Six Known Leishmania major Antigens and In Vitro Evaluation of Specific Epitopes Eliciting HLA-A2 Restricted CD8 T Cell Response

PubMed Central

Seyed, Negar; Zahedifard, Farnaz; Safaiyan, Shima; Gholami, Elham; Doustdari, Fatemeh; Azadmanesh, Kayhan; Mirzaei, Maryam; Saeedi Eslami, Nasir; Khadem Sadegh, Akbar; Eslami far, Ali; Sharifi, Iraj; Rafati, Sima

2011-01-01

Background As a potent CD8+ T cell activator, peptide vaccine has found its way in vaccine development against intracellular infections and cancer, but not against leishmaniasis. The first step toward a peptide vaccine is epitope mapping of different proteins according to the most frequent HLA types in a population. Methods and Findings Six Leishmania (L.) major-related candidate antigens (CPB,CPC,LmsTI-1,TSA,LeIF and LPG-3) were screened for potential CD8+ T cell activating 9-mer epitopes presented by HLA-A*0201 (the most frequent HLA-A allele). Online software including SYFPEITHI, BIMAS, EpiJen, Rankpep, nHLApred, NetCTL and Multipred were used. Peptides were selected only if predicted by almost all programs, according to their predictive scores. Pan-A2 presentation of selected peptides was confirmed by NetMHCPan1.1. Selected peptides were pooled in four peptide groups and the immunogenicity was evaluated by in vitro stimulation and intracellular cytokine assay of PBMCs from HLA-A2+ individuals recovered from L. major. HLA-A2− individuals recovered from L. major and HLA-A2+ healthy donors were included as control groups. Individual response of HLA-A2+ recovered volunteers as percent of CD8+/IFN-γ+ T cells after in vitro stimulation against peptide pools II and IV was notably higher than that of HLA-A2− recovered individuals. Based on cutoff scores calculated from the response of HLA-A2− recovered individuals, 31.6% and 13.3% of HLA-A2+ recovered persons responded above cutoff in pools II and IV, respectively. ELISpot and ELISA results confirmed flow cytometry analysis. The response of HLA-A2− recovered individuals against peptide pools I and III was detected similar and even higher than HLA-A2+ recovered individuals. Conclusion Using in silico prediction we demonstrated specific response to LmsTI-1 (pool II) and LPG-3- (pool IV) related peptides specifically presented in HLA-A*0201 context. This is among the very few reports mapping L. major epitopes for human HLA types. Studies like this will speed up polytope vaccine idea towards leishmaniasis. PMID:21909442

Peptide retention prediction using hydrophilic interaction liquid chromatography coupled to mass spectrometry.

PubMed

Badgett, Majors J; Boyes, Barry; Orlando, Ron

2018-02-16

A model that predicts retention for peptides using a HALO ® penta-HILIC column and gradient elution was created. Coefficients for each amino acid were derived using linear regression analysis and these coefficients can be summed to predict the retention of peptides. This model has a high correlation between experimental and predicted retention times (0.946), which is on par with previous RP and HILIC models. External validation of the model was performed using a set of H. pylori samples on the same LC-MS system used to create the model, and the deviation from actual to predicted times was low. Apart from amino acid composition, length and location of amino acid residues on a peptide were examined and two site-specific corrections for hydrophobic residues at the N-terminus as well as hydrophobic residues one spot over from the N-terminus were created. Copyright © 2017 Elsevier B.V. All rights reserved.

SVM-Based Prediction of Propeptide Cleavage Sites in Spider Toxins Identifies Toxin Innovation in an Australian Tarantula

PubMed Central

Wong, Emily S. W.; Hardy, Margaret C.; Wood, David; Bailey, Timothy; King, Glenn F.

2013-01-01

Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the SpiderP website. PMID:23894279

Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong

2017-01-01

Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell

PubMed Central

Sperschneider, Jana; Catanzariti, Ann-Maree; DeBoer, Kathleen; Petre, Benjamin; Gardiner, Donald M.; Singh, Karam B.; Dodds, Peter N.; Taylor, Jennifer M.

2017-01-01

Pathogens secrete effector proteins and many operate inside plant cells to enable infection. Some effectors have been found to enter subcellular compartments by mimicking host targeting sequences. Although many computational methods exist to predict plant protein subcellular localization, they perform poorly for effectors. We introduce LOCALIZER for predicting plant and effector protein localization to chloroplasts, mitochondria, and nuclei. LOCALIZER shows greater prediction accuracy for chloroplast and mitochondrial targeting compared to other methods for 652 plant proteins. For 107 eukaryotic effectors, LOCALIZER outperforms other methods and predicts a previously unrecognized chloroplast transit peptide for the ToxA effector, which we show translocates into tobacco chloroplasts. Secretome-wide predictions and confocal microscopy reveal that rust fungi might have evolved multiple effectors that target chloroplasts or nuclei. LOCALIZER is the first method for predicting effector localisation in plants and is a valuable tool for prioritizing effector candidates for functional investigations. LOCALIZER is available at http://localizer.csiro.au/. PMID:28300209

The Preferred Substrates for Transglutaminase 2 in a Complex Wheat Gluten Digest Are Peptide Fragments Harboring Celiac Disease T-Cell Epitopes

PubMed Central

Dørum, Siri; Arntzen, Magnus Ø.; Qiao, Shuo-Wang; Holm, Anders; Koehler, Christian J.; Thiede, Bernd; Sollid, Ludvig M.; Fleckenstein, Burkhard

2010-01-01

Background Celiac disease is a T-cell mediated chronic inflammatory disorder of the gut that is induced by dietary exposure to gluten proteins. CD4+ T cells of the intestinal lesion recognize gluten peptides in the context of HLA-DQ2.5 or HLA-DQ8 and the gluten derived peptides become better T-cell antigens after deamidation catalyzed by the enzyme transglutaminase 2 (TG2). In this study we aimed to identify the preferred peptide substrates of TG2 in a heterogeneous proteolytic digest of whole wheat gluten. Methods A method was established to enrich for preferred TG2 substrates in a complex gluten peptide mixture by tagging with 5-biotinamido-pentylamine. Tagged peptides were isolated and then identified by nano-liquid chromatography online-coupled to tandem mass spectrometry, database searching and final manual data validation. Results We identified 31 different peptides as preferred substrates of TG2. Strikingly, the majority of these peptides were harboring known gluten T-cell epitopes. Five TG2 peptide substrates that were predicted to bind to HLA-DQ2.5 did not contain previously characterized sequences of T-cell epitopes. Two of these peptides elicited T-cell responses when tested for recognition by intestinal T-cell lines of celiac disease patients, and thus they contain novel candidate T-cell epitopes. We also found that the intact 9mer core sequences of the respective epitopes were not present in all peptide substrates. Interestingly, those epitopes that were represented by intact forms were frequently recognized by T cells in celiac disease patients, whereas those that were present in truncated versions were infrequently recognized. Conclusion TG2 as well as gastrointestinal proteolysis play important roles in the selection of gluten T-cell epitopes in celiac disease. PMID:21124911

Investigating quantitation of phosphorylation using MALDI-TOF mass spectrometry.

PubMed

Parker, Laurie; Engel-Hall, Aaron; Drew, Kevin; Steinhardt, George; Helseth, Donald L; Jabon, David; McMurry, Timothy; Angulo, David S; Kron, Stephen J

2008-04-01

Despite advances in methods and instrumentation for analysis of phosphopeptides using mass spectrometry, it is still difficult to quantify the extent of phosphorylation of a substrate because of physiochemical differences between unphosphorylated and phosphorylated peptides. Here we report experiments to investigate those differences using MALDI-TOF mass spectrometry for a set of synthetic peptides by creating calibration curves of known input ratios of peptides/phosphopeptides and analyzing their resulting signal intensity ratios. These calibration curves reveal subtleties in sequence-dependent differences for relative desorption/ionization efficiencies that cannot be seen from single-point calibrations. We found that the behaviors were reproducible with a variability of 5-10% for observed phosphopeptide signal. Although these data allow us to begin addressing the issues related to modeling these properties and predicting relative signal strengths for other peptide sequences, it is clear that this behavior is highly complex and needs to be further explored. John Wiley & Sons, Ltd

Investigating quantitation of phosphorylation using MALDI-TOF mass spectrometry

PubMed Central

Parker, Laurie; Engel-Hall, Aaron; Drew, Kevin; Steinhardt, George; Helseth, Donald L.; Jabon, David; McMurry, Timothy; Angulo, David S.; Kron, Stephen J.

2010-01-01

Despite advances in methods and instrumentation for analysis of phosphopeptides using mass spectrometry, it is still difficult to quantify the extent of phosphorylation of a substrate due to physiochemical differences between unphosphorylated and phosphorylated peptides. Here we report experiments to investigate those differences using MALDI-TOF mass spectrometry for a set of synthetic peptides by creating calibration curves of known input ratios of peptides/phosphopeptides and analyzing their resulting signal intensity ratios. These calibration curves reveal subtleties in sequence-dependent differences for relative desorption/ionization efficiencies that cannot be seen from single-point calibrations. We found that the behaviors were reproducible with a variability of 5–10% for observed phosphopeptide signal. Although these data allow us to begin addressing the issues related to modeling these properties and predicting relative signal strengths for other peptide sequences, it is clear this behavior is highly complex and needs to be further explored. PMID:18064576

Chimeric peptide constructs comprising linear B-cell epitopes: application to the serodiagnosis of infectious diseases.

PubMed

Lu, Yudong; Li, Zhong; Teng, Huan; Xu, Hongke; Qi, Songnan; He, Jian'an; Gu, Dayong; Chen, Qijun; Ma, Hongwei

2015-08-21

Linear B-cell epitopes are ideal biomarkers for the serodiagnosis of infectious diseases. However, the long-predicted diagnostic value of epitopes has not been realized. Here, we demonstrated a method, diagnostic epitopes in four steps (DEIFS), that delivers a combination of epitopes for the serodiagnosis of infectious diseases with a high success rate. Using DEIFS for malaria, we identified 6 epitopes from 8 peptides and combined them into 3 chimeric peptide constructs. Along with 4 other peptides, we developed a rapid diagnostic test (RDT), which is able to differentiate Plasmodium falciparum (P. falciparum) from Plasmodium vivax (P. vivax) infections with 95.6% overall sensitivity and 99.1% overall specificity. In addition to applications in diagnosis, DEIFS could also be used in the diagnosis of virus and bacterium infections, discovery of vaccine candidates, evaluation of vaccine potency, and study of disease progression.

Chimeric peptide constructs comprising linear B-cell epitopes: application to the serodiagnosis of infectious diseases

PubMed Central

Lu, Yudong; Li, Zhong; Teng, Huan; Xu, Hongke; Qi, Songnan; He, Jian’an; Gu, Dayong; Chen, Qijun; Ma, Hongwei

2015-01-01

Linear B-cell epitopes are ideal biomarkers for the serodiagnosis of infectious diseases. However, the long-predicted diagnostic value of epitopes has not been realized. Here, we demonstrated a method, diagnostic epitopes in four steps (DEIFS), that delivers a combination of epitopes for the serodiagnosis of infectious diseases with a high success rate. Using DEIFS for malaria, we identified 6 epitopes from 8 peptides and combined them into 3 chimeric peptide constructs. Along with 4 other peptides, we developed a rapid diagnostic test (RDT), which is able to differentiate Plasmodium falciparum (P. falciparum) from Plasmodium vivax (P. vivax) infections with 95.6% overall sensitivity and 99.1% overall specificity. In addition to applications in diagnosis, DEIFS could also be used in the diagnosis of virus and bacterium infections, discovery of vaccine candidates, evaluation of vaccine potency, and study of disease progression. PMID:26293607

Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph.

PubMed

Saidemberg, Daniel M; Baptista-Saidemberg, Nicoli B; Palma, Mario S

2011-09-01

When searching for prospective novel peptides, it is difficult to determine the biological activity of a peptide based only on its sequence. The "trial and error" approach is generally laborious, expensive and time consuming due to the large number of different experimental setups required to cover a reasonable number of biological assays. To simulate a virtual model for Hymenoptera insects, 166 peptides were selected from the venoms and hemolymphs of wasps, bees and ants and applied to a mathematical model of multivariate analysis, with nine different chemometric components: GRAVY, aliphaticity index, number of disulfide bonds, total residues, net charge, pI value, Boman index, percentage of alpha helix, and flexibility prediction. Principal component analysis (PCA) with non-linear iterative projections by alternating least-squares (NIPALS) algorithm was performed, without including any information about the biological activity of the peptides. This analysis permitted the grouping of peptides in a way that strongly correlated to the biological function of the peptides. Six different groupings were observed, which seemed to correspond to the following groups: chemotactic peptides, mastoparans, tachykinins, kinins, antibiotic peptides, and a group of long peptides with one or two disulfide bonds and with biological activities that are not yet clearly defined. The partial overlap between the mastoparans group and the chemotactic peptides, tachykinins, kinins and antibiotic peptides in the PCA score plot may be used to explain the frequent reports in the literature about the multifunctionality of some of these peptides. The mathematical model used in the present investigation can be used to predict the biological activities of novel peptides in this system, and it may also be easily applied to other biological systems. Copyright © 2011 Elsevier Inc. All rights reserved.

BioPepDB: an integrated data platform for food-derived bioactive peptides.

PubMed

Li, Qilin; Zhang, Chao; Chen, Hongjun; Xue, Jitong; Guo, Xiaolei; Liang, Ming; Chen, Ming

2018-03-12

Food-derived bioactive peptides play critical roles in regulating most biological processes and have considerable biological, medical and industrial importance. However, a large number of active peptides data, including sequence, function, source, commercial product information, references and other information are poorly integrated. BioPepDB is a searchable database of food-derived bioactive peptides and their related articles, including more than four thousand bioactive peptide entries. Moreover, BioPepDB provides modules of prediction and hydrolysis-simulation for discovering novel peptides. It can serve as a reference database to investigate the function of different bioactive peptides. BioPepDB is available at http://bis.zju.edu.cn/biopepdbr/ . The web page utilises Apache, PHP5 and MySQL to provide the user interface for accessing the database and predict novel peptides. The database itself is operated on a specialised server.

A novel in chemico method to detect skin sensitizers in highly diluted reaction conditions.

PubMed

Yamamoto, Yusuke; Tahara, Haruna; Usami, Ryota; Kasahara, Toshihiko; Jimbo, Yoshihiro; Hioki, Takanori; Fujita, Masaharu

2015-11-01

The direct peptide reactivity assay (DPRA) is a simple and versatile alternative method for the evaluation of skin sensitization that involves the reaction of test chemicals with two peptides. However, this method requires concentrated solutions of test chemicals, and hydrophobic substances may not dissolve at the concentrations required. Furthermore, hydrophobic test chemicals may precipitate when added to the reaction solution. We previously established a high-sensitivity method, the amino acid derivative reactivity assay (ADRA). This method uses novel cysteine (NAC) and novel lysine derivatives (NAL), which were synthesized by introducing a naphthalene ring to the amine group of cysteine and lysine residues. In this study, we modified the ADRA method by reducing the concentration of the test chemicals 100-fold. We investigated the accuracy of skin sensitization predictions made using the modified method, which was designated the ADRA-dilutional method (ADRA-DM). The predictive accuracy of the ADRA-DM for skin sensitization was 90% for 82 test chemicals which were also evaluated via the ADRA, and the predictive accuracy in the ADRA-DM was higher than that in the ADRA and DPRA. Furthermore, no precipitation of test compounds was observed at the initiation of the ADRA-DM reaction. These results show that the ADRA-DM allowed the use of test chemicals at concentrations two orders of magnitude lower than that possible with the ADRA. In addition, ADRA-DM does not have the restrictions on test compound solubility that were a major problem with the DPRA. Therefore, the ADRA-DM is a versatile and useful method. Copyright © 2015 John Wiley & Sons, Ltd.

Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences

PubMed Central

2013-01-01

Background Comparison of short peptides which form amyloid-fibrils with their homologues that may form amorphous β-aggregates but not fibrils, can aid development of novel amyloid-containing nanomaterials with well defined morphologies and characteristics. The knowledge gained from the comparative analysis could also be applied towards identifying potential aggregation prone regions in proteins, which are important for biotechnology applications or have been implicated in neurodegenerative diseases. In this work we have systematically analyzed a set of 139 amyloid-fibril hexa-peptides along with a highly homologous set of 168 hexa-peptides that do not form amyloid fibrils for their position-wise as well as overall amino acid compositions and averages of 49 selected amino acid properties. Results Amyloid-fibril forming peptides show distinct preferences and avoidances for amino acid residues to occur at each of the six positions. As expected, the amyloid fibril peptides are also more hydrophobic than non-amyloid peptides. We have used the results of this analysis to develop statistical potential energy values for the 20 amino acid residues to occur at each of the six different positions in the hexa-peptides. The distribution of the potential energy values in 139 amyloid and 168 non-amyloid fibrils are distinct and the amyloid-fibril peptides tend to be more stable (lower total potential energy values) than non-amyloid peptides. The average frequency of occurrence of these peptides with lower than specific cutoff energies at different positions is 72% and 50%, respectively. The potential energy values were used to devise a statistical discriminator to distinguish between amyloid-fibril and non-amyloid peptides. Our method could identify the amyloid-fibril forming hexa-peptides to an accuracy of 89%. On the other hand, the accuracy of identifying non-amyloid peptides was only 54%. Further attempts were made to improve the prediction accuracy via machine learning. This resulted in an overall accuracy of 82.7% with the sensitivity and specificity of 81.3% and 83.9%, respectively, in 10-fold cross-validation method. Conclusions Amyloid-fibril forming hexa-peptides show position specific sequence features that are different from those which may form amorphous β-aggregates. These positional preferences are found to be important features for discriminating amyloid-fibril forming peptides from their homologues that don't form amyloid-fibrils. PMID:23815227

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction*

PubMed Central

Rahman, Kh. Shamsur; Chowdhury, Erfan Ullah; Sachse, Konrad; Kaltenboeck, Bernhard

2016-01-01

X-ray crystallography has shown that an antibody paratope typically binds 15–22 amino acids (aa) of an epitope, of which 2–5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6–11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7–12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, ≤11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16–30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences. PMID:27189949

Design-Based Peptidomimetic Ligand Discovery to Target HIV TAR RNA Using Comparative Analysis of Different Docking Methods.

PubMed

Fu, Junjie; Xia, Amy; Dai, Yao; Qi, Xin

2016-01-01

Discovering molecules capable of binding to HIV trans-activation responsive region (TAR) RNA thereby disrupting its interaction with Tat protein is an attractive strategy for developing novel antiviral drugs. Computational docking is considered as a useful tool for predicting binding affinity and conducting virtual screening. Although great progress in predicting protein-ligand interactions has been achieved in the past few decades, modeling RNA-ligand interactions is still largely unexplored due to the highly flexible nature of RNA. In this work, we performed molecular docking study with HIV TAR RNA using previously identified cyclic peptide L22 and its analogues with varying affinities toward HIV-1 TAR RNA. Furthermore, sarcosine scan was conducted to generate derivatives of CGP64222, a peptide-peptoid hybrid with inhibitory activity on Tat/TAR RNA interaction. Each compound was docked using CDOCKER, Surflex-Dock and FlexiDock to compare the effectiveness of each method. It was found that FlexiDock energy values correlated well with the experimental Kd values and could be used to predict the affinity of the ligands toward HIV-1 TAR RNA with a superior accuracy. Our results based on comparative analysis of different docking methods in RNA-ligand modeling will facilitate the structure-based discovery of HIV TAR RNA ligands for antiviral therapy.

Prediction of the neuropeptidomes of members of the Astacidea (Crustacea, Decapoda) using publicly accessible transcriptome shotgun assembly (TSA) sequence data.

PubMed

Christie, Andrew E; Chi, Megan

2015-12-01

The decapod infraorder Astacidea is comprised of clawed lobsters and freshwater crayfish. Due to their economic importance and their use as models for investigating neurochemical signaling, much work has focused on elucidating their neurochemistry, particularly their peptidergic systems. Interestingly, no astacidean has been the subject of large-scale peptidomic analysis via in silico transcriptome mining, this despite growing transcriptomic resources for members of this taxon. Here, the publicly accessible astacidean transcriptome shotgun assembly data were mined for putative peptide-encoding transcripts; these sequences were used to predict the structures of mature neuropeptides. One hundred seventy-six distinct peptides were predicted for Procambarus clarkii, including isoforms of adipokinetic hormone-corazonin-like peptide (ACP), allatostatin A (AST-A), allatostatin B, allatostatin C (AST-C) bursicon α, bursicon β, CCHamide, crustacean hyperglycemic hormone (CHH)/ion transport peptide (ITP), diuretic hormone 31 (DH31), eclosion hormone (EH), FMRFamide-like peptide, GSEFLamide, intocin, leucokinin, neuroparsin, neuropeptide F, pigment dispersing hormone, pyrokinin, RYamide, short neuropeptide F (sNPF), SIFamide, sulfakinin and tachykinin-related peptide (TRP). Forty-six distinct peptides, including isoforms of AST-A, AST-C, bursicon α, CCHamide, CHH/ITP, DH31, EH, intocin, myosuppressin, neuroparsin, red pigment concentrating hormone, sNPF and TRP, were predicted for Pontastacus leptodactylus, with a bursicon β and a neuroparsin predicted for Cherax quadricarinatus. The identification of ACP is the first from a decapod, while the predictions of CCHamide, EH, GSEFLamide, intocin, neuroparsin and RYamide are firsts for the Astacidea. Collectively, these data greatly expand the catalog of known astacidean neuropeptides and provide a foundation for functional studies of peptidergic signaling in members of this decapod infraorder. Copyright © 2015 Elsevier Inc. All rights reserved.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

Chemical synthesis and characterization of peptides and oligomeric proteins designed to form transmembrane ion channels.

PubMed

Iwamoto, T; Grove, A; Montal, M O; Montal, M; Tomich, J M

1994-06-01

A strategy for the synthesis of peptides and oligomeric proteins designed to form transmembrane ion channels is described. A folding motif that exhibits a functional ionic pore encompasses amphipathic alpha-helices organized as a four-helix bundle around a central hydrophilic pore. The channel-forming activity of monomeric amphipathic peptides may be examined after reconstitution in lipid bilayers in which peptides self-assemble into conductive oligomers. The covalent attachment of channel-forming peptides to the lysine epsilon-amino groups of a template molecule (KKKPGKEKG) specifies oligomeric number and facilitates the study of ionic permeation and channel blockade. Here we describe detailed protocols for the total synthesis of peptides and template-assembled four-helix bundle proteins, exemplified with the sequence of M2 delta (EKM-STAISVLLAQAVFLLLTSQR), considered involved in lining the pore of the nicotinic acetylcholine receptor channel. For comparison, the synthesis of a second four-helix bundle, T4CaIVS3 with the sequence of predicted transmembrane segment S3 (DPWNVFDFLIVIGSIIDVILSE) of the fourth repeat of the L-type voltage-gated calcium channel, is included. Peptides and proteins are synthesized step-wise by solid-phase methods, purified by reversed-phase HPLC, and homogeneity ascertained by analytical HPLC, capillary zone electrophoresis, SDS/PAGE, amino acid analysis and sequencing. Optimization of synthetic procedures for hydrophobic molecules include reducing resin substitution to avoid steric hindrance and aggregation of the final product. Protocols for the preparation of the samples prior to HPLC purification as well as the conditions and columns required for successful purification are presented. The methods developed are generally applicable for the chemical synthesis, purification and characterization of amphipathic peptides and template directed helical bundle proteins.

Multicenter comparison of 18F-FDG and 68Ga-DOTA-peptide PET/CT for pulmonary carcinoid.

PubMed

Lococo, Filippo; Perotti, Germano; Cardillo, Giuseppe; De Waure, Chiara; Filice, Angelina; Graziano, Paolo; Rossi, Giulio; Sgarbi, Giorgio; Stefanelli, Antonella; Giordano, Alessandro; Granone, Pierluigi; Rindi, Guido; Versari, Annibale; Rufini, Vittoria

2015-03-01

The aims of this study were to retrospectively evaluate and compare the detection rate (DR) of 68Ga-DOTA-peptide and 18F-FDG PET/CT in the preoperative workup of patients with pulmonary carcinoid (PC) and to assess the utility of various functional indices obtained with the 2 tracers in predicting the histological characterization of PC, that is, typical versus atypical. Thirty-three consecutive patients with confirmed PC referred for 18F-FDG and 68Ga-DOTA-peptide PET/CT in 2 centers between January 2009 and April 2013 were included. The semiquantitative evaluation included the SUV max, the SUV of the tumor relative to the maximal liver uptake for 18F-FDG (SUV T/L) or the maximal spleen uptake for 68Ga-DOTA-peptides (SUV T/S), the ratio between SUV max of 68Ga-DOTA-peptides PET/CT, and the SUV max of 18F-FDG PET/CT (SUV max ratio). Histology was used as reference standard. Definitive diagnosis consisted of 23 typical carcinoids (TCs) and 10 atypical carcinoids. 18F-FDG PET/CT was positive in 18 cases and negative in 15 (55% DR). 68Ga-DOTA-peptide PET/CT was positive in 26 cases and negative in 7 (79% DR). In the subgroup analysis, 68Ga-DOTA-peptide PET/CT was superior in detecting TC (91% DR; P < 0.001), whereas 18F-FDG PET/CT was superior in detecting atypical carcinoid (100% DR; P = 0.04). The SUV max ratio was the most accurate semiquantitative index in identifying TC. Overall diagnostic performance of PET/CT in detecting PC is optimal when integrating 18F-FDG and 68Ga-DOTA-peptide PET/CT findings. In the subgroup analysis, the SUV max ratio seems to be the most accurate index in predicting TC. Both methods should be performed when PC is suspected or when the histological subtype is undefined.

Prediction of binding modes between protein L-isoaspartyl (D-aspartyl) O-methyltransferase and peptide substrates including isomerized aspartic acid residues using in silico analytic methods for the substrate screening.

PubMed

Oda, Akifumi; Noji, Ikuhiko; Fukuyoshi, Shuichi; Takahashi, Ohgi

2015-12-10

Because the aspartic acid (Asp) residues in proteins are occasionally isomerized in the human body, not only l-α-Asp but also l-β-Asp, D-α-Asp and D-β-Asp are found in human proteins. In these isomerized aspartic acids, the proportion of D-β-Asp is the largest and the proportions of l-β-Asp and D-α-Asp found in human proteins are comparatively small. To explain the proportions of aspartic acid isomers, the possibility of an enzyme able to repair l-β-Asp and D-α-Asp is frequently considered. The protein L-isoaspartyl (D-aspartyl) O-methyltransferase (PIMT) is considered one of the possible repair enzymes for l-β-Asp and D-α-Asp. Human PIMT is an enzyme that recognizes both l-β-Asp and D-α-Asp, and catalyzes the methylation of their side chains. In this study, the binding modes between PIMT and peptide substrates containing l-β-Asp or D-α-Asp residues were investigated using computational protein-ligand docking and molecular dynamics simulations. The results indicate that carboxyl groups of both l-β-Asp and D-α-Asp were recognized in similar modes by PIMT and that the C-terminal regions of substrate peptides were located in similar positions on PIMT for both the l-β-Asp and D-α-Asp peptides. In contrast, for peptides containing l-α-Asp or D-β-Asp residues, which are not substrates of PIMT, the computationally constructed binding modes between PIMT and peptides greatly differed from those between PIMT and substrates. In the nonsubstrate peptides, not inter- but intra-molecular hydrogen bonds were observed, and the conformations of peptides were more rigid than those of substrates. Thus, the in silico analytical methods were able to distinguish substrates from nonsubstrates and the computational methods are expected to complement experimental analytical methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Bioinformatic prediction of arthropod/nematode-like peptides in non-arthropod, non-nematode members of the Ecdysozoa.

PubMed

Christie, Andrew E; Nolan, Daniel H; Garcia, Zachery A; McCoole, Matthew D; Harmon, Sarah M; Congdon-Jones, Benjamin; Ohno, Paul; Hartline, Niko; Congdon, Clare Bates; Baer, Kevin N; Lenz, Petra H

2011-02-01

The Onychophora, Priapulida and Tardigrada, along with the Arthropoda, Nematoda and several other small phyla, form the superphylum Ecdysozoa. Numerous peptidomic studies have been undertaken for both the arthropods and nematodes, resulting in the identification of many peptides from each group. In contrast, little is known about the peptides used as paracrines/hormones by species from the other ecdysozoan taxa. Here, transcriptome mining and bioinformatic peptide prediction were used to identify peptides in members of the Onychophora, Priapulida and Tardigrada, the only non-arthropod, non-nematode members of the Ecdysozoa for which there are publicly accessible expressed sequence tags (ESTs). The extant ESTs for each phylum were queried using 106 arthropod/nematode peptide precursors. Transcripts encoding calcitonin-like diuretic hormone and pigment-dispersing hormone (PDH) were identified for the onychophoran Peripatopsis sedgwicki, with transcripts encoding C-type allatostatin (C-AST) and FMRFamide-like peptide identified for the priapulid Priapulus caudatus. For the Tardigrada, transcripts encoding members of the A-type allatostatin, C-AST, insect kinin, orcokinin, PDH and tachykinin-related peptide families were identified, all but one from Hypsibius dujardini (the exception being a Milnesium tardigradum orcokinin-encoding transcript). The proteins deduced from these ESTs resulted in the prediction of 48 novel peptides, six onychophoran, eight priapulid and 34 tardigrade, which are the first described from these phyla. Copyright © 2010 Elsevier Inc. All rights reserved.

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry.

PubMed

Freudenmann, Lena Katharina; Marcu, Ana; Stevanović, Stefan

2018-07-01

The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments. © 2018 John Wiley & Sons Ltd.

Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction.

PubMed

Al-Jarrah, Omar Y; Yoo, Paul D; Taha, Kamal; Muhaidat, Sami; Shami, Abdallah; Zaki, Nazar

2015-01-01

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

MuPeXI: prediction of neo-epitopes from tumor sequencing data.

PubMed

Bjerregaard, Anne-Mette; Nielsen, Morten; Hadrup, Sine Reker; Szallasi, Zoltan; Eklund, Aron Charles

2017-09-01

Personalization of immunotherapies such as cancer vaccines and adoptive T cell therapy depends on identification of patient-specific neo-epitopes that can be specifically targeted. MuPeXI, the mutant peptide extractor and informer, is a program to identify tumor-specific peptides and assess their potential to be neo-epitopes. The program input is a file with somatic mutation calls, a list of HLA types, and optionally a gene expression profile. The output is a table with all tumor-specific peptides derived from nucleotide substitutions, insertions, and deletions, along with comprehensive annotation, including HLA binding and similarity to normal peptides. The peptides are sorted according to a priority score which is intended to roughly predict immunogenicity. We applied MuPeXI to three tumors for which predicted MHC-binding peptides had been screened for T cell reactivity, and found that MuPeXI was able to prioritize immunogenic peptides with an area under the curve of 0.63. Compared to other available tools, MuPeXI provides more information and is easier to use. MuPeXI is available as stand-alone software and as a web server at http://www.cbs.dtu.dk/services/MuPeXI .

Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations

PubMed Central

Chen, Jianzhong; Zhang, Dinglin; Zhang, Yuxin; Li, Guohui

2012-01-01

Inhibition of p53-MDM2/MDMX interaction is considered to be a promising strategy for anticancer drug design to activate wild-type p53 in tumors. We carry out molecular dynamics (MD) simulations to study the binding mechanisms of peptide and non-peptide inhibitors to MDM2/MDMX. The rank of binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) method agrees with one of the experimental values. The results suggest that van der Waals energy drives two kinds of inhibitors to MDM2/MDMX. We also find that the peptide inhibitors can produce more interaction contacts with MDM2/MDMX than the non-peptide inhibitors. Binding mode predictions based on the inhibitor-residue interactions show that the π–π, CH–π and CH–CH interactions dominated by shape complimentarity, govern the binding of the inhibitors in the hydrophobic cleft of MDM2/MDMX. Our studies confirm the residue Tyr99 in MDMX can generate a steric clash with the inhibitors due to energy and structure. This finding may theoretically provide help to develop potent dual-specific or MDMX inhibitors. PMID:22408446

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides.

PubMed

Agyei, Dominic; Tsopmo, Apollinaire; Udenigwe, Chibuike C

2018-06-01

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

Computational smart polymer design based on elastin protein mutability.

PubMed

Tarakanova, Anna; Huang, Wenwen; Weiss, Anthony S; Kaplan, David L; Buehler, Markus J

2017-05-01

Soluble elastin-like peptides (ELPs) can be engineered into a range of physical forms, from hydrogels and scaffolds to fibers and artificial tissues, finding numerous applications in medicine and engineering as "smart polymers". Elastin-like peptides are attractive candidates as a platform for novel biomaterial design because they exhibit a highly tunable response spectrum, with reversible phase transition capabilities. Here, we report the design of the first virtual library of elastin-like protein models using methods for enhanced sampling to study the effect of peptide chemistry, chain length, and salt concentration on the structural transitions of ELPs, exposing associated molecular mechanisms. We describe the behavior of the local molecular structure under increasing temperatures and the effect of peptide interactions with nearest hydration shell water molecules on peptide mobility and propensity to exhibit structural transitions. Shifts in the magnitude of structural transitions at the single-molecule scale are explained from the perspective of peptide-ion-water interactions in a library of four unique elastin-like peptide systems. Predictions of structural transitions are subsequently validated in experiment. This library is a valuable resource for recombinant protein design and synthesis as it elucidates mechanisms at the single-molecule level, paving a feedback path between simulation and experiment for smart material designs, with applications in biomedicine and diagnostic devices. Copyright © 2017. Published by Elsevier Ltd.

Computational Amide I Spectroscopy for Refinement of Disordered Peptide Ensembles: Maximum Entropy and Related Approaches

NASA Astrophysics Data System (ADS)

Reppert, Michael; Tokmakoff, Andrei

The structural characterization of intrinsically disordered peptides (IDPs) presents a challenging biophysical problem. Extreme heterogeneity and rapid conformational interconversion make traditional methods difficult to interpret. Due to its ultrafast (ps) shutter speed, Amide I vibrational spectroscopy has received considerable interest as a novel technique to probe IDP structure and dynamics. Historically, Amide I spectroscopy has been limited to delivering global secondary structural information. More recently, however, the method has been adapted to study structure at the local level through incorporation of isotope labels into the protein backbone at specific amide bonds. Thanks to the acute sensitivity of Amide I frequencies to local electrostatic interactions-particularly hydrogen bonds-spectroscopic data on isotope labeled residues directly reports on local peptide conformation. Quantitative information can be extracted using electrostatic frequency maps which translate molecular dynamics trajectories into Amide I spectra for comparison with experiment. Here we present our recent efforts in the development of a rigorous approach to incorporating Amide I spectroscopic restraints into refined molecular dynamics structural ensembles using maximum entropy and related approaches. By combining force field predictions with experimental spectroscopic data, we construct refined structural ensembles for a family of short, strongly disordered, elastin-like peptides in aqueous solution.

Characterizing the Conformational Landscape of Flavivirus Fusion Peptides via Simulation and Experiment

PubMed Central

Marzinek, Jan K.; Lakshminarayanan, Rajamani; Goh, Eunice; Huber, Roland G.; Panzade, Sadhana; Verma, Chandra; Bond, Peter J.

2016-01-01

Conformational changes in the envelope proteins of flaviviruses help to expose the highly conserved fusion peptide (FP), a region which is critical to membrane fusion and host cell infection, and which represents a significant target for antiviral drugs and antibodies. In principle, extended timescale atomic-resolution simulations may be used to characterize the dynamics of such peptides. However, the resultant accuracy is critically dependent upon both the underlying force field and sufficient conformational sampling. In the present study, we report a comprehensive comparison of three simulation methods and four force fields comprising a total of more than 40 μs of sampling. Additionally, we describe the conformational landscape of the FP fold across all flavivirus family members. All investigated methods sampled conformations close to available X-ray structures, but exhibited differently populated ensembles. The best force field / sampling combination was sufficiently accurate to predict that the solvated peptide fold is less ordered than in the crystallographic state, which was subsequently confirmed via circular dichroism and spectrofluorometric measurements. Finally, the conformational landscape of a mutant incapable of membrane fusion was significantly shallower than wild-type variants, suggesting that dynamics should be considered when therapeutically targeting FP epitopes. PMID:26785994

Mitochondrial transit peptide exhibits cell penetration ability and efficiently delivers macromolecules to mitochondria.

PubMed

Jain, Aastha; Chugh, Archana

2016-09-01

Mitochondrial malfunction under various circumstances can lead to a variety of disorders. Effective targeting of macromolecules (drugs) is important for restoration of mitochondrial function and treatment of related disorders. We have designed a novel cell-penetrating mitochondrial transit peptide (CpMTP) for delivery of macromolecules to mitochondria. Comparison between properties of cell-penetrating peptides (CPPs) and mitochondrial signal sequences enabled prediction of peptides with dual ability for cellular translocation and mitochondrial localization. Among the predicted peptides, CpMTP translocates across HeLa cells and shows successful delivery of noncovalently conjugated cargo molecules to mitochondria. CpMTP may have applications in transduction and transfection of mitochondria for therapeutics. © 2016 Federation of European Biochemical Societies.

In Silico Prediction of Neuropeptides/Peptide Hormone Transcripts in the Cheilostome Bryozoan Bugula neritina

PubMed Central

Zhang, Gen; He, Li-Sheng; Qian, Pei-Yuan

2016-01-01

The bryozoan Bugula neritina has a biphasic life cycle that consists of a planktonic larval stage and a sessile juvenile/adult stage. The transition between these two stages is crucial for the development and recruitment of B. neritina. Metamorphosis in B. neritina is mediated by both the nervous system and the release of developmental signals. However, no research has been conducted to investigate the expression of neuropeptides (NP)/peptide hormones in B. neritina larvae. Here, we report a comprehensive study of the NP/peptide hormones in the marine bryozoan B. neritina based on in silico identification methods. We recovered 22 transcripts encompassing 11 NP/peptide hormone precursor transcript sequences. The transcript sequences of the 11 isolated NP precursors were validated by cDNA cloning using gene-specific primers. We also examined the expression of three peptide hormone precursor transcripts (BnFDSIG, BnILP1, BnGPB) in the coronate larvae of B. neritina, demonstrating their distinct expression patterns in the larvae. Overall, our findings serve as an important foundation for subsequent investigations of the peptidergic control of bryozoan larval behavior and settlement. PMID:27537380

Factors that drive peptide assembly and fibril formation: experimental and theoretical analysis of Sup35 NNQQNY mutants.

PubMed

Do, Thanh D; Economou, Nicholas J; LaPointe, Nichole E; Kincannon, William M; Bleiholder, Christian; Feinstein, Stuart C; Teplow, David B; Buratto, Steven K; Bowers, Michael T

2013-07-18

Residue mutations have substantial effects on aggregation kinetics and propensities of amyloid peptides and their aggregate morphologies. Such effects are attributed to conformational transitions accessed by various types of oligomers such as steric zipper or single β-sheet. We have studied the aggregation propensities of six NNQQNY mutants: NVVVVY, NNVVNV, NNVVNY, VIQVVY, NVVQIY, and NVQVVY in water using a combination of ion-mobility mass spectrometry, transmission electron microscopy, atomic force microscopy, and all-atom molecular dynamics simulations. Our data show a strong correlation between the tendency to form early β-sheet oligomers and the subsequent aggregation propensity. Our molecular dynamics simulations indicate that the stability of a steric zipper structure can enhance the propensity for fibril formation. Such stability can be attained by either hydrophobic interactions in the mutant peptide or polar side-chain interdigitations in the wild-type peptide. The overall results display only modest agreement with the aggregation propensity prediction methods such as PASTA, Zyggregator, and RosettaProfile, suggesting the need for better parametrization and model peptides for these algorithms.

STRUCTURAL CHARACTERISTICS AND ANTIHYPERTENSIVE EFFECTS OF ANGIOTENSIN-I-CONVERTING ENZYME INHIBITORY PEPTIDES IN THE RENIN-ANGIOTENSIN AND KALLIKREIN KININ SYSTEMS

PubMed Central

Manoharan, Sivananthan; Shuib, Adawiyah Suriza; Abdullah, Noorlidah

2017-01-01

Background: The commercially available synthetic angiotensin-I-converting enzyme (ACE) inhibitors are known to exert negative side effects which have driven many research groups globally to discover the novel ACE inhibitors. Method: Literature search was performed within the PubMed, ScienceDirect.com and Google Scholar. Results: The presence of proline at the C-terminal tripeptide of ACE inhibitor can competitively inhibit the ACE activity. The effects of other amino acids are less studied leading to difficulties in predicting potent peptide sequences. The broad specificity of the enzyme may be due to the dual active sites observed on the somatic ACE. The inhibitors may not necessarily competitively inhibit the enzyme which explains why some reported inhibitors do not have the common ACE inhibitor characteristics. Finally, the in vivo assay has to be carried out before the peptides as the antihypertensive agents can be claimed. The peptides must be absorbed into circulation without being degraded, which will affect their bioavailability and potency. Thus, peptides with strong in vitro IC50 values do not necessarily have the same effect in vivo and vice versa. Conclusion: The relationship between peptide amino acid sequence and inhibitory activity, in vivo studies of the active peptides and bioavailability must be studied before the peptides as antihypertensive agents can be claimed. PMID:28573254

Scrutinizing MHC-I binding peptides and their limits of variation.

PubMed

Koch, Christian P; Perna, Anna M; Pillong, Max; Todoroff, Nickolay K; Wrede, Paul; Folkers, Gerd; Hiss, Jan A; Schneider, Gisbert

2013-01-01

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K(b) is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K(b) in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).

Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs.

PubMed

Cherkasov, Artem; Hilpert, Kai; Jenssen, Håvard; Fjell, Christopher D; Waldbrook, Matt; Mullaly, Sarah C; Volkmer, Rudolf; Hancock, Robert E W

2009-01-16

Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrug-resistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models.

Can DCE-MRI Explain the Heterogeneity in Radiopeptide Uptake Imaged by SPECT in a Pancreatic Neuroendocrine Tumor Model?

PubMed Central

Groen, Harald C.; Niessen, Wiro J.; Bernsen, Monique R.; de Jong, Marion; Veenland, Jifke F.

2013-01-01

Although efficient delivery and distribution of treatment agents over the whole tumor is essential for successful tumor treatment, the distribution of most of these agents cannot be visualized. However, with single-photon emission computed tomography (SPECT), both delivery and uptake of radiolabeled peptides can be visualized in a neuroendocrine tumor model overexpressing somatostatin receptors. A heterogeneous peptide uptake is often observed in these tumors. We hypothesized that peptide distribution in the tumor is spatially related to tumor perfusion, vessel density and permeability, as imaged and quantified by DCE-MRI in a neuroendocrine tumor model. Four subcutaneous CA20948 tumor-bearing Lewis rats were injected with the somatostatin-analog 111In-DTPA-Octreotide (50 MBq). SPECT-CT and MRI scans were acquired and MRI was spatially registered to SPECT-CT. DCE-MRI was analyzed using semi-quantitative and quantitative methods. Correlation between SPECT and DCE-MRI was investigated with 1) Spearman’s rank correlation coefficient; 2) SPECT uptake values grouped into deciles with corresponding median DCE-MRI parametric values and vice versa; and 3) linear regression analysis for median parameter values in combined datasets. In all tumors, areas with low peptide uptake correlated with low perfusion/density/ /permeability for all DCE-MRI-derived parameters. Combining all datasets, highest linear regression was found between peptide uptake and semi-quantitative parameters (R2>0.7). The average correlation coefficient between SPECT and DCE-MRI-derived parameters ranged from 0.52-0.56 (p<0.05) for parameters primarily associated with exchange between blood and extracellular extravascular space. For these parameters a linear relation with peptide uptake was observed. In conclusion, the ‘exchange-related’ DCE-MRI-derived parameters seemed to predict peptide uptake better than the ‘contrast amount- related’ parameters. Consequently, fast and efficient diffusion through the vessel wall into tissue is an important factor for peptide delivery. DCE-MRI helps to elucidate the relation between vascular characteristics, peptide delivery and treatment efficacy, and may form a basis to predict targeting efficiency. PMID:24116203

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

PubMed Central

Junaid, Muhammad; Kaushik, Aman Chandra; Ali, Arif; Ali, Syed Shujait; Mehmood, Aamir; Wei, Dong-Qing

2018-01-01

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections. PMID:29715318

De novo peptide sequencing by deep learning

PubMed Central

Tran, Ngoc Hieu; Zhang, Xianglilan; Xin, Lei; Shan, Baozhen; Li, Ming

2017-01-01

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5–100% coverage and 97.2–99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming. PMID:28720701

Peptidergic signaling in the crab Cancer borealis: Tapping the power of transcriptomics for neuropeptidome expansion.

PubMed

Christie, Andrew E; Pascual, Micah G

2016-10-01

The crab Cancer borealis has long been used as a model for understanding neural control of rhythmic behavior. One significant discovery made through its use is that even numerically simple neural circuits are capable of producing an essentially infinite array of distinct motor outputs via the actions of locally released and circulating neuromodulators, the largest class being peptides. While much work has focused on elucidating the peptidome of C. borealis, no investigation has used in silico transcriptome mining for peptide discovery in this species, a strategy proven highly effective for identifying neuropeptides in other crustaceans. Here, we mined a C. borealis neural transcriptome for putative peptide-encoding transcripts, and predicted 200 distinct mature neuropeptides from the proteins deduced from these sequences. The identified peptides include isoforms of allatostatin A, allatostatin B, allatostatin C, CCHamide, crustacean cardioactive peptide, crustacean hyperglycemic hormone, diuretic hormone 31 (DH31), diuretic hormone 44 (DH44), FMRFamide-like peptide, GSEFLamide, HIGSLYRamide, insulin-like peptide (ILP), intocin, leucokinin, neuroparsin, pigment dispersing hormone, pyrokinin, red pigment concentrating hormone, short neuropeptide F and SIFamide. While some of the predicted peptides were known previously from C. borealis, most (159) are new discoveries for the species, e.g., the isoforms of CCHamide, DH31, DH44, GSEFLamide, ILP, intocin and neuroparsin, which are the first members of these peptide families identified from C. borealis. Collectively, the peptides predicted here approximately double the peptidome known for C. borealis, and in so doing provide an expanded platform from which to launch new investigations of peptidergic neuromodulation in this species. Copyright © 2016 Elsevier Inc. All rights reserved.

Structure activity relationship modelling of milk protein-derived peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activity.

PubMed

Nongonierma, Alice B; FitzGerald, Richard J

2016-05-01

Quantitative structure activity type models were developed in an attempt to predict the key features of peptide sequences having dipeptidyl peptidase IV (DPP-IV) inhibitory activity. The models were then employed to help predict the potential of peptides, which are currently reported in the literature to be present in the intestinal tract of humans following milk/dairy product ingestion, to act as inhibitors of DPP-IV. Two models (z- and v-scale) for short (2-5 amino acid residues) bovine milk peptides, behaving as competitive inhibitors of DPP-IV, were developed. The z- and the v-scale models (p<0.05, R(2) of 0.829 and 0.815, respectively) were then applied to 56 milk protein-derived peptides previously reported in the literature to be found in the intestinal tract of humans which possessed a structural feature of DPP-IV inhibitory peptides (P at the N2 position). Ten of these peptides were synthetized and tested for their in vitro DPP-IV inhibitory properties. There was no agreement between the predicted and experimentally determined DPP-IV half maximal inhibitory concentrations (IC50) for the competitive peptide inhibitors. However, the ranking for DPP-IV inhibitory potency of the competitive peptide inhibitors was conserved. Furthermore, potent in vitro DPP-IV inhibitory activity was observed with two peptides, LPVPQ (IC50=43.8±8.8μM) and IPM (IC50=69.5±8.7μM). Peptides present within the gastrointestinal tract of human may have promise for the development of natural DPP-IV inhibitors for the management of serum glucose. Copyright © 2016 Elsevier Inc. All rights reserved.

Antimicrobial Peptides in Reptiles

PubMed Central

van Hoek, Monique L.

2014-01-01

Reptiles are among the oldest known amniotes and are highly diverse in their morphology and ecological niches. These animals have an evolutionarily ancient innate-immune system that is of great interest to scientists trying to identify new and useful antimicrobial peptides. Significant work in the last decade in the fields of biochemistry, proteomics and genomics has begun to reveal the complexity of reptilian antimicrobial peptides. Here, the current knowledge about antimicrobial peptides in reptiles is reviewed, with specific examples in each of the four orders: Testudines (turtles and tortosises), Sphenodontia (tuataras), Squamata (snakes and lizards), and Crocodilia (crocodilans). Examples are presented of the major classes of antimicrobial peptides expressed by reptiles including defensins, cathelicidins, liver-expressed peptides (hepcidin and LEAP-2), lysozyme, crotamine, and others. Some of these peptides have been identified and tested for their antibacterial or antiviral activity; others are only predicted as possible genes from genomic sequencing. Bioinformatic analysis of the reptile genomes is presented, revealing many predicted candidate antimicrobial peptides genes across this diverse class. The study of how these ancient creatures use antimicrobial peptides within their innate immune systems may reveal new understandings of our mammalian innate immune system and may also provide new and powerful antimicrobial peptides as scaffolds for potential therapeutic development. PMID:24918867

Designing Antibacterial Peptides with Enhanced Killing Kinetics

PubMed Central

Waghu, Faiza H.; Joseph, Shaini; Ghawali, Sanket; Martis, Elvis A.; Madan, Taruna; Venkatesh, Kareenhalli V.; Idicula-Thomas, Susan

2018-01-01

Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide. PMID:29527201

A Mass Spectrometry-Based Predictive Strategy Reveals ADAP1 is Phosphorylated at Tyrosine 364

DOE Office of Scientific and Technical Information (OSTI.GOV)

Littrell, BobbiJo R

The goal of this work was to identify phosphorylation sites within the amino acid sequence of human ADAP1. Using traditional mass spectrometry-based techniques we were unable to produce interpretable spectra demonstrating modification by phosphorylation. This prompted us to employ a strategy in which phosphorylated peptides were first predicted using peptide mapping followed by targeted MS/MS acquisition. ADAP1 was immunoprecipitated from extracts of HEK293 cells stably-transfected with ADAP1 cDNA. Immunoprecipitated ADAP1 was digested with proteolytic enzymes and analyzed by LC-MS in MS1 mode by high-resolution quadrupole time-of-flight mass spectrometry (QTOF-MS). Peptide molecular features were extracted using an untargeted data mining algorithm.more » Extracted peptide neutral masses were matched against the ADAP1 amino acid sequence with phosphorylation included as a predicted modification. Peptides with predicted phosphorylation sites were analyzed by targeted LC-MS2. Acquired MS2 spectra were then analyzed using database search engines to confirm phosphorylation. Spectra of phosphorylated peptides were validated by manual interpretation. Further confirmation was performed by manipulating phospho-peptide abundance using calf intestinal phosphatase (CIP) and the phorbol ester, phorbol 12-myristate 13-acetate (PMA). Of five predicted phosphopeptides, one, comprised of the sequence AVDRPMLPQEYAVEAHFK, was confirmed to be phosphorylated on a Tyrosine at position 364. Pre-treatment of cells with PMA prior to immunoprecipitation increased the ratio of phosphorylated to unphosphorylated peptide as determined by area counts of extracted ion chromatograms (EIC). Addition of CIP to immunoprecipitation reactions eliminated the phosphorylated form. A novel phosphorylation site was identified at Tyrosine 364. Phosphorylation at this site is increased by treatment with PMA. PMA promotes membrane translocation and activation of protein kinase C (PKC), indicating that Tyrosine 364 is phosphorylated by a PKC-dependent mechanism.« less

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping.

PubMed

Mukherjee, Sumanta; Warwicker, Jim; Chandra, Nagasuma

2015-07-01

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Interplay of charge distribution and conformation in peptides: comparison of theory and experiment.

PubMed

Makowska, Joanna; Bagińska, Katarzyna; Kasprzykowski, F; Vila, Jorge A; Jagielska, Anna; Liwo, Adam; Chmurzyński, Lech; Scheraga, Harold A

2005-01-01

We assessed the correlation between charge distribution and conformation of flexible peptides by comparing the theoretically calculated potentiometric-titration curves of two model peptides, Ac-Lys5-NHMe (a model of poly-L-lysine) and Ac-Lys-Ala11-Lys-Gly2-Tyr-NH2 (P1) in water and methanol, with the experimental curves. The calculation procedure consisted of three steps: (i) global conformational search of the peptide under study using the electrostatically driven Monte Carlo (EDMC) method with the empirical conformational energy program for peptides (ECEPP)/3 force field plus the surface-hydration (SRFOPT) or the generalized Born surface area (GBSA) solvation model as well as a molecular dynamics method with the assisted model building and energy refinement (AMBER)99/GBSA force field; (ii) reevaluation of the energy in the pH range considered by using the modified Poisson-Boltzmann approach and taking into account all possible protonation microstates of each conformation, and (iii) calculation of the average degree of protonation of the peptide at a given pH value by Boltzmann averaging over conformations. For Ac-Lys5-NHMe, the computed titration curve agrees qualitatively with the experimental curve of poly-L-lysine in 95% methanol. The experimental titration curves of peptide P1 in water and methanol indicate a remarkable downshift of the first pK(a) value compared to the values for reference compounds (n-butylamine and phenol, respectively), suggesting the presence of a hydrogen bond between the tyrosine hydroxyl oxygen and the H(epsilon) proton of a protonated lysine side chain. The theoretical titration curves agree well with the experimental curves, if conformations with such hydrogen bonds constitute a significant part of the ensemble; otherwise, the theory predicts too small a downward pH shift. Copyright 2005 Wiley Periodicals, Inc

Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

DOE PAGES

Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

2013-03-07

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

A Computational Tool to Detect and Avoid Redundancy in Selected Reaction Monitoring

PubMed Central

Röst, Hannes; Malmström, Lars; Aebersold, Ruedi

2012-01-01

Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www.srmcollider.org to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1–Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider. PMID:22535207

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

PubMed

Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

2013-01-01

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

Mass spectrometric survey of peptides in cephalopods with an emphasis on the FMRFamide-related peptides.

PubMed

Sweedler, J V; Li, L; Floyd, P; Gilly, W

2000-12-01

A matrix-assisted laser desorption/ionization (MALDI) mass spectrometric (MS) survey of the major peptides in the stellar, fin and pallial nerves and the posterior chromatophore lobe of the cephalopods Sepia officinalis, Loligo opalescens and Dosidicus gigas has been performed. Although a large number of putative peptides are distinct among the three species, several molecular masses are conserved. In addition to peptides, characterization of the lipid content of the nerves is reported, and these lipid peaks account for many of the lower molecular masses observed. One conserved set of peaks corresponds to the FMRFamide-related peptides (FRPs). The Loligo opalescens FMRFa gene has been sequenced. It encodes a 331 amino acid residue prohormone that is processed into 14 FRPs, which are both predicted by the nucleotide sequence and confirmed by MALDI MS. The FRPs predicted by this gene (FMRFa, FLRFa/FIRFa and ALSGDAFLRFa) are observed in all three species, indicating that members of this peptide family are highly conserved across cephalopods.

Fall in C-Peptide During First 4 Years From Diagnosis of Type 1 Diabetes: Variable Relation to Age, HbA1c, and Insulin Dose

PubMed Central

Gitelman, Steven; DiMeglio, Linda A.; Boulware, David; Greenbaum, Carla J.

2016-01-01

OBJECTIVE We aimed to describe the natural history of residual insulin secretion in Type 1 Diabetes TrialNet participants over 4 years from diagnosis and relate this to previously reported alternative clinical measures reflecting β-cell secretory function. RESEARCH DESIGN AND METHODS Data from 407 subjects from 5 TrialNet intervention studies were analyzed. All subjects had baseline stimulated C-peptide values of ≥0.2 nmol/L from mixed-meal tolerance tests (MMTTs). During semiannual visits, C-peptide values from MMTTs, HbA1c, and insulin doses were obtained. RESULTS The percentage of individuals with stimulated C-peptide of ≥0.2 nmol/L or detectable C-peptide of ≥0.017 nmol/L continued to diminish over 4 years; this was markedly influenced by age. At 4 years, only 5% maintained their baseline C-peptide secretion. The expected inverse relationships between C-peptide and HbA1c or insulin doses varied over time and with age. Combined clinical variables, such as insulin-dose adjusted HbA1c (IDAA1C) and the relationship of IDAA1C to C-peptide, also were influenced by age and time from diagnosis. Models using these clinical measures did not fully predict C-peptide responses. IDAA1C ≤9 underestimated the number of individuals with stimulated C-peptide ≥0.2 nmol/L, especially in children. CONCLUSIONS Current trials of disease-modifying therapy for type 1 diabetes should continue to use C-peptide as a primary end point of β-cell secretory function. Longer duration of follow-up is likely to provide stronger evidence of the effect of disease-modifying therapy on preservation of β-cell function. PMID:27422577

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

PubMed Central

Skinnider, Michael A.; Merwin, Nishanth J.; Johnston, Chad W.

2017-01-01

Abstract Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/. PMID:28460067

Immunoinformatic Analysis of Crimean Congo Hemorrhagic Fever Virus Glycoproteins and Epitope Prediction for Synthetic Peptide Vaccine.

PubMed

Tipu, Hamid Nawaz

2016-02-01

To determine the Crimean Congo Hemorrhagic Fever (CCHF) virus M segement glycoprotein's immunoinformatic parameters, and identify Human Leukocyte Antigen (HLA) class I binders as candidates for synthetic peptide vaccines. Cross-sectional study. Combined Military Hospital, Khuzdar Cantt, in May 2015. Data acquisition, antigenicity prediction, secondary and tertiary structure prediction, residue analysis were done using immunoinformatics tools. HLAclass I binders in glycoprotein's sequence were identified at nanomer length using NetMHC 3.4 and mapped onto tertiary structure. Docking was done for strongest binder against its corresponding allele with CABS-dock. HLAA*0101, 0201, 0301, 2402, 2601 and B*0702, 0801, 2705, 3901, 4001, 5801, 1501 were analyzed against two glycoprotein components of the virus. Atotal of 35 nanomers from GP1, and 3 from GP2 were identified. HLAB*0702 bound maximum number of peptides (6), while HLAB*4001 showed strongest binding affinity. HLAspecific glycoproteins epitope prediction can help identify synthetic peptide vaccine candidates.

Artificial neural network study on organ-targeting peptides

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

The property distance index PD predicts peptides that cross-react with IgE antibodies

PubMed Central

Ivanciuc, Ovidiu; Midoro-Horiuti, Terumi; Schein, Catherine H.; Xie, Liping; Hillman, Gilbert R.; Goldblum, Randall M.; Braun, Werner

2009-01-01

Similarities in the sequence and structure of allergens can explain clinically observed cross-reactivities. Distinguishing sequences that bind IgE in patient sera can be used to identify potentially allergenic protein sequences and aid in the design of hypo-allergenic proteins. The property distance index PD, incorporated in our Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/), may identify potentially cross-reactive segments of proteins, based on their similarity to known IgE epitopes. We sought to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to three linear IgE epitopes of Jun a 1, the dominant allergen from mountain cedar pollen. For each of the three epitopes, 60 peptides were designed with increasing PD values (decreasing physicochemical similarity) to the starting sequence. The peptides synthesized on a derivatized cellulose membrane were probed with sera from patients who were allergic to Jun a 1, and the experimental data were interpreted with a PD classification method. Peptides with low PD values relative to a given epitope were more likely to bind IgE from the sera than were those with PD values larger than 6. Control sequences, with PD values between 18 and 20 to all the three epitopes, did not bind patient IgE, thus validating our procedure for identifying negative control peptides. The PD index is a statistically validated method to detect discrete regions of proteins that have a high probability of cross-reacting with IgE from allergic patients. PMID:18950868

Elucidation of the binding preferences of peptide recognition modules: SH3 and PDZ domains.

PubMed

Teyra, Joan; Sidhu, Sachdev S; Kim, Philip M

2012-08-14

Peptide-binding domains play a critical role in regulation of cellular processes by mediating protein interactions involved in signalling. In recent years, the development of large-scale technologies has enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. These efforts have provided significant insights into the binding specificities of these modular domains. Many research groups have taken advantage of this unprecedented volume of specificity data and have developed a variety of new algorithms for the prediction of binding specificities of peptide-binding domains and for the prediction of their natural binding targets. This knowledge has also been applied to the design of synthetic peptide-binding domains in order to rewire protein-protein interaction networks. Here, we describe how these experimental technologies have impacted on our understanding of peptide-binding domain specificities and on the elucidation of their natural ligands. We discuss SH3 and PDZ domains as well characterized examples, and we explore the feasibility of expanding high-throughput experiments to other peptide-binding domains. Copyright © 2012. Published by Elsevier B.V.

An FPGA Implementation to Detect Selective Cationic Antibacterial Peptides

PubMed Central

Polanco González, Carlos; Nuño Maganda, Marco Aurelio; Arias-Estrada, Miguel; del Rio, Gabriel

2011-01-01

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides. PMID:21738652

Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro.

PubMed

Natsch, A; Gfeller, H; Rothaupt, M; Ellis, G

2007-10-01

A key step in the skin sensitization process is the formation of a covalent adduct between the skin sensitizer and endogenous proteins and/or peptides in the skin. A published peptide depletion assay was used to relate the in vitro reactivity of fragrance molecules to LLNA data. Using the classical assay, 22 of 28 tested moderate to strong sensitizers were positive. The prediction of weak sensitizers proved to be more difficult with only 50% of weak sensitizers giving a positive response, but for some compounds this could also be due to false-positive results from the LLNA. LC-MS analysis yielded the expected mass of the peptide adducts in several cases, whereas in other cases putative oxidation reactions led to adducts of unexpected molecular weight. Several moderately sensitizing aldehydes were correctly predicted by the depletion assay, but no adducts were found and the depletion appears to be due to an oxidation of the parent peptide catalyzed by the test compound. Finally, alternative test peptides derived from a physiological reactive protein with enhanced sensitivity for weak Michael acceptors were found, further increasing the sensitivity of the assay.

A novel computer algorithm improves antibody epitope prediction using affinity-selected mimotopes: a case study using monoclonal antibodies against the West Nile virus E protein.

PubMed

Denisova, Galina F; Denisov, Dimitri A; Yeung, Jeffrey; Loeb, Mark B; Diamond, Michael S; Bramson, Jonathan L

2008-11-01

Understanding antibody function is often enhanced by knowledge of the specific binding epitope. Here, we describe a computer algorithm that permits epitope prediction based on a collection of random peptide epitopes (mimotopes) isolated by antibody affinity purification. We applied this methodology to the prediction of epitopes for five monoclonal antibodies against the West Nile virus (WNV) E protein, two of which exhibit therapeutic activity in vivo. This strategy was validated by comparison of our results with existing F(ab)-E protein crystal structures and mutational analysis by yeast surface display. We demonstrate that by combining the results of the mimotope method with our data from mutational analysis, epitopes could be predicted with greater certainty. The two methods displayed great complementarity as the mutational analysis facilitated epitope prediction when the results with the mimotope method were equivocal and the mimotope method revealed a broader number of residues within the epitope than the mutational analysis. Our results demonstrate that the combination of these two prediction strategies provides a robust platform for epitope characterization.

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

PubMed Central

Reynolds, Sheila M.; Käll, Lukas; Riffle, Michael E.; Bilmes, Jeff A.; Noble, William Stafford

2008-01-01

Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr. PMID:18989393

Mycobacterium leprae virulence-associated peptides are indicators of exposure to M. leprae in Brazil, Ethiopia and Nepal.

PubMed

Bobosha, Kidist; Tang, Sheila Tuyet; van der Ploeg-van Schip, Jolien J; Bekele, Yonas; Martins, Marcia V S B; Lund, Ole; Franken, Kees L M C; Khadge, Saraswoti; Pontes, Maria Araci de Andrade; Gonçalves, Heitor de Sá; Hussien, Jemal; Thapa, Pratibha; Kunwar, Chhatra B; Hagge, Deanna A; Aseffa, Abraham; Pessolani, Maria Cristina Vidal; Pereira, Geraldo M B; Ottenhoff, Tom H M; Geluk, Annemieke

2012-12-01

Silent transmission of Mycobacterium leprae, as evidenced by stable leprosy incidence rates in various countries, remains a health challenge despite the implementation of multidrug therapy worldwide. Therefore, the development of tools for the early diagnosis of M. leprae infection should be emphasised in leprosy research. As part of the continuing effort to identify antigens that have diagnostic potential, unique M. leprae peptides derived from predicted virulence-associated proteins (group IV.A) were identified using advanced genome pattern programs and bioinformatics. Based on human leukocyte antigen (HLA)-binding motifs, we selected 21 peptides that were predicted to be promiscuous HLA-class I T-cell epitopes and eight peptides that were predicted to be HLA-class II restricted T-cell epitopes for field-testing in Brazil, Ethiopia and Nepal. High levels of interferon (IFN)-γ were induced when peripheral blood mononuclear cells (PBMCs) from tuberculoid/borderline tuberculoid leprosy patients located in Brazil and Ethiopia were stimulated with the ML2055 p35 peptide. PBMCs that were isolated from healthy endemic controls living in areas with high leprosy prevalence (EChigh) in Ethiopia also responded to the ML2055 p35 peptide. The Brazilian EChigh group recognised the ML1358 p20 and ML1358 p24 peptides. None of the peptides were recognised by PBMCs from healthy controls living in non-endemic region. In Nepal, mixtures of these peptides induced the production of IFN-γ by the PBMCs of leprosy patients and EChigh. Therefore, the M. leprae virulence-associated peptides identified in this study may be useful for identifying exposure to M. leprae in population with differing HLA polymorphisms.

Definition of Proteasomal Peptide Splicing Rules for High-Efficiency Spliced Peptide Presentation by MHC Class I Molecules

PubMed Central

Berkers, Celia R.; de Jong, Annemieke; Schuurman, Karianne G.; Linnemann, Carsten; Meiring, Hugo D.; Janssen, Lennert; Neefjes, Jacques J.; Schumacher, Ton N. M.; Rodenko, Boris

2015-01-01

Peptide splicing, in which two distant parts of a protein are excised and then ligated to form a novel peptide, can generate unique MHC class I–restricted responses. Because these peptides are not genetically encoded and the rules behind proteasomal splicing are unknown, it is difficult to predict these spliced Ags. In the current study, small libraries of short peptides were used to identify amino acid sequences that affect the efficiency of this transpeptidation process. We observed that splicing does not occur at random, neither in terms of the amino acid sequences nor through random splicing of peptides from different sources. In contrast, splicing followed distinct rules that we deduced and validated both in vitro and in cells. Peptide ligation was quantified using a model peptide and demonstrated to occur with up to 30% ligation efficiency in vitro, provided that optimal structural requirements for ligation were met by both ligating partners. In addition, many splicing products could be formed from a single protein. Our splicing rules will facilitate prediction and detection of new spliced Ags to expand the peptidome presented by MHC class I Ags. PMID:26401003

Folding molecular dynamics simulations accurately predict the effect of mutations on the stability and structure of a vammin-derived peptide.

PubMed

Koukos, Panagiotis I; Glykos, Nicholas M

2014-08-28

Folding molecular dynamics simulations amounting to a grand total of 4 μs of simulation time were performed on two peptides (with native and mutated sequences) derived from loop 3 of the vammin protein and the results compared with the experimentally known peptide stabilities and structures. The simulations faithfully and accurately reproduce the major experimental findings and show that (a) the native peptide is mostly disordered in solution, (b) the mutant peptide has a well-defined and stable structure, and (c) the structure of the mutant is an irregular β-hairpin with a non-glycine β-bulge, in excellent agreement with the peptide's known NMR structure. Additionally, the simulations also predict the presence of a very small β-hairpin-like population for the native peptide but surprisingly indicate that this population is structurally more similar to the structure of the native peptide as observed in the vammin protein than to the NMR structure of the isolated mutant peptide. We conclude that, at least for the given system, force field, and simulation protocol, folding molecular dynamics simulations appear to be successful in reproducing the experimentally accessible physical reality to a satisfactory level of detail and accuracy.

Selective enrichment and desalting of hydrophilic peptides using graphene oxide.

PubMed

Jiang, Miao; Qi, Linyu; Liu, Peiru; Wang, Zijun; Duan, Zhigui; Wang, Ying; Liu, Zhonghua; Chen, Ping

2016-08-01

The wide variety and low abundance of peptides in tissue brought great difficulties to the separation and identification of peptides, which is not in favor of the development of peptidomics. RP-HPLC, which could purify small molecules based on their hydrophobicity, has been widely used in the separation and enrichment of peptide due to its fast, good reproducibility and high resolution. However, RP-HPLC requires the instrument and expensive C18 column and its sample capacity is also limited. Recently, graphene oxide has been applied to the adsorption of amino acids. However, the enrichment efficiency and selectivity of graphene oxide for peptides remain unclear. In this study, the adsorption efficiency and selectivity of graphene oxide and RP-C18 matrix were compared on trypsinized α-actin and also on tissue extracts from pituitary gland and hippocampus. For α-actin, there exhibit similar elution peaks for total trypsinized products and those adsorpted by GO and C18 matrix. But peptides adsorbed by GO showed the higher hydrophilic peaks than which adsorbed by C18 matrix. The resulted RP-HPLC profile showed that most of peptides enriched by graphene oxide were eluted at low concentration of organic solvent, while peptides adsorbed by RP-C18 matrix were mostly eluted at relatively high concentration. Moreover, mass spectrometry analysis suggested that, in pituitary sample, there were 495 peptides enriched by graphene oxide, 447 peptides enriched by RP-C18 matrix while in hippocampus sample 333 and 243 peptides respectively. The GRAVY value analysis suggested that the graphene oxide has a stronger adsorption for highly hydrophilic peptides compared to the RP-C18 matrix. Furthermore, the combination of these two methods could notably increase the number of identification peptides but also the number of predicted protein precursors. Our study provided a new thought to the role of graphene oxide during the enrichment of peptides from tissue which should be useful for peptidomics study. Copyright © 2016 Elsevier B.V. All rights reserved.

Self-assembly of keratin peptides: Its implication on the performance of electrospun PVA nanofibers

PubMed Central

Kadirvelu, Kavitha; Fathima, Nishter Nishad

2016-01-01

Drawing inspiration from the field of designer self-assembling materials, this work is aimed to focus on the self-assembling nature of extracted peptides. Hair keratin, a proteinacious reject in tanning industry has been chosen since they have been extracted and used for wide range of applications. Keratin source was subjected to five hydrolysis treatments (viz., sulphitolysis, β-mercaptoethanol, ionic liquid, thioglycolic acid and alkali) and assayed for functional groups. This was followed by the prediction of secondary structure using circular dichroism, determining the microstructural level to which the extracted peptide has self-assembled. Sulphitolysis and thioglycolic acid based hydrolysates exist in monomeric conformation, whereas β-mercaptoethanol based hydrolysate exhibited dimeric conformation. The subsequent part of the study is to incorporate these peptides into the nanofibers to study the structural implication of keratin peptides on its characteristics. Accordingly, the peptides were electrospun with PVA and subjected to morphological, mechanical, thermal and biological characterizations. Monomeric nanofiber mat has high tensile strength of around 5.5 MPa and offered lower mass transport resistance, whereas dimeric mat has high Tm of around 290 °C and was more biocompatible. These results help in understanding the extraction-structure-function aspect of the hydrolysates stressing the role of extraction methods on the choice of application. PMID:27812004

A further step toward an optimal ensemble of classifiers for peptide classification, a case study: HIV protease.

PubMed

Nanni, Loris; Lumini, Alessandra

2009-01-01

The focuses of this work are: to propose a novel method for building an ensemble of classifiers for peptide classification based on substitution matrices; to show the importance to select a proper set of the parameters of the classifiers that build the ensemble of learning systems. The HIV-1 protease cleavage site prediction problem is here studied. The results obtained by a blind testing protocol are reported, the comparison with other state-of-the-art approaches, based on ensemble of classifiers, allows to quantify the performance improvement obtained by the systems proposed in this paper. The simulation based on experimentally determined protease cleavage data has demonstrated the success of these new ensemble algorithms. Particularly interesting it is to note that also if the HIV-1 protease cleavage site prediction problem is considered linearly separable we obtain the best performance using an ensemble of non-linear classifiers.

A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

USDA-ARS?s Scientific Manuscript database

Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8positive T-cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presenta...

NetMHCstab – predicting stability of peptide–MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery

PubMed Central

Jørgensen, Kasper W; Rasmussen, Michael; Buus, Søren; Nielsen, Morten

2014-01-01

Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide–MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide–MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at http://www.cbs.dtu.dk/services/NetMHCstab. PMID:23927693

Dissociation of POMC Peptides after Self-Injury Predicts Responses To Centrally Acting Opiate Blockers.

ERIC Educational Resources Information Center

Sandman, Curt A.; Hetrick, William; Taylor, Derek V.; Chicz-DeMet, Aleksandra

1997-01-01

This study investigated whether blood plasma levels of pro-opiomelanocortin-derived (POMC) peptides, beta-endorphin-like activity, adrenocorticotrophic hormone, and adrenal cortisol immediately after self injurious behavior (SIB) episodes predicted subsequent response to an opiate blocker in 10 patients with mental retardation. Results suggest…

High Throughput T Epitope Mapping and Vaccine Development

PubMed Central

Li Pira, Giuseppina; Ivaldi, Federico; Moretti, Paolo; Manca, Fabrizio

2010-01-01

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost. PMID:20617148

Virtual Screening Approach of Bacterial Peptide Deformylase Inhibitors Results in New Antibiotics.

PubMed

Merzoug, Amina; Chikhi, Abdelouahab; Bensegueni, Abderrahmane; Boucherit, Hanane; Okay, Sezer

2018-03-01

The increasing resistance of bacteria to antibacterial therapy poses an enormous health problem, it renders the development of new antibacterial agents with novel mechanism of action an urgent need. Peptide deformylase, a metalloenzyme which catalytically removes N-formyl group from N-terminal methionine of newly synthesized polypeptides, is an important target in antibacterial drug discovery. In this study, we report the structure-based virtual screening of ZINC database in order to discover potential hits as bacterial peptide deformylase enzyme inhibitors with more affinity as compared to GSK1322322, previously known inhibitor. After virtual screening, fifteen compounds of the top hits predicted were purchased and evaluated in vitro for their antibacterial activities against one Gram positive (Staphylococcus aureus) and three Gram negative (Escherichia coli, Pseudomonas aeruginosa and Klebsiella. pneumoniae) bacteria in different concentrations by disc diffusion method. Out of these, three compounds, ZINC00039650, ZINC03872971 and ZINC00126407, exhibited significant zone of inhibition. The results obtained were confirmed using the dilution method. Thus, these proposed compounds may aid the development of more efficient antibacterial agents. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

PubMed

Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

2015-11-01

The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Controlling the Surface Chemistry of Graphite by Engineered Self-Assembled Peptides

PubMed Central

Khatayevich, Dmitriy; So, Christopher R.; Hayamizu, Yuhei; Gresswell, Carolyn; Sarikaya, Mehmet

2012-01-01

The systematic control over surface chemistry is a long-standing challenge in biomedical and nanotechnological applications for graphitic materials. As a novel approach, we utilize graphite-binding dodecapeptides that self-assemble into dense domains to form monolayer thick long-range ordered films on graphite. Specifically, the peptides are rationally designed through their amino acid sequences to predictably display hydrophilic and hydrophobic characteristics while maintaining their self-assembly capabilities on the solid substrate. The peptides are observed to maintain a high tolerance for sequence modification, allowing the control over surface chemistry via their amino acid sequence. Furthermore, through a single step co-assembly of two different designed peptides, we predictably and precisely tune the wettability of the resulting functionalized graphite surfaces from 44 to 83 degrees. The modular molecular structures and predictable behavior of short peptides demonstrated here give rise to a novel platform for functionalizing graphitic materials that offers numerous advantages, including non-invasive modification of the substrate, bio-compatible processing in an aqueous environment, and simple fusion with other functional biological molecules. PMID:22428620

Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1

NASA Astrophysics Data System (ADS)

Anisimov, Victor M.; Ziemys, Arturas; Kizhake, Smitha; Yuan, Ziyan; Natarajan, Amarnath; Cavasotto, Claudio N.

2011-11-01

The C-terminal domain of BRCA1(BRCT) is involved in the DNA repair pathway by recognizing the pSXXF motif in interacting proteins. It has been reported that short peptides containing this motif bind to BRCA1(BRCT) in the micromolar range with high specificity. In this work, the binding of pSXXF peptides has been studied computationally and experimentally in order to characterize their interaction with BRCA1(BRCT). Elucidation of the contacts that drive the protein-ligand interaction is critical for the development of high affinity small-molecule BRCA1 inhibitors. Molecular dynamics (MD) simulations revealed the key role of threonine at the peptide P+2 position in providing structural rigidity to the ligand in the bound state. The mutation at P+1 had minor effects. Peptide extension at the N-terminal position with the naphthyl amino acid exhibited a modest increase in binding affinity, what could be explained by the dispersion interaction of the naphthyl side-chain with a hydrophobic patch. Three in silico end-point methods were considered for the calculation of binding free energy. The Molecular Mechanics Poisson-Boltzmann Surface Area and the Solvated Interaction Energy methods gave reasonable agreement with experimental data, exhibiting a Pearlman predictive index of 0.71 and 0.78, respectively. The MM-quantum mechanics-surface area method yielded improved results, which was characterized by a Pearlman index of 0.78. The correlation coefficients were 0.59, 0.61 and 0.69, respectively. The ability to apply a QM level of theory within an end-point binding free energy protocol may provide a way for a consistent improvement of accuracy in computer-aided drug design.

Relevance of PET for pretherapeutic prediction of doses in peptide receptor radionuclide therapy.

PubMed

Blaickner, Matthias; Baum, Richard P

2014-01-01

Personalized dosimetry in radionuclide therapy has gained much attention in recent years. This attention has also an impact on peptide receptor radionuclide therapy (PRRT). This article reviews the PET-based imaging techniques that can be used for pretherapeutic prediction of doses in PRRT. More specifically the usage of (86)Y, (90)Y, (68)Ga, and (44)Sc are discussed: their characteristics for PET acquisition, the available peptides for labeling, the specifics of the imaging protocols, and the experiences gained from phantom and clinical studies. These techniques are evaluated with regard to their usefulness for dosimetry predictions in PRRT, and future perspectives are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Transcriptomic Analysis of Neuropeptides and Peptide Hormones in the Barnacle Balanus amphitrite: Evidence of Roles in Larval Settlement

PubMed Central

Yan, Xing-Cheng; Chen, Zhang-Fan; Sun, Jin; Matsumura, Kiyotaka; Wu, Rudolf S. S.; Qian, Pei-Yuan

2012-01-01

The barnacle Balanus amphitrite is a globally distributed marine crustacean and has been used as a model species for intertidal ecology and biofouling studies. Its life cycle consists of seven planktonic larval stages followed by a sessile juvenile/adult stage. The transitional processes between larval stages and juveniles are crucial for barnacle development and recruitment. Although some studies have been conducted on the neuroanatomy and neuroactive substances of the barnacle, a comprehensive understanding of neuropeptides and peptide hormones remains lacking. To better characterize barnacle neuropeptidome and its potential roles in larval settlement, an in silico identification of putative transcripts encoding neuropeptides/peptide hormones was performed, based on transcriptome of the barnacle B. amphitrite that has been recently sequenced. Potential cleavage sites andstructure of mature peptides were predicted through homology search of known arthropod peptides. In total, 16 neuropeptide families/subfamilies were predicted from the barnacle transcriptome, and 14 of them were confirmed as genuine neuropeptides by Rapid Amplification of cDNA Ends. Analysis of peptide precursor structures and mature sequences showed that some neuropeptides of B. amphitrite are novel isoforms and shared similar characteristics with their homologs from insects. The expression profiling of predicted neuropeptide genes revealed that pigment dispersing hormone, SIFamide, calcitonin, and B-type allatostatin had the highest expression level in cypris stage, while tachykinin-related peptide was down regulated in both cyprids and juveniles. Furthermore, an inhibitor of proprotein convertase related to peptide maturation effectively delayed larval metamorphosis. Combination of real-time PCR results and bioassay indicated that certain neuropeptides may play an important role in cypris settlement. Overall, new insight into neuropeptides/peptide hormones characterized in this study shall provide a platform for unraveling peptidergic control of barnacle larval behavior and settlement process. PMID:23056329

Definition of the HLA-A29 peptide ligand motif allows prediction of potential T-cell epitopes from the retinal soluble antigen, a candidate autoantigen in birdshot retinopathy.

PubMed Central

Boisgerault, F; Khalil, I; Tieng, V; Connan, F; Tabary, T; Cohen, J H; Choppin, J; Charron, D; Toubert, A

1996-01-01

The peptide-binding motif of HLA-A29, the predisposing allele for birdshot retinopathy, was determined after acid-elution of endogenous peptides from purified HLA-A29 molecules. Individual and pooled HPLC fractions were sequenced by Edman degradation. Major anchor residues could be defined as glutamate at the second position of the peptide and as tyrosine at the carboxyl terminus. In vitro binding of polyglycine synthetic peptides to purified HLA-A29 molecules also revealed the need for an auxiliary anchor residue at the third position, preferably phenylalanine. By using this motif, we synthesized six peptides from the retinal soluble antigen, a candidate autoantigen in autoimmune uveoretinitis. Their in vitro binding was tested on HLA-A29 and also on HLA-B44 and HLA-B61, two alleles sharing close peptide-binding motifs. Two peptides derived from the carboxyl-terminal sequence of the human retinal soluble antigen bound efficiently to HLA-A29. This study could contribute to the prediction of T-cell epitopes from retinal autoantigens implicated in birdshot retinopathy. PMID:8622959

What happens after the activation of ascaridole? Reactive compounds and their implications for skin sensitization

USDA-ARS?s Scientific Manuscript database

To replace animal testing and to improve the prediction of skin sensitization, significant attention has been directed to the use of alternative methods. Along with induction of Nrf2 target gene and upregulation of CD86 and C54 markers, the direct peptide reactivity assay (DPRA), the regulatory agen...

High-throughput serum proteomics for the identification of protein biomarkers of mortality in older men

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orwoll, Eric S.; Wiedrick, Jack; Jacobs, Jon

The biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high throughput proteomics approach to identify serum peptides and proteins associated with 5 year mortality in community dwelling men age >65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri-annual questionnaire. Rigorous statistical methods were utilized tomore » identify 56 peptides (31 proteins) that were associated with 5-year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality-associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g. C reactive protein, alpha 1-antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy-associated plasma protein, VE cadherin, leucine-rich α-2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5-year mortality risk. This work may serve to identify novel biomarkers for near term mortality.« less

Relation of Natriuretic Peptide Concentrations to Central Sleep Apnea in Patients With Heart Failure

PubMed Central

Calvin, Andrew D.; Somers, Virend K.; van der Walt, Christelle; Scott, Christopher G.

2011-01-01

Background: Central sleep apnea (CSA) is frequent among patients with heart failure (HF) and associated with increased morbidity and mortality. Elevated cardiac filling pressures promote CSA and atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) secretion. We hypothesized that circulating natriuretic peptide concentrations predict CSA. Methods: Consecutive patients with HF (n = 44) with left ventricular ejection fraction (LVEF) ≤ 35% underwent polysomnography for detection of CSA. CSA was defined as an apnea-hypopnea index ≥ 15 with ≥ 50% central apneic events. The relation of natriuretic peptide concentrations to CSA was evaluated by estimation of ORs and receiver operator characteristics (ROCs). Results: Twenty-seven subjects (61%) had CSA, with men more frequently affected than women (73% vs 27%; OR, 7.1; P = .01); given that only three women had CSA, further analysis was restricted to men. Subjects with CSA had higher mean ANP (4,336 pg/mL vs 2,510 pg/mL, P = .03) and BNP concentrations (746 pg/mL vs 379 pg/mL, P = .05). ANP and BNP concentrations were significantly related to CSA (OR, 3.7 per 3,000 pg/mL, P = .03 and OR, 1.5 per 200 pg/mL, P = .04, respectively), whereas age, LVEF, and New York Heart Association functional class were not. Concentrations of ANP and BNP were predictive of CSA as ROC demonstrated areas under the curve of 0.75 and 0.73, respectively. Conclusions: Risk of CSA is related to severity of HF. ANP and BNP concentrations performed similarly for detection of CSA; low concentrations appear associated with low risk for CSA in men. PMID:21636668

PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides.

PubMed

Li, Ning; Kang, Juanjuan; Jiang, Lixu; He, Bifang; Lin, Hao; Huang, Jian

2017-01-01

Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.

Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library*

PubMed Central

Yen, Chia-Yu; Houel, Stephane; Ahn, Natalie G.; Old, William M.

2011-01-01

The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a reference library of confidently assigned spectra. Two problems relate to library size. First, reference spectral libraries are limited to rediscovery of previously identified peptides and are not applicable to new peptides, because of their incomplete coverage of the human proteome. Second, problems arise when searching a spectral library the size of the entire human proteome. We observed that traditional dot product scoring methods do not scale well with spectral library size, showing reduction in sensitivity when library size is increased. We show that this problem can be addressed by optimizing scoring metrics for spectrum-to-spectrum searches with large spectral libraries. MS/MS spectra for the 1.3 million predicted tryptic peptides in the human proteome are simulated using a kinetic fragmentation model (MassAnalyzer version2.1) to create a proteome-wide simulated spectral library. Searches of the simulated library increase MS/MS assignments by 24% compared with Mascot, when using probabilistic and rank based scoring methods. The proteome-wide coverage of the simulated library leads to 11% increase in unique peptide assignments, compared with parallel searches of a reference spectral library. Further improvement is attained when reference spectra and simulated spectra are combined into a hybrid spectral library, yielding 52% increased MS/MS assignments compared with Mascot searches. Our study demonstrates the advantages of using probabilistic and rank based scores to improve performance of spectrum-to-spectrum search strategies. PMID:21532008

A Novel Trypsin Inhibitor-Like Cysteine-Rich Peptide from the Frog Lepidobatrachus laevis Containing Proteinase-Inhibiting Activity.

PubMed

Wang, Yu-Wei; Tan, Ji-Min; Du, Can-Wei; Luan, Ning; Yan, Xiu-Wen; Lai, Ren; Lu, Qiu-Min

2015-08-01

Various bio-active substances in amphibian skins play important roles in survival of the amphibians. Many protease inhibitor peptides have been identified from amphibian skins, which are supposed to negatively modulate the activity of proteases to avoid premature degradation or release of skin peptides, or to inhibit extracellular proteases produced by invading bacteria. However, there is no information on the proteinase inhibitors from the frog Lepidobatrachus laevis which is unique in South America. In this work, a cDNA encoding a novel trypsin inhibitor-like (TIL) cysteine-rich peptide was identified from the skin cDNA library of L. laevis. The 240-bp coding region encodes an 80-amino acid residue precursor protein containing 10 half-cysteines. By sequence comparison and signal peptide prediction, the precursor was predicted to release a 55-amino acid mature peptide with amino acid sequence, IRCPKDKIYKFCGSPCPPSCKDLTPNCIAVCKKGCFCRDGTVDNNHGKCVKKENC. The mature peptide was named LL-TIL. LL-TIL shares significant domain similarity with the peptides from the TIL supper family. Antimicrobial and trypsin-inhibitory abilities of recombinant LL-TIL were tested. Recombinant LL-TIL showed no antimicrobial activity, while it had trypsin-inhibiting activity with a Ki of 16.5178 μM. These results suggested there was TIL peptide with proteinase-inhibiting activity in the skin of frog L. laevis. To the best of our knowledge, this is the first report of TIL peptide from frog skin.

HomoSAR: bridging comparative protein modeling with quantitative structural activity relationship to design new peptides.

PubMed

Borkar, Mahesh R; Pissurlenkar, Raghuvir R S; Coutinho, Evans C

2013-11-15

Peptides play significant roles in the biological world. To optimize activity for a specific therapeutic target, peptide library synthesis is inevitable; which is a time consuming and expensive. Computational approaches provide a promising way to simply elucidate the structural basis in the design of new peptides. Earlier, we proposed a novel methodology termed HomoSAR to gain insight into the structure activity relationships underlying peptides. Based on an integrated approach, HomoSAR uses the principles of homology modeling in conjunction with the quantitative structural activity relationship formalism to predict and design new peptide sequences with the optimum activity. In the present study, we establish that the HomoSAR methodology can be universally applied to all classes of peptides irrespective of sequence length by studying HomoSAR on three peptide datasets viz., angiotensin-converting enzyme inhibitory peptides, CAMEL-s antibiotic peptides, and hAmphiphysin-1 SH3 domain binding peptides, using a set of descriptors related to the hydrophobic, steric, and electronic properties of the 20 natural amino acids. Models generated for all three datasets have statistically significant correlation coefficients (r(2)) and predictive r2 (r(pred)2) and cross validated coefficient ( q(LOO)2). The daintiness of this technique lies in its simplicity and ability to extract all the information contained in the peptides to elucidate the underlying structure activity relationships. The difficulties of correlating both sequence diversity and variation in length of the peptides with their biological activity can be addressed. The study has been able to identify the preferred or detrimental nature of amino acids at specific positions in the peptide sequences. Copyright © 2013 Wiley Periodicals, Inc.

Blind test of physics-based prediction of protein structures.

PubMed

Shell, M Scott; Ozkan, S Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A

2009-02-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

Blind Test of Physics-Based Prediction of Protein Structures

PubMed Central

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

Mass spectrometry analysis and in silico prediction of allergenicity of peptides in tryptic hydrolysates of the proteins from Ruditapes philippinarum.

PubMed

Yu, Yue; Liu, Hongwei; Tu, Maolin; Qiao, Meiling; Wang, Zhenyu; Du, Ming

2017-12-01

Ruditapes philippinarum is nutrient-rich and widely-distributed, but little attention has been paid to the identification and characterization of the bioactive peptides in the bivalve. In the present study, we evaluated the peptides of the R. philippinarum that were enzymolysised by trypsin using a combination of ultra-performance liquid chromatography separation and electrospray ionization quadrupole time-of-flight tandem mass spectrometry, followed by data processing and sequence-similarity database searching. The potential allergenicity of the peptides was assessed in silico. The enzymolysis was performed under the conditions: E:S 3:100 (w/w), pH 9.0, 45 °C for 4 h. After separation and detection, the Swiss-Prot database and a Ruditapes philippinarum sequence database were used: 966 unique peptides were identified by non-error tolerant database searching; 173 peptides matching 55 precursor proteins comprised highly conserved cytoskeleton proteins. The remaining 793 peptides were identified from the R. philippinarum sequence database. The results showed that 510 peptides were labeled as allergens and 31 peptides were potential allergens; 425 peptides were predicted to be nonallergenic. The abundant peptide information contributes to further investigations of the structure and potential function of R. philippinarum. Additional in vitro studies are required to demonstrate and ensure the correct production of the hydrolysates for use in the food industry with respect to R. philippinarum. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

CCTOP: a Consensus Constrained TOPology prediction web server.

PubMed

Dobson, László; Reményi, István; Tusnády, Gábor E

2015-07-01

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes.

PubMed

Skinnider, Michael A; Merwin, Nishanth J; Johnston, Chad W; Magarvey, Nathan A

2017-07-03

Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

CGM-measured glucose values have a strong correlation with C-peptide, HbA1c and IDAAC, but do poorly in predicting C-peptide levels in the two years following onset of diabetes.

PubMed

Buckingham, Bruce; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Weinzimer, Stuart A; Slover, Robert; Bremer, Andrew A; Fuqua, John; Tamborlane, William

2015-06-01

The aim of this work was to assess the association between continuous glucose monitoring (CGM) data, HbA1c, insulin-dose-adjusted HbA1c (IDAA1c) and C-peptide responses during the first 2 years following diagnosis of type 1 diabetes. A secondary analysis was conducted of data collected from a randomised trial assessing the effect of intensive management initiated within 1 week of diagnosis of type 1 diabetes, in which mixed-meal tolerance tests were performed at baseline and at eight additional time points through 24 months. CGM data were collected at each visit. Among 67 study participants (mean age [± SD] 13.3 ± 5.7 years), HbA1c was inversely correlated with C-peptide at each time point (p < 0.001), as were changes in each measure between time points (p < 0.001). However, C-peptide at one visit did not predict the change in HbA1c at the next visit and vice versa. Higher C-peptide levels correlated with increased proportion of CGM glucose values between 3.9 and 7.8 mmol/l and lower CV (p = 0.001 and p = 0.02, respectively) but not with CGM glucose levels <3.9 mmol/l. Virtually all participants with IDAA1c < 9 retained substantial insulin secretion but when evaluated together with CGM, time in the range of 3.9-7.8 mmol/l and CV did not provide additional value in predicting C-peptide levels. In the first 2 years after diagnosis of type 1 diabetes, higher C-peptide levels are associated with increased sensor glucose levels in the target range and with lower glucose variability but not hypoglycaemia. CGM metrics do not provide added value over the IDAA1c in predicting C-peptide levels.

In silico design of smart binders to anthrax PA

NASA Astrophysics Data System (ADS)

Sellers, Michael; Hurley, Margaret M.

2012-06-01

The development of smart peptide binders requires an understanding of the fundamental mechanisms of recognition which has remained an elusive grail of the research community for decades. Recent advances in automated discovery and synthetic library science provide a wealth of information to probe fundamental details of binding and facilitate the development of improved models for a priori prediction of affinity and specificity. Here we present the modeling portion of an iterative experimental/computational study to produce high affinity peptide binders to the Protective Antigen (PA) of Bacillus anthracis. The result is a general usage, HPC-oriented, python-based toolkit based upon powerful third-party freeware, which is designed to provide a better understanding of peptide-protein interactions and ultimately predict and measure new smart peptide binder candidates. We present an improved simulation protocol with flexible peptide docking to the Anthrax Protective Antigen, reported within the context of experimental data presented in a companion work.

Sequencing Cyclic Peptides by Multistage Mass Spectrometry

PubMed Central

Mohimani, Hosein; Yang, Yu-Liang; Liu, Wei-Ting; Hsieh, Pei-Wen; Dorrestein, Pieter C.; Pevzner, Pavel A.

2012-01-01

Some of the most effective antibiotics (e.g., Vancomycin and Daptomycin) are cyclic peptides produced by non-ribosomal biosynthetic pathways. While hundreds of biomedically important cyclic peptides have been sequenced, the computational techniques for sequencing cyclic peptides are still in their infancy. Previous methods for sequencing peptide antibiotics and other cyclic peptides are based on Nuclear Magnetic Resonance spectroscopy, and require large amount (miligrams) of purified materials that, for most compounds, are not possible to obtain. Recently, development of mass spectrometry based methods has provided some hope for accurate sequencing of cyclic peptides using picograms of materials. In this paper we develop a method for sequencing of cyclic peptides by multistage mass spectrometry, and show its advantages over single stage mass spectrometry. The method is tested on known and new cyclic peptides from Bacillus brevis, Dianthus superbus and Streptomyces griseus, as well as a new family of cyclic peptides produced by marine bacteria. PMID:21751357

Overlooked Short Toxin-Like Proteins: A Shortcut to Drug Design

PubMed Central

Linial, Michal

2017-01-01

Short stable peptides have huge potential for novel therapies and biosimilars. Cysteine-rich short proteins are characterized by multiple disulfide bridges in a compact structure. Many of these metazoan proteins are processed, folded, and secreted as soluble stable folds. These properties are shared by both marine and terrestrial animal toxins. These stable short proteins are promising sources for new drug development. We developed ClanTox (classifier of animal toxins) to identify toxin-like proteins (TOLIPs) using machine learning models trained on a large-scale proteomic database. Insects proteomes provide a rich source for protein innovations. Therefore, we seek overlooked toxin-like proteins from insects (coined iTOLIPs). Out of 4180 short (<75 amino acids) secreted proteins, 379 were predicted as iTOLIPs with high confidence, with as many as 30% of the genes marked as uncharacterized. Based on bioinformatics, structure modeling, and data-mining methods, we found that the most significant group of predicted iTOLIPs carry antimicrobial activity. Among the top predicted sequences were 120 termicin genes from termites with antifungal properties. Structural variations of insect antimicrobial peptides illustrate the similarity to a short version of the defensin fold with antifungal specificity. We also identified 9 proteins that strongly resemble ion channel inhibitors from scorpion and conus toxins. Furthermore, we assigned functional fold to numerous uncharacterized iTOLIPs. We conclude that a systematic approach for finding iTOLIPs provides a rich source of peptides for drug design and innovative therapeutic discoveries. PMID:29109389

The electrostatic surface of MDM2 modulates the specificity of its interaction with phosphorylated and unphosphorylated p53 peptides.

PubMed

Brown, Christopher John; Srinivasan, Deepa; Jun, Lee Hui; Coomber, David; Verma, Chandra S; Lane, David P

2008-03-01

Florescence anisotropy measurements using FAM-labelled p53 peptides showed that the binding of the peptides to MDM2 was dependant upon the phosphorylation of p53 at Thr18 and that this binding was modulated by the electrostatic properties of MDM2. In agreement with computational predictions, the binding to phosphorylated p53 peptide, in comparison to the unphosphorylated p53 peptide, was enhanced upon mutation of 3 key residues on the MDM2 surface.

Machine learning-based methods for prediction of linear B-cell epitopes.

PubMed

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

Purification, characterization and application of a novel antimicrobial peptide from Andrias davidianus blood.

PubMed

Pei, J; Feng, Z; Ren, T; Sun, H; Han, H; Jin, W; Dang, J; Tao, Y

2018-01-01

The Andrias davidianus has been known as a traditional Chinese medicine for a long time. Its blood is considered as a waste or by-product of the meat production industry. Although there are reports on isolation of the antimicrobial peptides from different resources, there are no reports of their isolation from A. davidianus blood. In this work, an antimicrobial peptide, andricin B, was isolated from the blood of A. davidianus by an innovative method in which the magnetic liposome adsorption was combined with reversed-phase high-performance liquid chromatography. The structure, antimicrobial activity and safety of andricin B were further investigated. Amino acid sequence was determined by N-terminal sequencing and found to be Gly-Leu-Thr-Arg-Leu-Phe-Ser-Val-Ile-Lys. Circular dichroism (CD) spectra and prediction of three-dimensional structure by bioinformatics software suggested the presence of a well-defined random coil conformation. Andricin B was found to be active against all bacteria tested in this study as well as some fungi. The minimum inhibitory concentrations (MICs) were in the range 8-64 μg ml -1 . Moreover, the haemolytic testing also suggested that andricin B could be considered safe at the MICs. Finally, andricin B was shown to inhibit the growth of Staphylococcus aureus in the cooked meat of A. davidianus. This study shows that andricin B is a promising novel antimicrobial peptide that may provide further insights towards the development of new drugs. This is the pioneer study on screening and isolation of antimicrobial peptide from the blood of Andrias davidianus. Here, we have developed a novel method by combining magnetic liposomes adsorption with reversed-phase high-performance liquid chromatography to purify and screen the antimicrobial peptides. From this screen, we identified a novel antimicrobial peptide which we name as andricin B. Andricin B is unique as it checks the growth of both Gram-positive and Gram-negative bacteria as well as few fungal species. © 2017 The Society for Applied Microbiology.

N-terminal pro-B-type natriuretic peptide and the prediction of primary cardiovascular events: results from 15-year follow-up of WOSCOPS

PubMed Central

Welsh, Paul; Doolin, Orla; Willeit, Peter; Packard, Chris; Macfarlane, Peter; Cobbe, Stuart; Gudnason, Vilmundur; Di Angelantonio, Emanuele; Ford, Ian; Sattar, Naveed

2013-01-01

Aims To test whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) was independently associated with, and improved the prediction of, cardiovascular disease (CVD) in a primary prevention cohort. Methods and results In the West of Scotland Coronary Prevention Study (WOSCOPS), a cohort of middle-aged men with hypercholesterolaemia at a moderate risk of CVD, we related the baseline NT-proBNP (geometric mean 28 pg/mL) in 4801 men to the risk of CVD over 15 years during which 1690 experienced CVD events. Taking into account the competing risk of non-CVD death, NT-proBNP was associated with an increased risk of all CVD [HR: 1.17 (95% CI: 1.11–1.23) per standard deviation increase in log NT-proBNP] after adjustment for classical and clinical cardiovascular risk factors plus C-reactive protein. N-terminal pro-B-type natriuretic peptide was more strongly related to the risk of fatal [HR: 1.34 (95% CI: 1.19–1.52)] than non-fatal CVD [HR: 1.17 (95% CI: 1.10–1.24)] (P= 0.022). The addition of NT-proBNP to traditional risk factors improved the C-index (+0.013; P < 0.001). The continuous net reclassification index improved with the addition of NT-proBNP by 19.8% (95% CI: 13.6–25.9%) compared with 9.8% (95% CI: 4.2–15.6%) with the addition of C-reactive protein. N-terminal pro-B-type natriuretic peptide correctly reclassified 14.7% of events, whereas C-reactive protein correctly reclassified 3.4% of events. Results were similar in the 4128 men without evidence of angina, nitrate prescription, minor ECG abnormalities, or prior cerebrovascular disease. Conclusion N-terminal pro-B-type natriuretic peptide predicts CVD events in men without clinical evidence of CHD, angina, or history of stroke, and appears related more strongly to the risk for fatal events. N-terminal pro-B-type natriuretic peptide also provides moderate risk discrimination, in excess of that provided by the measurement of C-reactive protein. Clinical trial registration WOSCOPS was carried out and completed prior to the requirement for clinical trial registration. PMID:22942340

Use of galerina marginata genes and proteins for peptide production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hallen-Adams, Heather E.; Scott-Craig, John S.; Walton, Jonathan D.

The present invention relates to compositions and methods comprising genes and peptides associated with cyclic peptides and cyclic peptide production in mushrooms. In particular, the present invention relates to using genes and proteins from Galerina species encoding peptides specifically relating to amatoxins in addition to proteins involved with processing cyclic peptide toxins. In a preferred embodiment, the present invention also relates to methods for making small peptides and small cyclic peptides including peptides similar to amanitin. Further, the present inventions relate to providing kits for making small peptides.

Use of Galerina marginata genes and proteins for peptide production

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hallen-Adams, Heather E.; Scott-Craig, John S.; Walton, Jonathan D.

The present invention relates to compositions and methods comprising genes and peptides associated with cyclic peptides and cyclic peptide production in mushrooms. In particular, the present invention relates to using genes and proteins from Galerina species encoding peptides specifically relating to amatoxins in addition to proteins involved with processing cyclic peptide toxins. In a preferred embodiment, the present invention also relates to methods for making small peptides and small cyclic peptides including peptides similar to amanitin. Further, the present inventions relate to providing kits for making small peptides.

Use of Galerina marginata genes and proteins for peptide production

DOEpatents

Hallen-Adams, Heather E.; Scott-Craig, John S.; Walton, Jonathan D.; Luo, Hong

2016-03-01

The present invention relates to compositions and methods comprising genes and peptides associated with cyclic peptides and cyclic peptide production in mushrooms. In particular, the present invention relates to using genes and proteins from Galerina species encoding peptides specifically relating to amatoxins in addition to proteins involved with processing cyclic peptide toxins. In a preferred embodiment, the present invention also relates to methods for making small peptides and small cyclic peptides including peptides similar to amanitin. Further, the present inventions relate to providing kits for making small peptides.

Impact of graphene-based nanomaterials (GBNMs) on the structural and functional conformations of hepcidin peptide

NASA Astrophysics Data System (ADS)

Singh, Krishna P.; Baweja, Lokesh; Wolkenhauer, Olaf; Rahman, Qamar; Gupta, Shailendra K.

2018-03-01

Graphene-based nanomaterials (GBNMs) are widely used in various industrial and biomedical applications. GBNMs of different compositions, size and shapes are being introduced without thorough toxicity evaluation due to the unavailability of regulatory guidelines. Computational toxicity prediction methods are used by regulatory bodies to quickly assess health hazards caused by newer materials. Due to increasing demand of GBNMs in various size and functional groups in industrial and consumer based applications, rapid and reliable computational toxicity assessment methods are urgently needed. In the present work, we investigate the impact of graphene and graphene oxide nanomaterials on the structural conformations of small hepcidin peptide and compare the materials for their structural and conformational changes. Our molecular dynamics simulation studies revealed conformational changes in hepcidin due to its interaction with GBMNs, which results in a loss of its functional properties. Our results indicate that hepcidin peptide undergo severe structural deformations when superimposed on the graphene sheet in comparison to graphene oxide sheet. These observations suggest that graphene is more toxic than a graphene oxide nanosheet of similar area. Overall, this study indicates that computational methods based on structural deformation, using molecular dynamics (MD) simulations, can be used for the early evaluation of toxicity potential of novel nanomaterials.

Organization of model helical peptides in lipid bilayers: insight into the behavior of single-span protein transmembrane domains.

PubMed Central

Sharpe, Simon; Barber, Kathryn R; Grant, Chris W M; Goodyear, David; Morrow, Michael R

2002-01-01

Selectively deuterated transmembrane peptides comprising alternating leucine-alanine subunits were examined in fluid bilayer membranes by solid-state nuclear magnetic resonance (NMR) spectroscopy in an effort to gain insight into the behavior of membrane proteins. Two groups of peptides were studied: 21-mers having a 17-amino-acid hydrophobic domain calculated to be close in length to the hydrophobic thickness of 1-palmitoyl-2-oleoyl phosphatidylcholine and 26-mers having a 22-amino-acid hydrophobic domain calculated to exceed the membrane hydrophobic thickness. (2)H NMR spectral features similar to ones observed for transmembrane peptides from single-span receptors of higher animal cells were identified which apparently correspond to effectively monomeric peptide. Spectral observations suggested significant distortion of the transmembrane alpha-helix, and/or potential for restriction of rotation about the tilted helix long axis for even simple peptides. Quadrupole splittings arising from the 26-mer were consistent with greater peptide "tilt" than were those of the analogous 21-mer. Quadrupole splittings associated with monomeric peptide were relatively insensitive to concentration and temperature over the range studied, indicating stable average conformations, and a well-ordered rotation axis. At high peptide concentration (6 mol% relative to phospholipid) it appeared that the peptide predicted to be longer than the membrane thickness had a particular tendency toward reversible peptide-peptide interactions occurring on a timescale comparable with or faster than approximately 10(-5) s. This interaction may be direct or lipid-mediated and was manifest as line broadening. Peptide rotational diffusion rates within the membrane, calculated from quadrupolar relaxation times, T(2e), were consistent with such interactions. In the case of the peptide predicted to be equal to the membrane thickness, at low peptide concentration spectral lineshape indicated the additional presence of a population of peptide having rotational motion that was restricted on a timescale of 10(-5) s. PMID:12080125

A Peptide Amphiphile Organogelator of Polar Organic Solvents.

PubMed

Rouse, Charlotte K; Martin, Adam D; Easton, Christopher J; Thordarson, Pall

2017-03-03

A peptide amphiphile is reported, that gelates a range of polar organic solvents including acetonitrile/water, N,N-dimethylformamide and acetone, in a process dictated by β-sheet interactions and facilitated by the presence of an alkyl chain. Similarities with previously reported peptide amphiphile hydrogelators indicate analogous underlying mechanisms of gelation and structure-property relationships, suggesting that peptide amphiphile organogel design may be predictably based on hydrogel precedents.

A Peptide Amphiphile Organogelator of Polar Organic Solvents

PubMed Central

Rouse, Charlotte K.; Martin, Adam D.; Easton, Christopher J.; Thordarson, Pall

2017-01-01

A peptide amphiphile is reported, that gelates a range of polar organic solvents including acetonitrile/water, N,N-dimethylformamide and acetone, in a process dictated by β-sheet interactions and facilitated by the presence of an alkyl chain. Similarities with previously reported peptide amphiphile hydrogelators indicate analogous underlying mechanisms of gelation and structure-property relationships, suggesting that peptide amphiphile organogel design may be predictably based on hydrogel precedents. PMID:28255169

AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50).

PubMed

Qureshi, Abid; Tandon, Himani; Kumar, Manoj

2015-11-01

Peptide-based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC50 values involves a lot of effort. Therefore, we have developed "AVP-IC50 Pred," a regression-based algorithm to predict the antiviral activity in terms of IC50 values (μM). A total of 759 non-redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino-acid compositions, binary profile of N8-C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance-based classifier, and K-Star were employed. During 10-fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP-IC50 Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp. © 2015 Wiley Periodicals, Inc.

Peptide interfaces with graphene: an emerging intersection of analytical chemistry, theory, and materials.

PubMed

Russell, Shane R; Claridge, Shelley A

2016-04-01

Because noncovalent interface functionalization is frequently required in graphene-based devices, biomolecular self-assembly has begun to emerge as a route for controlling substrate electronic structure or binding specificity for soluble analytes. The remarkable diversity of structures that arise in biological self-assembly hints at the possibility of equally diverse and well-controlled surface chemistry at graphene interfaces. However, predicting and analyzing adsorbed monolayer structures at such interfaces raises substantial experimental and theoretical challenges. In contrast with the relatively well-developed monolayer chemistry and characterization methods applied at coinage metal surfaces, monolayers on graphene are both less robust and more structurally complex, levying more stringent requirements on characterization techniques. Theory presents opportunities to understand early binding events that lay the groundwork for full monolayer structure. However, predicting interactions between complex biomolecules, solvent, and substrate is necessitating a suite of new force fields and algorithms to assess likely binding configurations, solvent effects, and modulations to substrate electronic properties. This article briefly discusses emerging analytical and theoretical methods used to develop a rigorous chemical understanding of the self-assembly of peptide-graphene interfaces and prospects for future advances in the field.

Bringing the fathead minnow into the genomic era | Science ...

EPA Pesticide Factsheets

The fathead minnow is a well-established ecotoxicological model organism that has been widely used for regulatory ecotoxicity testing and research for over a half century. While a large amount of molecular information has been gathered on the fathead minnow over the years, the lack of genomic sequence data has limited the utility of the fathead minnow for certain applications. To address this limitation, high-throughput Illumina sequencing technology was employed to sequence the fathead minnow genome. Approximately 100X coverage was achieved by sequencing several libraries of paired-end reads with differing genome insert sizes. Two draft genome assemblies were generated using the SOAPdenovo and String Graph Assembler (SGA) methods, respectively. When these were compared, the SOAPdenovo assembly had a higher scaffold N50 value of 60.4 kbp versus 15.4 kbp, and it also performed better in a Core Eukaryotic Genes Mapping Analysis (CEGMA), mapping 91% versus 67% of genes. As such, this assembly was selected for further development and annotation. The foundation for genome annotation was generated using AUGUSTUS, an ab initio method for gene prediction. A total of 43,345 potential coding sequences were predicted on the genome assembly. These predicted sequences were translated to peptides and queried in a BLAST search against all vertebrates, with 28,290 of these sequences corresponding to zebrafish peptides and 5,242 producing no significant alignments. Additional ty

The RAPID method for blood processing yields new insight in plasma concentrations and molecular forms of circulating gut peptides.

PubMed

Stengel, Andreas; Keire, David; Goebel, Miriam; Evilevitch, Lena; Wiggins, Brian; Taché, Yvette; Reeve, Joseph R

2009-11-01

The correct identification of circulating molecular forms and measurement of peptide levels in blood entails that the endocrine peptide being studied is stable and recovered in good yields during blood processing. However, it is not clear whether this is achieved in studies using standard blood processing. Therefore, we compared peptide concentration and form of 12 (125)I-labeled peptides using the standard procedure (EDTA-blood on ice) and a new method employing Reduced temperatures, Acidification, Protease inhibition, Isotopic exogenous controls, and Dilution (RAPID). During standard processing there was at least 80% loss for calcitonin-gene-related peptide and cholecystokinin-58 (CCK-58) and more than 35% loss for amylin, insulin, peptide YY forms (PYY((1-36)) and PYY((3-36))), and somatostatin-28. In contrast, the RAPID method significantly improved the recovery for 11 of 12 peptides (P < 0.05) and eliminated the breakdown of endocrine peptides occurring after standard processing as reflected in radically changed molecular forms for CCK-58, gastrin-releasing peptide, somatostatin-28, and ghrelin. For endogenous ghrelin, this led to an acyl/total ghrelin ratio of 1:5 instead of 1:19 by the standard method. These results show that the RAPID method enables accurate assessment of circulating gut peptide concentrations and forms such as CCK-58, acylated ghrelin, and somatostatin-28. Therefore, the RAPID method represents an efficacious means to detect circulating variations in peptide concentrations and form relevant to the understanding of physiological function of endocrine peptides.

Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides.

PubMed

Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A

2018-06-07

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor and principal regulator of the antioxidant pathway. The Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) binds to motifs in the N-terminal region of Nrf2, promoting its degradation. There is interest in developing ligands that can compete with Nrf2 for binding to Kelch, thereby activating its transcriptional activities and increasing antioxidant levels. Using experimental Δ G bind values of Kelch-binding motifs determined previously, a revised hydrophobicity-based model was developed for estimating Δ G bind from amino acid sequence and applied to rank potential uncharacterized Kelch-binding motifs identified from interaction databases and BLAST searches. Model predictions and molecular dynamics (MD) simulations suggested that full-length MAD2A binds Kelch more favorably than a high-affinity 20-mer Nrf2 E78P peptide, but that the motif in isolation is not a particularly strong binder. Endeavoring to develop shorter peptides for activating Nrf2, new designs were created based on the E78P peptide, some of which showed considerable propensity to form binding-competent structures in MD, and were predicted to interact with Kelch more favorably than the E78P peptide. The peptides could be promising new ligands for enhancing the oxidative stress response.

An improved stable isotope N-terminal labeling approach with light/heavy TMPP to automate proteogenomics data validation: dN-TOP.

PubMed

Bertaccini, Diego; Vaca, Sebastian; Carapito, Christine; Arsène-Ploetze, Florence; Van Dorsselaer, Alain; Schaeffer-Reiss, Christine

2013-06-07

In silico gene prediction has proven to be prone to errors, especially regarding precise localization of start codons that spread in subsequent biological studies. Therefore, the high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and especially proteogenomics fields. The trimethoxyphenyl phosphonium (TMPP) labeling approach (N-TOP) is an efficient N-terminomic approach that allows the characterization of both N-terminal and internal peptides in a single experiment. Due to its permanent positive charge, TMPP labeling strongly affects MS/MS fragmentation resulting in unadapted scoring of TMPP-derivatized peptide spectra by classical search engines. This behavior has led to difficulties in validating TMPP-derivatized peptide identifications with usual score filtering and thus to low/underestimated numbers of identified N-termini. We present herein a new strategy (dN-TOP) that overwhelmed the previous limitation allowing a confident and automated N-terminal peptide validation thanks to a combined labeling with light and heavy TMPP reagents. We show how this double labeling allows increasing the number of validated N-terminal peptides. This strategy represents a considerable improvement to the well-established N-TOP method with an enhanced and accelerated data processing making it now fully compatible with high-throughput proteogenomics studies.

Establishment of HLA-DR4 Transgenic Mice for the Identification of CD4+ T Cell Epitopes of Tumor-Associated Antigens

PubMed Central

Harada, Kumiko; Michibata, Yayoi; Tsukamoto, Hirotake; Senju, Satoru; Tomita, Yusuke; Yuno, Akira; Hirayama, Masatoshi; Abu Sayem, Mohammad; Takeda, Naoki; Shibuya, Isao; Sogo, Shinji; Fujiki, Fumihiro; Sugiyama, Haruo; Eto, Masatoshi; Nishimura, Yasuharu

2013-01-01

Reports have shown that activation of tumor-specific CD4+ helper T (Th) cells is crucial for effective anti-tumor immunity and identification of Th-cell epitopes is critical for peptide vaccine-based cancer immunotherapy. Although computer algorithms are available to predict peptides with high binding affinity to a specific HLA class II molecule, the ability of those peptides to induce Th-cell responses must be evaluated. We have established HLA-DR4 (HLA-DRA*01:01/HLA-DRB1*04:05) transgenic mice (Tgm), since this HLA-DR allele is most frequent (13.6%) in Japanese population, to evaluate HLA-DR4-restricted Th-cell responses to tumor-associated antigen (TAA)-derived peptides predicted to bind to HLA-DR4. To avoid weak binding between mouse CD4 and HLA-DR4, Tgm were designed to express chimeric HLA-DR4/I-Ed, where I-Ed α1 and β1 domains were replaced with those from HLA-DR4. Th cells isolated from Tgm immunized with adjuvant and HLA-DR4-binding cytomegalovirus-derived peptide proliferated when stimulated with peptide-pulsed HLA-DR4-transduced mouse L cells, indicating chimeric HLA-DR4/I-Ed has equivalent antigen presenting capacity to HLA-DR4. Immunization with CDCA155-78 peptide, a computer algorithm-predicted HLA-DR4-binding peptide derived from TAA CDCA1, successfully induced Th-cell responses in Tgm, while immunization of HLA-DR4-binding Wilms' tumor 1 antigen-derived peptide with identical amino acid sequence to mouse ortholog failed. This was overcome by using peptide-pulsed syngeneic bone marrow-derived dendritic cells (BM-DC) followed by immunization with peptide/CFA booster. BM-DC-based immunization of KIF20A494-517 peptide from another TAA KIF20A, with an almost identical HLA-binding core amino acid sequence to mouse ortholog, successfully induced Th-cell responses in Tgm. Notably, both CDCA155-78 and KIF20A494-517 peptides induced human Th-cell responses in PBMCs from HLA-DR4-positive donors. Finally, an HLA-DR4 binding DEPDC1191-213 peptide from a new TAA DEPDC1 overexpressed in bladder cancer induced strong Th-cell responses both in Tgm and in PBMCs from an HLA-DR4-positive donor. Thus, the HLA-DR4 Tgm combined with computer algorithm was useful for preliminary screening of candidate peptides for vaccination. PMID:24386437

Protein domain analysis of genomic sequence data reveals regulation of LRR related domains in plant transpiration in Ficus.

PubMed

Lang, Tiange; Yin, Kangquan; Liu, Jinyu; Cao, Kunfang; Cannon, Charles H; Du, Fang K

2014-01-01

Predicting protein domains is essential for understanding a protein's function at the molecular level. However, up till now, there has been no direct and straightforward method for predicting protein domains in species without a reference genome sequence. In this study, we developed a functionality with a set of programs that can predict protein domains directly from genomic sequence data without a reference genome. Using whole genome sequence data, the programming functionality mainly comprised DNA assembly in combination with next-generation sequencing (NGS) assembly methods and traditional methods, peptide prediction and protein domain prediction. The proposed new functionality avoids problems associated with de novo assembly due to micro reads and small single repeats. Furthermore, we applied our functionality for the prediction of leucine rich repeat (LRR) domains in four species of Ficus with no reference genome, based on NGS genomic data. We found that the LRRNT_2 and LRR_8 domains are related to plant transpiration efficiency, as indicated by the stomata index, in the four species of Ficus. The programming functionality established in this study provides new insights for protein domain prediction, which is particularly timely in the current age of NGS data expansion.

HLA mismatches and hematopoietic cell transplantation: structural simulations assess the impact of changes in peptide binding specificity on transplant outcome

PubMed Central

Yanover, Chen; Petersdorf, Effie W.; Malkki, Mari; Gooley, Ted; Spellman, Stephen; Velardi, Andrea; Bardy, Peter; Madrigal, Alejandro; Bignon, Jean-Denis; Bradley, Philip

2013-01-01

The success of hematopoietic cell transplantation from an unrelated donor depends in part on the degree of Human Histocompatibility Leukocyte Antigen (HLA) matching between donor and patient. We present a structure-based analysis of HLA mismatching, focusing on individual amino acid mismatches and their effect on peptide binding specificity. Using molecular modeling simulations of HLA-peptide interactions, we find evidence that amino acid mismatches predicted to perturb peptide binding specificity are associated with higher risk of mortality in a large and diverse dataset of patient-donor pairs assembled by the International Histocompatibility Working Group in Hematopoietic Cell Transplantation consortium. This analysis may represent a first step toward sequence-based prediction of relative risk for HLA allele mismatches. PMID:24482668

Optimization for Peptide Sample Preparation for Urine Peptidomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sigdel, Tara K.; Nicora, Carrie D.; Hsieh, Szu-Chuan

2014-02-25

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput ‘peptidomics’ methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides andmore » the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.« less

BagReg: Protein inference through machine learning.

PubMed

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting unfolding thermodynamics and stable intermediates for alanine-rich helical peptides with the aid of coarse-grained molecular simulation.

PubMed

Calero-Rubio, Cesar; Paik, Bradford; Jia, Xinqiao; Kiick, Kristi L; Roberts, Christopher J

2016-10-01

This report focuses on the molecular-level processes and thermodynamics of unfolding of a series of helical peptides using a coarse-grained (CG) molecular model. The CG model was refined to capture thermodynamics and structural changes as a function of temperature for a set of published peptide sequences. Circular dichroism spectroscopy (CD) was used to experimentally monitor the temperature-dependent conformational changes and stability of published peptides and new sequences introduced here. The model predictions were quantitatively or semi-quantitatively accurate in all cases. The simulations and CD results showed that, as expected, in most cases the unfolding of helical peptides is well described by a simply 2-state model, and conformational stability increased with increased length of the helices. A notable exception in a 19-residue helix was when two Ala residues were each replaced with Phe. This stabilized a partly unfolded intermediate state via hydrophobic contacts, and also promoted aggregates at higher peptide concentrations. Copyright © 2016 Elsevier B.V. All rights reserved.

Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands.

PubMed

Zhao, Y; Gran, B; Pinilla, C; Markovic-Plese, S; Hemmer, B; Tzou, A; Whitney, L W; Biddison, W E; Martin, R; Simon, R

2001-08-15

The interaction of TCRs with MHC peptide ligands can be highly flexible, so that many different peptides are recognized by the same TCR in the context of a single restriction element. We provide a quantitative description of such interactions, which allows the identification of T cell epitopes and molecular mimics. The response of T cell clones to positional scanning synthetic combinatorial libraries is analyzed with a mathematical approach that is based on a model of independent contribution of individual amino acids to peptide Ag recognition. This biometric analysis compares the information derived from these libraries composed of trillions of decapeptides with all the millions of decapeptides contained in a protein database to rank and predict the most stimulatory peptides for a given T cell clone. We demonstrate the predictive power of the novel strategy and show that, together with gene expression profiling by cDNA microarrays, it leads to the identification of novel candidate autoantigens in the inflammatory autoimmune disease, multiple sclerosis.

Apolipoprotein C-II Is a Potential Serum Biomarker as a Prognostic Factor of Locally Advanced Cervical Cancer After Chemoradiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harima, Yoko, E-mail: harima@takii.kmu.ac.jp; Ikeda, Koshi; Utsunomiya, Keita

Purpose: To determine pretreatment serum protein levels for generally applicable measurement to predict chemoradiation treatment outcomes in patients with locally advanced squamous cell cervical carcinoma (CC). Methods and Materials: In a screening study, measurements were conducted twice. At first, 6 serum samples from CC patients (3 with no evidence of disease [NED] and 3 with cancer-caused death [CD]) and 2 from healthy controls were tested. Next, 12 serum samples from different CC patients (8 NED, 4 CD) and 4 from healthy controls were examined. Subsequently, 28 different CC patients (18 NED, 10 CD) and 9 controls were analyzed in themore » validation study. Protein chips were treated with the sample sera, and the serum protein pattern was detected by surface-enhanced laser desorption and ionization–time-of-flight mass spectrometry (SELDI-TOF MS). Then, single MS-based peptide mass fingerprinting (PMF) and tandem MS (MS/MS)-based peptide/protein identification methods, were used to identify protein corresponding to the detected peak. And then, turbidimetric assay was used to measure the levels of a protein that indicated the best match with this peptide peak. Results: The same peak 8918 m/z was identified in both screening studies. Neither the screening study nor the validation study had significant differences in the appearance of this peak in the controls and NED. However, the intensity of the peak in CD was significantly lower than that of controls and NED in both pilot studies (P=.02, P=.04) and validation study (P=.01, P=.001). The protein indicated the best match with this peptide peak at 8918 m/z was identified as apolipoprotein C-II (ApoC-II) using PMF and MS/MS methods. Turbidimetric assay showed that the mean serum levels of ApoC-II tended to decrease in CD group when compared with NED group (P=.078). Conclusion: ApoC-II could be used as a biomarker for detection in predicting and estimating the radiation treatment outcome of patients with CC.« less

Human milk peptides differentiate between the preterm and term infant and across varying lactational stages.

PubMed

Dingess, Kelly A; de Waard, Marita; Boeren, Sjef; Vervoort, Jacques; Lambers, Tim T; van Goudoever, Johannes B; Hettinga, Kasper

2017-10-18

Variations in endogenous peptide profiles, functionality, and the enzymes responsible for the formation of these peptides in human milk are understudied. Additionally, there is a lack of knowledge regarding peptides in donor human milk, which is used to feed preterm infants when mother's own milk is not (sufficiently) available. To assess this, 29 human milk samples from the Dutch Human Milk Bank were analyzed as three groups, preterm late lactation stage (LS) (n = 12), term early (n = 8) and term late LS (n = 9). Gestational age (GA) groups were defined as preterm (24-36 weeks) and term (≥37 weeks). LS was determined as days postpartum as early (16-36 days) or late (55-88 days). Peptides, analyzed by LC-MS/MS, and parent proteins (proteins from matched peptide sequences) were identified and quantified, after which peptide functionality and the enzymes responsible for protein cleavage were determined. A total of 16 different parent proteins were identified from human milk, with no differences by GA or LS. We identified 1104 endogenous peptides, of which, the majority were from the parent proteins β-casein, polymeric immunoglobulin receptor, α s1 -casein, osteopontin, and κ-casein. The absolute number of peptides differed by GA and LS with 30 and 41 differing sequences respectively (p < 0.05) Odds likelihood tests determined that 32 peptides had a predicted bioactive functionality, with no significant differences between groups. Enzyme prediction analysis showed that plasmin/trypsin enzymes most likely cleaved the identified human milk peptides. These results explain some of the variation in endogenous peptides in human milk, leading to future targets that may be studied for functionality.

Quantitative Protein Topography Analysis and High-Resolution Structure Prediction Using Hydroxyl Radical Labeling and Tandem-Ion Mass Spectrometry (MS)*

PubMed Central

Kaur, Parminder; Kiselar, Janna; Yang, Sichun; Chance, Mark R.

2015-01-01

Hydroxyl radical footprinting based MS for protein structure assessment has the goal of understanding ligand induced conformational changes and macromolecular interactions, for example, protein tertiary and quaternary structure, but the structural resolution provided by typical peptide-level quantification is limiting. In this work, we present experimental strategies using tandem-MS fragmentation to increase the spatial resolution of the technique to the single residue level to provide a high precision tool for molecular biophysics research. Overall, in this study we demonstrated an eightfold increase in structural resolution compared with peptide level assessments. In addition, to provide a quantitative analysis of residue based solvent accessibility and protein topography as a basis for high-resolution structure prediction; we illustrate strategies of data transformation using the relative reactivity of side chains as a normalization strategy and predict side-chain surface area from the footprinting data. We tested the methods by examination of Ca+2-calmodulin showing highly significant correlations between surface area and side-chain contact predictions for individual side chains and the crystal structure. Tandem ion based hydroxyl radical footprinting-MS provides quantitative high-resolution protein topology information in solution that can fill existing gaps in structure determination for large proteins and macromolecular complexes. PMID:25687570

N-terminal pro-brain natriuretic peptide in acute Kawasaki disease correlates with coronary artery involvement.

PubMed

Adjagba, Philippe M; Desjardins, Laurent; Fournier, Anne; Spigelblatt, Linda; Montigny, Martine; Dahdah, Nagib

2015-10-01

We have lately documented the importance of N-terminal pro-brain natriuretic peptide in aiding the diagnosis of Kawasaki disease. We sought to investigate the potential value of N-terminal pro-brain natriuretic peptide pertaining to the prediction of coronary artery dilatation (Z-score>2.5) and/or of resistance to intravenous immunoglobulin therapy. We hypothesised that increased serum N-terminal pro-brain natriuretic peptide level correlates with increased coronary artery dilatation and/or resistance to intravenous immunoglobulin. We carried out a prospective study involving newly diagnosed patients treated with 2 g/kg intravenous immunoglobulin within 5-10 days of onset of fever. Echocardiography was performed in all patients at onset, then weekly for 3 weeks, then at month 2, and month 3. Coronary arteries were measured at each visit, and coronary artery Z-score was calculated. All the patients had N-terminal pro-brain natriuretic peptide serum level measured at onset, and the Z-score calculated. There were 109 patients enrolled at 6.58±2.82 days of fever, age 3.79±2.92 years. High N-terminal pro-brain natriuretic peptide level was associated with coronary artery dilatation at onset in 22.2 versus 5.6% for normal N-terminal pro-brain natriuretic peptide levels (odds ratio 4.8 [95% confidence interval 1.05-22.4]; p=0.031). This was predictive of cumulative coronary artery dilatation for the first 3 months (p=0.04-0.02), but not during convalescence at 2-3 months (odds ratio 1.28 [95% confidence interval 0.23-7.3]; p=non-significant). Elevated N-terminal pro-brain natriuretic peptide levels did not predict intravenous immunoglobulin resistance, 15.3 versus 13.5% (p=1). Elevated N-terminal pro-brain natriuretic peptide level correlates with acute coronary artery dilatation in treated Kawasaki disease, but not with intravenous immunoglobulin resistance.

PEP-on-DEP: A competitive peptide-based disposable electrochemical aptasensor for renin diagnostics.

PubMed

Biyani, Manish; Kawai, Keiko; Kitamura, Koichiro; Chikae, Miyuki; Biyani, Madhu; Ushijima, Hiromi; Tamiya, Eiichi; Yoneda, Takashi; Takamura, Yuzuru

2016-10-15

Antibody-based immunosensors are relatively less accessible to a wide variety of unreachable targets, such as low-molecular-weight biomarkers that represent a rich untapped source of disease-specific diagnostic information. Here, we present a peptide aptamer-based electrochemical sensor technology called 'PEP-on-DEP' to detect less accessible target molecules, such as renin, and to improve the quality of life. Peptide-based aptamers represent a relatively smart class of affinity binders and show great promise in biosensor development. Renin is involved in the regulation of arterial blood pressure and is an emerging biomarker protein for predicting cardiovascular risk and prognosis. To our knowledge, no studies have described aptamer molecules that can be used as new potent probes for renin. Here, we describe a portable electrochemical biosensor platform based on the newly identified peptide aptamer molecules for renin. We constructed a randomized octapeptide library pool with diversified sequences and selected renin specific peptide aptamers using cDNA display technology. We identified a few peptide aptamer sequences with a KD in the µM binding affinity range for renin. Next, we grafted the selected peptide aptamers onto gold nanoparticles and detected renin in a one-step competitive assay using our originally developed DEP (Disposable Electrochemical Printed) chip and a USB powered portable potentiostat system. We successfully detected renin in as little as 300ngmL(-1) using the PEP-on-DEP method. Thus, the generation and characterization of novel probes for unreachable target molecules by merging a newly identified peptide aptamer with electrochemical transduction allowed for the development of a more practical biosensor that, in principle, can be adapted to develop a portable, low-cost and mass-producible biosensor for point-of-care applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of metalloprotease/disintegrins in Xenopus laevis testis with a potential role in fertilization.

PubMed

Shilling, F M; Krätzschmar, J; Cai, H; Weskamp, G; Gayko, U; Leibow, J; Myles, D G; Nuccitelli, R; Blobel, C P

1997-06-15

Proteins containing a membrane-anchored metalloprotease domain, a disintegrin domain, and a cysteine-rich region (MDC proteins) are thought to play an important role in mammalian fertilization, as well as in somatic cell-cell interactions. We have identified PCR sequence tags encoding the disintegrin domain of five distinct MDC proteins from Xenopus laevis testis cDNA. Four of these sequence tags (xMDC9, xMDC11.1, xMDC11.2, and xMDC13) showed strong similarity to known mammalian MDC proteins, whereas the fifth (xMDC16) apparently represents a novel family member. Northern blot analysis revealed that the mRNA for xMDC16 was only expressed in testis, and not in heart, muscle, liver, ovaries, or eggs, whereas the mRNAs corresponding to the four other PCR products were expressed in testis and in some or all somatic tissues tested. The xMDC16 protein sequence, as predicted from the full-length cDNA, contains a metalloprotease domain with the active-site sequence HEXXH, a disintegrin domain, a cysteine-rich region, an EGF repeat, a transmembrane domain, and a short cytoplasmic tail. To study a potential role for these xMDC proteins in fertilization, peptides corresponding to the predicted integrin-binding domain of each protein were tested for their ability to inhibit X. laevis fertilization. Cyclic and linear xMDC16 peptides inhibited fertilization in a concentration-dependent manner, whereas xMDC16 peptides that were scrambled or had certain amino acid replacements in the predicted integrin-binding domain did not affect fertilization. Cyclic and linear xMDC9 peptides and linear xMDC13 peptides also inhibited fertilization similarly to xMDC16 peptides, whereas peptides corresponding to the predicted integrin-binding site of xMDC11.1 and xMDC11.2 did not. These results are discussed in the context of a model in which multiple MDC protein-receptor interactions are necessary for fertilization to occur.

Natural antibody responses to the capsid protein in sera of Dengue infected patients from Sri Lanka.

PubMed

Nadugala, Mahesha N; Jeewandara, Chandima; Malavige, Gathsaurie N; Premaratne, Prasad H; Goonasekara, Charitha L

2017-01-01

This study aims to characterize the antigenicity of the Capsid (C) protein and the human antibody responses to C protein from the four dengue virus (DENV) serotypes. Parker hydrophilicity prediction, Emini surface accessibility prediction and Karplus & Schulz flexibility predictions were used to bioinformatically characterize antigenicity. The human antibody response to C protein was assessed by ELISA using immune sera and an array of overlapping DENV2 C peptides. DENV2 C protein peptides P1 (located on C protein at 2-18 a.a), P11 (79-95 a.a) and P12 (86-101 a.a) were recognized by most individuals exposed to infections with only one of the 4 DENV serotypes as well as people exposed to infections with two serotypes. These conserved peptide epitopes are located on the amino (1-40 a.a) and carboxy (70-100 a.a) terminal regions of C protein, which were predicted to be antigenic using different bioinformatic tools. DENV2 C peptide P6 (39-56 a.a) was recognized by all individuals exposed to DENV2 infections, some individuals exposed to DENV4 infections and none of the individuals exposed to DENV1 or 3 infections. Thus, unlike C peptides P1, P11 and P12, which contain epitopes, recognized by DENV serotype cross-reactive antibodies, DENV2 peptide P6 contains an epitope that is preferentially recognized by antibodies in people exposed to this serotype compared to other serotypes. We discuss our results in the context of the known structure of C protein and recent work on the human B-cell response to DENV infection.

In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors.

PubMed

Nongonierma, Alice B; Mooney, Catherine; Shields, Denis C; FitzGerald, Richard J

2014-07-01

Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC50 values of 3.5 and 44.2μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening. Copyright © 2014 Elsevier Inc. All rights reserved.

Peptide Identification by Database Search of Mixture Tandem Mass Spectra*

PubMed Central

Wang, Jian; Bourne, Philip E.; Bandeira, Nuno

2011-01-01

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision. PMID:21862760

Toward the definition of a peptidome signature and protease profile in chronic periodontitis.

PubMed

Trindade, Fábio; Amado, Francisco; Oliveira-Silva, Rui P; Daniel-da-Silva, Ana L; Ferreira, Rita; Klein, Julie; Faria-Almeida, Ricardo; Gomes, Pedro S; Vitorino, Rui

2015-10-01

Chronic periodontitis (CP) is a complex immuno-inflammatory disease that results from preestablished gingivitis. We investigated potential differences in salivary peptidome in health and CP. Saliva was collected from nine CP patients and ten healthy subjects, from which five CP and five healthy were enriched following endoProteoFASP approach, separated and identified by nanoHPLC-MALDI-TOF/TOF. Protease prediction was carried out in silico with Proteasix. Parallel gelatin and collagen (I) zymographies were performed to study proteolytic activity in CP. An association of CP with increased gelatinolytic and collagenolytic activity was observed, which is mainly attributed to metalloproteases, remarkably MMP9. Protease prediction revealed distinct protease profiles in CP and in health. Peptidomic data corroborated the inflammatory status, and demonstrated that intact histatin 1 may play an important role in the defense response against oral pathogens. The application of the endoProteoFASP approach to study the salivary peptidome of CP subjects resulted in the identification of eight surrogate peptide markers, which may be used in multiplex to identify CP. These peptides belong to acidic PRP and to P-B peptide. Particularly, P-B peptide fragments exhibited domains with potential predicted antimicrobial activity, corroborating an antimicrobial function. The comparison between the salivary peptidome obtained by control and CP samples showed a specific association of eight peptides to CP, with remarkable predicted antimicrobial activity, which should be further validated in studies with large number of subjects. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Signal peptide discrimination and cleavage site identification using SVM and NN.

PubMed

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods

PubMed Central

Shen, Yufeng; Tolić, Nikola; Xie, Fang; Zhao, Rui; Purvine, Samuel O.; Schepmoes, Athena A.; Ronald, J. Moore; Anderson, Gordon A.; Smith, Richard D.

2011-01-01

We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides isolated from human blood plasma without the use of specific “enzyme rules”. In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the numbers of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide datasets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than with SEQUEST (by 1.3–2.3 fold) at the same confidence levels, and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more sequence consecutive residues (e.g., ≥7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide datasets that were affected by the decoy database and mass tolerances applied (e.g., the identical peptides between the datasets could be limited to ~70%), while the UStags method provided the most consistent peptide datasets (>90% overlap) with extremely low (near zero) numbers of false positive identifications. The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary, and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs. PMID:21678914

Crystallographic and Computational Studies of a Class II MHC Complex with a Nonconforming Peptide: HLA-DRA/DRB3*0101

NASA Astrophysics Data System (ADS)

Parry, Christian S.; Gorski, Jack; Stern, Lawrence J.

2003-03-01

The stable binding of processed foreign peptide to a class II major histocompatibility (MHC) molecule and subsequent presentation to a T cell receptor is a central event in immune recognition and regulation. Polymorphic residues on the floor of the peptide binding site form pockets that anchor peptide side chains. These and other residues in the helical wall of the groove determine the specificity of each allele and define a motif. Allele specific motifs allow the prediction of epitopes from the sequence of pathogens. There are, however, known epitopes that do not satisfy these motifs: anchor motifs are not adequate for predicting epitopes as there are apparently major and minor motifs. We present crystallographic studies into the nature of the interactions that govern the binding of these so called nonconforming peptides. We would like to understand the role of the P10 pocket and find out whether the peptides that do not obey the consensus anchor motif bind in the canonical conformation observed in in prior structures of class II MHC-peptide complexes. HLA-DRB3*0101 complexed with peptide crystallized in unit cell 92.10 x 92.10 x 248.30 (90, 90, 90), P41212, and the diffraction data is reliable to 2.2ÅWe are complementing our studies with dynamical long time simulations to answer these questions, particularly the interplay of the anchor motifs in peptide binding, the range of protein and ligand conformations, and water hydration structures.

Accurate de novo design of hyperstable constrained peptides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhardwaj, Gaurav; Mulligan, Vikram Khipple; Bahl, Christopher D.

Covalently-crosslinked peptides present attractive opportunities for developing new therapeutics. Lying between small molecule and protein therapeutics in size, natural crosslinked peptides play critical roles in signaling, virulence and immunity. Engineering novel peptides with precise control over their three-dimensional structures is a significant challenge. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design hyperstable disulfide-stabilized miniproteins, heterochiral peptides, and N-C cyclic peptides. Experimentally-determined X-ray and NMR structures for 12 of the designs are nearly identical to the computational models. The computational design methods and stable scaffolds providemore » the basis for a new generation of peptide-based drugs.« less

Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35.

PubMed

Yu, Jinchao; Andreani, Jessica; Ochsenbein, Françoise; Guerois, Raphaël

2017-03-01

Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Effect of collision energy optimization on the measurement of peptides by selected reaction monitoring (SRM) mass spectrometry.

PubMed

Maclean, Brendan; Tomazela, Daniela M; Abbatiello, Susan E; Zhang, Shucha; Whiteaker, Jeffrey R; Paulovich, Amanda G; Carr, Steven A; Maccoss, Michael J

2010-12-15

Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool ( http://proteome.gs.washington.edu/software/skyline ).

A serendipitous survey of prediction algorithms for amyloidogenicity

PubMed Central

Roland, Bartholomew P.; Kodali, Ravindra; Mishra, Rakesh; Wetzel, Ronald

2014-01-01

SUMMARY The 17- amino acid N-terminal segment of the Huntingtin protein, httNT, grows into stable α-helix rich oligomeric aggregates when incubated under physiological conditions. We examined 15 scrambled sequence versions of an httNT peptide for their stabilities against aggregation in aqueous solution at low micromolar concentration and physiological conditions. Surprisingly, given their derivation from a sequence that readily assembles into highly stable α-helical aggregates that fail to convert into β-structure, we found that three of these scrambled peptides rapidly grow into amyloid-like fibrils, while two others also develop amyloid somewhat more slowly. The other 10 scrambled peptides do not detectibly form any aggregates after 100 hrs incubation under these conditions. We then analyzed these sequences using four previously described algorithms for predicting the tendencies of peptides to grow into amyloid or other β-aggregates. We found that these algorithms – Zyggregator, Tango, Waltz and Zipper – varied greatly in the number of sequences predicted to be amyloidogenic and in their abilities to correctly identify the amyloid forming members of scrambled peptide collection. The results are discussed in the context of a review of the sequence and structural factors currently thought to be important in determining amyloid formation kinetics and thermodynamics. PMID:23893755

Binding free energy and counterion release for adsorption of the antimicrobial peptide lactoferricin B on a POPG membrane.

PubMed

Tolokh, Igor S; Vivcharuk, Victor; Tomberli, Bruno; Gray, C G

2009-09-01

Molecular dynamics (MD) simulations are used to study the interaction of an anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with a POPG bilayer is employed as a model system for studying the details of membrane adsorption selectivity of cationic antimicrobial peptides. Seventy eight 4 ns MD production run trajectories of the equilibrated system, with six restrained orientations of LFCinB at 13 different separations from the POPG membrane, are generated to determine the free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the profile for this relatively large system, a variant of constrained MD and thermodynamic integration is used. A simplified method for relating the free energy profile to the LFCinB-POPG membrane binding constant is employed to predict a free energy of adsorption of -5.4+/-1.3 kcal/mol and a corresponding maximum adsorption binding force of about 58 pN. We analyze the results using Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions and polarized water from the region between the charged membrane and peptide, as the two approach each other. We contrast these results with those found earlier for adsorption of LFCinB on the mammalianlike palmitoyl-oleoyl-phosphatidylcholine membrane.

Galectin 3 complements BNP in risk stratification in acute heart failure

PubMed Central

Fermann, Gregory J.; Lindsell, Christopher J.; Storrow, Alan B.; Hart, Kimberly; Sperling, Matthew; Roll, Susan; Weintraub, Neal L.; Miller, Karen F.; Maron, David J.; Naftilan, Allen J.; Mcpherson, John A.; Sawyer, Douglas B.; Christenson, Robert; Collins, Sean P.

2013-01-01

Background Galectin 3 (G3) is a mediator of fibrosis and remodeling in heart failure. Methods Patients diagnosed with and treated for Acute Heart Failure Syndromes were prospectively enrolled in the Decision Making in Acute Decompensated Heart Failure multicenter trial. Results Patients with a higher G3 had a history of renal disease, a lower heart rate and acute kidney injury. They also tended to have a history of HF and 30-day adverse events compared with B-type natriuretic peptide. Conclusion In Acute Heart Failure Syndromes, G3 levels do not provide prognostic value, but when used complementary to B-type natriuretic peptide, G3 is associated with renal dysfunction and may predict 30-day events. PMID:22998064

Cyanine-based probe\\tag-peptide pair fluorescence protein imaging and fluorescence protein imaging methods

DOEpatents

Mayer-Cumblidge, M. Uljana; Cao, Haishi

2013-01-15

A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

Cyanine-based probe\\tag-peptide pair for fluorescence protein imaging and fluorescence protein imaging methods

DOEpatents

Mayer-Cumblidge, M Uljana [Richland, WA; Cao, Haishi [Richland, WA

2010-08-17

A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

Distinct mechanisms of a phosphotyrosyl peptide binding to two SH2 domains.

PubMed

Pang, Xiaodong; Zhou, Huan-Xiang

2014-05-01

Protein phosphorylation is very common post-translational modification, catalyzed by kinases, for signaling and regulation. Phosphotyrosines frequently target SH2 domains. The spleen tyrosine kinase (Syk) is critical for tyrosine phosphorylation of multiple proteins and for regulation of important pathways. Phosphorylation of both Y342 and Y346 in Syk linker B is required for optimal signaling. The SH2 domains of Vav1 and PLC-γ both bind this doubly phosphorylated motif. Here we used a recently developed method to calculate the effects of Y342 and Y346 phosphorylation on the rate constants of a peptide from Syk linker B binding to the SH2 domains of Vav1 and PLC-γ. The predicted effects agree well with experimental observations. Moreover, we found that the same doubly phosphorylated peptide binds the two SH2 domains via distinct mechanisms, with apparent rigid docking for Vav1 SH2 and dock-and-coalesce for PLC-γ SH2.

Chemical methods for peptide and protein production.

PubMed

Chandrudu, Saranya; Simerska, Pavla; Toth, Istvan

2013-04-12

Since the invention of solid phase synthetic methods by Merrifield in 1963, the number of research groups focusing on peptide synthesis has grown exponentially. However, the original step-by-step synthesis had limitations: the purity of the final product decreased with the number of coupling steps. After the development of Boc and Fmoc protecting groups, novel amino acid protecting groups and new techniques were introduced to provide high quality and quantity peptide products. Fragment condensation was a popular method for peptide production in the 1980s, but unfortunately the rate of racemization and reaction difficulties proved less than ideal. Kent and co-workers revolutionized peptide coupling by introducing the chemoselective reaction of unprotected peptides, called native chemical ligation. Subsequently, research has focused on the development of novel ligating techniques including the famous click reaction, ligation of peptide hydrazides, and the recently reported α-ketoacid-hydroxylamine ligations with 5-oxaproline. Several companies have been formed all over the world to prepare high quality Good Manufacturing Practice peptide products on a multi-kilogram scale. This review describes the advances in peptide chemistry including the variety of synthetic peptide methods currently available and the broad application of peptides in medicinal chemistry.

Simple method to assess stability of immobilized peptide ligands against proteases.

PubMed

Giudicessi, Silvana L; Salum, María L; Saavedra, Soledad L; Martínez-Ceron, María C; Cascone, Osvaldo; Erra-Balsells, Rosa; Camperi, Silvia A

2017-09-01

Although peptides are used as affinity chromatography ligands, they could be digested by proteases. Usually, peptide stability is evaluated in solution, which differs from the resin-bounded peptide behavior. Furthermore, the study of the degradation products requires purification steps before analysis. Here, we describe an easy method to assess immobilized peptide stability. Sample peptides were synthesized on hydroxymethylbenzamide-ChemMatrix resin. Peptidyl-resin beads were then incubated with solutions containing proteases. Peptides were detached from the solid support with ammonia vapor and analyzed by matrix-assisted laser desorption/ionization and electrospray ionization mass spectrometry, allowing the detection of the whole peptides as well as their C-terminal degradation products. The method allowed a fast evaluation of peptide ligand stability in solid phase towards proteases that may be present in the crude sample before their use as ligands in affinity chromatography. Copyright © 2017 European Peptide Society and John Wiley & Sons, Ltd. Copyright © 2017 European Peptide Society and John Wiley & Sons, Ltd.

Prospective screening for occult cardiomyopathy in dogs by measurement of plasma atrial natriuretic peptide, B-type natriuretic peptide, and cardiac troponin-I concentrations.

PubMed

Oyama, Mark A; Sisson, D David; Solter, Phil F

2007-01-01

To evaluate the use of measuring plasma concentrations of atrial natriuretic peptide (ANP), B-type natriuretic peptide (BNP), and cardiac troponin-I (cTnI) to detect dogs with occult dilated cardiomyopathy (DCM). 118 client-owned dogs. Dogs were prospectively examined by use of ECG; echocardiography; and evaluation of concentrations of ANP, BNP, and cTnI. Occult DCM was diagnosed by evaluation of echocardiographic left ventricular dimensions and detection of ventricular arrhythmias on ECG. Sensitivity and specificity of assays for measurement of plasma concentrations of ANP, BNP, and cTnI to detect dogs with occult DCM were determined. Occult DCM was diagnosed in 21 dogs. A concentration of > 6.21 pg/mL for BNP had a sensitivity of 95.2% and specificity of 61.9% for identifying dogs with occult DCM. In contrast, concentrations of ANP and cTnI had relatively low predictive values. Blood-based screening for occult DCM in dogs can be accomplished by use of a BNP assay. Additional studies should be performed to optimize this method of screening dogs to detect occult DCM.

Identification of ageing-associated naturally occurring peptides in human urine

PubMed Central

Nkuipou-Kenfack, Esther; Bhat, Akshay; Klein, Julie; Jankowski, Vera; Mullen, William; Vlahou, Antonia; Dakna, Mohammed; Koeck, Thomas; Schanstra, Joost P.; Zürbig, Petra; Rudolph, Karl L.; Schumacher, Björn; Pich, Andreas; Mischak, Harald

2015-01-01

To assess normal and pathological peptidomic changes that may lead to an improved understanding of molecular mechanisms underlying ageing, urinary peptidomes of 1227 healthy and 10333 diseased individuals between 20 and 86 years of age were investigated. The diseases thereby comprised diabetes mellitus, renal and cardiovascular diseases. Using age as a continuous variable, 116 peptides were identified that significantly (p < 0.05; |ρ|≥0.2) correlated with age in the healthy cohort. The same approach was applied to the diseased cohort. Upon comparison of the peptide patterns of the two cohorts 112 common age-correlated peptides were identified. These 112 peptides predominantly originated from collagen, uromodulin and fibrinogen. While most fibrillar and basement membrane collagen fragments showed a decreased age-related excretion, uromodulin, beta-2-microglobulin and fibrinogen fragments showed an increase. Peptide-based in silico protease analysis was performed and 32 proteases, including matrix metalloproteinases and cathepsins, were predicted to be involved in ageing. Identified peptides, predicted proteases and patient information were combined in a systems biology pathway analysis to identify molecular pathways associated with normal and/or pathological ageing. While perturbations in collagen homeostasis, trafficking of toll-like receptors and endosomal pathways were commonly identified, degradation of insulin-like growth factor-binding proteins was uniquely identified in pathological ageing. PMID:26431327

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference.

PubMed

Trolle, Thomas; McMurtrey, Curtis P; Sidney, John; Bardet, Wilfried; Osborn, Sean C; Kaever, Thomas; Sette, Alessandro; Hildebrand, William H; Nielsen, Morten; Peters, Bjoern

2016-02-15

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8(+) T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8(+) T cell epitopes. Copyright © 2016 by The American Association of Immunologists, Inc.

Small angle X-ray scattering as a high-throughput method to classify antimicrobial modes of action.

PubMed

von Gundlach, A R; Garamus, V M; Gorniak, T; Davies, H A; Reischl, M; Mikut, R; Hilpert, K; Rosenhahn, A

2016-05-01

Multi-drug resistant bacteria are currently undermining our health care system worldwide. While novel antimicrobial drugs, such as antimicrobial peptides, are urgently needed, identification of new modes of action is money and time consuming, and in addition current approaches are not available in a high throughput manner. Here we explore how small angle X-ray scattering (SAXS) as high throughput method can contribute to classify the mode of action for novel antimicrobials and therefore supports fast decision making in drug development. Using data bases for natural occurring antimicrobial peptides or predicting novel artificial peptides, many candidates can be discovered that will kill a selected target bacterium. However, in order to narrow down the selection it is important to know if these peptides follow all the same mode of action. In addition, the mode of action should be different from conventional antibiotics, in consequence peptide candidates can be developed further into drugs against multi-drug resistant bacteria. Here we used one short antimicrobial peptide with unknown mode of action and compared the ultrastructural changes of Escherichia coli cells after treatment with the peptide to cells treated with classic antibiotics. The key finding is that SAXS as a structure sensitive tool provides a rapid feedback on drug induced ultrastructural alterations in whole E. coli cells. We could demonstrate that ultrastructural changes depend on the used antibiotics and their specific mode of action. This is demonstrated using several well characterized antimicrobial compounds and the analysis of resulting SAXS curves by principal component analysis. To understand the result of the PCA analysis, the data is correlated with TEM images. In contrast to real space imaging techniques, SAXS allows to obtain nanoscale information averaged over approximately one million cells. The measurement takes only seconds, while conventional tests to identify a mode of action require days or weeks per single substance. The antimicrobial peptide showed a different mode of action as all tested antibiotics including polymyxin B and is therefore a good candidate for further drug development. We envision SAXS to become a useful tool within the high-throughput screening pipeline of modern drug discovery. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification and Validation of Human Missing Proteins and Peptides in Public Proteome Databases: Data Mining Strategy.

PubMed

Elguoshy, Amr; Hirao, Yoshitoshi; Xu, Bo; Saito, Suguru; Quadery, Ali F; Yamamoto, Keiko; Mitsui, Toshiaki; Yamamoto, Tadashi

2017-12-01

In an attempt to complete human proteome project (HPP), Chromosome-Centric Human Proteome Project (C-HPP) launched the journey of missing protein (MP) investigation in 2012. However, 2579 and 572 protein entries in the neXtProt (2017-1) are still considered as missing and uncertain proteins, respectively. Thus, in this study, we proposed a pipeline to analyze, identify, and validate human missing and uncertain proteins in open-access transcriptomics and proteomics databases. Analysis of RNA expression pattern for missing proteins in Human protein Atlas showed that 28% of them, such as Olfactory receptor 1I1 ( O60431 ), had no RNA expression, suggesting the necessity to consider uncommon tissues for transcriptomic and proteomic studies. Interestingly, 21% had elevated expression level in a particular tissue (tissue-enriched proteins), indicating the importance of targeting such proteins in their elevated tissues. Additionally, the analysis of RNA expression level for missing proteins showed that 95% had no or low expression level (0-10 transcripts per million), indicating that low abundance is one of the major obstacles facing the detection of missing proteins. Moreover, missing proteins are predicted to generate fewer predicted unique tryptic peptides than the identified proteins. Searching for these predicted unique tryptic peptides that correspond to missing and uncertain proteins in the experimental peptide list of open-access MS-based databases (PA, GPM) resulted in the detection of 402 missing and 19 uncertain proteins with at least two unique peptides (≥9 aa) at <(5 × 10 -4 )% FDR. Finally, matching the native spectra for the experimentally detected peptides with their SRMAtlas synthetic counterparts at three transition sources (QQQ, QTOF, QTRAP) gave us an opportunity to validate 41 missing proteins by ≥2 proteotypic peptides.

Mass Spectrometry of Human Leukocyte Antigen Class I Peptidomes Reveals Strong Effects of Protein Abundance and Turnover on Antigen Presentation*

PubMed Central

Bassani-Sternberg, Michal; Pletscher-Frankild, Sune; Jensen, Lars Juhl; Mann, Matthias

2015-01-01

HLA class I molecules reflect the health state of cells to cytotoxic T cells by presenting a repertoire of endogenously derived peptides. However, the extent to which the proteome shapes the peptidome is still largely unknown. Here we present a high-throughput mass-spectrometry-based workflow that allows stringent and accurate identification of thousands of such peptides and direct determination of binding motifs. Applying the workflow to seven cancer cell lines and primary cells, yielded more than 22,000 unique HLA peptides across different allelic binding specificities. By computing a score representing the HLA-I sampling density, we show a strong link between protein abundance and HLA-presentation (p < 0.0001). When analyzing overpresented proteins – those with at least fivefold higher density score than expected for their abundance – we noticed that they are degraded almost 3 h faster than similar but nonpresented proteins (top 20% abundance class; median half-life 20.8h versus 23.6h, p < 0.0001). This validates protein degradation as an important factor for HLA presentation. Ribosomal, mitochondrial respiratory chain, and nucleosomal proteins are particularly well presented. Taking a set of proteins associated with cancer, we compared the predicted immunogenicity of previously validated T-cell epitopes with other peptides from these proteins in our data set. The validated epitopes indeed tend to have higher immunogenic scores than the other detected HLA peptides. Remarkably, we identified five mutated peptides from a human colon cancer cell line, which have very recently been predicted to be HLA-I binders. Altogether, we demonstrate the usefulness of combining MS-analysis with immunogenesis prediction for identifying, ranking, and selecting peptides for therapeutic use. PMID:25576301

3D hydrophobic moment vectors as a tool to characterize the surface polarity of amphiphilic peptides.

PubMed

Reißer, Sabine; Strandberg, Erik; Steinbrecher, Thomas; Ulrich, Anne S

2014-06-03

The interaction of membranes with peptides and proteins is largely determined by their amphiphilic character. Hydrophobic moments of helical segments are commonly derived from their two-dimensional helical wheel projections, and the same is true for β-sheets. However, to the best of our knowledge, there exists no method to describe structures in three dimensions or molecules with irregular shape. Here, we define the hydrophobic moment of a molecule as a vector in three dimensions by evaluating the surface distribution of all hydrophilic and lipophilic regions over any given shape. The electrostatic potential on the molecular surface is calculated based on the atomic point charges. The resulting hydrophobic moment vector is specific for the instantaneous conformation, and it takes into account all structural characteristics of the molecule, e.g., partial unfolding, bending, and side-chain torsion angles. Extended all-atom molecular dynamics simulations are then used to calculate the equilibrium hydrophobic moments for two antimicrobial peptides, gramicidin S and PGLa, under different conditions. We show that their effective hydrophobic moment vectors reflect the distribution of polar and nonpolar patches on the molecular surface and the calculated electrostatic surface potential. A comparison of simulations in solution and in lipid membranes shows how the peptides undergo internal conformational rearrangement upon binding to the bilayer surface. A good correlation with solid-state NMR data indicates that the hydrophobic moment vector can be used to predict the membrane binding geometry of peptides. This method is available as a web application on http://www.ibg.kit.edu/HM/. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

The Proteasix Ontology.

PubMed

Arguello Casteleiro, Mercedes; Klein, Julie; Stevens, Robert

2016-06-04

The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides) The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis. The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results. The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone.

Peptide reranking with protein-peptide correspondence and precursor peak intensity information.

PubMed

Yang, Chao; He, Zengyou; Yang, Can; Yu, Weichuan

2012-01-01

Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification. Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information directly from database search scores or by training machine learning models. Information in the protein database and MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a score regularization method SRPI. The source codes of PPMRanker, PPIRanker, and MIRanker, and all supplementary documents are available at our website: http://bioinformatics.ust.hk/pepreranking/. Alternatively, these documents can also be downloaded from: http://sourceforge.net/projects/pepreranking/.

Influence of Running and Walking on Hormonal Regulators of Appetite in Women

PubMed Central

Larson-Meyer, D. Enette; Palm, Sonnie; Bansal, Aasthaa; Austin, Kathleen J.; Hart, Ann Marie; Alexander, Brenda M.

2012-01-01

Nine female runners and ten walkers completed a 60 min moderate-intensity (70% VO2max) run or walk, or 60 min rest in counterbalanced order. Plasma concentrations of the orexogenic peptide ghrelin, anorexogenic peptides peptide YY (PYY), glucagon-like peptide-1 (GLP-1), and appetite ratings were measured at 30 min interval for 120 min, followed by a free-choice meal. Both orexogenic and anorexogenic peptides were elevated after running, but no changes were observed after walking. Relative energy intake (adjusted for cost of exercise/rest) was negative in the meal following running (−194 ± 206 kcal) versus walking (41 ± 196 kcal) (P = 0.015), although both were suppressed (P < 0.05) compared to rest (299 ± 308 and 284 ± 121 kcal, resp.). The average rate of change in PYY and GLP-1 over time predicted appetite in runners, but only the change in GLP-1 predicted hunger (P = 0.05) in walkers. Results provide evidence that exercise-induced alterations in appetite are likely driven by complex changes in appetite-regulating hormones rather than change in a single gut peptide. PMID:22619704

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.

PubMed

Pérez, Germán M; Salomón, Luis A; Montero-Cabrera, Luis A; de la Vega, José M García; Mascini, Marcello

2016-05-01

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides.

PubMed

Spainhour, John Christian G; Janech, Michael G; Schwacke, John H; Velez, Juan Carlos Q; Ramakrishnan, Viswanathan

2014-01-01

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.

Comprehensive solid-phase extraction of multitudinous bioactive peptides from equine plasma and urine for doping detection.

PubMed

Guan, Fuyu; Robinson, Mary A

2017-09-08

The ability to analyze biological samples for multitudinous exogenous peptides with a single analytical method is desired for doping control in horse racing. The key to achieving this goal is the capability of extracting all target peptides from the sample matrix. In the present study, theory of mixed-mode solid-phase extraction (SPE) of peptides from plasma is described, and a generic mixed-mode SPE procedure has been developed for recovering multitudinous exogenous peptides with remarkable sequence diversity, from equine plasma and urine in a single procedure. Both the theory and the developed SPE procedure have led to the development of a novel analytical method for comprehensive detection of multitudinous bioactive peptides in equine plasma and urine using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). Thirty nine bioactive peptides were extracted with strong anion-exchange mixed-mode SPE sorbent, separated on a reversed-phase C 18 column and detected by HRMS and data-dependent tandem mass spectrometry. The limit of detection (LOD) was 10-50 pg mL -1 in plasma for most of the peptides and 100 pg mL -1 for the remaining. For urine, LOD was 20-400 pg mL -1 for most of the peptides and 1-4 ng mL -1 for the others. In vitro degradation of the peptides in equine plasma and urine was examined at ambient temperature; the peptides except those with a D-amino acid at position 2 were unstable not only in plasma but also in urine. The developed method was successful in analysis of plasma and urine samples from horses administered dermorphin. Additionally, dermorphin metabolites were identified in the absence of reference standards. The developed SPE procedure and LC-HRMS method can theoretically detect virtually all peptides present at a sufficient concentration in a sample. New peptides can be readily included in the method to be detected without method re-development. The developed method also generates such data that can be retrospectively analyzed for peptides unknown at the time of sample analysis. It is the first generic analytical method for comprehensive detection of multitudinous exogenous peptides in biological samples, to the authors' knowledge. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Engineering antimicrobial peptides with improved antimicrobial and hemolytic activities.

PubMed

Zhao, Jun; Zhao, Chao; Liang, Guizhao; Zhang, Mingzhen; Zheng, Jie

2013-12-23

The rapid rise of antibiotic resistance in pathogens becomes a serious and growing threat to medicine and public health. Naturally occurring antimicrobial peptides (AMPs) are an important line of defense in the immune system against invading bacteria and microbial infection. In this work, we present a combined computational and experimental study of the biological activity and membrane interaction of the computationally designed Bac2A-based peptide library. We used the MARTINI coarse-grained molecular dynamics with adaptive biasing force method and the umbrella sampling technique to investigate the translocation of a total of 91 peptides with different amino acid substitutions through a mixed anionic POPE/POPG (3:1) bilayer and a neutral POPC bilayer, which mimic the bacterial inner membrane and the human red blood cell (hRBC) membrane, respectively. Potential of mean force (PMF, free energy profile) was obtained to measure the free energy barrier required to transfer the peptides from the bulk water phase to the water-membrane interface and to the bilayer interior. Different PMF profiles can indeed identify different membrane insertion scenarios by mapping out peptide-lipid energy landscapes, which are correlated with antimicrobial activity and hemolytic activity. Computationally designed peptides were further tested experimentally for their antimicrobial and hemolytic activities using bacteria growth inhibition assay and hemolysis assay. Comparison of PMF data with cell assay results reveals a good correlation of the peptides between predictive transmembrane activity and antimicrobial/hemolytic activity. Moreover, the most active mutants with the balanced substitutions of positively charged Arg and hydrophobic Trp residues at specific positions were discovered to achieve the improved antimicrobial activity while minimizing red blood cell lysis. Such substitutions provide more effective and cooperative interactions to distinguish the peptide interaction with different lipid bilayers. This work provides a useful computational tool to better understand the mechanism and energetics of membrane insertion of AMPs and to rationally design more effective AMPs.

Bioinformatics study of the mangrove actin genes

NASA Astrophysics Data System (ADS)

Basyuni, M.; Wasilah, M.; Sumardi

2017-01-01

This study describes the bioinformatics methods to analyze eight actin genes from mangrove plants on DDBJ/EMBL/GenBank as well as predicted the structure, composition, subcellular localization, similarity, and phylogenetic. The physical and chemical properties of eight mangroves showed variation among the genes. The percentage of the secondary structure of eight mangrove actin genes followed the order of a helix > random coil > extended chain structure for BgActl, KcActl, RsActl, and A. corniculatum Act. In contrast to this observation, the remaining actin genes were random coil > extended chain structure > a helix. This study, therefore, shown the prediction of secondary structure was performed for necessary structural information. The values of chloroplast or signal peptide or mitochondrial target were too small, indicated that no chloroplast or mitochondrial transit peptide or signal peptide of secretion pathway in mangrove actin genes. These results suggested the importance of understanding the diversity and functional of properties of the different amino acids in mangrove actin genes. To clarify the relationship among the mangrove actin gene, a phylogenetic tree was constructed. Three groups of mangrove actin genes were formed, the first group contains B. gymnorrhiza BgAct and R. stylosa RsActl. The second cluster which consists of 5 actin genes the largest group, and the last branch consist of one gene, B. sexagula Act. The present study, therefore, supported the previous results that plant actin genes form distinct clusters in the tree.

A peptide N-terminal protection strategy for comprehensive glycoproteome analysis using hydrazide chemistry based method

PubMed Central

Huang, Junfeng; Qin, Hongqiang; Sun, Zhen; Huang, Guang; Mao, Jiawei; Cheng, Kai; Zhang, Zhang; Wan, Hao; Yao, Yating; Dong, Jing; Zhu, Jun; Wang, Fangjun; Ye, Mingliang; Zou, Hanfa

2015-01-01

Enrichment of glycopeptides by hydrazide chemistry (HC) is a popular method for glycoproteomics analysis. However, possible side reactions of peptide backbones during the glycan oxidation in this method have not been comprehensively studied. Here, we developed a proteomics approach to locate such side reactions and found several types of the side reactions that could seriously compromise the performance of glycoproteomics analysis. Particularly, the HC method failed to identify N-terminal Ser/Thr glycopeptides because the oxidation of vicinal amino alcohol on these peptides generates aldehyde groups and after they are covalently coupled to HC beads, these peptides cannot be released by PNGase F for identification. To overcome this drawback, we apply a peptide N-terminal protection strategy in which primary amine groups on peptides are chemically blocked via dimethyl labeling, thus the vicinal amino alcohols on peptide N-termini are eliminated. Our results showed that this strategy successfully prevented the oxidation of peptide N-termini and significantly improved the coverage of glycoproteome. PMID:25959593

A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

PubMed Central

Trellet, Mikael; Melquiond, Adrien S. J.; Bonvin, Alexandre M. J. J.

2013-01-01

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking. PMID:23516555

A unified conformational selection and induced fit approach to protein-peptide docking.

PubMed

Trellet, Mikael; Melquiond, Adrien S J; Bonvin, Alexandre M J J

2013-01-01

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

Genome-Wide Prediction and Validation of Peptides That Bind Human Prosurvival Bcl-2 Proteins

PubMed Central

DeBartolo, Joe; Taipale, Mikko; Keating, Amy E.

2014-01-01

Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models. PMID:24967846

Bioinformatics and immunologic investigation on B and T cell epitopes of Cur l 3, a major allergen of Curvularia lunata.

PubMed

Sharma, Vidhu; Singh, Bhanu P; Gaur, Shailendra N; Pasha, Santosh; Arora, Naveen

2009-06-01

The knowledge on epitopes of proteins can help in devising new therapeutic modalities for allergic disorders. In the present study, five B (P1-P5) and five T cell (P6-P10) epitopes were predicted in silico based on sequence homology model of Cur l 3, a major allergen of Curvularia lunata. Peptides (epitopes) were synthesized and assessed for biological activity by ELISA, competitive ELISA, lymphoproliferation and cytokine profiling using Curvularia allergic patients' sera. B cell peptides showed higher IgE binding by ELISA than T cell epitopes except P6. Peptides P1-P6 achieved EC(50) at 100 ng, whereas P7-P10 required 10 mug in inhibition assays. Peripheral blood mononuclear cells from Curvularia allergic patients (n = 20) showed higher lymphoproliferation for T cell epitopes than B cell epitopes except P6 confirming the properties of B and T cell prediction. The supernatant from these patients show highest interleukin-4 release on stimulation with P6 followed by B cell peptides. P4 and P6 together identified 35/37 of Curvularia positive patients by skin tests. In summary, experimental analysis confirmed in silico predicted epitopes containing important antigenic regions of Cur l 3. P6, a predicted T cell epitope, showed the presence of a cryptic B cell epitope. Peptides P4 and P6 have potential for clinical application. The approach used here is relevant and may be used to delineate epitopes of other proteins.

Order within disorder: Aggrecan chondroitin sulphate-attachment region provides new structural insights into protein sequences classified as disordered

PubMed Central

Jowitt, Thomas A; Murdoch, Alan D; Baldock, Clair; Berry, Richard; Day, Joanna M; Hardingham, Timothy E

2010-01-01

Structural investigation of proteins containing large stretches of sequences without predicted secondary structure is the focus of much increased attention. Here, we have produced an unglycosylated 30 kDa peptide from the chondroitin sulphate (CS)-attachment region of human aggrecan (CS-peptide), which was predicted to be intrinsically disordered and compared its structure with the adjacent aggrecan G3 domain. Biophysical analyses, including analytical ultracentrifugation, light scattering, and circular dichroism showed that the CS-peptide had an elongated and stiffened conformation in contrast to the globular G3 domain. The results suggested that it contained significant secondary structure, which was sensitive to urea, and we propose that the CS-peptide forms an elongated wormlike molecule based on a dynamic range of energetically equivalent secondary structures stabilized by hydrogen bonds. The dimensions of the structure predicted from small-angle X-ray scattering analysis were compatible with EM images of fully glycosylated aggrecan and a partly glycosylated aggrecan CS2-G3 construct. The semiordered structure identified in CS-peptide was not predicted by common structural algorithms and identified a potentially distinct class of semiordered structure within sequences currently identified as disordered. Sequence comparisons suggested some evidence for comparable structures in proteins encoded by other genes (PRG4, MUC5B, and CBP). The function of these semiordered sequences may serve to spatially position attached folded modules and/or to present polypeptides for modification, such as glycosylation, and to provide templates for the multiple pleiotropic interactions proposed for disordered proteins. Proteins 2010. © 2010 Wiley-Liss, Inc. PMID:20806220

Peptide arrays on cellulose support: SPOT synthesis, a time and cost efficient method for synthesis of large numbers of peptides in a parallel and addressable fashion.

PubMed

Hilpert, Kai; Winkler, Dirk F H; Hancock, Robert E W

2007-01-01

Peptide synthesis on cellulose using SPOT technology allows the parallel synthesis of large numbers of addressable peptides in small amounts. In addition, the cost per peptide is less than 1% of peptides synthesized conventionally on resin. The SPOT method follows standard fluorenyl-methoxy-carbonyl chemistry on conventional cellulose sheets, and can utilize more than 600 different building blocks. The procedure involves three phases: preparation of the cellulose membrane, stepwise coupling of the amino acids and cleavage of the side-chain protection groups. If necessary, peptides can be cleaved from the membrane for assays performed using soluble peptides. These features make this method an excellent tool for screening large numbers of peptides for many different purposes. Potential applications range from simple binding assays, to more sophisticated enzyme assays and studies with living microbes or cells. The time required to complete the protocol depends on the number and length of the peptides. For example, 400 9-mer peptides can be synthesized within 6 days.

Predicting the Retention Behavior of Specific O-Linked Glycopeptides.

PubMed

Badgett, Majors J; Boyes, Barry; Orlando, Ron

2017-09-01

O -Linked glycosylation is a common post-translational modification that can alter the overall structure, polarity, and function of proteins. Reverse-phase (RP) chromatography is the most common chromatographic approach to analyze O -glycosylated peptides and their unmodified counterparts, even though this approach often does not provide adequate separation of these two species. Hydrophilic interaction liquid chromatography (HILIC) can be a solution to this problem, as the polar glycan interacts with the polar stationary phase and potentially offers the ability to resolve the peptide from its modified form(s). In this paper, HILIC is used to separate peptides with O - N -acetylgalactosamine ( O -GalNAc), O - N -acetylglucosamine ( O -GlcNAc), and O -fucose additions from their native forms, and coefficients representing the extent of hydrophilicity were derived using linear regression analysis as a means to predict the retention times of peptides with these modifications.

Predicting the Retention Behavior of Specific O-Linked Glycopeptides

PubMed Central

Badgett, Majors J.; Boyes, Barry; Orlando, Ron

2017-01-01

O-Linked glycosylation is a common post-translational modification that can alter the overall structure, polarity, and function of proteins. Reverse-phase (RP) chromatography is the most common chromatographic approach to analyze O-glycosylated peptides and their unmodified counterparts, even though this approach often does not provide adequate separation of these two species. Hydrophilic interaction liquid chromatography (HILIC) can be a solution to this problem, as the polar glycan interacts with the polar stationary phase and potentially offers the ability to resolve the peptide from its modified form(s). In this paper, HILIC is used to separate peptides with O-N-acetylgalactosamine (O-GalNAc), O-N-acetylglucosamine (O-GlcNAc), and O-fucose additions from their native forms, and coefficients representing the extent of hydrophilicity were derived using linear regression analysis as a means to predict the retention times of peptides with these modifications. PMID:28785176

Flanking signal and mature peptide residues influence signal peptide cleavage

PubMed Central

Choo, Khar Heng; Ranganathan, Shoba

2008-01-01

Background Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria. Results In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups. Conclusion We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs. PMID:19091014

Triiodothyronine and brain natriuretic peptide: similar long-term prognostic values for chronic heart failure.

PubMed

Kozdag, Guliz; Ertas, Gokhan; Kilic, Teoman; Acar, Eser; Sahin, Tayfun; Ural, Dilek

2010-01-01

Although low levels of free triiodothyronine and high levels of brain natriuretic peptide have been shown as independent predictors of death in chronic heart failure patients, few studies have compared their prognostic values. The aim of this prospective study was to measure free triiodothyronine and brain natriuretic peptide levels and to compare their prognostic values among such patients.A total of 334 patients (mean age, 62 ± 13 yr; 218 men) with ischemic and nonischemic dilated cardiomyopathy were included in the study. The primary endpoint was a major cardiac event.During the follow-up period, 92 patients (28%) experienced a major cardiac event. Mean free triiodothyronine levels were lower and median brain natriuretic peptide levels were higher in patients with major cardiac events than in those without. A significant negative correlation was found between free triiodothyronine and brain natriuretic peptide levels. Receiver operating characteristic curve analysis showed that the predictive cutoff values were < 2.12 pg/mL for free triiodothyronine and > 686 pg/mL for brain natriuretic peptide. Cumulative survival was significantly lower among patients with free triiodothyronine < 2.12 pg/mL and among patients with brain natriuretic peptide > 686 pg/mL. In multivariate analysis, the significant independent predictors of major cardiac events were age, free triiodothyronine, and brain natriuretic peptide.In the present study, free triiodothyronine and brain natriuretic peptide had similar prognostic values for predicting long-term prognosis in chronic heart failure patients. These results also suggested that combining these biomarkers may provide an important risk indicator for patients with heart failure.

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

PubMed Central

Mathiowetz, A. M.; Goddard, W. A.

1995-01-01

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better. PMID:7549885

Evaluation of neuroendocrine tumors with 99mTc-EDDA/HYNIC TOC.

PubMed

Artiko, Vera; Afgan, Aida; Petrović, Jelena; Radović, Branislava; Petrović, Nebojša; Vlajković, Marina; Šobić-Šaranović, Dragana; Obradović, Vladimir

2016-01-01

This paper is the short review of our preliminary results obtained with 99mTc-EDDA/HYNIC-TOC. The total of 495 patients with different neuroendocrine tumors were investigated during last few years. There have been 334 true positive (TP), 73 true negative (TN), 6 false positive (FP) and 82 false negative findings (FN). Diagnosis was made according to SPECT findings in 122 patients (25%). The mean T/NT ratio for TP cases was significantly higher (p < 0.01) on SPECT (3.12 ± 1.13) than on whole body scan (2.2 ± 0.75). According to our results, overall sensitivity of the method is 80%, specificity 92%, positive predictive value 98%, negative predictive value 47% and accuracy 82%. Fifteen TP patients underwent therapy with 90Y-DOTATATE. Scintigraphy of neuroendocrine tumors with 99mTc-Tektrotyd is a useful method for diagnosis, staging and follow up of the patients suspected to have neuroendocrine tumors. SPECT had important role in diagnosis. It is also helpful in the appropriate choice of the therapy, including the peptide receptor radionuclide therapy. In the absence of 68Ga-labeled peptides and PET/CT, the special emphasize should be given to application of SPECT/CT as well as to the radioguided surgery.

In silico methods for design of biological therapeutics.

PubMed

Roy, Ankit; Nair, Sanjana; Sen, Neeladri; Soni, Neelesh; Madhusudhan, M S

2017-12-01

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Detection of Peptide-Based Nanoparticles in Blood Plasma by ELISA

PubMed Central

Bode, Gerard H.; Pickl, Karin E.; Sanchez-Purrà, Maria; Albaiges, Berta; Borrós, Salvador; Pötgens, Andy J. G.; Schmitz, Christoph; Sinner, Frank M.; Losen, Mario; Steinbusch, Harry W. M.; Frank, Hans-Georg; Martinez-Martinez, Pilar

2015-01-01

Aims The aim of the current study was to develop a method to detect peptide-linked nanoparticles in blood plasma. Materials & Methods A convenient enzyme linked immunosorbent assay (ELISA) was developed for the detection of peptides functionalized with biotin and fluorescein groups. As a proof of principle, polymerized pentafluorophenyl methacrylate nanoparticles linked to biotin-carboxyfluorescein labeled peptides were intravenously injected in Wistar rats. Serial blood plasma samples were analyzed by ELISA and by liquid chromatography mass spectrometry (LC/MS) technology. Results The ELISA based method for the detection of FITC labeled peptides had a detection limit of 1 ng/mL. We were able to accurately measure peptides bound to pentafluorophenyl methacrylate nanoparticles in blood plasma of rats, and similar results were obtained by LC/MS. Conclusions We detected FITC-labeled peptides on pentafluorophenyl methacrylate nanoparticles after injection in vivo. This method can be extended to detect nanoparticles with different chemical compositions. PMID:25996618

BUSCA: an integrative web server to predict subcellular localization of proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Profiti, Giuseppe; Casadio, Rita

2018-04-30

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.

Inhibition on JAK-STAT3 Signaling Transduction Cascade Is Taken by Bioactive Peptide Alpha-S2 Casein Protein from Goat Ethawah Breed Milk

PubMed Central

Rohmah, Rista Nikmatu; Hardiyanti, Ferlany; Fatchiyah, Fatchiyah

2015-01-01

Background: RA is a systemic inflammatory disease that causes developing comorbidity conditions. This condition can cause by overproduction of pro-inflammatory cytokine. In a previous study, we have found bioactive peptide CSN1S2 from Ethawah goat milk for anti-inflammatory for repair the ileum destruction. However, the signaling transduction cascade of bioactive peptides inhibits inflammation still not clear yet. Therefore, we analyzed the signaling transduction cascade via JAK-STAT3 pathway by in vivo and in silico. Methods: The ileum was isolated DNA and amplification with specific primer. The sequence was analyzed using the Sanger sequencing method. Modeling 3D-structure was predicted by SWISS-MODEL and virtual interaction was analyzed by docking system using Pymol and Discovery Studio 4.0 software. Results: This study showed that STAT3 has target gene 480bp. The normal group and normal treating- CSN1S2 of goat milk have similarity from gene bank. Whereas, RA group had transversion mutation that the purine change into pyrimidine even cause frameshift mutation. Interestingly, after treating with the CSN1S2 protein of goat milk shows reverse to the normal acid sequence group. Based on in silico study, from eight peptides, only three peptides of CSN1S2 protein, which carried by PePT1 to enter the small intestine. The fragments are PepT1-41-NMAIHPR-47; PepT1-182-KISQYYQK-189 and PepT1-214-TNAIPYVR-221. We have found just one bioactive peptide of f182-KISQYYQK-189 is able bind to STAT3. The energy binding of f182-KISQYYQK-189 and RA-STAT3 amino acid, it was Σ = -402.43 kJ/mol and the energy binding of f182-KISQYYQK-189 and RAS-STAT3 amino acid is decreasing into Σ = -407.09 kJ/mol. Conclusion: This study suggested that the fragment 182-KISQYYQK-189 peptides from Ethawah goat milk may act as an anti-inflammatory agent via JAK-STAT3 signal transduction cascade at the cellular level. PMID:26483598

Comparative study of generalized born models: Born radii and peptide folding.

PubMed

Zhu, Jiang; Alexov, Emil; Honig, Barry

2005-02-24

In this study, we have implemented four analytical generalized Born (GB) models and investigated their performance in conjunction with the GROMOS96 force field. The four models include that of Still and co-workers, the HCT model of Cramer, Truhlar, and co-workers, a modified form of the AGB model of Levy and co-workers, and the GBMV2 model of Brooks and co-workers. The models were coded independently and implemented in the GROMOS software package and in TINKER. They were compared in terms of their ability to reproduce the results of Poisson-Boltzmann (PB) calculations and in their performance in the ab initio peptide folding of two peptides, one that forms a beta-hairpin in solution and one that forms an alpha-helix. In agreement with previous work, the GBMV2 model is most successful in reproducing PB results while the other models tend to underestimate the effective Born radii of buried atoms. In contrast, stochastic dynamics simulations on the folding of the two peptides, the C-terminus beta-hairpin of the B1 domain of protein G and the alanine-based alpha-helical peptide 3K(I), suggest that the simpler GB models are more effective in sampling conformational space. Indeed, the Still model used in conjunction with the GROMOS96 force field is able to fold the hairpin peptide to a native-like structure without the benefit of enhanced sampling techniques. This is due in part to the properties of the united-atom GROMOS96 force field which appears to be more flexible, and hence to sample more efficiently, than force fields such as OPLSAA. Our results suggest a general strategy which involves using different combinations of force fields and solvent models in different applications, for example, using GROMOS96 and a simple GB model in sampling and OPLSAA and a more accurate GB model in refinement. The fact that various methods have been implemented in a unified way should facilitate the testing and subsequent use of different methods to evaluate conformational free energies in different applications. Our results also bear on some general issues involved in peptide folding and structure prediction which are addressed in the Discussion.

Determination of the Adsorption Free Energy for Peptide–Surface Interactions by SPR Spectroscopy

PubMed Central

Wei, Yang; Latour, Robert A.

2009-01-01

To understand and predict protein adsorption behavior, we must first understand the fundamental interactions between the functional groups presented by the amino acid residues making up a protein and the functional groups presented by the surface. Limited quantitative information is available, however, on these types of submolecular interactions. The objective of this study was therefore to develop a reliable method to determine the standard state adsorption free energy (ΔG°ads) of amino acid residue–surface interactions using surface plasma resonance (SPR) spectroscopy. Two problems are commonly encountered when using SPR for peptide adsorption studies: the need to account for “bulk-shift” effects and the influence of peptide–peptide interactions at the surface. Bulk-shift effects represent the contribution of the bulk solute concentration to the SPR response that occurs in addition to the response due to adsorption. Peptide–peptide interactions, which are assumed to be zero for Langmuir adsorption, can greatly skew the isotherm shape and result in erroneous calculated values of ΔG°ads. To address these issues, we have developed a new approach for the determination of ΔG°ads using SPR that is based on the chemical potential. In this article, we present the development of this new approach and its application for the calculation of ΔG°ads for a set of peptide–surface systems where the peptide has a host–guest amino acid sequence of TGTG-X-GTGT (where G and T are glycine and threonine residues and X represents a variable residue) and the surface consists of alkanethiol self-assembled monolayers (SAMs) with methyl (CH3) and hydroxyl (OH) functionality. This new approach enables bulk-shift effects to be directly determined from the raw SPR versus peptide concentration data plots and the influence of peptide–peptide interaction effects to be minimized, thus providing a very straightforward and accurate method for the determination of ΔG °ads for peptide adsorption. Further studies are underway to characterize ΔG°ads for a large library of peptide–SAM combinations. PMID:18507411

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins.

PubMed

Langó, Tamás; Róna, Gergely; Hunyadi-Gulyás, Éva; Turiák, Lilla; Varga, Julia; Dobson, László; Várady, György; Drahos, László; Vértessy, Beáta G; Medzihradszky, Katalin F; Szakács, Gergely; Tusnády, Gábor E

2017-02-13

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

Human endogenous retrovirus K(HML-2) Gag and Env specific T-cell responses are not detected in HTLV-I-infected subjects using standard peptide screening methods.

PubMed

Jones, R Brad; Leal, Fabio E; Hasenkrug, Aaron M; Segurado, Aluisio C; Nixon, Douglas F; Ostrowski, Mario A; Kallas, Esper G

2013-01-10

An estimated 10-20 million individuals are infected with the retrovirus human T-cell leukemia virus type 1 (HTLV-1). While the majority of these individuals remain asymptomatic, 0.3-4% develop a neurodegenerative inflammatory disease, termed HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). HAM/TSP results in the progressive demyelination of the central nervous system and is a differential diagnosis of multiple sclerosis (MS). The etiology of HAM/TSP is unclear, but evidence points to a role for CNS-inflitrating T-cells in pathogenesis. Recently, the HTLV-1-Tax protein has been shown to induce transcription of the human endogenous retrovirus (HERV) families W, H and K. Intriguingly, numerous studies have implicated these same HERV families in MS, though this association remains controversial. Here, we explore the hypothesis that HTLV-1-infection results in the induction of HERV antigen expression and the elicitation of HERV-specific T-cells responses which, in turn, may be reactive against neurons and other tissues. PBMC from 15 HTLV-1-infected subjects, 5 of whom presented with HAM/TSP, were comprehensively screened for T-cell responses to overlapping peptides spanning HERV-K(HML-2) Gag and Env. In addition, we screened for responses to peptides derived from diverse HERV families, selected based on predicted binding to predicted optimal epitopes. We observed a lack of responses to each of these peptide sets. Thus, although the limited scope of our screening prevents us from conclusively disproving our hypothesis, the current study does not provide data supporting a role for HERV-specific T-cell responses in HTLV-1 associated immunopathology.

[A turning point in the knowledge of the structure-function-activity relations of elastin].

PubMed

Alix, A J

2001-01-01

In this review are presented the last new results of our research group dealing with the molecular structures (atomic level) of tropoelastin, elastin and elastin derived peptides studied by using essentially methods of bioinformatics (theoretical predictions and molecular modelling) linked to experimental circular dichroism spectroscopic studies. We already had characterized both the local secondary structure and some parts of the tertiary structure of the tropoelastin and elastin molecules (human, bovine...), by using either theoretical predictions (local secondary structure, linear epitopes...) and/or experimental data (optical spectroscopic methods: Raman scattering, infrared absorption, circular dichroism). Except the cross-linking regions which are in helical conformations, the whole tropoelastin structure displays a lot of beta-reverse turns which usually belong to irregular structures in proteins. These turns play a key role in other regularly structures orientation (alpha-helix, beta-strand), thus they are very important in the native protein 3D architecture. It is particularly true for human tropoelastin, because its sequence is rich in glycines and prolines, and these residues are frequently met in beta-turns (a beta-turn is made of four consecutive residues which are stabilized by an hydrogen bond). Several types of beta-turns can be defined with the dihedral angles values phi and psi of the two central residues. Thus, by using a very recent updated set of propensities for the amino acid residues to belong to given types of reverse beta-turns (extracted from a reference set of known 3-D structures of globular proteins), we have determined, (by using our home made software COUDES), for all possible tetrapeptides of the human tropoelastin sequence, the distribution and the characterization of the possible type of turns. Thus, it is shown that the locations and/or the types of these reverse beta-turns reveal a regularity and are not all random. This confirms our hypothesis that intra-molecular elasticity of tropoelastin could be explained by the possibility of transitions between conformations involving short beta-strands and beta-turns. This result is of great interest in the construction (by using molecular biology) of elastic biomaterials derived from the elastin sequence (particularly, the elastin derived peptides corresponding to the sequence exon 21--(exon 24--exon 24...). Our study permit also to predict the conformations of specific elastin derived peptides which could have interesting biological activity. Peptides resulting from the degradation of elastin, the insoluble polymer of tropoelastin and responsible for the elasticity of vertebrate tissues, can induce biological effects and notably the regulation of matrix metalloproteinases (MMP-s) activity. Recently, it was proposed that some elastin derived hexapeptides resulting from circular permutations of VGVAPG (a three fold repetition sequence in exon 24 of human tropoelastin) possess MMP-1 production and activation regulation properties. This effect depends on the presence of the tropoelastin specific membraneous receptor 67 KDa EBP (Elastin Binding Protein). Our results obtained by using both circular dichroism spectroscopy and linear predictions confirmed the hypothesis of a structure dependent mechanism with a possibly occurring type VIII beta-turn on the first four residues of the GXXPG sequence consensus which is only present among all active peptides. Thus, we have performed extensive molecular dynamics studies, in both implicit and explicit solvent, on these active and inactive elastin derived hexapeptides. Using our own analysis method of pattern recognition of the types of the beta-reverse-turns followed during the molecular dynamics trajectory, we found that active and inactive peptides effectively form two well distinct conformational groups in which active peptides preferentially adopt conformation close to type VIII GXXP (beta-reverse-turn. The structural role of the C terminal G residue could also be explained. Additional molecular simulations on (VGVAPG)2 and (VGVAPG)3 show the formation of two or three GXXP tetrapeptides adopting a structure close to type VIII beta-reverse-turn, suggesting a local conformational preference for this motif. This observation of a specific structural single and/or repeated motif is in agreement with the circular dichroism spectra of the involved (VGVAPG)1, (VGVAPG)2 and (VGVAPG)3 peptides and then it can be proposed that their biological activities have to be linear. The final aim of this type of work is to understand more about the sequence/structure/function/activity relationships of those structured peptides in order to propose specific sequences (corresponding to specific structures) for best biological activity results.

Entropic (de)stabilization of surface-bound peptides conjugated with polymers

NASA Astrophysics Data System (ADS)

Carmichael, Scott P.; Shell, M. Scott

2015-12-01

In many emerging biotechnologies, functional proteins must maintain their native structures on or near interfaces (e.g., tethered peptide arrays, protein coated nanoparticles, and amphiphilic peptide micelles). Because the presence of a surface is known to dramatically alter the thermostability of tethered proteins, strategies to stabilize surface-bound proteins are highly sought. Here, we show that polymer conjugation allows for significant control over the secondary structure and thermostability of a model surface-tethered peptide. We use molecular dynamics simulations to examine the folding behavior of a coarse-grained helical peptide that is conjugated to polymers of various lengths and at various conjugation sites. These polymer variations reveal surprisingly diverse behavior, with some stabilizing and some destabilizing the native helical fold. We show that ideal-chain polymer entropies explain these varied effects and can quantitatively predict shifts in folding temperature. We then develop a generic theoretical model, based on ideal-chain entropies, that predicts critical lengths for conjugated polymers to effect changes in the folding of a surface-bound protein. These results may inform new design strategies for the stabilization of surface-associated proteins important for a range technological applications.

Entropic (de)stabilization of surface-bound peptides conjugated with polymers.

PubMed

Carmichael, Scott P; Shell, M Scott

2015-12-28

In many emerging biotechnologies, functional proteins must maintain their native structures on or near interfaces (e.g., tethered peptide arrays, protein coated nanoparticles, and amphiphilic peptide micelles). Because the presence of a surface is known to dramatically alter the thermostability of tethered proteins, strategies to stabilize surface-bound proteins are highly sought. Here, we show that polymer conjugation allows for significant control over the secondary structure and thermostability of a model surface-tethered peptide. We use molecular dynamics simulations to examine the folding behavior of a coarse-grained helical peptide that is conjugated to polymers of various lengths and at various conjugation sites. These polymer variations reveal surprisingly diverse behavior, with some stabilizing and some destabilizing the native helical fold. We show that ideal-chain polymer entropies explain these varied effects and can quantitatively predict shifts in folding temperature. We then develop a generic theoretical model, based on ideal-chain entropies, that predicts critical lengths for conjugated polymers to effect changes in the folding of a surface-bound protein. These results may inform new design strategies for the stabilization of surface-associated proteins important for a range technological applications.

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: in silico studies.

PubMed

Moskovets, Eugene; Goloborodko, Anton A; Gorshkov, Alexander V; Gorshkov, Mikhail V

2012-07-01

A two-dimensional (2-D) liquid chromatography (LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2-D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2-D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of Major Histocompatibility Complex-Bound HIV Peptides Identified from Various Cell Types Reveals Common Nested Peptides and Novel T Cell Responses

PubMed Central

Rucevic, Marijana; Kourjian, Georgio; Boucau, Julie; Blatnik, Renata; Garcia Bertran, Wilfredo; Berberich, Matthew J.; Walker, Bruce D.; Riemer, Angelika B.

2016-01-01

ABSTRACT Despite the critical role of epitope presentation for immune recognition, we still lack a comprehensive definition of HIV peptides presented by HIV-infected cells. Here we identified 107 major histocompatibility complex (MHC)-bound HIV peptides directly from the surface of live HIV-transfected 293T cells, HIV-infected B cells, and primary CD4 T cells expressing a variety of HLAs. The majority of peptides were 8 to 12 amino acids (aa) long and mostly derived from Gag and Pol. The analysis of the total MHC-peptidome and of HLA-A02-bound peptides identified new noncanonical HIV peptides of up to 16 aa that could not be predicted by HLA anchor scanning and revealed an heterogeneous surface peptidome. Nested sets of surface HIV peptides included optimal and extended HIV epitopes and peptides partly overlapping or distinct from known epitopes, revealing new immune responses in HIV-infected persons. Surprisingly, in all three cell types, a majority of Gag peptides derived from p15 rather than from the most immunogenic p24. The cytosolic degradation of peptide precursors in corresponding cells confirmed the generation of identified surface-nested peptides. Cytosolic degradation revealed peptides commonly produced in all cell types and displayed by various HLAs, peptides commonly produced in all cell types and selectively displayed by specific HLAs, and peptides produced in only one cell type. Importantly, we identified areas of proteins leading to common presentations of noncanonical peptides by several cell types with distinct HLAs. These peptides may benefit the design of immunogens, focusing T cell responses on relevant markers of HIV infection in the context of HLA diversity. IMPORTANCE The recognition of HIV-infected cells by immune T cells relies on the presentation of HIV-derived peptides by diverse HLA molecules at the surface of cells. The landscape of HIV peptides displayed by HIV-infected cells is not well defined. Considering the diversity of HLA molecules in the human population, it is critical for vaccine design to identify HIV peptides that may be displayed despite the HLA diversity. We identified 107 HIV peptides directly from the surface of three cell types infected with HIV. They corresponded to nested sets of HIV peptides of canonical and novel noncanonical lengths not predictable by the presence of HLA anchors. Importantly, we identified areas of HIV proteins leading to presentation of noncanonical peptides by several cell types with distinct HLAs. Including such peptides in vaccine immunogen may help to focus immune responses on common markers of HIV infection in the context of HLA diversity. PMID:27440904

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

PubMed

Tenzer, S; Peters, B; Bulik, S; Schoor, O; Lemmel, C; Schatz, M M; Kloetzel, P-M; Rammensee, H-G; Schild, H; Holzhütter, H-G

2005-05-01

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

Predicting DPP-IV inhibitors with machine learning approaches

NASA Astrophysics Data System (ADS)

Cai, Jie; Li, Chanjuan; Liu, Zhihong; Du, Jiewen; Ye, Jiming; Gu, Qiong; Xu, Jun

2017-04-01

Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.

Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

NASA Astrophysics Data System (ADS)

Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

2005-03-01

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

State of the art of immunoassay methods for B-type natriuretic peptides: An update.

PubMed

Clerico, Aldo; Franzini, Maria; Masotti, Silvia; Prontera, Concetta; Passino, Claudio

2015-01-01

The aim of this review article is to give an update on the state of the art of the immunoassay methods for the measurement of B-type natriuretic peptide (BNP) and its related peptides. Using chromatographic procedures, several studies reported an increasing number of circulating peptides related to BNP in human plasma of patients with heart failure. These peptides may have reduced or even no biological activity. Furthermore, other studies have suggested that, using immunoassays that are considered specific for BNP, the precursor of the peptide hormone, proBNP, constitutes a major portion of the peptide measured in plasma of patients with heart failure. Because BNP immunoassay methods show large (up to 50%) systematic differences in values, the use of identical decision values for all immunoassay methods, as suggested by the most recent international guidelines, seems unreasonable. Since proBNP significantly cross-reacts with all commercial immunoassay methods considered specific for BNP, manufacturers should test and clearly declare the degree of cross-reactivity of glycosylated and non-glycosylated proBNP in their BNP immunoassay methods. Clinicians should take into account that there are large systematic differences between methods when they compare results from different laboratories that use different BNP immunoassays. On the other hand, clinical laboratories should take part in external quality assessment (EQA) programs to evaluate the bias of their method in comparison to other BNP methods. Finally, the authors believe that the development of more specific methods for the active peptide, BNP1-32, should reduce the systematic differences between methods and result in better harmonization of results.

Accurate de novo design of hyperstable constrained peptides

PubMed Central

Bhardwaj, Gaurav; Mulligan, Vikram Khipple; Bahl, Christopher D.; Gilmore, Jason M.; Harvey, Peta J.; Cheneval, Olivier; Buchko, Garry W.; Pulavarti, Surya V.S.R.K.; Kaas, Quentin; Eletsky, Alexander; Huang, Po-Ssu; Johnsen, William A.; Greisen, Per; Rocklin, Gabriel J.; Song, Yifan; Linsky, Thomas W.; Watkins, Andrew; Rettie, Stephen A.; Xu, Xianzhong; Carter, Lauren P.; Bonneau, Richard; Olson, James M.; Coutsias, Evangelos; Correnti, Colin E.; Szyperski, Thomas; Craik, David J.; Baker, David

2016-01-01

Summary Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for de novo design of conformationally-restricted peptides, and the use of these methods to design 15–50 residue disulfide-crosslinked and heterochiral N-C backbone-cyclized peptides. These peptides are exceptionally stable to thermal and chemical denaturation, and twelve experimentally-determined X-ray and NMR structures are nearly identical to the computational models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs. PMID:27626386

Isolation and characterization of anti-SEB peptides using magnetic sorting and bacterial peptide display library technology

NASA Astrophysics Data System (ADS)

Pennington, Joseph M.; Kogot, Joshua M.; Sarkes, Deborah A.; Pellegrino, Paul M.; Stratis-Cullum, Dimitra N.

2012-06-01

Peptide display libraries offer an alternative method to existing antibody development methods enabling rapid isolation of highly stable reagents for detection of new and emerging biological threats. Bacterial display libraries are used to isolate new peptide reagents within 1 week, which is simpler and timelier than using competing display library technology based on phage or yeast. Using magnetic sorting methods, we have isolated peptide reagents with high affinity and specificity to staphylococcal enterotoxin B (SEB), a suspected food pathogen. Flow cytometry methods were used for on-cell characterization and the binding affinity (Kd) of this new peptide reagent was determined to be 56 nm with minimal cross-reactivity to other proteins. These results demonstrated that magnetic sorting for new reagents using bacterial display libraries is a rapid and effective method and has the potential for current and new and emerging food pathogen targets.

Peptide-membrane Interactions by Spin-labeling EPR

PubMed Central

Smirnova, Tatyana I.; Smirnov, Alex I.

2016-01-01

Site-directed spin labeling (SDSL) in combination with Electron Paramagnetic Resonance (EPR) spectroscopy is a well-established method that has recently grown in popularity as an experimental technique, with multiple applications in protein and peptide science. The growth is driven by development of labeling strategies, as well as by considerable technical advances in the field, that are paralleled by an increased availability of EPR instrumentation. While the method requires an introduction of a paramagnetic probe at a well-defined position in a peptide sequence, it has been shown to be minimally destructive to the peptide structure and energetics of the peptide-membrane interactions. In this chapter, we describe basic approaches for using SDSL EPR spectroscopy to study interactions between small peptides and biological membranes or membrane mimetic systems. We focus on experimental approaches to quantify peptide-membrane binding, topology of bound peptides, and characterize peptide aggregation. Sample preparation protocols including spin-labeling methods and preparation of membrane mimetic systems are also described. PMID:26477253

Binding free energy and counterion release for adsorption of the antimicrobial peptide lactoferricin B on a POPG membrane

NASA Astrophysics Data System (ADS)

Tolokh, Igor S.; Vivcharuk, Victor; Tomberli, Bruno; Gray, C. G.

2009-09-01

Molecular dynamics (MD) simulations are used to study the interaction of an anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with a POPG bilayer is employed as a model system for studying the details of membrane adsorption selectivity of cationic antimicrobial peptides. Seventy eight 4 ns MD production run trajectories of the equilibrated system, with six restrained orientations of LFCinB at 13 different separations from the POPG membrane, are generated to determine the free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the profile for this relatively large system, a variant of constrained MD and thermodynamic integration is used. A simplified method for relating the free energy profile to the LFCinB-POPG membrane binding constant is employed to predict a free energy of adsorption of -5.4±1.3kcal/mol and a corresponding maximum adsorption binding force of about 58 pN. We analyze the results using Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions and polarized water from the region between the charged membrane and peptide, as the two approach each other. We contrast these results with those found earlier for adsorption of LFCinB on the mammalianlike palmitoyl-oleoyl-phosphatidylcholine membrane.

Is the C-terminal flanking peptide of rat cholecystokinin double sulphated?

PubMed

Adrian, T E; Domin, J; Bacarese-Hamilton, A J; Bloom, S R

1986-02-03

A specific radioimmunoassay was developed to the predicted nine amino acid C-terminal flanking peptide of cholecystokinin (peptide serine serine, PSS). In aqueous extracts of rat brain, PSS was undetectable unless the extracts were first treated with arylsulphatase, which also resulted in desulphation of cholecystokinin. The reverse-phase HPLC analysis of partially desulphated extracts showed the presence of two peaks intermediate to the naturally occurring and the completely desulphated forms. It is therefore proposed that the CCK-flanking peptide PSS has both tyrosine residues sulphated.

Rapid Identification of Staphylococcus aureus Directly from Blood Cultures by Fluorescence In Situ Hybridization with Peptide Nucleic Acid Probes

PubMed Central

Oliveira, Kenneth; Procop, Gary W.; Wilson, Deborah; Coull, James; Stender, Henrik

2002-01-01

A new fluorescence in situ hybridization (FISH) method with peptide nucleic acid (PNA) probes for identification of Staphylococcus aureus directly from positive blood culture bottles that contain gram-positive cocci in clusters (GPCC) is described. The test (the S. aureus PNA FISH assay) is based on a fluorescein-labeled PNA probe that targets a species-specific sequence of the 16S rRNA of S. aureus. Evaluations with 17 reference strains and 48 clinical isolates, including methicillin-resistant and methicillin-susceptible S. aureus species, coagulase-negative Staphylococcus species, and other clinically relevant and phylogenetically related bacteria and yeast species, showed that the assay had 100% sensitivity and 96% specificity. Clinical trials with 87 blood cultures positive for GPCC correctly identified 36 of 37 (97%) of the S. aureus-positive cultures identified by standard microbiological methods. The positive and negative predictive values were 100 and 98%, respectively. It is concluded that this rapid method (2.5 h) for identification of S. aureus directly from blood culture bottles that contain GPCC offers important information for optimal antibiotic therapy. PMID:11773123

Low-Cost Peptide Microarrays for Mapping Continuous Antibody Epitopes.

PubMed

McBride, Ryan; Head, Steven R; Ordoukhanian, Phillip; Law, Mansun

2016-01-01

With the increasing need for understanding antibody specificity in antibody and vaccine research, pepscan assays provide a rapid method for mapping and profiling antibody responses to continuous epitopes. We have developed a relatively low-cost method to generate peptide microarray slides for studying antibody binding. Using a setup of an IntavisAG MultiPep RS peptide synthesizer, a Digilab MicroGrid II 600 microarray printer robot, and an InnoScan 1100 AL scanner, the method allows the interrogation of up to 1536 overlapping, alanine-scanning, and mutant peptides derived from the target antigens. Each peptide is tagged with a polyethylene glycol aminooxy terminus to improve peptide solubility, orientation, and conjugation efficiency to the slide surface.

Bayesian functional integral method for inferring continuous data from discrete measurements.

PubMed

Heuett, William J; Miller, Bernard V; Racette, Susan B; Holloszy, John O; Chow, Carson C; Periwal, Vipul

2012-02-08

Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic β-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of nonparametric Bayesian model selection where a proposed ISR time-course is considered to be a "model". An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because it is a priori unclear how robust the inference is to the deletion of data points, and a closely related question, how much smoothness or continuity the data actually support. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior probability distribution for a Markov-chain Monte Carlo evaluation of the posterior for the full model. As a concrete application of our method, we calculate the ISR from actual clinical C-peptide measurements in human subjects with varying degrees of insulin sensitivity. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing timescale and the width of the prior probability distribution on the space of models. In particular, our model comparison method determines the discrete time-step for interpolation of the unobservable continuous variable that is supported by the data. Attempts to go to finer discrete time-steps lead to less likely models. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Strategies for generating peptide radical cations via ion/ion reactions.

PubMed

Gilbert, Joshua D; Fisher, Christine M; Bu, Jiexun; Prentice, Boone M; Redwine, James G; McLuckey, Scott A

2015-02-01

Several approaches for the generation of peptide radical cations using ion/ion reactions coupled with either collision induced dissociation (CID) or ultraviolet photo dissociation (UVPD) are described here. Ion/ion reactions are used to generate electrostatic or covalent complexes comprised of a peptide and a radical reagent. The radical site of the reagent can be generated multiple ways. Reagents containing a carbon-iodine (C-I) bond are subjected to UVPD with 266-nm photons, which selectively cleaves the C-I bond homolytically. Alternatively, reagents containing azo functionalities are collisionally activated to yield radical sites on either side of the azo group. Both of these methods generate an initial radical site on the reagent, which then abstracts a hydrogen from the peptide while the peptide and reagent are held together by either electrostatic interactions or a covalent linkage. These methods are demonstrated via ion/ion reactions between the model peptide RARARAA (doubly protonated) and various distonic anionic radical reagents. The radical site abstracts a hydrogen atom from the peptide, while the charge site abstracts a proton. The net result is the conversion of a doubly protonated peptide to a peptide radical cation. The peptide radical cations have been fragmented via CID and the resulting product ion mass spectra are compared to the control CID spectrum of the singly protonated, even-electron species. This work is then extended to bradykinin, a more broadly studied peptide, for comparison with other radical peptide generation methods. The work presented here provides novel methods for generating peptide radical cations in the gas phase through ion/ion reaction complexes that do not require modification of the peptide in solution or generation of non-covalent complexes in the electrospray process. Copyright © 2015 John Wiley & Sons, Ltd.

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

PubMed

Huang, Ri-Bo; Du, Qi-Shi; Wei, Yu-Tuo; Pang, Zong-Wen; Wei, Hang; Chou, Kuo-Chen

2009-02-07

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.

Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1

DOE PAGES

Leon, Deborah R.; Ytterberg, A. Jimmy; Boontheung, Pinmanee; ...

2015-03-05

Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to “mine” information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154more » proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.« less

Peptides and Anti-peptide Antibodies for Small and Medium Scale Peptide and Anti-peptide Affinity Microarrays: Antigenic Peptide Selection, Immobilization, and Processing.

PubMed

Zhang, Fan; Briones, Andrea; Soloviev, Mikhail

2016-01-01

This chapter describes the principles of selection of antigenic peptides for the development of anti-peptide antibodies for use in microarray-based multiplex affinity assays and also with mass-spectrometry detection. The methods described here are mostly applicable to small to medium scale arrays. Although the same principles of peptide selection would be suitable for larger scale arrays (with 100+ features) the actual informatics software and printing methods may well be different. Because of the sheer number of proteins/peptides to be processed and analyzed dedicated software capable of processing all the proteins and an enterprise level array robotics may be necessary for larger scale efforts. This report aims to provide practical advice to those who develop or use arrays with up to ~100 different peptide or protein features.

Enhanced Prediction of Src Homology 2 (SH2) Domain Binding Potentials Using a Fluorescence Polarization-derived c-Met, c-Kit, ErbB, and Androgen Receptor Interactome*

PubMed Central

Leung, Kin K.; Hause, Ronald J.; Barkinge, John L.; Ciaccio, Mark F.; Chuu, Chih-Pin; Jones, Richard B.

2014-01-01

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains. PMID:24728074

Unexpected Hydrolytic Instability of N-Acylated Amino Acid Amides and Peptides

PubMed Central

2015-01-01

Remote amide bonds in simple N-acyl amino acid amide or peptide derivatives 1 can be surprisingly unstable hydrolytically, affording, in solution, variable amounts of 3 under mild acidic conditions, such as trifluoroacetic acid/water mixtures at room temperature. This observation has important implications for the synthesis of this class of compounds, which includes N-terminal-acylated peptides. We describe the factors contributing to this instability and how to predict and control it. The instability is a function of the remote acyl group, R2CO, four bonds away from the site of hydrolysis. Electron-rich acyl R2 groups accelerate this reaction. In the case of acyl groups derived from substituted aromatic carboxylic acids, the acceleration is predictable from the substituent’s Hammett σ value. N-Acyl dipeptides are also hydrolyzed under typical cleavage conditions. This suggests that unwanted peptide truncation may occur during synthesis or prolonged standing in solution when dipeptides or longer peptides are acylated on the N-terminus with electron-rich aromatic groups. When amide hydrolysis is an undesired secondary reaction, as can be the case in the trifluoroacetic acid-catalyzed cleavage of amino acid amide or peptide derivatives 1 from solid-phase resins, conditions are provided to minimize that hydrolysis. PMID:24617596

Neo-epitope Peptides as Biomarkers of Disease Progression for Muscular Dystrophies and Other Myopathies

PubMed Central

Arvanitidis, A.; Henriksen, K.; Karsdal, M.A.; Nedergaard, A.

2016-01-01

For several decades, serological biomarkers of neuromuscular diseases as dystrophies, myopathies and myositis have been limited to routine clinical biochemistry panels. Gauging the pathological progression is a prerequisite for proper treatment and therefore identifying accessible, easy to monitor biomarkers that can predict the disease progression would be an important advancement. Most muscle diseases involve accelerated muscle fiber degradation, inflammation, fatty tissue substitution and/or fibrosis. All these pathological traits have been shown to give rise to serological peptide biomarkers in other tissues, underlining the potential application of existing biomarkers of such traits in muscle disorders. A significant quantity of tissue is involved in these pathological mechanisms alongside with qualitative changes in protein turnover in myofibrillar, extra-cellular matrix and immunological cell protein fractions accompanied by alterations in body fluids. We propose that protein and peptides can leak out of the afflicted muscles and can be of use in diagnosis, prediction of pathology trajectory and treatment efficacy. Proteolytic cleavage systems are especially modulated during a range of muscle pathologies, thereby giving rise to peptides that are differentially released during disease manifestation. Therefore, we believe that pathology-specific post-translational modifications like cleavages can give rise to neoepitope peptides that may represent a promising class of peptides for discovery of biomarkers pertaining to neuromuscular diseases. PMID:27854226

Identification of EGFRvIII-derived CTL epitopes restricted by HLA A0201 for dendritic cell based immunotherapy of gliomas.

PubMed

Wu, An-hua; Xiao, Jing; Anker, Lars; Hall, Walter A; Gregerson, Dale S; Cavenee, Webster K; Chen, Wei; Low, Walter C

2006-01-01

The type III variant of the epidermal growth factor receptor (EGFRvIII) mutation is present in 20-25% of patients with glioblastoma multiforme (GBM). EGFRvIII is not expressed in normal tissue and is therefore a suitable candidate antigen for dendritic cell (DC) based immunotherapy of GBM. To identify the antigenic epitope(s) that may serve as targets for EGFRvIII-specific cytotoxic T lymphocytes (CTLs), the peptide sequence of EGFRvIII was screened with two software programs to predict candidate epitopes restricted by the major histocompatibility complex class I subtype HLA-A0201, which is the predominant subtype in most ethnic groups. Three predicted peptides were constructed and loaded to mature human DCs generated from peripheral blood monocytes. Autologous CD8+ T cells were stimulated in vitro with the EGFRvIII peptide-pulsed DCs. One of the three peptides was found to induce EGFRvIII-specific CTLs as demonstrated by IFN-gamma production and cytotoxicity against HLA-A0201+ EGFRvIII transfected U87 glioma cells. These results suggest that vaccination with EGFRvIII peptide-pulsed DCs or adoptive transfer of in vitro elicited EGFRvIII-specific CTLs by EGFRvIII peptide-pulsed DCs are potential approaches to the treatment of glioma patients.

Protein and Peptide Gas-phase Structure Investigation Using Collision Cross Section Measurements and Hydrogen Deuterium Exchange

NASA Astrophysics Data System (ADS)

Khakinejad, Mahdiar

Protein and peptide gas-phase structure analysis provides the opportunity to study these species outside of their explicit environment where the interaction network with surrounding molecules makes the analysis difficult [1]. Although gas-phase structure analysis offers a unique opportunity to study the intrinsic behavior of these biomolecules [2-4], proteins and peptides exhibit very low vapor pressures [2]. Peptide and protein ions can be rendered in the gas-phase using electrospray ionization (ESI) [5]. There is a growing body of literature that shows proteins and peptides can maintain solution structures during the process of ESI and these structures can persist for a few hundred milliseconds [6-9]. Techniques for monitoring gas-phase protein and peptide ion structures are categorized as physical probes and chemical probes. Collision cross section (CCS) measurement, being a physical probe, is a powerful method to investigate gas-phase structure size [3, 7, 10-15]; however, CCS values alone do not establish a one to one relation with structure(i.e., the CCS value is an orientationally averaged value [15-18]. Here we propose the utility of gas-phase hydrogen deuterium exchange (HDX) as a second criterion of structure elucidation. The proposed approach incudes extensive MD simulations to sample biomolecular ion conformation space with the production of numerous, random in-silico structures. Subsequently a CCS can be calculated for these structures and theoretical CCS values are compared with experimental values to produce a pool of candidate structures. Utilizing a chemical reaction model based on the gas-phase HDX mechanism, the HDX kinetics behavior of these candidate structures are predicted and compared to experimental results to nominate the best in-silico structures which match (chemically and physically) with experimental observations. For the predictive approach to succeed, an extensive technique and method development is essential. To combine CCS measurements and gas-phase HDX studies at the amino acid residue level, for the first time a drift tube is connected to a linear ion trap (LIT) with electron transfer dissociation (ETD) capability[19, 20]. In this manner CCS and per-residue deuterium uptake measurements for a model peptide carried out successfully[19]. In this study, the gas-phase conformations of electrosprayed ions of the model peptide KKDDDDIIKIIK have been examined. Using ion structures obtained from molecular dynamics (MD) simulation and considering charge-site/exchange-site density the level of the maximum total deuterium uptake for the gas-phase ions is explained. Also a new hydrogen accessibility scoring (HAS) model that includes two distance calculations (charge site to carbonyl group and carbonyl group to exchange site) is applied to the in-silico structures to describe the expected HDX behavior for these structures. Further investigation to improve the accuracy of the model is accomplished by a "per-residue" HDX kinetics study of the model peptide [21]. In this study, the ion residence time and the deuterium uptake of each residue is measured at different partial pressures of D2O. Subsequently the contribution each residue to the overall HDX rate of the intact peptide ion is calculated. These rate contributions of the residues exhibit a better fit to HAS than their maximum deuterium uptake. Proteins and peptides with very frequent acidic residue in their sequence provide very poor signal levels when employing positive polarity ESI. Also, the comparison of protonated and deprotonated ions of these biomolecules offers the potential to provide a better structural characterization [22]. Per-residue deuterium uptake values resulting from collision-induced dissociation (CID) of the model peptide KKDDDDIIKIIK were used to investigated the degree of hydrogen deuterium scrambling for deprotonated ions [23]. Remarkably, limited isotopic scrambling was observed in this study of this small model peptide. This data and the per-residue deuterium uptake of the triply-protonated model peptide Acetyl-PAAAAKAAAAKAAAAKAAAAK are exploited to propose a lemma to allocate protonation and deprotonation sites for peptide ions in the gas-phase. Insulin ions, as a small protein model system, are examined to investigate the relation of the maximum deuterium uptake value for each insulin chain to the exposed surface area of each insulin subunit [22]. The results show that the methodology can be applied on the protein complexes to provide information about the exposed surface area of each subunit.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis.

PubMed

Park, In-Hee; Venable, John D; Steckler, Caitlin; Cellitti, Susan E; Lesley, Scott A; Spraggon, Glen; Brock, Ansgar

2015-09-28

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

PubMed Central

Park, In-Hee; Venable, John D.; Steckler, Caitlin; Cellitti, Susan E.; Lesley, Scott A.; Spraggon, Glen; Brock, Ansgar

2015-01-01

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure and dynamics. More recently, Hydrogen Exchange Mass Spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from Molecular Dynamics (MD) simulation snapshots is used to determine partitioning over bonded and non-bonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for Fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models. PMID:26241692

Solution structures, dynamics, and ice growth inhibitory activity of peptide fragments derived from an antarctic yeast protein.

PubMed

Shah, Syed Hussinien H; Kar, Rajiv K; Asmawi, Azren A; Rahman, Mohd Basyaruddin A; Murad, Abdul Munir A; Mahadi, Nor M; Basri, Mahiran; Rahman, Raja Noor Zaliha A; Salleh, Abu B; Chatterjee, Subhrangsu; Tejo, Bimo A; Bhunia, Anirban

2012-01-01

Exotic functions of antifreeze proteins (AFP) and antifreeze glycopeptides (AFGP) have recently been attracted with much interest to develop them as commercial products. AFPs and AFGPs inhibit ice crystal growth by lowering the water freezing point without changing the water melting point. Our group isolated the Antarctic yeast Glaciozyma antarctica that expresses antifreeze protein to assist it in its survival mechanism at sub-zero temperatures. The protein is unique and novel, indicated by its low sequence homology compared to those of other AFPs. We explore the structure-function relationship of G. antarctica AFP using various approaches ranging from protein structure prediction, peptide design and antifreeze activity assays, nuclear magnetic resonance (NMR) studies and molecular dynamics simulation. The predicted secondary structure of G. antarctica AFP shows several α-helices, assumed to be responsible for its antifreeze activity. We designed several peptide fragments derived from the amino acid sequences of α-helical regions of the parent AFP and they also showed substantial antifreeze activities, below that of the original AFP. The relationship between peptide structure and activity was explored by NMR spectroscopy and molecular dynamics simulation. NMR results show that the antifreeze activity of the peptides correlates with their helicity and geometrical straightforwardness. Furthermore, molecular dynamics simulation also suggests that the activity of the designed peptides can be explained in terms of the structural rigidity/flexibility, i.e., the most active peptide demonstrates higher structural stability, lower flexibility than that of the other peptides with lower activities, and of lower rigidity. This report represents the first detailed report of downsizing a yeast AFP into its peptide fragments with measurable antifreeze activities.

Solution Structures, Dynamics, and Ice Growth Inhibitory Activity of Peptide Fragments Derived from an Antarctic Yeast Protein

PubMed Central

Asmawi, Azren A.; Rahman, Mohd Basyaruddin A.; Murad, Abdul Munir A.; Mahadi, Nor M.; Basri, Mahiran; Rahman, Raja Noor Zaliha A.; Salleh, Abu B.; Chatterjee, Subhrangsu; Tejo, Bimo A.; Bhunia, Anirban

2012-01-01

Exotic functions of antifreeze proteins (AFP) and antifreeze glycopeptides (AFGP) have recently been attracted with much interest to develop them as commercial products. AFPs and AFGPs inhibit ice crystal growth by lowering the water freezing point without changing the water melting point. Our group isolated the Antarctic yeast Glaciozyma antarctica that expresses antifreeze protein to assist it in its survival mechanism at sub-zero temperatures. The protein is unique and novel, indicated by its low sequence homology compared to those of other AFPs. We explore the structure-function relationship of G. antarctica AFP using various approaches ranging from protein structure prediction, peptide design and antifreeze activity assays, nuclear magnetic resonance (NMR) studies and molecular dynamics simulation. The predicted secondary structure of G. antarctica AFP shows several α-helices, assumed to be responsible for its antifreeze activity. We designed several peptide fragments derived from the amino acid sequences of α-helical regions of the parent AFP and they also showed substantial antifreeze activities, below that of the original AFP. The relationship between peptide structure and activity was explored by NMR spectroscopy and molecular dynamics simulation. NMR results show that the antifreeze activity of the peptides correlates with their helicity and geometrical straightforwardness. Furthermore, molecular dynamics simulation also suggests that the activity of the designed peptides can be explained in terms of the structural rigidity/flexibility, i.e., the most active peptide demonstrates higher structural stability, lower flexibility than that of the other peptides with lower activities, and of lower rigidity. This report represents the first detailed report of downsizing a yeast AFP into its peptide fragments with measurable antifreeze activities. PMID:23209600

HLA Class I Binding 9mer Peptides from Influenza A Virus Induce CD4+ T Cell Responses

PubMed Central

Wang, Mingjun; Larsen, Mette V.; Nielsen, Morten; Harndahl, Mikkel; Justesen, Sune; Dziegiel, Morten H.; Buus, Søren; Tang, Sheila T.; Lund, Ole; Claesson, Mogens H.

2010-01-01

Background Identification of human leukocyte antigen class I (HLA-I) restricted cytotoxic T cell (CTL) epitopes from influenza virus is of importance for the development of new effective peptide-based vaccines. Methodology/Principal Findings In the present work, bioinformatics was used to predict 9mer peptides derived from available influenza A viral proteins with binding affinity for at least one of the 12 HLA-I supertypes. The predicted peptides were then selected in a way that ensured maximal coverage of the available influenza A strains. One hundred and thirty one peptides were synthesized and their binding affinities for the HLA-I supertypes were measured in a biochemical assay. Influenza-specific T cell responses towards the peptides were quantified using IFNγ ELISPOT assays with peripheral blood mononuclear cells (PBMC) from adult healthy HLA-I typed donors as responder cells. Of the 131 peptides, 21 were found to induce T cell responses in 19 donors. In the ELISPOT assay, five peptides induced responses that could be totally blocked by the pan-specific anti-HLA-I antibody W6/32, whereas 15 peptides induced responses that could be completely blocked in the presence of the pan-specific anti-HLA class II (HLA-II) antibody IVA12. Blocking of HLA-II subtype reactivity revealed that 8 and 6 peptide responses were blocked by anti-HLA-DR and -DP antibodies, respectively. Peptide reactivity of PBMC depleted of CD4+ or CD8+ T cells prior to the ELISPOT culture revealed that effectors are either CD4+ (the majority of reactivities) or CD8+ T cells, never a mixture of these subsets. Three of the peptides, recognized by CD4+ T cells showed binding to recombinant DRA1*0101/DRB1*0401 or DRA1*0101/DRB5*0101 molecules in a recently developed biochemical assay. Conclusions/Significance HLA-I binding 9mer influenza virus-derived peptides induce in many cases CD4+ T cell responses restricted by HLA-II molecules. PMID:20479886

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation

PubMed Central

Jameson-Lee, Max; Koparde, Vishal; Griffith, Phil; Scalora, Allison F.; Sampson, Juliana K.; Khalid, Haniya; Sheth, Nihar U.; Batalo, Michael; Serrano, Myrna G.; Roberts, Catherine H.; Hess, Michael L.; Buck, Gregory A.; Neale, Michael C.; Manjili, Masoud H.; Toor, Amir Ahmed

2014-01-01

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor–recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential. PMID:25414699

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation.

PubMed

Jameson-Lee, Max; Koparde, Vishal; Griffith, Phil; Scalora, Allison F; Sampson, Juliana K; Khalid, Haniya; Sheth, Nihar U; Batalo, Michael; Serrano, Myrna G; Roberts, Catherine H; Hess, Michael L; Buck, Gregory A; Neale, Michael C; Manjili, Masoud H; Toor, Amir Ahmed

2014-01-01

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor-recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential.

Strategies for the construction and use of peptide and antibody libraries displayed on phages.

PubMed

Pini, Alessandro; Giuliani, Andrea; Ricci, Claudia; Runci, Ylenia; Bracci, Luisa

2004-12-01

Combinatorial chemistry and biology have become popular methods for the identification of bio-active molecules in drug discovery. A widely used technique in combinatorial biology is "phage display", by which peptides, antibody fragments and enzymes are displayed on the surface of bacteriophages, and can be selected by simple procedures of biopanning. The construction of phage libraries of peptides or antibody fragments provides a huge source of ligands and bio-active molecules that can be isolated from the library without laborious studies on antigen characteristics and prediction of ligand structure. This "irrational" approach for the construction of new drugs is extremely rapid and is now used by thousands of laboratories world-wide. The bottleneck in this procedure is the availability of large reliable libraries that can be used repeatedly over the years without loss of ligand expression and diversity. Construction of personalized libraries is therefore important for public and private laboratories engaged in the isolation of specific molecules for therapeutic or diagnostic use. Here we report the general strategies for constructing large phage peptide and antibody libraries, based on the experience of researchers who built the world's most widely used libraries. Particular attention is paid to advanced strategies for the construction, preservation and panning.

Effect of charge on the conformation of highly basic peptides including the tail regions of histone proteins by ion mobility mass spectrometry.

PubMed

Akashi, Satoko; Downard, Kevin M

2016-09-01

The first systematic and comprehensive study of the charging behaviour and effect of charge on the conformation of specifically constructed arginine-rich peptides and its significance to the N- and C-terminal basic tail regions of histone proteins was conducted using ion mobility mass spectrometry (IM-MS). Among the basic amino acids, arginine has the greatest impact on the charging behaviour and structures of gas phase ions by virtue of its high proton affinity. A close linear correlation was found between either the maximum charge, or most abundant charge state, that the peptides support and their average collision cross section (CCS) values measured by ion mobility mass spectrometry. The calculated collision cross sections for the lowest energy solution state models predicted by the PEP-FOLD algorithm using a modified MOBCAL trajectory method were found to best correlate with the values measured by IM-MS. In the case of the histone peptides, more compact folded structures, supporting less than the maximum number of charges, were observed. These results are consistent with those previously reported for histone dimers where neutralization of the charge at the basic residues of the tail regions did not affect their CCS values.

Strategies to improve plasma half life time of peptide and protein drugs.

PubMed

Werle, M; Bernkop-Schnürch, A

2006-06-01

Due to the obvious advantages of long-acting peptide and protein drugs, strategies to prolong plasma half life time of such compounds are highly on demand. Short plasma half life times are commonly due to fast renal clearance as well as to enzymatic degradation occurring during systemic circulation. Modifications of the peptide/protein can lead to prolonged plasma half life times. By shortening the overall amino acid amount of somatostatin and replacing L: -analogue amino acids with D: -amino acids, plasma half life time of the derivate octreotide was 1.5 hours in comparison to only few minutes of somatostatin. A PEG(2,40 K) conjugate of INF-alpha-2b exhibited a 330-fold prolonged plasma half life time compared to the native protein. It was the aim of this review to provide an overview of possible strategies to prolong plasma half life time such as modification of N- and C-terminus or PEGylation as well as methods to evaluate the effectiveness of drug modifications. Furthermore, fundamental data about most important proteolytic enzymes of human blood, liver and kidney as well as their cleavage specificity and inhibitors for them are provided in order to predict enzymatic cleavage of peptide and protein drugs during systemic circulation.

Bioinformatics analysis of the oxidosqualene cyclase gene and the amino acid sequence in mangrove plants

NASA Astrophysics Data System (ADS)

Basyuni, M.; Wati, R.

2017-01-01

This study described the bioinformatics methods to analyze seven oxidosqualene cyclase (OSC) genes from mangrove plants on DDBJ/EMBL/GenBank as well as predicted the structure, composition, similarity, subcellular localization and phylogenetic. The physical and chemical properties of seven mangrove OSC showed variation among the genes. The percentage of the secondary structure of seven mangrove OSC genes followed the order of a helix > random coil > extended chain structure. The values of chloroplast or signal peptide were too low, indicated that no chloroplast transit peptide or signal peptide of secretion pathway in mangrove OSC genes. The target peptide value of mitochondria varied from 0.163 to 0.430, indicated it was possible to exist. These results suggested the importance of understanding the diversity and functional of properties of the different amino acids in mangrove OSC genes. To clarify the relationship among the mangrove OSC gene, a phylogenetic tree was constructed. The phylogenetic tree shows that there are three clusters, Kandelia KcMS join with Bruguiera BgLUS, Rhizophora RsM1 was close to Bruguiera BgbAS, and Rhizophora RcCAS join with Kandelia KcCAS. The present study, therefore, supported the previous results that plant OSC genes form distinct clusters in the tree.

Preoperative Beta Cell Function Is Predictive of Diabetes Remission After Bariatric Surgery.

PubMed

Souteiro, Pedro; Belo, Sandra; Neves, João Sérgio; Magalhães, Daniela; Silva, Rita Bettencourt; Oliveira, Sofia Castro; Costa, Maria Manuel; Saavedra, Ana; Oliveira, Joana; Cunha, Filipe; Lau, Eva; Esteves, César; Freitas, Paula; Varela, Ana; Queirós, Joana; Carvalho, Davide

2017-02-01

Bariatric surgery can improve glucose metabolism in obese patients with diabetes, but the factors that can predict diabetes remission are still under discussion. The present study aims to examine the impact of preoperative beta cell function on diabetes remission following surgery. We investigated a cohort of 363 obese diabetic patients who underwent bariatric surgery. The impact of several preoperative beta cell function indexes on diabetes remission was explored through bivariate logistic regression models. Postoperative diabetes remission was achieved in 39.9 % of patients. Younger patients (p < 0.001) and those with lower HbA1c (p = 0.001) at the baseline evaluation had higher odds of diabetes remission. Use of oral anti-diabetics and insulin therapy did not reach statistical significance when they were adjusted for age and HbA1c. Among the evaluated indexes of beta cell function, higher values of insulinogenix index, Stumvoll first- and second-phase indexes, fasting C-peptide, C-peptide area under the curve (AUC), C-peptide/glucose AUC, ISR (insulin secretion rate) AUC, and ISR/glucose AUC predicted diabetes remission even after adjustment for age and HbA1c. Among them, C-peptide AUC had the higher discriminative power (AUC 0.76; p < 0.001). Patients' age and preoperative HbA1c can forecast diabetes remission following surgery. Unlike other studies, our group found that the use of oral anti-diabetics and insulin therapy were not independent predictors of postoperative diabetes status. Preoperative beta cell function, mainly C-peptide AUC, is useful in predicting diabetes remission, and it should be assessed in all obese diabetic patients before bariatric or metabolic surgery.

Expansion for the Brachylophosaurus canadensis Collagen I Sequence and Additional Evidence of the Preservation of Cretaceous Protein.

PubMed

Schroeter, Elena R; DeHart, Caroline J; Cleland, Timothy P; Zheng, Wenxia; Thomas, Paul M; Kelleher, Neil L; Bern, Marshall; Schweitzer, Mary H

2017-02-03

Sequence data from biomolecules such as DNA and proteins, which provide critical information for evolutionary studies, have been assumed to be forever outside the reach of dinosaur paleontology. Proteins, which are predicted to have greater longevity than DNA, have been recovered from two nonavian dinosaurs, but these results remain controversial. For proteomic data derived from extinct Mesozoic organisms to reach their greatest potential for investigating questions of phylogeny and paleobiology, it must be shown that peptide sequences can be reliably and reproducibly obtained from fossils and that fragmentary sequences for ancient proteins can be increasingly expanded. To test the hypothesis that peptides can be repeatedly detected and validated from fossil tissues many millions of years old, we applied updated extraction methodology, high-resolution mass spectrometry, and bioinformatics analyses on a Brachylophosaurus canadensis specimen (MOR 2598) from which collagen I peptides were recovered in 2009. We recovered eight peptide sequences of collagen I: two identical to peptides recovered in 2009 and six new peptides. Phylogenetic analyses place the recovered sequences within basal archosauria. When only the new sequences are considered, B. canadensis is grouped more closely to crocodylians, but when all sequences (current and those reported in 2009) are analyzed, B. canadensis is placed more closely to basal birds. The data robustly support the hypothesis of an endogenous origin for these peptides, confirm the idea that peptides can survive in specimens tens of millions of years old, and bolster the validity of the 2009 study. Furthermore, the new data expand the coverage of B. canadensis collagen I (a 33.6% increase in collagen I alpha 1 and 116.7% in alpha 2). Finally, this study demonstrates the importance of reexamining previously studied specimens with updated methods and instrumentation, as we obtained roughly the same amount of sequence data as the previous study with substantially less sample material. Data are available via ProteomeXchange with identifier PXD005087.

A two-step database search method improves sensitivity in peptide sequence matches for metaproteomics and proteogenomics studies.

PubMed

Jagtap, Pratik; Goslinga, Jill; Kooren, Joel A; McGowan, Thomas; Wroblewski, Matthew S; Seymour, Sean L; Griffin, Timothy J

2013-04-01

Large databases (>10(6) sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database-search programs. Most notably, strict filtering to avoid false-positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two-step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target-decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two-step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one-step method. The two-step method captured almost all of the same peptides matched by the one-step method, with a majority of the additional matches being false negatives from the one-step method. Furthermore, the two-step method improved results regardless of the database search program used. Our results show that our two-step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prospects of pharmaceuticals and biopharmaceuticals loaded microparticles prepared by double emulsion technique for controlled delivery

PubMed Central

Giri, Tapan Kumar; Choudhary, Chhatrapal; Ajazuddin; Alexander, Amit; Badwaik, Hemant; Tripathi, Dulal Krishna

2012-01-01

Several methods and techniques are potentially useful for the preparation of microparticles in the field of controlled drug delivery. The type and the size of the microparticles, the entrapment, release characteristics and stability of drug in microparticles in the formulations are dependent on the method used. One of the most common methods of preparing microparticles is the single emulsion technique. Poorly soluble, lipophilic drugs are successfully retained within the microparticles prepared by this method. However, the encapsulation of highly water soluble compounds including protein and peptides presents formidable challenges to the researchers. The successful encapsulation of such compounds requires high drug loading in the microparticles, prevention of protein and peptide degradation by the encapsulation method involved and predictable release, both rate and extent, of the drug compound from the microparticles. The above mentioned problems can be overcome by using the double emulsion technique, alternatively called as multiple emulsion technique. Aiming to achieve this various techniques have been examined to prepare stable formulations utilizing w/o/w, s/o/w, w/o/o, and s/o/o type double emulsion methods. This article reviews the current state of the art in double emulsion based technologies for the preparation of microparticles including the investigation of various classes of substances that are pharmaceutically and biopharmaceutically active. PMID:23960828

Analysis of illegal peptide biopharmaceuticals frequently encountered by controlling agencies.

PubMed

Vanhee, Celine; Janvier, Steven; Desmedt, Bart; Moens, Goedele; Deconinck, Eric; De Beer, Jacques O; Courselle, Patricia

2015-09-01

Recent advances in genomics, recombinant expression technologies and peptide synthesis have led to an increased development of protein and peptide therapeutics. Unfortunately this goes hand in hand with a growing market of counterfeit and illegal biopharmaceuticals, including substances that are still under pre-clinical and clinical development. These counterfeit and illegal protein and peptide substances could imply severe health threats as has been demonstrated by numerous case reports. The Belgian Federal Agency for Medicines and Health Products (FAMHP) and customs are striving, together with their global counterparts, to curtail the trafficking and distributions of these substances. At their request, suspected protein and peptide preparations are analysed in our Official Medicines Control Laboratory (OMCL). It stands to reason that a general screening method would be beneficiary in the battle against counterfeit and illegal peptide drugs. In this paper we present such general screening method employing liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the identification of counterfeit and illegal injectable peptide preparations, extended with a subsequent quantification method using ultra-high performance liquid chromatography with diode array detection (UHPLC-DAD). The screening method, taking only 30 min, is able to selectively detect 25 different peptides and incorporates the proposed minimum of five identification points (IP) as has been recommended for sports drug testing applications. The group of peptides represent substances which have already been detected in illegal and counterfeit products seized by different European countries as well as some biopharmaceutical peptides which have not been confiscated yet by the controlling agencies, but are already being used according to the many internet users forums. Additionally, we also show that when applying the same LC gradient, it is also possible to quantify these peptides without the need for derivatization or the use of expensive labelled peptides. This quantification method was successfully validated for a representative subset of 10 different peptides by using the "total error" approach in accordance with the validation requirements of ISO-17025. Copyright © 2015 Elsevier B.V. All rights reserved.

A Web Server and Mobile App for Computing Hemolytic Potency of Peptides.

PubMed

Chaudhary, Kumardeep; Kumar, Ritesh; Singh, Sandeep; Tuknait, Abhishek; Gautam, Ankur; Mathur, Deepika; Anand, Priya; Varshney, Grish C; Raghava, Gajendra P S

2016-03-08

Numerous therapeutic peptides do not enter the clinical trials just because of their high hemolytic activity. Recently, we developed a database, Hemolytik, for maintaining experimentally validated hemolytic and non-hemolytic peptides. The present study describes a web server and mobile app developed for predicting, and screening of peptides having hemolytic potency. Firstly, we generated a dataset HemoPI-1 that contains 552 hemolytic peptides extracted from Hemolytik database and 552 random non-hemolytic peptides (from Swiss-Prot). The sequence analysis of these peptides revealed that certain residues (e.g., L, K, F, W) and motifs (e.g., "FKK", "LKL", "KKLL", "KWK", "VLK", "CYCR", "CRR", "RFC", "RRR", "LKKL") are more abundant in hemolytic peptides. Therefore, we developed models for discriminating hemolytic and non-hemolytic peptides using various machine learning techniques and achieved more than 95% accuracy. We also developed models for discriminating peptides having high and low hemolytic potential on different datasets called HemoPI-2 and HemoPI-3. In order to serve the scientific community, we developed a web server, mobile app and JAVA-based standalone software (http://crdd.osdd.net/raghava/hemopi/).

Synthetic peptides coupled to the surface of liposomes effectively induce SARS coronavirus-specific cytotoxic T lymphocytes and viral clearance in HLA-A*0201 transgenic mice.

PubMed

Ohno, Satoshi; Kohyama, Shunsuke; Taneichi, Maiko; Moriya, Osamu; Hayashi, Hidenori; Oda, Hiroshi; Mori, Masahito; Kobayashi, Akiharu; Akatsuka, Toshitaka; Uchida, Tetsuya; Matsui, Masanori

2009-06-12

We investigated whether the surface-linked liposomal peptide was applicable to a vaccine based on cytotoxic T lymphocytes (CTLs) against severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV). We first identified four HLA-A*0201-restricted CTL epitopes derived from SARS-CoV using HLA-A*0201 transgenic mice and recombinant adenovirus expressing predicted epitopes. These peptides were coupled to the surface of liposomes, and inoculated into mice. Two of the liposomal peptides were effective for peptide-specific CTL induction, and one of them was efficient for the clearance of vaccinia virus expressing epitopes of SARS-CoV, suggesting that the surface-linked liposomal peptide might offer an effective CTL-based vaccine against SARS.

Can natural proteins designed with 'inverted' peptide sequences adopt native-like protein folds?

PubMed

Sridhar, Settu; Guruprasad, Kunchur

2014-01-01

We have carried out a systematic computational analysis on a representative dataset of proteins of known three-dimensional structure, in order to evaluate whether it would possible to 'swap' certain short peptide sequences in naturally occurring proteins with their corresponding 'inverted' peptides and generate 'artificial' proteins that are predicted to retain native-like protein fold. The analysis of 3,967 representative proteins from the Protein Data Bank revealed 102,677 unique identical inverted peptide sequence pairs that vary in sequence length between 5-12 and 18 amino acid residues. Our analysis illustrates with examples that such 'artificial' proteins may be generated by identifying peptides with 'similar structural environment' and by using comparative protein modeling and validation studies. Our analysis suggests that natural proteins may be tolerant to accommodating such peptides.

Cloning of cDNAs encoding new peptides of the dermaseptin-family.

PubMed

Wechselberger, C

1998-10-14

Dermaseptins are a group of basic (lysine-rich) peptides, 27-34 amino acids in length and involved in the defense of frog skin against microbial invasion. By using a degenerated oligonucleotide primer binding to the 5'-untranslated region of previously characterized cDNAs of these peptides, it was possible to identify new members of the dermaseptin family in the South American frogs Agalychnis annae and Pachymedusa dacnicolor. Amino acid alignment and secondary structure prediction reveals, that only five of the deduced peptides can be supposed to be also functional homologs to the known dermaseptins from Phyllomedusa bicolor and Phyllomedusa sauvagei. The remaining six peptides described in this paper have not been isolated and characterized yet.

Rational design of class I MHC ligands

NASA Astrophysics Data System (ADS)

Rognan, D.; Scapozza, L.; Folkers, G.; Daser, Angelika

1995-04-01

From the knowledge of the three-dimensional structure of a class I MHC protein, several non natural peptides were designed in order to either optimize the interactions of one secondary anchor amino acid with its HLA binding pocket or to substitute the non interacting part with spacer residues. All peptides were synthesized and tested for binding to the class I MHC protein in an in vitro reconstitution assay. As predicted, the non natural peptides present an enhanced binding to the HLA-B27 molecule with respect to their natural parent peptides. This study constitutes the first step towards the rational design of non peptidic MHC ligands that should be very promising tools for the selective immunotherapy of autoimmune diseases.

F pocket flexibility influences the tapasin dependence of two differentially disease-associated MHC Class I proteins.

PubMed

Abualrous, Esam T; Fritzsche, Susanne; Hein, Zeynep; Al-Balushi, Mohammed S; Reinink, Peter; Boyle, Louise H; Wellbrock, Ursula; Antoniou, Antony N; Springer, Sebastian

2015-04-01

The human MHC class I protein HLA-B*27:05 is statistically associated with ankylosing spondylitis, unlike HLA-B*27:09, which differs in a single amino acid in the F pocket of the peptide-binding groove. To understand how this unique amino acid difference leads to a different behavior of the proteins in the cell, we have investigated the conformational stability of both proteins using a combination of in silico and experimental approaches. Here, we show that the binding site of B*27:05 is conformationally disordered in the absence of peptide due to a charge repulsion at the bottom of the F pocket. In agreement with this, B*27:05 requires the chaperone protein tapasin to a greater extent than the conformationally stable B*27:09 in order to remain structured and to bind peptide. Taken together, our data demonstrate a method to predict tapasin dependence and physiological behavior from the sequence and crystal structure of a particular class I allotype. Also watch the Video Abstract. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In silico, in vitro and in vivo analyses of dipeptidyl peptidase IV inhibitory activity and the antidiabetic effect of sodium caseinate hydrolysate.

PubMed

Hsieh, Cheng-Hong; Wang, Tzu-Yuan; Hung, Chuan-Chuan; Jao, Chia-Ling; Hsieh, You-Liang; Wu, Si-Xian; Hsu, Kuo-Chiang

2016-02-01

The frequency (A), a novel in silico parameter, was developed by calculating the ratio of the number of truncated peptides with Xaa-proline and Xaa-alanine to all peptide fragments from a protein hydrolyzed with a specific protease. The highest in vitro DPP-IV inhibitory activity (72.7%) was observed in the hydrolysate of sodium caseinate by bromelain (Cas/BRO), and the constituent proteins of bovine casein also had relatively high A values (0.10-0.17) with BRO hydrolysis. 1CBR (the <1 kDa fraction of Cas/BRO) showed the greatest in vitro DPP-IV inhibitory activity of 77.5% and was used for in vivo test by high-fat diet-fed and low-dose streptozotocin-induced diabetic rats. The daily administration of 1CBR for 6 weeks was effective to improve glycaemic control in diabetic rats. The results indicate that the novel in silico method has the potential as a screening tool to predict dietary proteins to generate DPP-IV inhibitory and antidiabetic peptides.

Computational study of the free energy landscape of the miniprotein CLN025 in explicit and implicit solvent.

PubMed

Rodriguez, Alex; Mokoema, Pol; Corcho, Francesc; Bisetty, Khrisna; Perez, Juan J

2011-02-17

The prediction capabilities of atomistic simulations of peptides are hampered by different difficulties, including the reliability of force fields, the treatment of the solvent or the adequate sampling of the conformational space. In this work, we have studied the conformational profile of the 10 residue miniprotein CLN025 known to exhibit a β-hairpin in its native state to understand the limitations of implicit methods to describe solvent effects and how these may be compensated by using different force fields. For this purpose, we carried out a thorough sampling of the conformational space of CLN025 in explicit solvent using the replica exchange molecular dynamics method as a sampling technique and compared the results with simulations of the system modeled using the analytical linearized Poisson-Boltzmann (ALPB) method with three different AMBER force fields: parm94, parm96, and parm99SB. The results show the peptide to exhibit a funnel-like free energy landscape with two minima in explicit solvent. In contrast, the higher minimum nearly disappears from the energy surface when the system is studied with an implicit representation of the solvent. Moreover, the different force fields used in combination with the ALPB method do not describe the system in the same manner. The results of this work suggest that the balance between intra- and intermolecular interactions is the cause of the differences between implicit and explicit solvent simulations in this system, stressing the role of the environment to define properly the conformational profile of a peptide in solution.

Computer-assisted prediction of HLA-DR binding and experimental analysis for human promiscuous Th1-cell peptides in the 24 kDa secreted lipoprotein (LppX) of Mycobacterium tuberculosis.

PubMed

Al-Attiyah, R; Mustafa, A S

2004-01-01

The secreted 24 kDa lipoprotein (LppX) is an antigen that is specific for Mycobacterium tuberculosis complex and M. leprae. The present study was carried out to identify the promiscuous T helper 1 (Th1)-cell epitopes of the M. tuberculosis LppX (MT24, Rv2945c) antigen by using 15 overlapping synthetic peptides (25 mers overlapping by 10 residues) covering the sequence of the complete protein. The analysis of Rv2945c sequence for binding to 51 alleles of nine serologically defined HLA-DR molecules, by using a virtual matrix-based prediction program (propred), showed that eight of the 15 peptides of Rv2945c were predicted to bind promiscuously to >/=10 alleles from more than or equal to three serologically defined HLA-DR molecules. The Th1-cell reactivity of all the peptides was assessed in antigen-induced proliferation and interferon-gamma (IFN-gamma)-secretion assays with peripheral blood mononuclear cells (PBMCs) from 37 bacille Calmette-Guérin (BCG)-vaccinated healthy subjects. The results showed that 17 of the 37 donors, which represented an HLA-DR-heterogeneous group, responded to one or more peptides of Rv2945c in the Th1-cell assays. Although each peptide stimulated PBMCs from one or more donors in the above assays, the best positive responses (12/17 (71%) responders) were observed with the peptide p14 (aa 196-220). This suggested a highly promiscuous presentation of p14 to Th1 cells. In addition, the sequence of p14 is completely identical among the LppX of M. tuberculosis, M. bovis and M. leprae, which further supports the usefulness of Rv2945c and p14 in the subunit vaccine design against both tuberculosis and leprosy.

N-terminal pro-brain natriuretic peptide and high-sensitivity troponin in the evaluation of acute chest pain of uncertain etiology. A PITAGORAS substudy.

PubMed

Sanchis, Juan; Bardají, Alfredo; Bosch, Xavier; Loma-Osorio, Pablo; Marín, Francisco; Sánchez, Pedro L; Calvo, Francisco; Avanzas, Pablo; Hernández, Carolina; Serrano, Silvia; Carratalá, Arturo; Barrabés, José A

2013-07-01

High-sensitivity troponin assays have improved the diagnosis of acute coronary syndrome in patients presenting with chest pain and normal troponin levels as measured by conventional assays. Our aim was to investigate whether N-terminal pro-brain natriuretic peptide provides additional information to troponin determination in these patients. A total of 398 patients, included in the PITAGORAS study, presenting to the emergency department with chest pain and normal troponin levels as measured by conventional assay in 2 serial samples (on arrival and 6 h to 8h later) were studied. The samples were also analyzed in a central laboratory for high-sensitivity troponin T (both samples) and for N-terminal pro-brain natriuretic peptide (second sample). The endpoints were diagnosis of acute coronary syndrome and the composite endpoint of in-hospital revascularization or a 30-day cardiac event. Acute coronary syndrome was adjudicated to 79 patients (20%) and the composite endpoint to 59 (15%). When the N-terminal pro-brain natriuretic peptide quartile increased, the diagnosis of acute coronary syndrome also increased (12%, 16%, 23% and 29%; P=.01), as did the risk of the composite endpoint (6%, 13%, 16% and 24%; P=.004). N-terminal pro-brain natriuretic peptide elevation (>125ng/L) was associated with both endpoints (relative risk= 2.0; 95% confidence interval, 1.2-3.3; P=.02; relative risk=2.4; 95% confidence interval, 1.4-4.2; P=.004). However, in the multivariable models adjusted by clinical and electrocardiographic data, a predictive value was found for high-sensitivity T troponin but not for N-terminal pro-brain natriuretic peptide. In low-risk patients with chest pain of uncertain etiology evaluated using high-sensitivity T troponin, N-terminal pro-brain natriuretic peptide does not contribute additional predictive value to diagnosis or the prediction of short-term outcomes. Copyright © 2012 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Quantitative PET Imaging with Novel HER3 Targeted Peptides Selected by Phage Display to Predict Androgen Independent Prostate Cancer Progression

DTIC Science & Technology

2017-08-01

9 4 1. Introduction The subject of this research is the design and testing of a PET imaging agent for the detection and...AWARD NUMBER: W81XWH-16-1-0447 TITLE: Quantitative PET Imaging with Novel HER3-Targeted Peptides Selected by Phage Display to Predict Androgen...MA 02114 REPORT DATE: August 2017 TYPE OF REPORT: Annual PREPARED FOR: U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland

Proven in vitro evolution of protease cathepsin E-inhibitors and -activators at pH 4.5 using a paired peptide method.

PubMed

Kitamura, Koichiro; Komatsu, Masayuki; Biyani, Madhu; Futakami, Masae; Kawakubo, Tomoyo; Yamamoto, Kenji; Nishigaki, Koichi

2012-12-01

Improving a particular function of molecules is often more difficult than identifying such molecules ab initio. Here, a method to acquire higher affinity and/or more functional peptides was developed as a progressive library selection method. The primary library selection products were utilized to build a secondary library composed of blocks of 4 amino acids, of which selection led to peptides with increased activity. These peptides were further converted to randomly generate paired peptides. Cathepsin E-inhibitors thus obtained exhibited the highest activities and affinities (pM order). This was also the case with cathepsin E-activating peptides, proving the methodological effectiveness. The primary, secondary, and tertiary library selections can be regarded as module-finding, module-shuffling, and module-pairing, respectively, which resembles the progression of the natural evolution of proteins. The mode of peptide binding to their target proteins is discussed in analogy to antibodies and epitopes of an antigen. Copyright © 2012 European Peptide Society and John Wiley & Sons, Ltd.

Characterization of angiotensin-I converting enzyme inhibiting peptide from Venerupis philippinarum with nano-liquid chromatography in combination with orbitrap mass spectrum detection and molecular docking

NASA Astrophysics Data System (ADS)

Shi, Lei; Wu, Tizhi; Sheng, Naijuan; Yang, Li; Wang, Qian; Liu, Rui; Wu, Hao

2017-06-01

The complexity and diversity of peptide mixture from protein hydrolysates make their characterization difficult. In this study, a method combining nano LC-MS/MS with molecular docking was applied to identifying and characterizing a peptide with angiotensin-I converting enzyme (ACE-I) inhibiting activity from Venerupis philippinarum hydrolysate. Firstly, ethanol supernatant of V. philippinarum hydrolysate was separated into active fractions with chromatographic methods such as ion-exchange chromatography and high performance liquid chromatography in combination. Then seven peptides from active fraction were identified according to the searching result of the MS/MS spectra against protein databases. Peptides were synthesized and subjected to ACE-I-inhibition assay. The peptide NTLTLIDTGIGMTK showed the highest potency with an IC50 of 5.75 μmol L-1. The molecular docking analysis showed that the ACE-I inhibiting peptide NTLTLIDTGIGMTK bond with residues Glu123, Glu403, Arg522, Glu376, Gln281 and Asn285 of ACE-I. Therefore, active peptides could be identified with the present method rather than the traditional purification and identification strategies. It may also be feasible to identify other food-derived peptides which target other enzymes and receptors with the method developed in this study.

Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data.

PubMed

Nielsen, Morten; Connelley, Tim; Ternette, Nicola

2018-01-05

Peptide binding to MHC class I molecules is the single most selective step in antigen presentation and the strongest single correlate to peptide cellular immunogenicity. The cost of experimentally characterizing the rules of peptide presentation for a given MHC-I molecule is extensive, and predictors of peptide-MHC interactions constitute an attractive alternative. Recently, an increasing amount of MHC presented peptides identified by mass spectrometry (MS ligands) has been published. Handling and interpretation of MS ligand data is, in general, challenging due to the polyspecificity nature of the data. We here outline a general pipeline for dealing with this challenge and accurately annotate ligands to the relevant MHC-I molecule they were eluted from by use of GibbsClustering and binding motif information inferred from in silico models. We illustrate the approach here in the context of MHC-I molecules (BoLA) of cattle. Next, we demonstrate how such annotated BoLA MS ligand data can readily be integrated with in vitro binding affinity data in a prediction model with very high and unprecedented performance for identification of BoLA-I restricted T-cell epitopes. The prediction model is freely available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan . The approach has here been applied to the BoLA-I system, but the pipeline is readily applicable to MHC systems in other species.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain.

PubMed

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or "PB/LIE" free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α 2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo . The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

PubMed Central

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J.; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future. PMID:29018806

Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens.

PubMed

Panni, Simona; Montecchi-Palazzi, Luisa; Kiemer, Lars; Cabibbo, Andrea; Paoluzi, Serena; Santonico, Elena; Landgraf, Christiane; Volkmer-Engert, Rudolf; Bachi, Angela; Castagnoli, Luisa; Cesareni, Gianni

2011-01-01

Large-scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co-purification of non-specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14-3-3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14-3-3 phospho-peptide partners. Notably, a proline next to the carboxy side of the phospho-amino acid functions as a potent inhibitor of 14-3-3 binding. The position-specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these "peptide features" with "protein features", such as protein co-expression and co-localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14-3-3 peptide target on the partner proteins. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Plasmatic levels of N-terminal pro-atrial natriuretic peptide in preeclamptic patients and healthy normotensive pregnant women.

PubMed

Reyna-Villasmil, Eduardo; Mejia-Montilla, Jorly; Reyna-Villasmil, Nadia; Mayner-Tresol, Gabriel; Herrera-Moya, Pedro; Fernández-Ramírez, Andreina; Rondón-Tapía, Marta

2018-05-11

To compare plasma N-terminal pro-atrial natriuretic peptide concentrations in preeclamptic patients and healthy normotensive pregnant women. A cases-controls study was done with 180 patients at Hospital Central Dr. Urquinaona, Maracaibo, Venezuela, that included 90 preeclamptic patients (group A; cases) and 90 healthy normotensive pregnant women selected with the same age and body mass index similar to group A (group B; controls). Blood samples were collected one hour after admission and prior to administration of any medication in group A to determine plasma N-terminal pro-atrial natriuretic peptide and other laboratory parameters. Plasma N-terminal pro-atrial natriuretic peptide concentrations in group A (mean 1.01 [0.26] pg/mL) showed a significant difference when compared with patients in group B (mean 0.55 [0.07] pg/mL; P<.001]. There was no significant correlation with systolic and diastolic blood pressure values in preeclamptic patients (P=ns). A cut-off value of 0.66ng/mL had an area under the curve of 0.93, sensitivity of 87.8%, specificity of 83.3%, a positive predictive value of 84.0% and a negative predictive value of 87.2%, with a diagnostic accuracy of 85.6%. Preeclamptic patients have significantly higher concentrations of plasma N-terminal pro-atrial natriuretic peptide compared with healthy normotensive pregnant women, with high predictive values for diagnosis. Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

Synthesis and screening of one-bead-one-compound cyclic peptide libraries.

PubMed

Qian, Ziqing; Upadhyaya, Punit; Pei, Dehua

2015-01-01

Cyclic peptides have been a rich source of biologically active molecules. Herein we present a method for the combinatorial synthesis and screening of large one-bead-one-compound (OBOC) libraries of cyclic peptides against biological targets such as proteins. Up to ten million different cyclic peptides are rapidly synthesized on TentaGel microbeads by the split-and-pool synthesis method and subjected to a multistage screening protocol which includes magnetic sorting, on-bead enzyme-linked and fluorescence-based assays, and in-solution binding analysis of cyclic peptides selectively released from single beads by fluorescence anisotropy. Finally, the most active hit(s) is identified by the partial Edman degradation-mass spectrometry (PED-MS) method. This method allows a single researcher to synthesize and screen up to ten million cyclic peptides and identify the most active ligand(s) in ~1 month, without the time-consuming and expensive hit resynthesis or the use of any special equipment.

Deriving Heterospecific Self-Assembling Protein-Protein Interactions Using a Computational Interactome Screen.

PubMed

Crooks, Richard O; Baxter, Daniel; Panek, Anna S; Lubben, Anneke T; Mason, Jody M

2016-01-29

Interactions between naturally occurring proteins are highly specific, with protein-network imbalances associated with numerous diseases. For designed protein-protein interactions (PPIs), required specificity can be notoriously difficult to engineer. To accelerate this process, we have derived peptides that form heterospecific PPIs when combined. This is achieved using software that generates large virtual libraries of peptide sequences and searches within the resulting interactome for preferentially interacting peptides. To demonstrate feasibility, we have (i) generated 1536 peptide sequences based on the parallel dimeric coiled-coil motif and varied residues known to be important for stability and specificity, (ii) screened the 1,180,416 member interactome for predicted Tm values and (iii) used predicted Tm cutoff points to isolate eight peptides that form four heterospecific PPIs when combined. This required that all 32 hypothetical off-target interactions within the eight-peptide interactome be disfavoured and that the four desired interactions pair correctly. Lastly, we have verified the approach by characterising all 36 pairs within the interactome. In analysing the output, we hypothesised that several sequences are capable of adopting antiparallel orientations. We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings. Our approach can be used to derive increasingly large and therefore complex sets of heterospecific PPIs with a wide range of potential downstream applications from disease modulation to the design of biomaterials and peptides in synthetic biology. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Detection of Peptide-based nanoparticles in blood plasma by ELISA.

PubMed

Bode, Gerard H; Pickl, Karin E; Sanchez-Purrà, Maria; Albaiges, Berta; Borrós, Salvador; Pötgens, Andy J G; Schmitz, Christoph; Sinner, Frank M; Losen, Mario; Steinbusch, Harry W M; Frank, Hans-Georg; Martinez-Martinez, Pilar

2015-01-01

The aim of the current study was to develop a method to detect peptide-linked nanoparticles in blood plasma. A convenient enzyme linked immunosorbent assay (ELISA) was developed for the detection of peptides functionalized with biotin and fluorescein groups. As a proof of principle, polymerized pentafluorophenyl methacrylate nanoparticles linked to biotin-carboxyfluorescein labeled peptides were intravenously injected in Wistar rats. Serial blood plasma samples were analyzed by ELISA and by liquid chromatography mass spectrometry (LC/MS) technology. The ELISA based method for the detection of FITC labeled peptides had a detection limit of 1 ng/mL. We were able to accurately measure peptides bound to pentafluorophenyl methacrylate nanoparticles in blood plasma of rats, and similar results were obtained by LC/MS. We detected FITC-labeled peptides on pentafluorophenyl methacrylate nanoparticles after injection in vivo. This method can be extended to detect nanoparticles with different chemical compositions.

Identification of novel HLA-A(*)0201-restricted CTL epitopes from Pokemon.

PubMed

Yuan, Bangqing; Zhao, Lin; Xian, Ronghua; Zhao, Gang

2012-01-01

Pokemon is a member of the POK family of transcriptional repressors and aberrant overexpressed in various human cancers. Therefore, the related peptide epitopes derived from Pokemon is essential for the development of specific immunotherapy of malignant tumors. In this study, we predicted and identified HLA-A(*)0201-restricted cytotoxic T lymphocyte (CTL) epitopes derived from Pokemon with computer-based epitope prediction, peptide-binding assay and testing of the induced CTLs toward different kinds of carcinoma cells. The results demonstrated that effectors induced by peptides of Pokemon containing residues 32-40, 61-69, 87-95, and 319-327 could specifically secrete IFN-γ and lyse tumor cell lines of Pokemon-positive and HLA-A2-matched. The results suggest that Pokemon32, Pokemon61, Pokemon87, and Pokemon319 peptides are novel HLA-A(*)0201-restricted restricted CTL epitopes, and could be utilized in the cancer immunotherapy against a broad spectrum of tumors. Copyright © 2012 Elsevier Inc. All rights reserved.

Exploring high-affinity binding properties of octamer peptides by principal component analysis of tetramer peptides.

PubMed

Kume, Akiko; Kawai, Shun; Kato, Ryuji; Iwata, Shinmei; Shimizu, Kazunori; Honda, Hiroyuki

2017-02-01

To investigate the binding properties of a peptide sequence, we conducted principal component analysis (PCA) of the physicochemical features of a tetramer peptide library comprised of 512 peptides, and the variables were reduced to two principal components. We selected IL-2 and IgG as model proteins and the binding affinity to these proteins was assayed using the 512 peptides mentioned above. PCA of binding affinity data showed that 16 and 18 variables were suitable for localizing IL-2 and IgG high-affinity binding peptides, respectively, into a restricted region of the PCA plot. We then investigated whether the binding affinity of octamer peptide libraries could be predicted using the identified region in the tetramer PCA. The results show that octamer high-affinity binding peptides were also concentrated in the tetramer high-affinity binding region of both IL-2 and IgG. The average fluorescence intensity of high-affinity binding peptides was 3.3- and 2.1-fold higher than that of low-affinity binding peptides for IL-2 and IgG, respectively. We conclude that PCA may be used to identify octamer peptides with high- or low-affinity binding properties from data from a tetramer peptide library. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Superior Antifouling Performance of a Zwitterionic Peptide Compared to an Amphiphilic, Non-Ionic Peptide.

PubMed

Ye, Huijun; Wang, Libing; Huang, Renliang; Su, Rongxin; Liu, Boshi; Qi, Wei; He, Zhimin

2015-10-14

The aim of this study was to explore the influence of amphiphilic and zwitterionic structures on the resistance of protein adsorption to peptide self-assembled monolayers (SAMs) and gain insight into the associated antifouling mechanism. Two kinds of cysteine-terminated heptapeptides were studied. One peptide had alternating hydrophobic and hydrophilic residues with an amphiphilic sequence of CYSYSYS. The other peptide (CRERERE) was zwitterionic. Both peptides were covalently attached onto gold substrates via gold-thiol bond formation. Surface plasmon resonance analysis results showed that both peptide SAMs had ultralow or low protein adsorption amounts of 1.97-11.78 ng/cm2 in the presence of single proteins. The zwitterionic peptide showed relatively higher antifouling ability with single proteins and natural complex protein media. We performed molecular dynamics simulations to understand their respective antifouling behaviors. The results indicated that strong surface hydration of peptide SAMs contributes to fouling resistance by impeding interactions with proteins. Compared to the CYSYSYS peptide, more water molecules were predicted to form hydrogen-bonding interactions with the zwitterionic CRERERE peptide, which is in agreement with the antifouling test results. These findings reveal a clear relation between peptide structures and resistance to protein adsorption, facilitating the development of novel peptide-containing antifouling materials.

Helicity of short E-R/K peptides.

PubMed

Sommese, Ruth F; Sivaramakrishnan, Sivaraj; Baldwin, Robert L; Spudich, James A

2010-10-01

Understanding the secondary structure of peptides is important in protein folding, enzyme function, and peptide-based drug design. Previous studies of synthetic Ala-based peptides (>12 a.a.) have demonstrated the role for charged side chain interactions involving Glu/Lys or Glu/Arg spaced three (i, i + 3) or four (i, i + 4) residues apart. The secondary structure of short peptides (<9 a.a.), however, has not been investigated. In this study, the effect of repetitive Glu/Lys or Glu/Arg side chain interactions, giving rise to E-R/K helices, on the helicity of short peptides was examined using circular dichroism. Short E-R/K-based peptides show significant helix content. Peptides containing one or more E-R interactions display greater helicity than those with similar E-K interactions. Significant helicity is achieved in Arg-based E-R/K peptides eight, six, and five amino acids long. In these short peptides, each additional i + 3 and i + 4 salt bridge has substantial contribution to fractional helix content. The E-R/K peptides exhibit a strongly linear melt curve indicative of noncooperative folding. The significant helicity of these short peptides with predictable dependence on number, position, and type of side chain interactions makes them an important consideration in peptide design.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Basis for substrate recognition and distinction by matrix metalloproteinases

PubMed Central

Ratnikov, Boris I.; Cieplak, Piotr; Gramatikoff, Kosi; Pierce, James; Eroshkin, Alexey; Igarashi, Yoshinobu; Kazanov, Marat; Sun, Qing; Godzik, Adam; Osterman, Andrei; Stec, Boguslaw; Strongin, Alex; Smith, Jeffrey W.

2014-01-01

Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure–function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221–227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure–function relationships among three phylogenetic branches of the matrix metalloproteinase (MMP) family by comparing their cleavage efficiencies toward an extended set of phage peptide substrates that were selected from ∼64 million peptide sequences (i.e., a large unbiased representation of substrate space). The observed second-order rate constants [k(obs)] across the substrate space provide a distance measure of functional similarity among the MMPs. These functional distances directly correlate with MMP phylogenetic distance. There is also a remarkable and near-perfect correlation between the MMP substrate preference and sequence identity of 50–57 discontinuous residues surrounding the catalytic groove. We conclude that these residues represent the specificity-determining positions (SDPs) that allowed for the expansion of MMP proteolytic function during evolution. A transmutation of only a few selected SDPs proximal to the bound substrate peptide, and contributing the most to selectivity among the MMPs, is sufficient to enact a global change in the substrate preference of one MMP to that of another, indicating the potential for the rational and focused redesign of cleavage specificity in MMPs. PMID:25246591

Selectivity of peptide bond dissociation on excitation of a core electron: Effects of a phenyl group

NASA Astrophysics Data System (ADS)

Tsai, Cheng-Cheng; Chen, Jien-Lian; Hu, Wei-Ping; Lin, Yi-Shiue; Lin, Huei-Ru; Lee, Tsai-Yun; Lee, Yuan T.; Ni, Chi-Kung; Liu, Chen-Lin

2016-09-01

The selective dissociation of a peptide bond upon excitation of a core electron in acetanilide and N-benzylacetamide was investigated. The total-ion-yield near-edge X-ray absorption fine structure spectra were recorded and compared with the predictions from time-dependent density functional theory. The branching ratios for the dissociation of a peptide bond are observed as 16-34% which is quite significant. This study explores the core-excitation, the X-ray photodissociation pathways, and the theoretical explanation of the NEXAFS spectra of organic molecules containing both a peptide bond and a phenyl group.

The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right.

PubMed

Audie, J; Boyd, C

2010-01-01

The case for peptide-based drugs is compelling. Due to their chemical, physical and conformational diversity, and relatively unproblematic toxicity and immunogenicity, peptides represent excellent starting material for drug discovery. Nature has solved many physiological and pharmacological problems through the use of peptides, polypeptides and proteins. If nature could solve such a diversity of challenging biological problems through the use of peptides, it seems reasonable to infer that human ingenuity will prove even more successful. And this, indeed, appears to be the case, as a number of scientific and methodological advances are making peptides and peptide-based compounds ever more promising pharmacological agents. Chief among these advances are powerful chemical and biological screening technologies for lead identification and optimization, methods for enhancing peptide in vivo stability, bioavailability and cell-permeability, and new delivery technologies. Other advances include the development and experimental validation of robust computational methods for peptide lead identification and optimization. Finally, scientific analysis, biology and chemistry indicate the prospect of designing relatively small peptides to therapeutically modulate so-called 'undruggable' protein-protein interactions. Taken together a clear picture is emerging: through the synergistic use of the scientific imagination and the computational, chemical and biological methods that are currently available, effective peptide therapeutics for novel targets can be designed that surpass even the proven peptidic designs of nature.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Camarero, J A; Hackel, B J; de Yoreo, J J

C-terminal peptide thioesters are key intermediates for the synthesis/semisynthesis of proteins and for the production of cyclic peptides by native chemical ligation. They can be synthetically prepared by solid-phase peptide synthesis (SPPS) methods or biosynthetically by protein splicing techniques. Until recently, the chemical synthesis of C-terminal a-thioester peptides by SPPS was largely restricted to the Boc/Benzyl methodology because of the poor stability of the thioester bond to the basic conditions employed for the deprotection of the N{sup {alpha}}-Fmoc group. In the present work, we describe a new method for the SPPS of C-terminal thioesters by Fmoc/t-Bu chemistry. This method ismore » based on the use of an aryl hydrazide linker, which is totally stable to the Fmoc-SPPS conditions. Once the peptide synthesis has been completed, activation of the linker can be achieved by mild oxidation. This step transforms the hydrazide group into a highly reactive diazene intermediate which can react with different H-AA-SEt to yield the corresponding {alpha}-thioester peptide in good yields. This method has been successfully used for the generation of different thioester peptides, circular peptides and a fully functional SH3 protein domain.« less

Synthesis of peptide .alpha.-thioesters

DOEpatents

Camarero, Julio A [Livermore, CA; Mitchell, Alexander R [Livermore, CA; De Yoreo, James J [Clayton, CA

2008-08-19

Disclosed herein is a new method for the solid phase peptide synthesis (SPPS) of C-terminal peptide .alpha. thioesters using Fmoc/t-Bu chemistry. This method is based on the use of an aryl hydrazine linker, which is totally stable to conditions required for Fmoc-SPPS. When the peptide synthesis has been completed, activation of the linker is achieved by mild oxidation. The oxidation step converts the acyl-hydrazine group into a highly reactive acyl-diazene intermediate which reacts with an .alpha.-amino acid alkylthioester (H-AA-SR) to yield the corresponding peptide .alpha.-thioester in good yield. A variety of peptide thioesters, cyclic peptides and a fully functional Src homology 3 (SH3) protein domain have been successfully prepared.

Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

DOEpatents

Callstrom, Matthew R.; Bednarski, Mark D.; Gruber, Patrick R.

1997-01-01

A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines.

Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

DOEpatents

Callstrom, M.R.; Bednarski, M.D.; Gruber, P.R.

1997-06-17

A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines. 19 figs.

A virtual screening method for inhibitory peptides of Angiotensin I-converting enzyme.

PubMed

Wu, Hongxi; Liu, Yalan; Guo, Mingrong; Xie, Jingli; Jiang, XiaMin

2014-09-01

Natural small peptides from foods have been proven to be efficient inhibitors of Angiotensin I-converting enzyme (ACE) for the regulation of blood pressure. The traditional ACE inhibitory peptides screening method is both time consuming and money costing, to the contrary, virtual screening method by computation can break these limitations. We establish a virtual screening method to obtain ACE inhibitory peptides with the help of Libdock module of Discovery Studio 3.5 software. A significant relationship between Libdock score and experimental IC(50) was found, Libdock score = 10.063 log(1/IC(50)) + 68.08 (R(2) = 0.62). The credibility of the relationship was confirmed by testing the coincidence of the estimated log(1/IC(50)) and measured log(1/IC(50)) (IC(50) is 50% inhibitory concentration toward ACE, in μmol/L) of 5 synthetic ACE inhibitory peptides, which was virtual hydrolyzed and screened from a kind of seafood, Phascolosoma esculenta. Accordingly, Libdock method is a valid IC(50) estimation tool and virtual screening method for small ACE inhibitory peptides. © 2014 Institute of Food Technologists®

Multiple loop conformations of peptides predicted by molecular dynamics simulations are compatible with nuclear magnetic resonance.

PubMed

Carstens, Heiko; Renner, Christian; Milbradt, Alexander G; Moroder, Luis; Tavan, Paul

2005-03-29

The affinity and selectivity of protein-protein interactions can be fine-tuned by varying the size, flexibility, and amino acid composition of involved surface loops. As a model for such surface loops, we study the conformational landscape of an octapeptide, whose flexibility is chemically steered by a covalent ring closure integrating an azobenzene dye into and by a disulfide bridge additionally constraining the peptide backbone. Because the covalently integrated azobenzene dyes can be switched by light between a bent cis state and an elongated trans state, six cyclic peptide models of strongly different flexibilities are obtained. The conformational states of these peptide models are sampled by NMR and by unconstrained molecular dynamics (MD) simulations. Prototypical conformations and the free-energy landscapes in the high-dimensional space spanned by the phi/psi angles at the peptide backbone are obtained by clustering techniques from the MD trajectories. Multiple open-loop conformations are shown to be predicted by MD particularly in the very flexible cases and are shown to comply with the NMR data despite the fact that such open-loop conformations are missing in the refined NMR structures.

Side-chain conformational space analysis (SCSA): A multi conformation-based QSAR approach for modeling and prediction of protein-peptide binding affinities

NASA Astrophysics Data System (ADS)

Zhou, Peng; Chen, Xiang; Shang, Zhicai

2009-03-01

In this article, the concept of multi conformation-based quantitative structure-activity relationship (MCB-QSAR) is proposed, and based upon that, we describe a new approach called the side-chain conformational space analysis (SCSA) to model and predict protein-peptide binding affinities. In SCSA, multi-conformations (rather than traditional single-conformation) have received much attention, and the statistical average information on multi-conformations of side chains is determined using self-consistent mean field theory based upon side chain rotamer library. Thereby, enthalpy contributions (including electrostatic, steric, hydrophobic interaction and hydrogen bond) and conformational entropy effects to the binding are investigated in terms of occurrence probability of residue rotamers. Then, SCSA was applied into the dataset of 419 HLA-A*0201 binding peptides, and nonbonding contributions of each position in peptide ligands are well determined. For the peptides, the hydrogen bond and electrostatic interactions of the two ends are essential to the binding specificity, van der Waals and hydrophobic interactions of all the positions ensure strong binding affinity, and the loss of conformational entropy at anchor positions partially counteracts other favorable nonbonding effects.

Peptide selection and antibody generation for the prospective immunorecognition of Cry1Ab16 protein of transgenic maize.

PubMed

Costa, Joana; Marani, Mariela M; Grazina, Liliana; Villa, Caterina; Meira, Liliana; Oliveira, M Beatriz P P; Leite, José R S A; Mafra, Isabel

2017-09-15

The introduction of genes isolated from different Bacillus thuringiensis strains to express Cry-type toxins in transgenic crops is a common strategy to confer insect resistance traits. This work intended to extensively in silico analyse Cry1A(b)16 protein for the identification of peptide markers for the biorecognition of transgenic crops. By combining two different strategies based on several bioinformatic tools for linear epitope prediction, a set of seven peptides was successfully selected as potential Cry1A(b)16 immunogens. For the prediction of conformational epitopes, Cry1A(b)16 models were built on the basis of three independent templates of homologue proteins of Cry1A(a) and Cry1A(c) using an integrated approach. PcH_736-746 and PcH_876-886 peptides were selected as the best candidates, being synthesised and used for the production of polyclonal antibodies. To the best of our knowledge, this is the first attempt of selecting and defining linear peptides as immunogenic markers of Cry1A(b)-type toxins in transgenic maize. Copyright © 2017 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hallen, Heather E.; Walton, Jonathan D.; Luo, Hong

The present invention relates to compositions and methods comprising genes and peptides associated with cyclic peptide toxins and toxin production in mushrooms. In particular, the present invention relates to using genes and proteins from Amanita species encoding Amanita peptides, specifically relating to amatoxins and phallotoxins. In a preferred embodiment, the present invention also relates to methods for detecting Amanita peptide toxin genes for identifying Amanita peptide-producing mushrooms and for diagnosing suspected cases of mushroom poisoning. Further, the present inventions relate to providing kits for diagnosing and monitoring suspected cases of mushroom poisoning in patients.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics.

PubMed

Gul, Ahmet; Erman, Burak

2018-01-16

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Ultrafast spectroscopy reveals subnanosecond peptide conformational dynamics and validates molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Spörlein, Sebastian; Carstens, Heiko; Satzger, Helmut; Renner, Christian; Behrendt, Raymond; Moroder, Luis; Tavan, Paul; Zinth, Wolfgang; Wachtveitl, Josef

2002-06-01

Femtosecond time-resolved spectroscopy on model peptides with built-in light switches combined with computer simulation of light-triggered motions offers an attractive integrated approach toward the understanding of peptide conformational dynamics. It was applied to monitor the light-induced relaxation dynamics occurring on subnanosecond time scales in a peptide that was backbone-cyclized with an azobenzene derivative as optical switch and spectroscopic probe. The femtosecond spectra permit the clear distinguishing and characterization of the subpicosecond photoisomerization of the chromophore, the subsequent dissipation of vibrational energy, and the subnanosecond conformational relaxation of the peptide. The photochemical cis/trans-isomerization of the chromophore and the resulting peptide relaxations have been simulated with molecular dynamics calculations. The calculated reaction kinetics, as monitored by the energy content of the peptide, were found to match the spectroscopic data. Thus we verify that all-atom molecular dynamics simulations can quantitatively describe the subnanosecond conformational dynamics of peptides, strengthening confidence in corresponding predictions for longer time scales.

Binding stability of peptides on major histocompatibility complex class I proteins: role of entropy and dynamics

NASA Astrophysics Data System (ADS)

Gul, Ahmet; Erman, Burak

2018-03-01

Prediction of peptide binding on specific human leukocyte antigens (HLA) has long been studied with successful results. We herein describe the effects of entropy and dynamics by investigating the binding stabilities of 10 nanopeptides on various HLA Class I alleles using a theoretical model based on molecular dynamics simulations. The fluctuational entropies of the peptides are estimated over a temperature range of 310-460 K. The estimated entropies correlate well with experimental binding affinities of the peptides: peptides that have higher binding affinities have lower entropies compared to non-binders, which have significantly larger entropies. The computation of the entropies is based on a simple model that requires short molecular dynamics trajectories and allows for approximate but rapid determination. The paper draws attention to the long neglected dynamic aspects of peptide binding, and provides a fast computation scheme that allows for rapid scanning of large numbers of peptides on selected HLA antigens, which may be useful in defining the right peptides for personal immunotherapy.

Using a combined computational-experimental approach to predict antibody-specific B cell epitopes.

PubMed

Sela-Culang, Inbal; Benhnia, Mohammed Rafii-El-Idrissi; Matho, Michael H; Kaever, Thomas; Maybeno, Matt; Schlossman, Andrew; Nimrod, Guy; Li, Sheng; Xiang, Yan; Zajonc, Dirk; Crotty, Shane; Ofran, Yanay; Peters, Bjoern

2014-04-08

Antibody epitope mapping is crucial for understanding B cell-mediated immunity and required for characterizing therapeutic antibodies. In contrast to T cell epitope mapping, no computational tools are in widespread use for prediction of B cell epitopes. Here, we show that, utilizing the sequence of an antibody, it is possible to identify discontinuous epitopes on its cognate antigen. The predictions are based on residue-pairing preferences and other interface characteristics. We combined these antibody-specific predictions with results of cross-blocking experiments that identify groups of antibodies with overlapping epitopes to improve the predictions. We validate the high performance of this approach by mapping the epitopes of a set of antibodies against the previously uncharacterized D8 antigen, using complementary techniques to reduce method-specific biases (X-ray crystallography, peptide ELISA, deuterium exchange, and site-directed mutagenesis). These results suggest that antibody-specific computational predictions and simple cross-blocking experiments allow for accurate prediction of residues in conformational B cell epitopes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Peptide/protein-polymer conjugates: synthetic strategies and design concepts.

PubMed

Gauthier, Marc A; Klok, Harm-Anton

2008-06-21

This feature article provides a compilation of tools available for preparing well-defined peptide/protein-polymer conjugates, which are defined as hybrid constructs combining (i) a defined number of peptide/protein segments with uniform chain lengths and defined monomer sequences (primary structure) with (ii) a defined number of synthetic polymer chains. The first section describes methods for post-translational, or direct, introduction of chemoselective handles onto natural or synthetic peptides/proteins. Addressed topics include the residue- and/or site-specific modification of peptides/proteins at Arg, Asp, Cys, Gln, Glu, Gly, His, Lys, Met, Phe, Ser, Thr, Trp, Tyr and Val residues and methods for producing peptides/proteins containing non-canonical amino acids by peptide synthesis and protein engineering. In the second section, methods for introducing chemoselective groups onto the side-chain or chain-end of synthetic polymers produced by radical, anionic, cationic, metathesis and ring-opening polymerization are described. The final section discusses convergent and divergent strategies for covalently assembling polymers and peptides/proteins. An overview of the use of chemoselective reactions such as Heck, Sonogashira and Suzuki coupling, Diels-Alder cycloaddition, Click chemistry, Staudinger ligation, Michael's addition, reductive alkylation and oxime/hydrazone chemistry for the convergent synthesis of peptide/protein-polymer conjugates is given. Divergent approaches for preparing peptide/protein-polymer conjugates which are discussed include peptide synthesis from synthetic polymer supports, polymerization from peptide/protein macroinitiators or chain transfer agents and the polymerization of peptide side-chain monomers.

A Novel Method to Predict Highly Expressed Genes Based on Radius Clustering and Relative Synonymous Codon Usage.

PubMed

Tran, Tuan-Anh; Vo, Nam Tri; Nguyen, Hoang Duc; Pham, Bao The

2015-12-01

Recombinant proteins play an important role in many aspects of life and have generated a huge income, notably in the industrial enzyme business. A gene is introduced into a vector and expressed in a host organism-for example, E. coli-to obtain a high productivity of target protein. However, transferred genes from particular organisms are not usually compatible with the host's expression system because of various reasons, for example, codon usage bias, GC content, repetitive sequences, and secondary structure. The solution is developing programs to optimize for designing a nucleotide sequence whose origin is from peptide sequences using properties of highly expressed genes (HEGs) of the host organism. Existing data of HEGs determined by practical and computer-based methods do not satisfy for qualifying and quantifying. Therefore, the demand for developing a new HEG prediction method is critical. We proposed a new method for predicting HEGs and criteria to evaluate gene optimization. Codon usage bias was weighted by amplifying the difference between HEGs and non-highly expressed genes (non-HEGs). The number of predicted HEGs is 5% of the genome. In comparison with Puigbò's method, the result is twice as good as Puigbò's one, in kernel ratio and kernel sensitivity. Concerning transcription/translation factor proteins (TF), the proposed method gives low TF sensitivity, while Puigbò's method gives moderate one. In summary, the results indicated that the proposed method can be a good optional applying method to predict optimized genes for particular organisms, and we generated an HEG database for further researches in gene design.

A motif detection and classification method for peptide sequences using genetic programming.

PubMed

Tomita, Yasuyuki; Kato, Ryuji; Okochi, Mina; Honda, Hiroyuki

2008-08-01

An exploration of common rules (property motifs) in amino acid sequences has been required for the design of novel sequences and elucidation of the interactions between molecules controlled by the structural or physical environment. In the present study, we developed a new method to search property motifs that are common in peptide sequence data. Our method comprises the following two characteristics: (i) the automatic determination of the position and length of common property motifs by calculating the physicochemical similarity of amino acids, and (ii) the quick and effective exploration of motif candidates that discriminates the positives and negatives by the introduction of genetic programming (GP). Our method was evaluated by two types of model data sets. First, the intentionally buried property motifs were searched in the artificially derived peptide data containing intentionally buried property motifs. As a result, the expected property motifs were correctly extracted by our algorithm. Second, the peptide data that interact with MHC class II molecules were analyzed as one of the models of biologically active peptides with buried motifs in various lengths. Twofold MHC class II binding peptides were identified with the rule using our method, compared to the existing scoring matrix method. In conclusion, our GP based motif searching approach enabled to obtain knowledge of functional aspects of the peptides without any prior knowledge.

Expansion and diversification of the MSDIN family of cyclic peptide genes in the poisonous agarics Amanita phalloides and A. bisporigera

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pulman, Jane A.; Childs, Kevin L.; Sgambelluri, R. Michael

Here, the cyclic peptide toxins of Amanita mushrooms, such as α-amanitin and phalloidin, are encoded by the “MSDIN” gene family and ribosomally biosynthesized. Based on partial genome sequence and PCR analysis, some members of the MSDIN family were previously identified in Amanita bisporigera, and several other members are known from other species of Amanita. However, the complete complement in any one species, and hence the genetic capacity for these fungi to make cyclic peptides, remains unknown. As a result, draft genome sequences of two cyclic peptide-producing mushrooms, the “Death Cap” A. phalloides and the “Destroying Angel” A. bisporigera, were obtained.more » Each species has ~30 MSDIN genes, most of which are predicted to encode unknown cyclic peptides. Some MSDIN genes were duplicated in one or the other species, but only three were common to both species. A gene encoding cycloamanide B, a previously described nontoxic cyclic heptapeptide, was also present in A. phalloides, but genes for antamanide and cycloamanides A, C, and D were not. In A. bisporigera, RNA expression was observed for 20 of the MSDIN family members. Based on their predicted sequences, novel cyclic peptides were searched for by LC/MS/MS in extracts of A. phalloides. The presence of two cyclic peptides, named cycloamanides E and F with structures cyclo(SFFFPVP) and cyclo(IVGILGLP), was thereby demonstrated. Of the MSDIN genes reported earlier from another specimen of A. bisporigera, 9 of 14 were not found in the current genome assembly. Differences between previous and current results for the complement of MSDIN genes and cyclic peptides in the two fungi probably represents natural variation among geographically dispersed isolates of A. phalloides and among the members of the poorly defined A. bisporigera species complex. Both A. phalloides and A. bisporigera contain two prolyl oligopeptidase genes, one of which (POPB) is probably dedicated to cyclic peptide biosynthesis as it is in Galerina marginata. Finally, the MSDIN gene family has expanded and diverged rapidly in Amanita section Phalloideae. Together, A. bisporigera and A. phalloides are predicted to have the capacity to make more than 50 cyclic hexa-, hepta-,octa-, nona- and decapeptides.« less

Expansion and diversification of the MSDIN family of cyclic peptide genes in the poisonous agarics Amanita phalloides and A. bisporigera

DOE PAGES

Pulman, Jane A.; Childs, Kevin L.; Sgambelluri, R. Michael; ...

2016-12-15

Here, the cyclic peptide toxins of Amanita mushrooms, such as α-amanitin and phalloidin, are encoded by the “MSDIN” gene family and ribosomally biosynthesized. Based on partial genome sequence and PCR analysis, some members of the MSDIN family were previously identified in Amanita bisporigera, and several other members are known from other species of Amanita. However, the complete complement in any one species, and hence the genetic capacity for these fungi to make cyclic peptides, remains unknown. As a result, draft genome sequences of two cyclic peptide-producing mushrooms, the “Death Cap” A. phalloides and the “Destroying Angel” A. bisporigera, were obtained.more » Each species has ~30 MSDIN genes, most of which are predicted to encode unknown cyclic peptides. Some MSDIN genes were duplicated in one or the other species, but only three were common to both species. A gene encoding cycloamanide B, a previously described nontoxic cyclic heptapeptide, was also present in A. phalloides, but genes for antamanide and cycloamanides A, C, and D were not. In A. bisporigera, RNA expression was observed for 20 of the MSDIN family members. Based on their predicted sequences, novel cyclic peptides were searched for by LC/MS/MS in extracts of A. phalloides. The presence of two cyclic peptides, named cycloamanides E and F with structures cyclo(SFFFPVP) and cyclo(IVGILGLP), was thereby demonstrated. Of the MSDIN genes reported earlier from another specimen of A. bisporigera, 9 of 14 were not found in the current genome assembly. Differences between previous and current results for the complement of MSDIN genes and cyclic peptides in the two fungi probably represents natural variation among geographically dispersed isolates of A. phalloides and among the members of the poorly defined A. bisporigera species complex. Both A. phalloides and A. bisporigera contain two prolyl oligopeptidase genes, one of which (POPB) is probably dedicated to cyclic peptide biosynthesis as it is in Galerina marginata. Finally, the MSDIN gene family has expanded and diverged rapidly in Amanita section Phalloideae. Together, A. bisporigera and A. phalloides are predicted to have the capacity to make more than 50 cyclic hexa-, hepta-,octa-, nona- and decapeptides.« less

Peptide-Directed PdAu Nanoscale Surface Segregation: Toward Controlled Bimetallic Architecture for Catalytic Materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bedford, Nicholas M.; Showalter, Allison R.; Woehl, Taylor J.

Bimetallic nanoparticles are of immense scientific and technological interest given the synergistic properties observed when mixing two different metallic species at the nanoscale. This is particularly prevalent in catalysis, where bimetallic nanoparticles often exhibit improved catalytic activity and durability over their monometallic counterparts. Yet despite intense research efforts, little is understood regarding how to optimize bimetallic surface composition and structure synthetically using rational design principles. Recently, it has been demonstrated that peptide-enabled routes for nanoparticle synthesis result in materials with sequence-dependent catalytic properties, providing an opportunity for rational design through sequence manipulation. In this study, bimetallic PdAu nanoparticles are synthesizedmore » with a small set of peptides containing known Pd and Au binding motifs. The resulting nanoparticles were extensively characterized using high-resolution scanning transmission electron microscopy, X-ray absorption spectroscopy and high-energy X-ray diffraction coupled to atomic pair distribution function analysis. Structural information obtained from synchrotron radiation methods were then used to generate model nanoparticle configurations using reverse Monte Carlo simulations, which illustrate sequence-dependence in both surface structure and surface composition. Replica exchange solute tempering molecular dynamic simulations were also used to predict the modes of peptide binding on monometallic surfaces, indicating that different sequences bind to the metal interfaces via different mechanisms. As a testbed reaction, electrocatalytic methanol oxidation experiments were performed, wherein differences in catalytic activity are clearly observed in materials with identical bimetallic composition. Finally, taken together, this study indicates that peptides could be used to arrive at bimetallic surfaces with enhanced catalytic properties, which could be leveraged for rational bimetallic nanoparticle design using peptide-enabled approaches.« less

Peptide-Directed PdAu Nanoscale Surface Segregation: Toward Controlled Bimetallic Architecture for Catalytic Materials

DOE PAGES

Bedford, Nicholas M.; Showalter, Allison R.; Woehl, Taylor J.; ...

2016-09-01

Bimetallic nanoparticles are of immense scientific and technological interest given the synergistic properties observed when mixing two different metallic species at the nanoscale. This is particularly prevalent in catalysis, where bimetallic nanoparticles often exhibit improved catalytic activity and durability over their monometallic counterparts. Yet despite intense research efforts, little is understood regarding how to optimize bimetallic surface composition and structure synthetically using rational design principles. Recently, it has been demonstrated that peptide-enabled routes for nanoparticle synthesis result in materials with sequence-dependent catalytic properties, providing an opportunity for rational design through sequence manipulation. In this study, bimetallic PdAu nanoparticles are synthesizedmore » with a small set of peptides containing known Pd and Au binding motifs. The resulting nanoparticles were extensively characterized using high-resolution scanning transmission electron microscopy, X-ray absorption spectroscopy and high-energy X-ray diffraction coupled to atomic pair distribution function analysis. Structural information obtained from synchrotron radiation methods were then used to generate model nanoparticle configurations using reverse Monte Carlo simulations, which illustrate sequence-dependence in both surface structure and surface composition. Replica exchange solute tempering molecular dynamic simulations were also used to predict the modes of peptide binding on monometallic surfaces, indicating that different sequences bind to the metal interfaces via different mechanisms. As a testbed reaction, electrocatalytic methanol oxidation experiments were performed, wherein differences in catalytic activity are clearly observed in materials with identical bimetallic composition. Finally, taken together, this study indicates that peptides could be used to arrive at bimetallic surfaces with enhanced catalytic properties, which could be leveraged for rational bimetallic nanoparticle design using peptide-enabled approaches.« less

Optimization by infusion of multiple reaction monitoring transitions for sensitive quantification of peptides by liquid chromatography/mass spectrometry.

PubMed

Alghanem, Bandar; Nikitin, Frédéric; Stricker, Thomas; Duchoslav, Eva; Luban, Jeremy; Strambio-De-Castillia, Caterina; Muller, Markus; Lisacek, Frédérique; Varesio, Emmanuel; Hopfgartner, Gérard

2017-05-15

In peptide quantification by liquid chromatography/mass spectrometry (LC/MS), the optimization of multiple reaction monitoring (MRM) parameters is essential for sensitive detection. We have compared different approaches to build MRM assays, based either on flow injection analysis (FIA) of isotopically labelled peptides, or on the knowledge and the prediction of the best settings for MRM transitions and collision energies (CE). In this context, we introduce MRMOptimizer, an open-source software tool that processes spectra and assists the user in selecting transitions in the FIA workflow. MS/MS spectral libraries with CE voltages from 10 to 70 V are automatically acquired in FIA mode for isotopically labelled peptides. Then MRMOptimizer determines the optimal MRM settings for each peptide. To assess the quantitative performance of our approach, 155 peptides, representing 84 proteins, were analysed by LC/MRM-MS and the peak areas were compared between: (A) the MRMOptimizer-based workflow, (B1) the SRMAtlas transitions set used 'as-is'; (B2) the same SRMAtlas set with CE parameters optimized by Skyline. 51% of the three most intense transitions per peptide were shown to be common to both A and B1/B2 methods, and displayed similar sensitivity and peak area distributions. The peak areas obtained with MRMOptimizer for transitions sharing either the precursor ion charge state or the fragment ions with the SRMAtlas set at unique transitions were increased 1.8- to 2.3-fold. The gain in sensitivity using MRMOptimizer for transitions with different precursor ion charge state and fragment ions (8% of the total), reaches a ~ 11-fold increase. Isotopically labelled peptides can be used to optimize MRM transitions more efficiently in FIA than by searching databases. The MRMOptimizer software is MS independent and enables the post-acquisition selection of MRM parameters. Coefficients of variation for optimal CE values are lower than those obtained with the SRMAtlas approach (B2) and one additional peptide was detected. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Investigating Endogenous Peptides and Peptidases using Peptidomics

PubMed Central

Tinoco, Arthur D.; Saghatelian, Alan

2012-01-01

Rather than simply being protein degradation products, peptides have proven to be important bioactive molecules. Bioactive peptides act as hormones, neurotransmitters and antimicrobial agents in vivo. The dysregulation of bioactive peptide signaling is also known to be involved in disease, and targeting peptide hormone pathways has been successful strategy in the development of novel therapeutics. The importance of bioactive peptides in biology has spurred research to elucidate the function and regulation of these molecules. Classical methods for peptide analysis have relied on targeted immunoassays, but certain scientific questions necessitated a broader and more detailed view of the peptidome–all the peptides in a cell, tissue or organism. In this review we discuss how peptidomics has emerged to fill this need through the application of advanced liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods that provide unique insights into peptide activity and regulation. PMID:21786763

Halobacterium salinarum NRC-1 PeptideAtlas: strategies for targeted proteomics

PubMed Central

Van, Phu T.; Schmid, Amy K.; King, Nichole L.; Kaur, Amardeep; Pan, Min; Whitehead, Kenia; Koide, Tie; Facciotti, Marc T.; Goo, Young-Ah; Deutsch, Eric W.; Reiss, David J.; Mallick, Parag; Baliga, Nitin S.

2009-01-01

The relatively small numbers of proteins and fewer possible posttranslational modifications in microbes provides a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a Peptide Atlas (PA) for 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636,000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has helped highlight plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics. PMID:18652504

Membrane Perturbation Induced by Interfacially Adsorbed Peptides

PubMed Central

Zemel, Assaf; Ben-Shaul, Avinoam; May, Sylvio

2004-01-01

The structural and energetic characteristics of the interaction between interfacially adsorbed (partially inserted) α-helical, amphipathic peptides and the lipid bilayer substrate are studied using a molecular level theory of lipid chain packing in membranes. The peptides are modeled as “amphipathic cylinders” characterized by a well-defined polar angle. Assuming two-dimensional nematic order of the adsorbed peptides, the membrane perturbation free energy is evaluated using a cell-like model; the peptide axes are parallel to the membrane plane. The elastic and interfacial contributions to the perturbation free energy of the “peptide-dressed” membrane are evaluated as a function of: the peptide penetration depth into the bilayer's hydrophobic core, the membrane thickness, the polar angle, and the lipid/peptide ratio. The structural properties calculated include the shape and extent of the distorted (stretched and bent) lipid chains surrounding the adsorbed peptide, and their orientational (C-H) bond order parameter profiles. The changes in bond order parameters attendant upon peptide adsorption are in good agreement with magnetic resonance measurements. Also consistent with experiment, our model predicts that peptide adsorption results in membrane thinning. Our calculations reveal pronounced, membrane-mediated, attractive interactions between the adsorbed peptides, suggesting a possible mechanism for lateral aggregation of membrane-bound peptides. As a special case of interest, we have also investigated completely hydrophobic peptides, for which we find a strong energetic preference for the transmembrane (inserted) orientation over the horizontal (adsorbed) orientation. PMID:15189858

Physical stability of arginine-glycine-aspartic acid peptide coated on anodized implants after installation.

PubMed

Huh, Jung-Bo; Lee, Jeong-Yeol; Jeon, Young-Chan; Shin, Sang-Wan; Ahn, Jin-Soo; Ryu, Jae-Jun

2013-05-01

The aim of this study was to evaluate the stability of arginine-glycine-aspartic acid (RGD) peptide coatings on implants by measuring the amount of peptide remaining after installation. Fluorescent isothiocyanate (FITC)-fixed RGD peptide was coated onto anodized titanium implants (width 4 mm, length 10 mm) using a physical adsorption method (P) or a chemical grafting method (C). Solid Rigid Polyurethane Foam (SRPF) was classified as either hard bone (H) or soft bone (S) according to its density. Two pieces of artificial bone were fixed in a customized jig, and coated implants were installed at the center of the boundary between two pieces of artificial bone. The test groups were classified as: P-H, P-S, C-H, or C-S. After each installation, implants were removed from the SRPF, and the residual amounts and rates of RGD peptide in implants were measured by fluorescence spectrometry. The Kruskal-Wallis test was used for the statistical analysis (α=0.05). Peptide-coating was identified by fluorescence microscopy and XPS. Total coating amount was higher for physical adsorption than chemical grafting. The residual rate of peptide was significantly larger in the P-S group than in the other three groups (P<.05). The result of this study suggests that coating doses depend on coating method. Residual amounts of RGD peptide were greater for the physical adsorption method than the chemical grafting method.

Are highly morphed peptide frameworks lurking silently in microbial genomes valuable as next generation antibiotic scaffolds?

PubMed

Walsh, Christopher T

2017-07-01

Antibiotics are a therapeutic class that, once deployed, select for resistant bacterial pathogens and so shorten their useful life cycles. As a consequence new versions of antibiotics are constantly needed. Among the antibiotic natural products, morphed peptide scaffolds, converting conformationally mobile, short-lived linear peptides into compact, rigidified small molecule frameworks, act on a wide range of bacterial targets. Advances in bacterial genome mining, biosynthetic gene cluster prediction and expression, and mass spectroscopic structure analysis suggests many more peptides, modified both in side chains and peptide backbones, await discovery. Such molecules may turn up new bacterial targets and be starting points for combinatorial or semisynthetic manipulations to optimize activity and pharmacology parameters.

NIST Libraries of Peptide Fragmentation Mass Spectra Databass

National Institute of Standards and Technology Data Gateway

SRD 4 NIST Libraries of Peptide Fragmentation Mass Spectra Databass (PC database for purchase)   Interactive computer program for predicting thermodynamic and transport properties of pure fluids and fluid mixtures containing up to 20 components. The components are selected from a database of 196 components, mostly hydrocarbons.

Computational identification of epitopes in the glycoproteins of novel bunyavirus (SFTS virus) recognized by a human monoclonal antibody (MAb 4-5)

NASA Astrophysics Data System (ADS)

Zhang, Wenshuai; Zeng, Xiaoyan; Zhang, Li; Peng, Haiyan; Jiao, Yongjun; Zeng, Jun; Treutlein, Herbert R.

2013-06-01

In this work, we have developed a new approach to predict the epitopes of antigens that are recognized by a specific antibody. Our method is based on the "multiple copy simultaneous search" (MCSS) approach which identifies optimal locations of small chemical functional groups on the surfaces of the antibody, and identifying sequence patterns of peptides that can bind to the surface of the antibody. The identified sequence patterns are then used to search the amino-acid sequence of the antigen protein. The approach was validated by reproducing the binding epitope of HIV gp120 envelop glycoprotein for the human neutralizing antibody as revealed in the available crystal structure. Our method was then applied to predict the epitopes of two glycoproteins of a newly discovered bunyavirus recognized by an antibody named MAb 4-5. These predicted epitopes can be verified by experimental methods. We also discuss the involvement of different amino acids in the antigen-antibody recognition based on the distributions of MCSS minima of different functional groups.

A computational method for selecting short peptide sequences for inorganic material binding.

PubMed

Nayebi, Niloofar; Cetinel, Sibel; Omar, Sara Ibrahim; Tuszynski, Jack A; Montemagno, Carlo

2017-11-01

Discovering or designing biofunctionalized materials with improved quality highly depends on the ability to manipulate and control the peptide-inorganic interaction. Various peptides can be used as assemblers, synthesizers, and linkers in the material syntheses. In another context, specific and selective material-binding peptides can be used as recognition blocks in mining applications. In this study, we propose a new in silico method to select short 4-mer peptides with high affinity and selectivity for a given target material. This method is illustrated with the calcite (104) surface as an example, which has been experimentally validated. A calcite binding peptide can play an important role in our understanding of biomineralization. A practical aspect of calcite is a need for it to be selectively depressed in mining sites. © 2017 Wiley Periodicals, Inc.

Comparative peptidomic profile between human hypertrophic scar tissue and matched normal skin for identification of endogenous peptides involved in scar pathology.

PubMed

Li, Jingyun; Chen, Ling; Li, Qian; Cao, Jing; Gao, Yanli; Li, Jun

2018-08-01

Endogenous peptides recently attract increasing attention for their participation in various biological processes. Their roles in the pathogenesis of human hypertrophic scar remains poorly understood. In this study, we used liquid chromatography-tandem mass spectrometry to construct a comparative peptidomic profiling between human hypertrophic scar tissue and matched normal skin. A total of 179 peptides were significantly differentially expressed in human hypertrophic scar tissue, with 95 upregulated and 84 downregulated peptides between hypertrophic scar tissue and matched normal skin. Further bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis) indicated that precursor proteins of these differentially expressed peptides correlate with cellular process, biological regulation, cell part, binding and structural molecule activity ribosome, and PPAR signaling pathway occurring during pathological changes of hypertrophic scar. Based on prediction database, we found that 78 differentially expressed peptides shared homology with antimicrobial peptides and five matched known immunomodulatory peptides. In conclusion, our results show significantly altered expression profiles of peptides in human hypertrophic scar tissue. These peptides may participate in the etiology of hypertrophic scar and provide beneficial scheme for scar evaluation and treatments. © 2017 Wiley Periodicals, Inc.

A Web Server and Mobile App for Computing Hemolytic Potency of Peptides

NASA Astrophysics Data System (ADS)

Chaudhary, Kumardeep; Kumar, Ritesh; Singh, Sandeep; Tuknait, Abhishek; Gautam, Ankur; Mathur, Deepika; Anand, Priya; Varshney, Grish C.; Raghava, Gajendra P. S.

2016-03-01

Numerous therapeutic peptides do not enter the clinical trials just because of their high hemolytic activity. Recently, we developed a database, Hemolytik, for maintaining experimentally validated hemolytic and non-hemolytic peptides. The present study describes a web server and mobile app developed for predicting, and screening of peptides having hemolytic potency. Firstly, we generated a dataset HemoPI-1 that contains 552 hemolytic peptides extracted from Hemolytik database and 552 random non-hemolytic peptides (from Swiss-Prot). The sequence analysis of these peptides revealed that certain residues (e.g., L, K, F, W) and motifs (e.g., “FKK”, “LKL”, “KKLL”, “KWK”, “VLK”, “CYCR”, “CRR”, “RFC”, “RRR”, “LKKL”) are more abundant in hemolytic peptides. Therefore, we developed models for discriminating hemolytic and non-hemolytic peptides using various machine learning techniques and achieved more than 95% accuracy. We also developed models for discriminating peptides having high and low hemolytic potential on different datasets called HemoPI-2 and HemoPI-3. In order to serve the scientific community, we developed a web server, mobile app and JAVA-based standalone software (http://crdd.osdd.net/raghava/hemopi/).

De Novo Design and Experimental Characterization of Ultrashort Self-Associating Peptides

PubMed Central

Xue, Bo; Robinson, Robert C.; Hauser, Charlotte A. E.; Floudas, Christodoulos A.

2014-01-01

Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a β-strand conformation that stack together to form parallel β-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins. PMID:25010703

A Hydrogen-Bonded Polar Network in the Core of the Glucagon-Like Peptide-1 Receptor Is a Fulcrum for Biased Agonism: Lessons from Class B Crystal Structures.

PubMed

Wootten, Denise; Reynolds, Christopher A; Koole, Cassandra; Smith, Kevin J; Mobarec, Juan C; Simms, John; Quon, Tezz; Coudrat, Thomas; Furness, Sebastian G B; Miller, Laurence J; Christopoulos, Arthur; Sexton, Patrick M

2016-03-01

The glucagon-like peptide 1 (GLP-1) receptor is a class B G protein-coupled receptor (GPCR) that is a key target for treatments for type II diabetes and obesity. This receptor, like other class B GPCRs, displays biased agonism, though the physiologic significance of this is yet to be elucidated. Previous work has implicated R2.60(190), N3.43(240), Q7.49(394), and H6.52(363) as key residues involved in peptide-mediated biased agonism, with R2.60(190), N3.43(240), and Q7.49(394) predicted to form a polar interaction network. In this study, we used novel insight gained from recent crystal structures of the transmembrane domains of the glucagon and corticotropin releasing factor 1 (CRF1) receptors to develop improved models of the GLP-1 receptor that predict additional key molecular interactions with these amino acids. We have introduced E6.53(364)A, N3.43(240)Q, Q7.49(394)N, and N3.43(240)Q/Q7.49(394)N mutations to probe the role of predicted H-bonding and charge-charge interactions in driving cAMP, calcium, or extracellular signal-regulated kinase (ERK) signaling. A polar interaction between E6.53(364) and R2.60(190) was predicted to be important for GLP-1- and exendin-4-, but not oxyntomodulin-mediated cAMP formation and also ERK1/2 phosphorylation. In contrast, Q7.49(394), but not R2.60(190)/E6.53(364) was critical for calcium mobilization for all three peptides. Mutation of N3.43(240) and Q7.49(394) had differential effects on individual peptides, providing evidence for molecular differences in activation transition. Collectively, this work expands our understanding of peptide-mediated signaling from the GLP-1 receptor and the key role that the central polar network plays in these events. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

B and T Cell Epitope-Based Peptides Predicted from Evolutionarily Conserved and Whole Protein Sequences of Ebola Virus as Vaccine Targets.

PubMed

Yasmin, T; Nabi, A H M Nurun

2016-05-01

Ebola virus (EBV) has become a serious threat to public health. Different approaches were applied to predict continuous and discontinuous B cell epitopes as well as T cell epitopes from the sequence-based and available three-dimensional structural analyses of each protein of EBV. Peptides '(79) VPSATKRWGFRSGVPP(94) ' from GP1 and '(515) LHYWTTQDEGAAIGLA(530) ' from GP2 of Ebola were found to be the consensus peptidic sequences predicted as linear B cell epitope of which the latter contains a region (519) TTQDEG(524) that fulfilled all the criteria of accessibility, hydrophilicity, flexibility and beta turn region for becoming an ideal B cell epitope. Different nonamers as T cell epitopes were obtained that interacted with different numbers of MHC class I and class II alleles with a binding affinity of <100 nm. Interestingly, these alleles also bound to the MHC class I alleles mostly prevalent in African and South Asian regions. Of these, 'LANETTQAL' and 'FLYDRLAST' nonamers were predicted to be the most potent T cell epitopes and they, respectively, interacted with eight and twelve class I alleles that covered 63.79% and 54.16% of world population, respectively. These nonamers were found to be the core sequences of 15mer peptides that interacted with the most common class II allele, HLA-DRB1*01:01. They were further validated for their binding to specific class I alleles using docking technique. Thus, these predicted epitopes may be used as vaccine targets against EBV and can be validated in model hosts to verify their efficacy as vaccine. © 2016 The Foundation for the Scandinavian Journal of Immunology.

Neuropeptides of the beetle, Tenebrio molitor identified using MALDI-TOF mass spectrometry and deduced sequences from the Tribolium castaneum genome.

PubMed

Weaver, Robert J; Audsley, Neil

2008-02-01

Four neuropeptides were identified from the brain and corpora cardiaca-corpora allata (CC-CA) of the mealworm beetle Tenebrio molitor using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and information derived from the genome of the red flour beetle, Tribolium castaneum. Leucomyosuppressin (a FLRFamide), previously associated with cockroaches, but also subsequently identified from honey bee seen as a prominent peptide in both brain and CC-CA of T.molitor. A coding sequence for this peptide is found in the genome of T. castaneum. In addition, three FXPRLamides (pyrokinins), provisionally Tenmo-PK-1, Tenmo-PK-2 and Tenmo-PK-3 (HVVNFTPRLamide, SPPFAPRLamide, HL(I)SPFSPRLamide) were identified in both CC-CA and brain of T. molitor, again on the basis of predicted occurrence or similarity in T. castaneum. The sequence of Tenmo-PK-2 is the same as the PK-2 of the cockroach, Periplaneta americana. Other peptides readily predicted from the genome of T. castaneum include two AKH/HrTH peptides (Trica-AKH-1; pELNFSTDWamide and Trica-AKH-2; pELNFTPNWamide), the second of which is identical to Pyrap-AKH, an AKH-related peptide (Trica AKH-L; pEVTFSRDWPamide), two CRF-related diuretic factors (Trica-DH 37 and Trica-DH 47), the latter identical to Tenmo-DH 47, a putative antidiuretic factor (Trica-ADFb; LYDDGSYKPHVYGF-OH), two sulfakinin-like peptides (Trica-SK-1; pETSDDY(SO(3))GHLRFamide, and Trica SK-2; GEEPFDDYGHMRFamide), a potential allatostatin-C (Trica-AS; pESRYRQCYFNPISCF-OH), six allatostatin-B/myoinhibitory peptides (Trica-AST-B-1,2,3,4,5 & 6; DWNKDLHIWamide, GWNNLHEGWamide, AWQSLQSGWamide, NWGQFHGGWamide, SKWDNFRGSWamide, EPAWSNLGIWamide), an allatotropin-like peptide (Trica-ATL; GIEALKYHNMDLGTARGYamide), four 'CAPA'-related peptides (Trica-CAPA-1,2,3,4; NKLASVYALTPSLRVamide, RIGKMVSFPRIamide, PGANSGGMWFGPRLamide, SENFTPWAYIILNGEAPIIREVHYSPRLamide), proctolin (RYLPT), a potential SIFamide (Trica-SIFa; TYRKPPFNGSIFamide), an arginine-vasopressin-related peptide (Trica-AVP; CLITNCPRGamide) and an ITP-related peptide (Trica-ITP). No evidence was found for the presence of 'A' allatostatins (Y/FxFGLamides) or corazonin, either in T. molitor, or in the genome of T. castaneum.

Using ensemble of classifiers for predicting HIV protease cleavage sites in proteins.

PubMed

Nanni, Loris; Lumini, Alessandra

2009-03-01

The focus of this work is the use of ensembles of classifiers for predicting HIV protease cleavage sites in proteins. Due to the complex relationships in the biological data, several recent works show that often ensembles of learning algorithms outperform stand-alone methods. We show that the fusion of approaches based on different encoding models can be useful for improving the performance of this classification problem. In particular, in this work four different feature encodings for peptides are described and tested. An extensive evaluation on a large dataset according to a blind testing protocol is reported which demonstrates how different feature extraction methods and classifiers can be combined for obtaining a robust and reliable system. The comparison with other stand-alone approaches allows quantifying the performance improvement obtained by the ensembles proposed in this work.

Protein asparagine deamidation prediction based on structures with machine learning methods.

PubMed

Jia, Lei; Sun, Yaxiong

2017-01-01

Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

Influence of the yeast strain on the changes of the amino acids, peptides and proteins during sparkling wine production by the traditional method.

PubMed

Martínez-Rodríguez, A J; Carrascosa, A V; Martín-Alvarez, P J; Moreno-Arribas, V; Polo, M C

2002-12-01

The influence of five yeast strains on the nitrogen fractions, amino acids, peptides and proteins, during 12 months of aging of sparkling wines produced by the traditional or Champenoise method, was studied. High-performance liquid chromatography (HPLC) techniques were used for analysis of the amino acid and peptide fractions. Proteins plus polypeptides were determined by the colorimetric Bradford method. Four main stages were detected in the aging of wines with yeast. In the first stage, a second fermentation took place; amino acids and proteins plus polypeptides diminished, and peptides were liberated. In the second stage, there was a release of amino acids and proteins, and peptides were degraded. In the third stage, the release of proteins and peptides predominated. In the fourth stage, the amino acid concentration diminished. The yeast strain used influenced the content of free amino acids and peptides and the aging time in all the nitrogen fractions.

Chemical Cleavage of an Asp-Cys Sequence Allows Efficient Production of Recombinant Peptides with an N-Terminal Cysteine Residue.

PubMed

Pane, Katia; Verrillo, Mariavittoria; Avitabile, Angela; Pizzo, Elio; Varcamonti, Mario; Zanfardino, Anna; Di Maro, Antimo; Rega, Camilla; Amoresano, Angela; Izzo, Viviana; Di Donato, Alberto; Cafaro, Valeria; Notomista, Eugenio

2018-04-18

Peptides with an N-terminal cysteine residue allow site-specific modification of proteins and peptides and chemical synthesis of proteins. They have been widely used to develop new strategies for imaging, drug discovery, diagnostics, and chip technologies. Here we present a method to produce recombinant peptides with an N-terminal cysteine residue as a convenient alternative to chemical synthesis. The method is based on the release of the desired peptide from a recombinant fusion protein by mild acid hydrolysis of an Asp-Cys sequence. To test the general validity of the method we prepared four fusion proteins bearing three different peptides (20-37 amino acid long) at the C-terminus of a ketosteroid isomerase-derived and two Onconase-derived carriers for the production of toxic peptides in E. coli. The chosen peptides were (C)GKY20, an antimicrobial peptide from the C-terminus of human thrombin, (C)ApoB L , an antimicrobial peptide from an inner region of human Apolipoprotein B, and (C)p53pAnt, an anticancer peptide containing the C-terminal region of the p53 protein fused to the cell penetrating peptide Penetratin. Cleavage efficiency of Asp-Cys bonds in the four fusion proteins was studied as a function of pH, temperature, and incubation time. In spite of the differences in the amino acid sequence (GTGDCGKY, GTGDCHVA, GSGTDCGSR, SQGSDCGSR) we obtained for all the proteins a cleavage efficiency of about 70-80% after 24 h incubation at 60 °C and pH 2. All the peptides were produced with very good yield (5-16 mg/L of LB cultures), high purity (>96%), and the expected content of free thiol groups (1 mol per mole of peptide). Furthermore, (C)GKY20 was modified with PyMPO-maleimide, a commercially available fluorophore bearing a thiol reactive group, and with 6-hydroxy-2-cyanobenzothiazole, a reagent specific for N-terminal cysteines, with yields of 100% thus demonstrating that our method is very well suited for the production of fully reactive peptides with an N-terminal cysteine residue.

Sequence-Dependent Structure/Function Relationships of Catalytic Peptide-Enabled Gold Nanoparticles Generated under Ambient Synthetic Conditions.

PubMed

Bedford, Nicholas M; Hughes, Zak E; Tang, Zhenghua; Li, Yue; Briggs, Beverly D; Ren, Yang; Swihart, Mark T; Petkov, Valeri G; Naik, Rajesh R; Knecht, Marc R; Walsh, Tiffany R

2016-01-20

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

MINS2: revisiting the molecular code for transmembrane-helix recognition by the Sec61 translocon.

PubMed

Park, Yungki; Helms, Volkhard

2008-08-15

To be fully functional, membrane proteins should not only fold, but also get inserted into the membrane, which is mediated by the Sec61 translocon. Recent experimental studies have attempted to elucidate how the Sec61 translocon accomplishes this delicate task by measuring the translocon-mediated membrane insertion free energies of 357 systematically designed peptides. On the basis of this data set, we have developed MINS2, a novel sequence-based computational method for predicting the membrane insertion free energies of protein sequences. A benchmark analysis of MINS2 shows that MINS2 signi.cantly outperforms previously proposed methods. Importantly, the application of MINS2 to known membrane protein structures shows that a better prediction of membrane insertion free energies does not lead to a better prediction of transmembrane segments of polytopic membrane proteins. A web server for MINS2 is publicly available at http://service.bioinformatik.uni-saarland.de/mins. Supplementary data are available at Bioinformatics online.

Ocean acidification affects marine chemical communication by changing structure and function of peptide signalling molecules.

PubMed

Roggatz, Christina C; Lorch, Mark; Hardege, Jörg D; Benoit, David M

2016-12-01

Ocean acidification is a global challenge that faces marine organisms in the near future with a predicted rapid drop in pH of up to 0.4 units by the end of this century. Effects of the change in ocean carbon chemistry and pH on the development, growth and fitness of marine animals are well documented. Recent evidence also suggests that a range of chemically mediated behaviours and interactions in marine fish and invertebrates will be affected. Marine animals use chemical cues, for example, to detect predators, for settlement, homing and reproduction. But, while effects of high CO 2 conditions on these behaviours are described across many species, little is known about the underlying mechanisms, particularly in invertebrates. Here, we investigate the direct influence of future oceanic pH conditions on the structure and function of three peptide signalling molecules with an interdisciplinary combination of methods. NMR spectroscopy and quantum chemical calculations were used to assess the direct molecular influence of pH on the peptide cues, and we tested the functionality of the cues in different pH conditions using behavioural bioassays with shore crabs (Carcinus maenas) as a model system. We found that peptide signalling cues are susceptible to protonation in future pH conditions, which will alter their overall charge. We also show that structure and electrostatic properties important for receptor binding differ significantly between the peptide forms present today and the protonated signalling peptides likely to be dominating in future oceans. The bioassays suggest an impaired functionality of the signalling peptides at low pH. Physiological changes due to high CO 2 conditions were found to play a less significant role in influencing the investigated behaviour. From our results, we conclude that the change of charge, structure and consequently function of signalling molecules presents one possible mechanism to explain altered behaviour under future oceanic pH conditions. © 2016 John Wiley & Sons Ltd.

Molecular modeling of the human sperm associated antigen 11 B (SPAG11B) proteins.

PubMed

Narmadha, Ganapathy; Yenugu, Suresh

2015-04-01

Antimicrobial proteins and peptides are ubiquitous in nature with diverse structural and biological properties. Among them, the human beta-defensins are known to contribute to the innate immune response. Besides the defensins, a number of defensin-like proteins and peptides are expressed in many organ systems including the male reproductive system. Some of the protein isoforms encoded by the sperm associated antigen 11B (SPAG11) gene in humans are beta-defensin-like and exhibit structure dependent and salt tolerant antimicrobial activity, besides contributing to sperm maturation. Though some of the functional roles of these proteins are reported, the structural and molecular features that contribute to their antimicrobial activity is not yet reported. In this study, using in silico tools, we report the three dimensional structure of the human SPAG11B proteins and their C-terminal peptides. web-based hydropathy, amphipathicity, and topology (WHAT) analyses and grand average of hydropathy (GRAVY) indices show that these proteins and peptides are amphipathic and highly hydrophilic. Self-optimized prediction method with alignment (SOPMA) analyses and circular dichroism data suggest that the secondary structure of these proteins and peptides primarily contain beta-sheet and random coil structure and alpha-helix to a lesser extent. Ramachandran plots show that majority of the amino acids in these proteins and peptides fall in the permissible regions, thus indicating stable structures. The secondary structure of SPAG11B isoforms and their peptides were not perturbed with increasing NaCl concentration (0-300 mM) and at different pH (3, 7, and 10), thus reinforcing our previously reported observation that their antimicrobial activity is salt tolerant. To the best of our knowledge, for the first time, results of our study provide vital information on the structural features of SPAG11B protein isoforms and their contribution to antimicrobial activity.

Serum dihydroxyacetone kinase peptide m/z 520.3 as predictor of disease severity in patients with compensated chronic hepatitis B

PubMed Central

2013-01-01

Background & aim Due to known limitations of liver biopsy, reliable non-invasive serum biomarkers for chronic liver diseases are needed. We performed serum peptidomics for such investigation in compensated chronic hepatitis B (CHB) patients. Methods Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed peptides in sera from 40 CHB patients (20 with S0G0-S1G1 and 20 with S3G3-S4G4). Ion pair quantification from differentially expressed peptides in a validation set of sera from 86 CHB patients was done with multiple reaction monitoring (MRM). Results 21 differentially represented peptide peaks were found through LC-MS/MS. Ion pairs generated from eleven of these peptides (m/z < 800) were quantified by MRM. Summed peak area ratios of 6 ion pairs from peptide m/z 520.3 (176.1, 353.7, 459.8, 503.3, 351.3, 593.1), which was identified as dihydroxyacetone kinase (DAK) fragment, decreased from mild to advanced stages of fibrosis or inflammation. Area Under Receiver Operating Characteristic Curves (AUROCs) of five ion models discriminating fibrosis degrees were 0.871 ~ 0.915 (S2-4 versus S0-1) and 0.804 ~ 0.924 (S3-4 versus S0-2). AUROCs discriminating inflammation grades were 0.840 ~ 0.902 (G2-4 versus G0-1) and 0.787 ~ 0.888 (G3-4 versus G0-2). The diagnostic power of these models provides improved sensitivity and specificity for predicting disease progression as compared to aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Forn’s index and serum DAK protein. Conclusions The peptide fragment (m/z 520.3) of DAK is a promising biomarker to guide timing of antiviral treatment and to avoid liver biopsy in compensated CHB patients. PMID:24289155

Quantitative PET Imaging with Novel HER3-Targeted Peptides Selected by Phage Display to Predict Androgen-Independent Prostate Cancer Progression

DTIC Science & Technology

2017-12-01

peptide in tumors that was linearly correlated with HER3 levels. Biodistribution analysis revealed low off-target accumulation and rapid clearance...Internal Lab 15-22 Dr. Larimer 5 Stock) Subtask 2: Correlate changes in peptide uptake with protein expression and cell signaling changes ex vivo...signal for each individual tumor was plotted against its corresponding HER3 protein level, the TBR correlated linearly with the amount of protein

Neuromedin and FN-38 Peptides for Treating Psychiatric Diseases

USDA-ARS?s Scientific Manuscript database

Methods and compositions for treating psychiatric diseases and disorders are disclosed. The methods provided generally involve the administration of an NMX peptide, an FNX peptide, or an NMX receptor agonist, or analogs or derivatives thereof, to a subject in order to treat psychiatric diseases and ...

Improving short antimicrobial peptides despite elusive rules for activity.

PubMed

Mikut, Ralf; Ruden, Serge; Reischl, Markus; Breitling, Frank; Volkmer, Rudolf; Hilpert, Kai

2016-05-01

Antimicrobial peptides (AMPs) can effectively kill a broad range of life threatening multidrug-resistant bacteria, a serious threat to public health worldwide. However, despite great hopes novel drugs based on AMPs are still rare. To accelerate drug development we studied different approaches to improve the antibacterial activity of short antimicrobial peptides. Short antimicrobial peptides seem to be ideal drug candidates since they can be synthesized quickly and easily, modified and optimized. In addition, manufacturing a short peptide drug will be more cost efficient than long and structured ones. In contrast to longer and structured peptides short AMPs seem hard to design and predict. Here, we designed, synthesized and screened five different peptide libraries, each consisting of 600 9-mer peptides, against Pseudomonas aeruginosa. Each library is presenting a different approach to investigate effectiveness of an optimization strategy. The data for the 3000 peptides were analyzed using models based on fuzzy logic bioinformatics and plausible descriptors. The rate of active or superior active peptides was improved from 31.0% in a semi-random library from a previous study to 97.8% in the best new designed library. This article is part of a Special Issue entitled: Antimicrobial peptides edited by Karl Lohner and Kai Hilpert. Copyright © 2015 Elsevier B.V. All rights reserved.

Clinical value of rapid urine trypsinogen-2 test strip, urinary trypsinogen activation peptide, and serum and urinary activation peptide of carboxypeptidase B in acute pancreatitis

PubMed Central

Sáez, Jesús; Martínez, Juan; Trigo, Celia; Sánchez-Payá, José; Compañy, Luis; Laveda, Raquel; Griñó, Pilar; García, Cristina; Pérez-Mateo, Miguel

2005-01-01

AIM: To assess the usefulness of urinary trypsinogen-2 test strip, urinary trypsinogen activation peptide (TAP), and serum and urine concentrations of the activation peptide of carboxypeptidase B (CAPAP) in the diagnosis of acute pancreatitis. METHODS: Patients with acute abdominal pain and hospitalized within 24 h after the onset of symptoms were prospectively studied. Urinary trypsinogen-2 was considered positive when a clear blue line was observed (detection limit 50 μg/L). Urinary TAP was measured using a quantitative solid-phase ELISA, and serum and urinary CAPAP by a radioimmunoassay method. RESULTS: Acute abdominal pain was due to acute pancreatitis in 50 patients and turned out to be extrapancreatic in origin in 22 patients. Patients with acute pancreatitis showed significantly higher median levels of serum and urinary CAPAP levels, as well as amylase and lipase than extrapancreatic controls. Median TAP levels were similar in both groups. The urinary trypsinogen-2 test strip was positive in 68% of patients with acute pancreatitis and 13.6% in extrapancreatic controls (P<0.01). Urinary CAPAP was the most reliable test for the diagnosis of acute pancreatitis (sensitivity 66.7%, specificity 95.5%, positive and negative predictive values 96.6% and 56.7%, respectively), with a 14.6 positive likelihood ratio for a cut-off value of 2.32 nmol/L. CONCLUSION: In patients with acute abdominal pain, hospitalized within 24 h of symptom onset, CAPAP in serum and urine was a reliable diagnostic marker of acute pancreatitis. Urinary trypsinogen-2 test strip showed a clinical value similar to amylase and lipase. Urinary TAP was not a useful screening test for the diagnosis of acute pancreatitis. PMID:16437625

The pepATTRACT web server for blind, large-scale peptide-protein docking.

PubMed

de Vries, Sjoerd J; Rey, Julien; Schindler, Christina E M; Zacharias, Martin; Tuffery, Pierre

2017-07-03

Peptide-protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein-peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein-peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Selection of High-Affinity Peptidic Serine Protease Inhibitors with Increased Binding Entropy from a Back-Flip Library of Peptide-Protease Fusions.

PubMed

Sørensen, Hans Peter; Xu, Peng; Jiang, Longguang; Kromann-Hansen, Tobias; Jensen, Knud J; Huang, Mingdong; Andreasen, Peter A

2015-09-25

We have developed a new concept for designing peptidic protein modulators, by recombinantly fusing the peptidic modulator, with randomized residues, directly to the target protein via a linker and screening for internal modulation of the activity of the protein. We tested the feasibility of the concept by fusing a 10-residue-long, disulfide-bond-constrained inhibitory peptide, randomized in selected positions, to the catalytic domain of the serine protease murine urokinase-type plasminogen activator. High-affinity inhibitory peptide variants were identified as those that conferred to the fusion protease the lowest activity for substrate hydrolysis. The usefulness of the strategy was demonstrated by the selection of peptidic inhibitors of murine urokinase-type plasminogen activator with a low nanomolar affinity. The high affinity could not have been predicted by rational considerations, as the high affinity was associated with a loss of polar interactions and an increased binding entropy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Competitor analogs for defined T cell antigens: peptides incorporating a putative binding motif and polyproline or polyglycine spacers.

PubMed

Maryanski, J L; Verdini, A S; Weber, P C; Salemme, F R; Corradin, G

1990-01-12

We describe a new approach for modeling antigenic peptides recognized by T cells. Peptide A24 170-182 can compete with other antigenic peptides that are recognized by H-2kd-restricted cytolytic T cells, presumably by binding to the Kd molecule. By comparing substituted A24 peptides as competitors in a functional competition assay, the A24 residues Tyr-171, Thr-178, and Leu-179 were identified as possible contact residues for Kd. A highly active competitor peptide analog was synthesized in which Tyr was separated from the Thr-Leu pair by a pentaproline spacer. The choice of proline allowed the prediction of a probable conformation for the analog when bound to the Kd molecule. The simplest conformation of the A24 peptide that allows the same spacing and orientation of the motif as in the analog would be a nearly extended polypeptide chain incorporating a single 3(10) helical turn or similar structural kink.

Can currently available non-animal methods detect pre and pro-haptens relevant for skin sensitization?

PubMed

Patlewicz, Grace; Casati, Silvia; Basketter, David A; Asturiol, David; Roberts, David W; Lepoittevin, Jean-Pierre; Worth, Andrew P; Aschberger, Karin

2016-12-01

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors have provided strong impetus to develop non-animal alternatives. Three test methods have undergone OECD validation: the direct peptide reactivity assay (DPRA), the KeratinoSens™ and the human Cell Line Activation Test (h-CLAT). Whilst these methods perform relatively well in predicting LLNA results, a concern raised is their ability to predict chemicals that need activation to be sensitizing (pre- or pro-haptens). This current study reviewed an EURL ECVAM dataset of 127 substances for which information was available in the LLNA and three non-animal test methods. Twenty eight of the sensitizers needed to be activated, with the majority being pre-haptens. These were correctly identified by 1 or more of the test methods. Six substances were categorized exclusively as pro-haptens, but were correctly identified by at least one of the cell-based assays. The analysis here showed that skin metabolism was not likely to be a major consideration for assessing sensitization potential and that sensitizers requiring activation could be identified correctly using one or more of the current non-animal methods. Published by Elsevier Inc.

RAFP-Pred: Robust Prediction of Antifreeze Proteins Using Localized Analysis of n-Peptide Compositions.

PubMed

Khan, Shujaat; Naseem, Imran; Togneri, Roberto; Bennamoun, Mohammed

2018-01-01

In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction of the AFPs is considered to be a challenging task. In this research, we propose to handle this arduous manifold learning task using the notion of localized processing. In particular, an AFP sequence is segmented into two sub-segments each of which is analyzed for amino acid and di-peptide compositions. We propose to use only the most significant features using the concept of information gain (IG) followed by a random forest classification approach. The proposed RAFP-Pred achieved an excellent performance on a number of standard datasets. We report a high Youden's index (sensitivity+specificity-1) value of 0.75 on the standard independent test data set outperforming the AFP-PseAAC, AFP_PSSM, AFP-Pred, and iAFP by a margin of 0.05, 0.06, 0.14, and 0.68, respectively. The verification rate on the UniProKB dataset is found to be 83.19 percent which is substantially superior to the 57.18 percent reported for the iAFP method.

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

PubMed Central

He, Zhisong; Shi, Xiaohe; Feng, Kaiyan; Ma, Buyong; Cai, Yu-Dong

2012-01-01

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues. PMID:22761773

Ligand-regulated peptides: a general approach for modulating protein-peptide interactions with small molecules.

PubMed

Binkowski, Brock F; Miller, Russell A; Belshaw, Peter J

2005-07-01

We engineered a novel ligand-regulated peptide (LiRP) system where the binding activity of intracellular peptides is controlled by a cell-permeable small molecule. In the absence of ligand, peptides expressed as fusions in an FKBP-peptide-FRB-GST LiRP scaffold protein are free to interact with target proteins. In the presence of the ligand rapamycin, or the nonimmunosuppressive rapamycin derivative AP23102, the scaffold protein undergoes a conformational change that prevents the interaction of the peptide with the target protein. The modular design of the scaffold enables the creation of LiRPs through rational design or selection from combinatorial peptide libraries. Using these methods, we identified LiRPs that interact with three independent targets: retinoblastoma protein, c-Src, and the AMP-activated protein kinase. The LiRP system should provide a general method to temporally and spatially regulate protein function in cells and organisms.