sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

PubMed

Han, Youngmahn; Kim, Dongsup

2017-12-28

Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc

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.

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

PubMed Central

2015-01-01

Background 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. Methods 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. Results 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. Conclusions Network analysis is a

MHC2NNZ: A novel peptide binding prediction approach for HLA DQ molecules

NASA Astrophysics Data System (ADS)

Xie, Jiang; Zeng, Xu; Lu, Dongfang; Liu, Zhixiang; Wang, Jiao

2017-07-01

The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

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.

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

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.

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

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

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.

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.

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.

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/.

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.

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

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

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/).

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

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

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.

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

PubMed Central

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-01-01

Background Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion The SMM-align method was

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

PubMed

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-07-04

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. The SMM-align method was shown to outperform other

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.

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.

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

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.

New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

PubMed

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

2004-11-21

Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).

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

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.

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.

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.

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 .

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

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

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

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.

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.

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

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.

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

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

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

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.

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

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/.

Peptides and methods against diabetes

DOEpatents

Albertini, Richard J.; Falta, Michael T.

2000-01-01

This invention relates to methods of preventing or reducing the severity of diabetes. In one embodiment, the method involves administering to the individual a peptide having substantially the sequence of a on-conserved region sequence of a T cell receptor present on the surface of T cells mediating diabetes or a fragment thereof, wherein the peptide or fragment is capable of causing an effect on the immune system to regulate the T cells. In particular, the T cell receptor has the V.beta. regional V.beta.6 or V.beta.14. In another embodiment, the method involves gene therapy. The invention also relates to methods of diagnosing diabetes by determining the presence of diabetes predominant T cell receptors.

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.

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

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.

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.

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.

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

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.

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

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.

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.

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

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.

Knowledge-based grouping of modeled HLA peptide complexes.

PubMed

Kangueane, P; Sakharkar, M K; Lim, K S; Hao, H; Lin, K; Chee, R E; Kolatkar, P R

2000-05-01

Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%.

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

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.

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

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.

Prediction of Peptide and Protein Propensity for Amyloid Formation

PubMed Central

Família, Carlos; Dennison, Sarah R.; Quintas, Alexandre; Phoenix, David A.

2015-01-01

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation. PMID:26241652

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Detection of co-eluted peptides using database search methods

PubMed Central

Alves, Gelio; Ogurtsov, Aleksey Y; Kwok, Siwei; Wu, Wells W; Wang, Guanghui; Shen, Rong-Fong; Yu, Yi-Kuo

2008-01-01

Background Current experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS2) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost. Results We have computationally investigated the possibility of enhancing the information retrieval during a given LC/MS experiment by selecting the two or three most intense parent ions for simultaneous fragmentation. A set of spectra is created via superimposing a number of MS2 spectra, each can be identified by all search methods tested with high confidence, to mimick the spectra of co-eluted peptides. The generated convoluted spectra were used to evaluate the capability of several database search methods – SEQUEST, Mascot, X!Tandem, OMSSA, and RAId_DbS – in identifying true peptides from superimposed spectra of co-eluted peptides. We show that using these simulated spectra, all the database search methods will gain eventually in the number of true peptides identified by using the compound spectra of co-eluted peptides. Open peer review Reviewed by Vlad Petyuk (nominated by Arcady Mushegian), King Jordan and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section. PMID:18597684

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

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.

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.

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.

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.

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

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.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

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

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/

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.

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®

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

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

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 Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

PubMed

Gautam, Aditi; Sharma, Asuda; Jaiswal, Sarika; Fatma, Samar; Arora, Vasu; Iquebal, M A; Nandi, S; Sundaray, J K; Jayasankar, P; Rai, Anil; Kumar, Dinesh

2016-09-01

Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

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.

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.

Combinatorial Labeling Method for Improving Peptide Fragmentation in Mass Spectrometry

NASA Astrophysics Data System (ADS)

Kuchibhotla, Bhanuramanand; Kola, Sankara Rao; Medicherla, Jagannadham V.; Cherukuvada, Swamy V.; Dhople, Vishnu M.; Nalam, Madhusudhana Rao

2017-06-01

Annotation of peptide sequence from tandem mass spectra constitutes the central step of mass spectrometry-based proteomics. Peptide mass spectra are obtained upon gas-phase fragmentation. Identification of the protein from a set of experimental peptide spectral matches is usually referred as protein inference. Occurrence and intensity of these fragment ions in the MS/MS spectra are dependent on many factors such as amino acid composition, peptide basicity, activation mode, protease, etc. Particularly, chemical derivatizations of peptides were known to alter their fragmentation. In this study, the influence of acetylation, guanidinylation, and their combination on peptide fragmentation was assessed initially on a lipase (LipA) from Bacillus subtilis followed by a bovine six protein mix digest. The dual modification resulted in improved fragment ion occurrence and intensity changes, and this resulted in the equivalent representation of b- and y-type fragment ions in an ion trap MS/MS spectrum. The improved representation has allowed us to accurately annotate the peptide sequences de novo. Dual labeling has significantly reduced the false positive protein identifications in standard bovine six peptide digest. Our study suggests that the combinatorial labeling of peptides is a useful method to validate protein identifications for high confidence protein inference. [Figure not available: see fulltext.

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.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

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.

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.

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

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.

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 .

Improved Methods for the Enrichment and Analysis of Glycated Peptides

PubMed Central

Zhang, Qibin; Schepmoes, Athena A.; Brock, Jonathan W. C.; Wu, Si; Moore, Ronald J.; Purvine, Samuel O.; Baynes, John W.; Smith, Richard D.; Metz, Thomas O.

2009-01-01

Nonenzymatic glycation of tissue proteins has important implications in the development of complications of diabetes mellitus. Herein we report improved methods for the enrichment and analysis of glycated peptides using boronate affinity chromatography and electron-transfer dissociation mass spectrometry, respectively. The enrichment of glycated peptides was improved by replacing an off-line desalting step with an online wash of column-bound glycated peptides using 50 mM ammonium acetate, followed by elution with 100 mM acetic acid. The analysis of glycated peptides by MS/MS was improved by considering only higher charged (≥3) precursor ions during data-dependent acquisition, which increased the number of glycated peptide identifications. Similarly, the use of supplemental collisional activation after electron transfer (ETcaD) resulted in more glycated peptide identifications when the MS survey scan was acquired with enhanced resolution. Acquiring ETD-MS/MS data at a normal MS survey scan rate, in conjunction with the rejection of both 1+ and 2+ precursor ions, increased the number of identified glycated peptides relative to ETcaD or the enhanced MS survey scan rate. Finally, an evaluation of trypsin, Arg-C, and Lys-C showed that tryptic digestion of glycated proteins was comparable to digestion with Lys-C and that both were better than Arg-C in terms of the number of glycated peptides and corresponding glycated proteins identified by LC–MS/MS. PMID:18989935

PIPI: PTM-Invariant Peptide Identification Using Coding Method.

PubMed

Yu, Fengchao; Li, Ning; Yu, Weichuan

2016-12-02

In computational proteomics, the identification of peptides with an unlimited number of post-translational modification (PTM) types is a challenging task. The computational cost associated with database search increases exponentially with respect to the number of modified amino acids and linearly with respect to the number of potential PTM types at each amino acid. The problem becomes intractable very quickly if we want to enumerate all possible PTM patterns. To address this issue, one group of methods named restricted tools (including Mascot, Comet, and MS-GF+) only allow a small number of PTM types in database search process. Alternatively, the other group of methods named unrestricted tools (including MS-Alignment, ProteinProspector, and MODa) avoids enumerating PTM patterns with an alignment-based approach to localizing and characterizing modified amino acids. However, because of the large search space and PTM localization issue, the sensitivity of these unrestricted tools is low. This paper proposes a novel method named PIPI to achieve PTM-invariant peptide identification. PIPI belongs to the category of unrestricted tools. It first codes peptide sequences into Boolean vectors and codes experimental spectra into real-valued vectors. For each coded spectrum, it then searches the coded sequence database to find the top scored peptide sequences as candidates. After that, PIPI uses dynamic programming to localize and characterize modified amino acids in each candidate. We used simulation experiments and real data experiments to evaluate the performance in comparison with restricted tools (i.e., Mascot, Comet, and MS-GF+) and unrestricted tools (i.e., Mascot with error tolerant search, MS-Alignment, ProteinProspector, and MODa). Comparison with restricted tools shows that PIPI has a close sensitivity and running speed. Comparison with unrestricted tools shows that PIPI has the highest sensitivity except for Mascot with error tolerant search and Protein

Method of identity analyte-binding peptides

DOEpatents

Kauvar, Lawrence M.

1990-01-01

A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4-20 amino acids for specific affinity to the analyte.

Method of identity analyte-binding peptides

DOEpatents

Kauvar, L.M.

1990-10-16

A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4--20 amino acids for specific affinity to the analyte. 5 figs.

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.

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.

PChopper: high throughput peptide prediction for MRM/SRM transition design.

PubMed

Afzal, Vackar; Huang, Jeffrey T-J; Atrih, Abdel; Crowther, Daniel J

2011-08-15

The use of selective reaction monitoring (SRM) based LC-MS/MS analysis for the quantification of phosphorylation stoichiometry has been rapidly increasing. At the same time, the number of sites that can be monitored in a single LC-MS/MS experiment is also increasing. The manual processes associated with running these experiments have highlighted the need for computational assistance to quickly design MRM/SRM candidates. PChopper has been developed to predict peptides that can be produced via enzymatic protein digest; this includes single enzyme digests, and combinations of enzymes. It also allows digests to be simulated in 'batch' mode and can combine information from these simulated digests to suggest the most appropriate enzyme(s) to use. PChopper also allows users to define the characteristic of their target peptides, and can automatically identify phosphorylation sites that may be of interest. Two application end points are available for interacting with the system; the first is a web based graphical tool, and the second is an API endpoint based on HTTP REST. Service oriented architecture was used to rapidly develop a system that can consume and expose several services. A graphical tool was built to provide an easy to follow workflow that allows scientists to quickly and easily identify the enzymes required to produce multiple peptides in parallel via enzymatic digests in a high throughput manner.

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

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

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

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.

Manual method of visually identifying candidate signals for a targeted peptide.

PubMed

Filimonov, Aleksey; Kopylov, Arthur; Lisitsa, Andrey; Archakov, Alexander

2018-04-15

The purpose of this study is to improve peptide signal identification in groups of extracted ion chromatograms (XICs) obtained with the liquid chromatography-selected reaction monitoring (LC-SRM) technique and a triple quadrupole mass spectrometer (QqQ) operating in one of the supported multiple reaction monitoring (MRM) modes. The imperfection of quadrupole mass analyzers causes ion interference, which impedes the identification of peptide signals as chromatographic peak groups in relevant retention time intervals. To investigate this problem in depth, the QqQ conversion of the eluate into XIC groups was considered as the consecutive transformations of the particles' abundances as the corresponding functions of retention time. In this study, the hypothesis that, during this conversion, the same chromatographic profile should be preserved as an implicit sign in each chromatographic peak of the signal was confirmed for peptides. To examine chromatographic profiles, continuous transformations of XIC groups were derived and implemented in srm2prot Express software (s2pe, http://msr.ibmc.msk.ru/s2pe). Because of ion interference, several peptide-like signals may appear in one XIC group. Therefore, these signals must be considered candidates for a targeted peptide's signal and should be resolved after identification. The theoretical investigation of intensity functions as XICs that are not distorted by noise produced three rules for Identifying Candidate Signals for a targeted Peptide (ICSP, http://msr.ibmc.msk.ru/ICSP) that constitute the proposed manual visual method. We theoretically and experimentally compared this method with the conventional semiempirical intuitive technique and found that the former significantly streamlines peptide signal identification and avoids typical errors. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

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.

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.

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…

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

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

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.

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

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

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.

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

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

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.

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.

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

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/

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

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

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.

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.

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.

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

Modified filter-aided sample preparation (FASP) method increases peptide and protein identifications for shotgun proteomics.

PubMed

Ni, Mao-Wei; Wang, Lu; Chen, Wei; Mou, Han-Zhou; Zhou, Jie; Zheng, Zhi-Guo

2017-01-30

Mass spectrometry (MS)-based protein identification depends mainly on protein extraction and digestion. Although sodium dodecyl sulfate (SDS) can preclude enzymatic digestion and interfere with MS analysis, it is still the most widely used surfactant in these steps. To overcome these disadvantages, a SDS-compatible proteomic technique for SDS removal prior to MS-based analyses was developed, namely filter-aided sample preparation (FASP). Herein, based on the effectiveness of sodium deoxycholate and a detergent removal spin column, we developed a modified FASP (mFASP) method and compared its overall performance, total number of peptides and proteins identified for shotgun proteomic experiments with that of the FASP method. Identification of 4570 ± 392 and 9139 ± 317 peptides and description of 862 ± 46 and 1377 ± 33 protein groups with two or more peptides from the ovarian cancer cell line A2780 was accomplished by FASP and mFASP methods, respectively. The mFASP method (21.2 ± 0.2%) had higher average peptide to protein coverage than FASP method (13.2 ± 0.5%). More hydrophobic peptides were identified by mFASP than by FASP, as indicated by the GRAVY score distribution. The reported method enables reliable and efficient identification of proteins and peptides in whole-cell extracts containing SDS. The new approach allows for higher throughput (the simultaneous identification of more proteins), a more comprehensive investigation of proteins, and potentially the discovery of new biomarkers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 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.

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.

Empirical Methods for Identifying Specific Peptide-protein Interactions for Smart Reagent Development

DTIC Science & Technology

2012-09-01

orientated immobilization of proteins,” Biotechnology Progress, 22(2), 401-405 ( 2006 ). [26] J. M. Kogot, D. A. Sarkes , I. Val-Addo et al...Empirical Methods for Identifying Specific Peptide-protein Interactions for Smart Reagent Development by Joshua M. Kogot, Deborah A. Sarkes ...Peptide-protein Interactions for Smart Reagent Development Joshua M. Kogot, Deborah A. Sarkes , Dimitra N. Stratis-Cullum, and Paul M

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/.

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

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

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.

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.

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.

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.

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.

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.

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.

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

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

SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.

PubMed

He, Bifang; Kang, Juanjuan; Ru, Beibei; Ding, Hui; Zhou, Peng; Huang, Jian

2016-01-01

Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community.

Self-assembling peptide amphiphiles and related methods for growth factor delivery

DOEpatents

Stupp, Samuel I [Chicago, IL; Donners, Jack J. J. M.; Silva, Gabriel A [Chicago, IL; Behanna, Heather A [Chicago, IL; Anthony, Shawn G [New Stanton, PA

2009-06-09

Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

Self-assembling peptide amphiphiles and related methods for growth factor delivery

DOEpatents

Stupp, Samuel I [Chicago, IL; Donners, Jack J. J. M.; Silva, Gabriel A [Chicago, IL; Behanna, Heather A [Chicago, IL; Anthony, Shawn G [New Stanton, PA

2012-03-20

Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

Self-assembling peptide amphiphiles and related methods for growth factor delivery

DOEpatents

Stupp, Samuel I; Donners, Jack J.J.M.; Silva, Gabriel A; Behanna, Heather A; Anthony, Shawn G

2013-11-12

Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

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.

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

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.

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.

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.

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

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.

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

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.

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.

A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization

PubMed Central

Goldstein, Leonard D.; Howarth, Mark; Cardelli, Luca; Emmott, Stephen; Elliott, Tim; Werner, Joern M.

2011-01-01

Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human

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.

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

High-Throughput Method for Ranking the Affinity of Peptide Ligands Selected from Phage Display Libraries

PubMed Central

González-Techera, A.; Umpiérrez-Failache, M.; Cardozo, S.; Obal, G.; Pritsch, O.; Last, J. A.; Gee, S. J.; Hammock, B. D.; González-Sapienza, G.

2010-01-01

The use of phage display peptide libraries allows rapid isolation of peptide ligands for any target selector molecule. However, due to differences in peptide expression and the heterogeneity of the phage preparations, there is no easy way to compare the binding properties of the selected clones, which operates as a major “bottleneck” of the technology. Here, we present the development of a new type of library that allows rapid comparison of the relative affinity of the selected peptides in a high-throughput screening format. As a model system, a phage display peptide library constructed on a phagemid vector that contains the bacterial alkaline phosphatase gene (BAP) was selected with an antiherbicide antibody. Due to the intrinsic switching capacity of the library, the selected peptides were transferred “en masse” from the phage coat protein to BAP. This was coupled to an optimized affinity ELISA where normalized amounts of the peptide–BAP fusion allow direct comparison of the binding properties of hundreds of peptide ligands. The system was validated by plasmon surface resonance experiments using synthetic peptides, showing that the method discriminates among the affinities of the peptides within 3 orders of magnitude. In addition, the peptide–BAP protein can find direct application as a tracer reagent. PMID:18393454

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

Urinary C-peptide as a method for monitoring body mass changes in captive bonobos (Pan paniscus).

PubMed

Deschner, Tobias; Kratzsch, Jürgen; Hohmann, Gottfried

2008-11-01

In recent years methodological improvements have allowed for more precise estimates of nutrient intake in wild primates. However, estimates of energetic condition have remained relatively imprecise due to the difficulties of estimating digestive efficiency and energy expenditure in these animals. In the absence of a reliable intake-expenditure calculation, a method is needed that directly links changes in energetic condition, such as body mass, to physiological changes that can be detected via markers in body excretions such as urine or feces. One promising marker is C-peptide, a metabolic byproduct of insulin synthesis. Here we present the results of a food restriction experiment carried out in a group of captive bonobos (Pan paniscus). We measured changes in food availability and body mass and determined urinary C-peptide levels with the help of a time-resolved fluoroimmunoassay routinely used for measuring C-peptide in human blood. Urinary C-peptide levels decreased during a period of food restriction and increased again when food availability was continuously increased. During this refeeding phase an increase in body mass was significantly correlated with an increase in urinary C-peptide levels. Our results suggest that urinary C-peptide levels are an accurate indicator of individual energy balance. In conclusion, measuring C-peptide in urine is a promising method to quantify the energetic condition of wild apes.

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

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.

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.

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.

Minimizing Postsampling Degradation of Peptides by a Thermal Benchtop Tissue Stabilization Method

PubMed Central

Segerström, Lova; Gustavsson, Jenny

2016-01-01

Enzymatic degradation is a major concern in peptide analysis. Postmortem metabolism in biological samples entails considerable risk for measurements misrepresentative of true in vivo concentrations. It is therefore vital to find reliable, reproducible, and easy-to-use procedures to inhibit enzymatic activity in fresh tissues before subjecting them to qualitative and quantitative analyses. The aim of this study was to test a benchtop thermal stabilization method to optimize measurement of endogenous opioids in brain tissue. Endogenous opioid peptides are generated from precursor proteins through multiple enzymatic steps that include conversion of one bioactive peptide to another, often with a different function. Ex vivo metabolism may, therefore, lead to erroneous functional interpretations. The efficacy of heat stabilization was systematically evaluated in a number of postmortem handling procedures. Dynorphin B (DYNB), Leu-enkephalin-Arg6 (LARG), and Met-enkephalin-Arg6-Phe7 (MEAP) were measured by radioimmunoassay in rat hypothalamus, striatum (STR), and cingulate cortex (CCX). Also, simplified extraction protocols for stabilized tissue were tested. Stabilization affected all peptide levels to varying degrees compared to those prepared by standard dissection and tissue handling procedures. Stabilization increased DYNB in hypothalamus, but not STR or CCX, whereas LARG generally decreased. MEAP increased in hypothalamus after all stabilization procedures, whereas for STR and CCX, the effect was dependent on the time point for stabilization. The efficacy of stabilization allowed samples to be left for 2 hours in room temperature (20°C) without changes in peptide levels. This study shows that conductive heat transfer is an easy-to-use and efficient procedure for the preservation of the molecular composition in biological samples. Region- and peptide-specific critical steps were identified and stabilization enabled the optimization of tissue handling and opioid

Two-dimensional replica exchange approach for peptide-peptide interactions

NASA Astrophysics Data System (ADS)

Gee, Jason; Shell, M. Scott

2011-02-01

The replica exchange molecular dynamics (REMD) method has emerged as a standard approach for simulating proteins and peptides with rugged underlying free energy landscapes. We describe an extension to the original methodology—here termed umbrella-sampling REMD (UREMD)—that offers specific advantages in simulating peptide-peptide interactions. This method is based on the use of two dimensions in the replica cascade, one in temperature as in conventional REMD, and one in an umbrella sampling coordinate between the center of mass of the two peptides that aids explicit exploration of the complete association-dissociation reaction coordinate. To mitigate the increased number of replicas required, we pursue an approach in which the temperature and umbrella dimensions are linked at only fully associated and dissociated states. Coupled with the reweighting equations, the UREMD method aids accurate calculations of normalized free energy profiles and structural or energetic measures as a function of interpeptide separation distance. We test the approach on two families of peptides: a series of designed tetrapeptides that serve as minimal models for amyloid fibril formation, and a fragment of a classic leucine zipper peptide and its mutant. The results for these systems are compared to those from conventional REMD simulations, and demonstrate good convergence properties, low statistical errors, and, for the leucine zippers, an ability to sample near-native structures.

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

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.

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

Usefulness of ELISA Methods for Assessing LPS Interactions with Proteins and Peptides

PubMed Central

Martínez-Sernández, Victoria; Orbegozo-Medina, Ricardo A.; Romarís, Fernanda; Paniagua, Esperanza; Ubeira, Florencio M.

2016-01-01

Lipopolysaccharide (LPS), the major constituent of the outer membrane of Gram-negative bacteria, can trigger severe inflammatory responses during bacterial infections, possibly leading to septic shock. One approach to combatting endotoxic shock is to neutralize the most conserved part and major mediator of LPS activity (lipid A) with LPS-binding proteins or peptides. Although several available assays evaluate the biological activity of these molecules on LPS (e.g. inhibition of LPS-induced TNF-α production in macrophages), the development of simple and cost-effective methods that would enable preliminary screening of large numbers of potential candidate molecules is of great interest. Moreover, it would be also desirable that such methods could provide information about the possible biological relevance of the interactions between proteins and LPS, which may enhance or neutralize LPS-induced inflammatory responses. In this study, we designed and evaluated different types of ELISA that could be used to study possible interactions between LPS and any protein or peptide. We also analysed the usefulness and limitations of the different ELISAs. Specifically, we tested the capacity of several proteins and peptides to bind FITC-labeled LPSs from Escherichia coli serotypes O111:B4 and O55:B5 in an indirect ELISA and in two competitive ELISAs including casein hydrolysate (hCAS) and biotinylated polymyxin B (captured by deglycosylated avidin; PMX) as LPS-binding agents in the solid phase. We also examined the influence of pH, detergents and different blocking agents on LPS binding. Our results showed that the competitive hCAS-ELISA performed under mildly acidic conditions can be used as a general method for studying LPS interactions, while the more restrictive PMX-ELISA may help to identify proteins/peptides that are likely to have neutralizing properties in vitro or in vivo. PMID:27249227

Improved nonlinear prediction method

NASA Astrophysics Data System (ADS)

Adenan, Nur Hamiza; Md Noorani, Mohd Salmi

2014-06-01

The analysis and prediction of time series data have been addressed by researchers. Many techniques have been developed to be applied in various areas, such as weather forecasting, financial markets and hydrological phenomena involving data that are contaminated by noise. Therefore, various techniques to improve the method have been introduced to analyze and predict time series data. In respect of the importance of analysis and the accuracy of the prediction result, a study was undertaken to test the effectiveness of the improved nonlinear prediction method for data that contain noise. The improved nonlinear prediction method involves the formation of composite serial data based on the successive differences of the time series. Then, the phase space reconstruction was performed on the composite data (one-dimensional) to reconstruct a number of space dimensions. Finally the local linear approximation method was employed to make a prediction based on the phase space. This improved method was tested with data series Logistics that contain 0%, 5%, 10%, 20% and 30% of noise. The results show that by using the improved method, the predictions were found to be in close agreement with the observed ones. The correlation coefficient was close to one when the improved method was applied on data with up to 10% noise. Thus, an improvement to analyze data with noise without involving any noise reduction method was introduced to predict the time series data.

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.

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.

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.

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.

PubMed

Nielsen, Morten; Justesen, Sune; Lund, Ole; Lundegaard, Claus; Buus, Søren

2010-11-13

Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option. Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy. The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be

Spectrum-based method to generate good decoy libraries for spectral library searching in peptide identifications.

PubMed

Cheng, Chia-Ying; Tsai, Chia-Feng; Chen, Yu-Ju; Sung, Ting-Yi; Hsu, Wen-Lian

2013-05-03

As spectral library searching has received increasing attention for peptide identification, constructing good decoy spectra from the target spectra is the key to correctly estimating the false discovery rate in searching against the concatenated target-decoy spectral library. Several methods have been proposed to construct decoy spectral libraries. Most of them construct decoy peptide sequences and then generate theoretical spectra accordingly. In this paper, we propose a method, called precursor-swap, which directly constructs decoy spectral libraries directly at the "spectrum level" without generating decoy peptide sequences by swapping the precursors of two spectra selected according to a very simple rule. Our spectrum-based method does not require additional efforts to deal with ion types (e.g., a, b or c ions), fragment mechanism (e.g., CID, or ETD), or unannotated peaks, but preserves many spectral properties. The precursor-swap method is evaluated on different spectral libraries and the results of obtained decoy ratios show that it is comparable to other methods. Notably, it is efficient in time and memory usage for constructing decoy libraries. A software tool called Precursor-Swap-Decoy-Generation (PSDG) is publicly available for download at http://ms.iis.sinica.edu.tw/PSDG/.

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.

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

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.

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.

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

Prediction of the effect on antihyperglycaemic action of sitagliptin by plasma active form glucagon-like peptide-1

PubMed Central

Kushiyama, Akifumi; Kikuchi, Takako; Tanaka, Kentaro; Tahara, Tazu; Takao, Toshiko; Onishi, Yukiko; Yoshida, Yoko; Kawazu, Shoji; Iwamoto, Yasuhiko

2016-01-01

AIM: To investigate whether active glucagon-like peptide-1 (GLP-1) is a prediction Factor of Effect of sitagliptin on patients with type 2 diabetes mellitus (GLP-1 FEST:UMIN000010645). METHODS: Seventy-six patients with type 2 diabetes, who had insufficient glycemic control [Hemoglobin A1c (HbA1c) ≥ 7%] in spite of treatment with metformin and/or sulfonylurea, were included in the investigation. Patients were divided into three groups by tertiles of fasting plasma active GLP-1 level, before the administration of 50 mg sitagliptin. RESULTS: At baseline, body mass index, serum UA, insulin and HOMA-IR were higher in the high active GLP-1 group than in the other two groups. The high active GLP-1 group did not show any decline of HbA1c (7.6% ± 1.4% to 7.5% ± 1.5%), whereas the middle and low groups indicated significant decline of HbA1c (7.4 ± 0.7 to 6.8 ± 0.6 and 7.4 ± 1.2 to 6.9 ± 1.3, respectively) during six months. Only the low and middle groups showed a significant increment of active GLP-1, C-peptide level, a decreased log and proinsulin/insulin ratio after administration. In logistic analysis, the low or middle group is a significant explanatory variable for an HbA1c decrease of ≥ 0.5%, and its odds ratio is 4.5 (1.40-17.6) (P = 0.01) against the high active GLP-1 group. This remains independent when adjusted for HbA1c level before administration, patients’ medical history, medications, insulin secretion and insulin resistance. CONCLUSION: Plasma fasting active GLP-1 is an independent predictive marker for the efficacy of dipeptidyl peptidase 4 inhibitor sitagliptin. PMID:27326345

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.

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

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.

Cathepsin-Mediated Cleavage of Peptides from Peptide Amphiphiles Leads to Enhanced Intracellular Peptide Accumulation

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

Acar, Handan; Samaeekia, Ravand; Schnorenberg, Mathew R.

Peptides synthesized in the likeness of their native interaction domain(s) are natural choices to target protein protein interactions (PPIs) due to their fidelity of orthostatic contact points between binding partners. Despite therapeutic promise, intracellular delivery of biofunctional peptides at concentrations necessary for efficacy remains a formidable challenge. Peptide amphiphiles (PAs) provide a facile method of intracellular delivery and stabilization of bioactive peptides. PAs consisting of biofunctional peptide headgroups linked to hydrophobic alkyl lipid-like tails prevent peptide hydrolysis and proteolysis in circulation, and PA monomers are internalized via endocytosis. However, endocytotic sequestration and steric hindrance from the lipid tail are twomore » major mechanisms that limit PA efficacy to target intracellular PPIs. To address these problems, we have constructed a PA platform consisting of cathepsin-B cleavable PAs in which a selective p53-based inhibitory peptide is cleaved from its lipid tail within endosomes, allowing for intracellular peptide accumulation and extracellular recycling of the lipid moiety. We monitor for cleavage and follow individual PA components in real time using a resonance energy transfer (FRET)-based tracking system. Using this platform, components in real time using a Forster we provide a better understanding and quantification of cellular internalization, trafficking, and endosomal cleavage of PAs and of the ultimate fates of each component.« less

A RAPID Method for Blood Processing to Increase the Yield of Plasma Peptide Levels in Human Blood.

PubMed

Teuffel, Pauline; Goebel-Stengel, Miriam; Hofmann, Tobias; Prinz, Philip; Scharner, Sophie; Körner, Jan L; Grötzinger, Carsten; Rose, Matthias; Klapp, Burghard F; Stengel, Andreas

2016-04-28

Research in the field of food intake regulation is gaining importance. This often includes the measurement of peptides regulating food intake. For the correct determination of a peptide's concentration, it should be stable during blood processing. However, this is not the case for several peptides which are quickly degraded by endogenous peptidases. Recently, we developed a blood processing method employing Reduced temperatures, Acidification, Protease inhibition, Isotopic exogenous controls and Dilution (RAPID) for the use in rats. Here, we have established this technique for the use in humans and investigated recovery, molecular form and circulating concentration of food intake regulatory hormones. The RAPID method significantly improved the recovery for (125)I-labeled somatostatin-28 (+39%), glucagon-like peptide-1 (+35%), acyl ghrelin and glucagon (+32%), insulin and kisspeptin (+29%), nesfatin-1 (+28%), leptin (+21%) and peptide YY3-36 (+19%) compared to standard processing (EDTA blood on ice, p <0.001). High performance liquid chromatography showed the elution of endogenous acyl ghrelin at the expected position after RAPID processing, while after standard processing 62% of acyl ghrelin were degraded resulting in an earlier peak likely representing desacyl ghrelin. After RAPID processing the acyl/desacyl ghrelin ratio in blood of normal weight subjects was 1:3 compared to 1:23 following standard processing (p = 0.03). Also endogenous kisspeptin levels were higher after RAPID compared to standard processing (+99%, p = 0.02). The RAPID blood processing method can be used in humans, yields higher peptide levels and allows for assessment of the correct molecular form.

Prediction of clinically relevant hyperkalemia in patients treated with peptide receptor radionuclide therapy

PubMed Central

2014-01-01

Background Peptide receptor radionuclide therapy (PRRT) is applied in patients with advanced neuroendocrine tumors. Co-infused amino acids (AA) should prevent nephrotoxicity. The aims of this study were to correlate the incidence of AA-induced hyperkalemia (HK) (≥5.0 mmol/l) and to identify predictors of AA-induced severe HK (>6.0). Methods In 38 patients, standard activity of 177Lu-labelled somatostatin analogs was administered. Pre-therapeutic kidney function was assessed by renal scintigraphy and laboratory tests. For kidney protection, AA was co-infused. Biochemical parameters (potassium, glomerular filtration rate, creatinine, blood urea nitrogen (BUN), sodium, phosphate, chloride, and lactate dehydrogenase (LDH)) were obtained prior to 4 and 24 h after the AA infusion. Incidence of HK (≥5.0) was correlated with pre-therapeutic kidney function and serum parameters. Formulas for the prediction of severe hyperkalemia (>6.0) were computed and prospectively validated. Results At 4 h, HK (≥5.0) was present in 94.7% with severe HK (>6.0) in 36.1%. Values normalized after 24 h in 84.2%. Pre-therapeutic kidney function did not correlate with the incidence of severe HK. Increases in K+ were significantly correlated with decreases in phosphate (r = −0.444, p < 0.005) and increases in BUN (r = 0.313, p = 0.056). A baseline BUN of >28 mg/dl had a sensitivity of 84.6% and a specificity of 60.0% (AUC = 0.75) in predicting severe HK of >6.0 (phosphate, AUC = 0.37). Computing of five standard serum parameters (potassium, BUN, sodium, phosphate, LDH) resulted in a sensitivity of 88.9% and a specificity of 79.3% for the prediction of severe HK >6.0 (accuracy = 81.6%). Conclusions A combination of serum parameters predicted prospectively the occurrence of relevant HK with an accuracy of 81.6% underlining its potential utility for identifying ‘high-risk’ patients prone to PRRT. PMID:25977880

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.

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.

Simple detection method of amyloid-beta peptide using p-FET with optical filtering layer and magnetic particle

NASA Astrophysics Data System (ADS)

Kim, Kwan-Soo; Kim, Chang-Beom; Song, Ki-Bong

2013-05-01

This article describes a novel method for detection of amyloid-β (Aβ) peptide that utilizes a photo-sensitive field-effect transistor (p-FET). According to a recent study, Aβ protein is known to play a central role in the pathogenesis of Alzheimer's disease (AD). Accordingly, we investigated the variation of photo current of the p-FET generated by the magnetic beads conjugated with Aβ peptides which are placed on the p-FET sensing areas. Additionally, in order to amplify the output signal, we used the lock-in amplifier (LIA) and confirmed the generating the photo current by a small incident light power under 100 μW. It means that it is possible to simply detect a certain protein using magnetic beads conjugated with Aβ peptide and fluorescent label located on the p-FET device. Therefore, in this paper, we suggest that our method could detect tiny amounts of Aβ peptide for early diagnosis of AD using the p-FET devices.

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

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.

Pathways to Structure-Property Relationships of Peptide-Materials Interfaces: Challenges in Predicting Molecular Structures.

PubMed

Walsh, Tiffany R

2017-07-18

challenges in their successful application to model the biotic-abiotic interface, related to several factors. For instance, simulations require a plausible description of the chemistry and the physics of the interface, which comprises two very different states of matter, in the presence of liquid water. Also, it is essential that the conformational ensemble be comprehensively characterized under these conditions; this is especially challenging because intrinsically disordered peptides do not typically admit one single structure or set of structures. Moreover, a plausible structural model of the substrate is required, which may require a high level of detail, even for single-element materials such as Au surfaces or graphene. Developing and applying strategies to make credible predictions of the conformational ensemble of adsorbed peptides and using these to construct structure-property relationships of these interfaces have been the goals of our efforts. We have made substantial progress in developing interatomic potentials for these interfaces and adapting advanced conformational sampling approaches for these purposes. This Account summarizes our progress in the development and deployment of interfacial force fields and molecular simulation techniques for the purpose of elucidating these insights at biomolecule-materials interfaces, using examples from our laboratories ranging from noble-metal interfaces to graphitic substrates (including carbon nanotubes and graphene) and oxide materials (such as titania). In addition to the well-established application areas of plasmonic materials, biosensing, and the production of medical implant materials, we outline new directions for this field that have the potential to bring new advances in areas such as energy materials and regenerative medicine.

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.

PepPat, a pattern-based oligopeptide homology search method and the identification of a novel tachykinin-like peptide.

PubMed

Jiang, Ying; Gao, Ge; Fang, Gang; Gustafson, Eric L; Laverty, Maureen; Yin, Yanbin; Zhang, Yong; Luo, Jingchu; Greene, Jonathan R; Bayne, Marvin L; Hedrick, Joseph A; Murgolo, Nicholas J

2003-05-01

PepPat, a hybrid method that combines pattern matching with similarity scoring, is described. We also report PepPat's application in the identification of a novel tachykinin-like peptide. PepPat takes as input a query peptide and a user-specified regular expression pattern within the peptide. It first performs a database pattern match and then ranks candidates on the basis of their similarity to the query peptide. PepPat calculates similarity over the pattern spanning region, enhancing PepPat's sensitivity for short query peptides. PepPat can also search for a user-specified number of occurrences of a repeated pattern within the target sequence. We illustrate PepPat's application in short peptide ligand mining. As a validation example, we report the identification of a novel tachykinin-like peptide, C14TKL-1, and show it is an NK1 (neuokinin receptor 1) agonist whose message is widely expressed in human periphery. PepPat is offered online at: http://peppat.cbi.pku.edu.cn.

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.

Cytotoxic T lymphocyte response to peptide vaccination predicts survival in stage III colorectal cancer.

PubMed

Kawamura, Junichiro; Sugiura, Fumiaki; Sukegawa, Yasushi; Yoshioka, Yasumasa; Hida, Jin-Ichi; Hazama, Shoichi; Okuno, Kiyotaka

2018-02-23

We previously reported a phase I clinical trial of a peptide vaccine ring finger protein 43 (RNF43) and 34-kDa translocase of the outer mitochondrial membrane (TOMM34) combined with uracil-tegafur (UFT)/LV for patients with metastatic colorectal cancer (CRC), and demonstrated the safety and immunological responsiveness of this combination therapy. In this study, we evaluated vaccination-induced immune responses to clarify the survival benefit of the combination therapy as adjuvant treatment. We enrolled 44 patients initially in an HLA-masked fashion. After the disclosure of HLA, 28 patients were in the HLA-A*2402-matched and 16 were in the unmatched group. In the HLA-matched group, 14 patients had positive CTL responses specific for the RNF43 and/or TOMM34 peptides after 2 cycles of treatment and 9 had negative responses; in the HLA-unmatched group, 10 CTL responses were positive and 2 negative. In the HLA-matched group, 3-year relapse-free survival (RFS) was significantly better in the positive CTL subgroup than in the negative-response subgroup. Patients with negative vaccination-induced CTL responses showed a significant trend towards shorter RFS than those with positive responses. Moreover, in the HLA-unmatched group, the positive CTL response subgroup showed an equally good 3-year RFS as in the HLA-matched group. In conclusion, vaccination-induced CTL response to peptide vaccination could predict survival in the adjuvant setting for stage III CRC. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library.

PubMed

Ru, Beibei; 't Hoen, Peter A C; Nie, Fulei; Lin, Hao; Guo, Feng-Biao; Huang, Jian

2014-02-01

Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.

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

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

PubMed Central

Nesvizhskii, Alexey I.

2010-01-01

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues. PMID:20816881

Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.

PubMed

Gao, JianZhao; Tao, Xue-Wen; Zhao, Jia; Feng, Yuan-Ming; Cai, Yu-Dong; Zhang, Ning

2017-01-01

Lysine acetylation, as one type of post-translational modifications (PTM), plays key roles in cellular regulations and can be involved in a variety of human diseases. However, it is often high-cost and time-consuming to use traditional experimental approaches to identify the lysine acetylation sites. Therefore, effective computational methods should be developed to predict the acetylation sites. In this study, we developed a position-specific method for epsilon lysine acetylation site prediction. Sequences of acetylated proteins were retrieved from the UniProt database. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed. Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, which suggested some important factors determining the lysine acetylation sites. We developed a position-specific method for epsilon lysine acetylation site prediction. A set of optimal features was selected. Analysis of the optimal features provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

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.

Prediction of antiviral peptides derived from viral fusion proteins potentially active against herpes simplex and influenza A viruses

PubMed Central

Jesús, Torres; Rogelio, López; Abraham, Cetina; Uriel, López; J- Daniel, García; Alfonso, Méndez-Tenorio; Lilia, Barrón Blanca

2012-01-01

There are very few antiviral drugs available to fight viral infections and the appearance of viral strains resistant to these antivirals is not a rare event. Hence, the design of new antiviral drugs is important. We describe the prediction of peptides with antiviral activity (AVP) derived from the viral glycoproteins involved in the entrance of herpes simplex (HSV) and influenza A viruses into their host cells. It is known, that during this event viral glycoproteins suffer several conformational changes due to protein-protein interactions, which lead to membrane fusion between the viral envelope and the cellular membrane. Our hypothesis is that AVPs can be derived from these viral glycoproteins, specifically from regions highly conserved in amino acid sequences, which at the same time have the physicochemical properties of being highly exposed (antigenic), hydrophilic, flexible, and charged, since these properties are important for protein-protein interactions. For that, we separately analyzed the HSV glycoprotein H and B, and influenza A viruses hemagglutinin (HA), using several bioinformatics tools. A set of multiple alignments was carried out, to find the most conserved regions in the amino acid sequences. Then, the physicochemical properties indicated above were analyzed. We predicted several peptides 12-20 amino acid length which by docking analysis were able to interact with the fusion viral glycoproteins and thus may prevent conformational changes in them, blocking the viral infection. Our strategy to design AVPs seems to be very promising since the peptides were synthetized and their antiviral activities have produced very encouraging results. PMID:23144542

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

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.

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.

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.

Peptide chemistry toolbox - Transforming natural peptides into peptide therapeutics.

PubMed

Erak, Miloš; Bellmann-Sickert, Kathrin; Els-Heindl, Sylvia; Beck-Sickinger, Annette G

2018-06-01

The development of solid phase peptide synthesis has released tremendous opportunities for using synthetic peptides in medicinal applications. In the last decades, peptide therapeutics became an emerging market in pharmaceutical industry. The need for synthetic strategies in order to improve peptidic properties, such as longer half-life, higher bioavailability, increased potency and efficiency is accordingly rising. In this mini-review, we present a toolbox of modifications in peptide chemistry for overcoming the main drawbacks during the transition from natural peptides to peptide therapeutics. Modifications at the level of the peptide backbone, amino acid side chains and higher orders of structures are described. Furthermore, we are discussing the future of peptide therapeutics development and their impact on the pharmaceutical market. Copyright © 2018 Elsevier Ltd. All rights reserved.

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).

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.

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.

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/.

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

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

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.

AJIPHASE®: A Highly Efficient Synthetic Method for One-Pot Peptide Elongation in the Solution Phase by an Fmoc Strategy.

PubMed

Takahashi, Daisuke; Inomata, Tatsuji; Fukui, Tatsuya

2017-06-26

We previously reported an efficient peptide synthesis method, AJIPHASE®, that comprises repeated reactions and isolations by precipitation. This method utilizes an anchor molecule with long-chain alkyl groups as a protecting group for the C-terminus. To further improve this method, we developed a one-pot synthesis of a peptide sequence wherein the synthetic intermediates were isolated by solvent extraction instead of precipitation. A branched-chain anchor molecule was used in the new process, significantly enhancing the solubility of long peptides and the operational efficiency compared with the previous method, which employed precipitation for isolation and a straight-chain aliphatic group. Another prerequisite for this solvent-extraction-based strategy was the use of thiomalic acid and DBU for Fmoc deprotection, which facilitates the removal of byproducts, such as the fulvene adduct. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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.

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

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

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

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.

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.

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

Improving density functional tight binding predictions of free energy surfaces for peptide condensation reactions in solution

NASA Astrophysics Data System (ADS)

Kroonblawd, Matthew; Goldman, Nir

First principles molecular dynamics using highly accurate density functional theory (DFT) is a common tool for predicting chemistry, but the accessible time and space scales are often orders of magnitude beyond the resolution of experiments. Semi-empirical methods such as density functional tight binding (DFTB) offer up to a thousand-fold reduction in required CPU hours and can approach experimental scales. However, standard DFTB parameter sets lack good transferability and calibration for a particular system is usually necessary. Force matching the pairwise repulsive energy term in DFTB to short DFT trajectories can improve the former's accuracy for chemistry that is fast relative to DFT simulation times (<10 ps), but the effects on slow chemistry and the free energy surface are not well-known. We present a force matching approach to increase the accuracy of DFTB predictions for free energy surfaces. Accelerated sampling techniques are combined with path collective variables to generate the reference DFT data set and validate fitted DFTB potentials without a priori knowledge of transition states. Accuracy of force-matched DFTB free energy surfaces is assessed for slow peptide-forming reactions by direct comparison to DFT results for particular paths. Extensions to model prebiotic chemistry under shock conditions are discussed. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

The PeptideAtlas Project.

PubMed

Deutsch, Eric W

2010-01-01

PeptideAtlas is a multi-species compendium of peptides observed with tandem mass spectrometry methods. Raw mass spectrometer output files are collected from the community and reprocessed through a uniform analysis and validation pipeline that continues to advance. The results are loaded into a database and the information derived from the raw data is returned to the community via several web-based data exploration tools. The PeptideAtlas resource is useful for experiment planning, improving genome annotation, and other data mining projects. PeptideAtlas has become especially useful for planning targeted proteomics experiments.

Developing a capillary electrophoresis based method for dynamically monitoring enzyme cleavage activity using quantum dots-peptide assembly.

PubMed

Wang, Jianhao; Fan, Jie; Liu, Li; Ding, Shumin; Liu, Xiaoqian; Wang, Jianpeng; Gao, Liqian; Chattopadhaya, Souvik; Miao, Peng; Xia, Jiang; Qiu, Lin; Jiang, Pengju

2017-10-01

Herein, a novel assay has been developed for monitoring PreScission protease (His-PSP) mediated enzyme cleavage of ATTO 590 labeled peptide substrate (ATTO-LEV). This novel method is based on combining the use of capillary electrophoresis and fluorescence detection (CE-FL) to dynamically monitor the enzyme cleavage activity. A multivalent peptide substrate was first constructed by immobilizing His-tagged ATTO 590 labeled peptide substrate (ATTO-LEVH6) onto the surface of CdSe/ZnS quantum dots (QDs). Once successfully immobilized, the novel multivalent peptide substrate resulted in the Förster resonance energy transfer (FRET) from QDs to ATTO 590. The ATTO-LEVH6-QD assembly was then incubated with His-PSP to study the proteolytic cleavage of surface bound ATTO-LEVH6 by CE-FL. Our data suggests that PreScission-mediated proteolytic cleavage is enzyme concentration- and incubation time-dependent. By combining capillary electrophoresis, QDs and FRET, our study herein not only provides a new method for the detection and dynamically monitoring of PSP enzyme cleavage activity, but also can be extended to the detection of many other enzymes and proteases. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

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.

Synthesis, molecular docking and anticancer studies of peptides and iso-peptides.

PubMed

Jabeen, Farukh; Panda, Siva S; Kondratyuk, Tamara P; Park, Eun-Jung; Pezzuto, John M; Ihsan-ul-Haq; Hall, C Dennis; Katritzky, Alan R

2015-08-01

Chiral peptides and iso-peptides were synthesized in excellent yield by using benzotriazole mediated solution phase synthesis. Benzotriazole acted both as activating and leaving group, eliminating frequent use of protection and subsequent deprotection. The procedure was based on the hypothesis that epimerization should be suppressed in solution due to a faster coupling rate than SPPS. All the synthesized peptides complied with Lipinski's Ro5 except for the rotatable bonds. Inhibition of cell proliferation of cancer cell lines is one of the most commonly used methods to study the effectiveness of any anticancer agents. Synthesized peptides and iso-peptides were tested against three cancer cell lines (MCF-7, MDA-MB 231) to determine their anti-proliferative potential. NFkB was also determined. Molecular docking studies were also carried out to complement the experimental results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Composition and method for self-assembly and mineralization of peptide-amphiphiles

DOEpatents

Stupp, Samuel I [Chicago, IL; Beniash, Elia [Newton, MA; Hartgerink, Jeffrey D [Pearland, TX

2012-02-28

The present invention is directed to a composition useful for making homogeneously mineralized self assembled peptide-amphiphile nanofibers and nanofiber gels. The composition is generally a solution comprised of a positively or negatively charged peptide-amphiphile and a like signed ion from the mineral. Mixing this solution with a second solution containing a dissolved counter-ion of the mineral and/or a second oppositely charged peptide amphiphile, results in the rapid self assembly of the peptide-amphiphiles into a nanofiber gel and templated mineralization of the ions. Templated mineralization of the initially dissolved mineral cations and anions in the mixture occurs with preferential orientation of the mineral crystals along the fiber surfaces within the nanofiber gel. One advantage of the present invention is that it results in homogenous growth of the mineral throughout the nanofiber gel. Another advantage of the present invention is that the nanofiber gel formation and mineralization reactions occur in a single mixing step and under substantially neutral or physiological pH conditions. These homogeneous nanostructured composite materials are useful for medical applications especially the regeneration of damaged bone in mammals. This invention is directed to the synthesis of peptide-amphiphiles with more than one amphiphilic moment and to supramolecular compositions comprised of such multi-dimensional peptide-amphiphiles. Supramolecular compositions can be formed by self assembly of multi-dimensional peptide-amphiphiles by mixing them with a solution comprising a monovalent cation.

Composition and method for self-assembly and mineralization of peptide amphiphiles

DOEpatents

Stupp, Samuel I [Chicago, IL; Beniash, Elia [Newton, MA; Hartgerink, Jeffrey D [Houston, TX

2009-06-30

The present invention is directed to a composition useful for making homogeneously mineralized self assembled peptide-amphiphile nanofibers and nanofiber gels. The composition is generally a solution comprised of a positively or negatively charged peptide-amphiphile and a like signed ion from the mineral. Mixing this solution with a second solution containing a dissolved counter-ion of the mineral and/or a second oppositely charged peptide amphiphile, results in the rapid self assembly of the peptide-amphiphiles into a nanofiber gel and templated mineralization of the ions. Templated mineralization of the initially dissolved mineral cations and anions in the mixture occurs with preferential orientation of the mineral crystals along the fiber surfaces within the nanofiber gel. One advantage of the present invention is that it results in homogenous growth of the mineral throughout the nanofiber gel. Another advantage of the present invention is that the nanofiber gel formation and mineralization reactions occur in a single mixing step and under substantially neutral or physiological pH conditions. These homogeneous nanostructured composite materials are useful for medical applications especially the regeneration of damaged bone in mammals. This invention is directed to the synthesis of peptide-amphiphiles with more than one amphiphilic moment and to supramolecular compositions comprised of such multi-dimensional peptide-amphiphiles. Supramolecular compositions can be formed by self assembly of multi-dimensional peptide-amphiphiles by mixing them with a solution comprising a monovalent cation.

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.

[The predictive value of plasma B-type natriuretic peptide levels on outcome in children with pulmonary hypertension undergoing congenital heart surgery].

PubMed

Baysal, Ayse; Saşmazel, Ahmet; Yildirim, Ayse; Ozyaprak, Buket; Gundogus, Narin; Kocak, Tuncer

2014-01-01

In children undergoing congenital heart surgery, plasma brain natriuretic peptide levels may have a role in development of low cardiac output syndrome that is defined as a combination of clinical findings and interventions to augment cardiac output in children with pulmonary hypertension. In a prospective observational study, fifty-one children undergoing congenital heart surgery with preoperative echocardiographic study showing pulmonary hypertension were enrolled. The plasma brain natriuretic peptide levels were collected before operation, 12, 24 and 48h after operation. The patients enrolled into the study were divided into two groups depending on: (1) Development of LCOS which is defined as a combination of clinical findings or interventions to augment cardiac output postoperatively; (2) Determination of preoperative brain natriuretic peptide cut-off value by receiver operating curve analysis for low cardiac output syndrome. The secondary end points were: (1) duration of mechanical ventilation ≥72h, (2) intensive care unit stay >7days, and (3) mortality. The differences in preoperative and postoperative brain natriuretic peptide levels of patients with or without low cardiac output syndrome (n=35, n=16, respectively) showed significant differences in repeated measurement time points (p=0.0001). The preoperative brain natriuretic peptide cut-off value of 125.5pgmL-1 was found to have the highest sensitivity of 88.9% and specificity of 96.9% in predicting low cardiac output syndrome in patients with pulmonary hypertension. A good correlation was found between preoperative plasma brain natriuretic peptide level and duration of mechanical ventilation (r=0.67, p=0.0001). In patients with pulmonary hypertension undergoing congenital heart surgery, 91% of patients with preoperative plasma brain natriuretic peptide levels above 125.5pgmL-1 are at risk of developing low cardiac output syndrome which is an important postoperative outcome. Copyright © 2013 Sociedade

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

Single-subunit oligosaccharyltransferases of Trypanosoma brucei display different and predictable peptide acceptor specificities.

PubMed

Jinnelov, Anders; Ali, Liaqat; Tinti, Michele; Güther, Maria Lucia S; Ferguson, Michael A J

2017-12-08

Trypanosoma brucei causes African trypanosomiasis and contains three full-length oligosaccharyltransferase (OST) genes; two of which, Tb STT3A and Tb STT3B, are expressed in the bloodstream form of the parasite. These OSTs have different peptide acceptor and lipid-linked oligosaccharide donor specificities, and trypanosomes do not follow many of the canonical rules developed for other eukaryotic N -glycosylation pathways, raising questions as to the basic architecture and detailed function of trypanosome OSTs. Here, we show by blue-native gel electrophoresis and stable isotope labeling in cell culture proteomics that the Tb STT3A and Tb STT3B proteins associate with each other in large complexes that contain no other detectable protein subunits. We probed the peptide acceptor specificities of the OSTs in vivo using a transgenic glycoprotein reporter system and performed glycoproteomics on endogenous parasite glycoproteins using sequential endoglycosidase H and peptide: N -glycosidase-F digestions. This allowed us to assess the relative occupancies of numerous N -glycosylation sites by endoglycosidase H-resistant N -glycans originating from Man 5 GlcNAc 2 -PP-dolichol transferred by Tb STT3A, and endoglycosidase H-sensitive N -glycans originating from Man 9 GlcNAc 2 -PP-dolichol transferred by Tb STT3B. Using machine learning, we assessed the features that best define Tb STT3A and Tb STT3B substrates in vivo and built an algorithm to predict the types of N -glycan most likely to predominate at all the putative N -glycosylation sites in the parasite proteome. Finally, molecular modeling was used to suggest why Tb STT3A has a distinct preference for sequons containing and/or flanked by acidic amino acid residues. Together, these studies provide insights into how a highly divergent eukaryote has re-wired protein N -glycosylation to provide protein sequence-specific N -glycan modifications. Data are available via ProteomeXchange with identifiers PXD007236, PXD007267

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

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

[Distiller Yeasts Producing Antibacterial Peptides].

PubMed

Klyachko, E V; Morozkina, E V; Zaitchik, B Ts; Benevolensky, S V

2015-01-01

A new method of controlling lactic acid bacteria contamination was developed with the use of recombinant Saccharomyces cerevisiae strains producing antibacterial peptides. Genes encoding the antibacterial peptides pediocin and plantaricin with codons preferable for S. cerevisiae were synthesized, and a system was constructed for their secretory expression. Recombinant S. cerevisiae strains producing antibacterial peptides effectively inhibit the growth of Lactobacillus sakei, Pediacoccus pentasaceus, Pediacoccus acidilactici, etc. The application of distiller yeasts producing antibacterial peptides enhances the ethanol yield in cases of bacterial contamination. Recombinant yeasts producing the antibacterial peptides pediocin and plantaricin can successfully substitute the available industrial yeast strains upon ethanol production.

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

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.

Computational prediction of the pKas of small peptides through Conceptual DFT descriptors

NASA Astrophysics Data System (ADS)

Frau, Juan; Hernández-Haro, Noemí; Glossman-Mitnik, Daniel

2017-03-01

The experimental pKa of a group of simple amines have been plotted against several Conceptual DFT descriptors calculated by means of different density functionals, basis sets and solvation schemes. It was found that the best fits are those that relate the pKa of the amines with the global hardness η through the MN12SX density functional in connection with the Def2TZVP basis set and the SMD solvation model, using water as a solvent. The parameterized equation resulting from the linear regression analysis has then been used for the prediction of the pKa of small peptides of interest in the study of diabetes and Alzheimer disease. The accuracy of the results is relatively good, with a MAD of 0.36 units of pKa.

Predicting Three-Dimensional Conformations of Peptides Constructed of Only Glycine, Alanine, Aspartic Acid, and Valine

NASA Astrophysics Data System (ADS)

Oda, Akifumi; Fukuyoshi, Shuichi

2015-06-01

The GADV hypothesis is a form of the protein world hypothesis, which suggests that life originated from proteins (Lacey et al. 1999; Ikehara 2002; Andras 2006). In the GADV hypothesis, life is thought to have originated from primitive proteins constructed of only glycine, alanine, aspartic acid, and valine ([GADV]-proteins). In this study, the three-dimensional (3D) conformations of randomly generated short [GADV]-peptides were computationally investigated using replica-exchange molecular dynamics (REMD) simulations (Sugita and Okamoto 1999). Because the peptides used in this study consisted of only 20 residues each, they could not form certain 3D structures. However, the conformational tendencies of the peptides were elucidated by analyzing the conformational ensembles generated by REMD simulations. The results indicate that secondary structures can be formed in several randomly generated [GADV]-peptides. A long helical structure was found in one of the hydrophobic peptides, supporting the conjecture of the GADV hypothesis that many peptides aggregated to form peptide multimers with enzymatic activity in the primordial soup. In addition, these results indicate that REMD simulations can be used for the structural investigation of short peptides.

Time-resolved method to distinguish protein/peptide oxidation during electrospray ionization mass spectrometry.

PubMed

Pei, Jiying; Hsu, Cheng-Chih; Yu, Kefu; Wang, Yinghui; Huang, Guangming

2018-06-29

Electrospray ionization mass spectrometry (ESI-MS) is one of the most prevalent techniques used to monitor protein/peptide oxidation induced by reactive oxygen species (ROSs). However, both corona discharge (CD) and electrochemistry (EC) can also lead to protein/peptide oxidation during ESI. Because the two types of oxidation occur almost simultaneously, determining the extent to which the two pathways contribute to protein/peptide oxidation is difficult. Herein, a time-resolved method was introduced to identify and differentiate CD- and EC-induced oxidation. Using this approach, we separated the instantaneous CD-induced oxidation from the hysteretic EC-induced oxidation, and the effects of the spray voltage and flow rate of the ESI source on both oxidation types were investigated with a homemade ESI source. For angiotensin II analogue (b-DRVYVHPF-y), the dehydrogenation and oxygenation species were the detected EC-induced oxidation products, while the oxygenation species were the major CD-induced oxidation products. This time-resolved approach was also applicable to a commercial HESI source, in which both CD and EC were responsible for hemoglobin and cytochrome c oxidation with upstream grounding while CD dominated the oxidation without upstream grounding. Copyright © 2018 Elsevier B.V. All rights reserved.

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

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.

Gold nanoparticles-based electrochemical method for the detection of protein kinase with a peptide-like inhibitor as the bioreceptor

PubMed Central

Sun, Kai; Chang, Yong; Zhou, Binbin; Wang, Xiaojin; Liu, Lin

2017-01-01

This article presents a general method for the detection of protein kinase with a peptide-like kinase inhibitor as the bioreceptor, and it was done by converting gold nanoparticles (AuNPs)-based colorimetric assay into sensitive electrochemical analysis. In the colorimetric assay, the kinase-specific aptameric peptide triggered the aggregation of AuNPs in solution. However, the specific binding of peptide to the target protein (kinase) inhibited its ability to trigger the assembly of AuNPs. In the electrochemical analysis, peptides immobilized on a gold electrode and presented as solution triggered together the in situ formation of AuNPs-based network architecture on the electrode surface. Nevertheless, the formation of peptide–kinase complex on the electrode surface made the peptide-triggered AuNPs assembly difficult. Electrochemical impedance spectroscopy was used to measure the change in surface property in the binding events. When a ferrocene-labeled peptide (Fc-peptide) was used in this design, the network of AuNPs/Fc-peptide produced a good voltammetric signal. The competitive assay allowed for the detection of protein kinase A with a detection limit of 20 mU/mL. This work should be valuable for designing novel optical or electronic biosensors and likely lead to many detection applications. PMID:28331314

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.

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

C-STrap Sample Preparation Method--In-Situ Cysteinyl Peptide Capture for Bottom-Up Proteomics Analysis in the STrap Format.

PubMed

Zougman, Alexandre; Banks, Rosamonde E

2015-01-01

Recently we introduced the concept of Suspension Trapping (STrap) for bottom-up proteomics sample processing that is based upon SDS-mediated protein extraction, swift detergent removal and rapid reactor-type protein digestion in a quartz depth filter trap. As the depth filter surface is made of silica, it is readily modifiable with various functional groups using the silane coupling chemistries. Thus, during the digest, peptides possessing specific features could be targeted for enrichment by the functionalized depth filter material while non-targeted peptides could be collected as an unbound distinct fraction after the digest. In the example presented here the quartz depth filter surface is functionalized with the pyridyldithiol group therefore enabling reversible in-situ capture of the cysteine-containing peptides generated during the STrap-based digest. The described C-STrap method retains all advantages of the original STrap methodology and provides robust foundation for the conception of the targeted in-situ peptide fractionation in the STrap format for bottom-up proteomics. The presented data support the method's use in qualitative and semi-quantitative proteomics experiments.

Comparison of copeptin, B-type natriuretic peptide, and amino-terminal pro-B-type natriuretic peptide in patients with chronic heart failure: prediction of death at different stages of the disease.

PubMed

Neuhold, Stephanie; Huelsmann, Martin; Strunk, Guido; Stoiser, Brigitte; Struck, Joachim; Morgenthaler, Nils G; Bergmann, Andreas; Moertl, Deddo; Berger, Rudolf; Pacher, Richard

2008-07-22

This study sought to evaluate the predictive value of copeptin over the entire spectrum of heart failure (HF) and compare it to the current benchmark markers, B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP). Vasopressin has been shown to increase with the severity of chronic HF. Copeptin is a fragment of pre-pro-vasopressin that is synthesized and secreted in equimolar amounts to vasopressin. Both hormones have a short lifetime in vivo, similar to BNPs, but in contrast to vasopressin, copeptin is very stable in vitro. The predictive value of copeptin has been shown in advanced HF, where it was superior to BNP for predicting 24-month mortality. This was a long-term observational study in 786 HF patients from the whole spectrum of heart failure (New York Heart Association [NYHA] functional class I to IV, BNP 688 +/- 948 pg/ml [range 3 to 8,536 pg/ml], left ventricular ejection fraction 25 +/- 10% [range 5% to 65%]). The NYHA functional class was the most potent single predictor of 24-month outcome in a stepwise Cox regression model. The BNP, copeptin, and glomerular filtration rate were related to NYHA functional class (p < 0.0001 for trend). Copeptin was the most potent single predictor of mortality in patients with NYHA functional class II (p < 0.0001) and class III (p < 0.0001). In NYHA functional class IV, the outcome of patients was best predicted by serum sodium, but again, copeptin added additional independent information. Increased levels of copeptin are linked to excess mortality, and this link is maintained irrespective of the clinical signs of severity of the disease. Copeptin was superior to BNP or NT-proBNP in this study, but the markers seem to be closely related.

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.

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.

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

Analysis of the endogenous peptide profile of milk: identification of 248 mainly casein-derived peptides.

PubMed

Baum, Florian; Fedorova, Maria; Ebner, Jennifer; Hoffmann, Ralf; Pischetsrieder, Monika

2013-12-06

Milk is an excellent source of bioactive peptides. However, the composition of the native milk peptidome has only been partially elucidated. The present study applied matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) directly or after prefractionation of the milk peptides by reverse-phase high-performance liquid chromatography (RP-HPLC) or OFFGEL fractionation for the comprehensive analysis of the peptide profile of raw milk. The peptide sequences were determined by MALDI-TOF/TOF or nano-ultra-performance liquid chromatography-nanoelectrospray ionization-LTQ-Orbitrap-MS. Direct MALDI-TOF-MS analysis led to the assignment of 57 peptides. Prefractionation by both complementary methods led to the assignment of another 191 peptides. Most peptides originate from α(S1)-casein, followed by β-casein, and α(S2)-casein. κ-Casein and whey proteins seem to play only a minor role as peptide precursors. The formation of many, but not all, peptides could be explained by the activity of the endogenous peptidases, plasmin or cathepsin D, B, and G. Database searches revealed the presence of 22 peptides with established physiological function, including those with angiotensin-converting-enzyme (ACE) inhibitory, immunomodulating, or antimicrobial activity.

Development of a group contribution method for estimating free energy of peptides in a dodecane-water system via molecular dynamic simulations.

PubMed

Mora Osorio, Camilo Andrés; González Barrios, Andrés Fernando

2016-12-07

Calculation of the Gibbs free energy changes of biological molecules at the oil-water interface is commonly performed with Molecular Dynamics simulations (MD). It is a process that could be performed repeatedly in order to find some molecules of high stability in this medium. Here, an alternative method of calculation has been proposed: a group contribution method (GCM) for peptides based on MD of the twenty classic amino acids to obtain free energy change during the insertion of any peptide chain in water-dodecane interfaces. Multiple MD of the twenty classic amino acids located at the interface of rectangular simulation boxes with a dodecane-water medium were performed. A GCM to calculate the free energy of entire peptides is then proposed. The method uses the summation of the Gibbs free energy of each amino acid adjusted in function of its presence or absence in the chain as well as its hydrophobic characteristics. Validation of the equation was performed with twenty-one peptides all simulated using MD in dodecane-water rectangular boxes in previous work, obtaining an average relative error of 16%.

Peptide synthesis triggered by comet impacts: A possible method for peptide delivery to the early Earth and icy satellites

NASA Astrophysics Data System (ADS)

Sugahara, Haruna; Mimura, Koichi

2015-09-01

We performed shock experiments simulating natural comet impacts in an attempt to examine the role that comet impacts play in peptide synthesis. In the present study, we selected a mixture of alanine (DL-alanine), water ice, and silicate (forsterite) to make a starting material for the experiments. The shock experiments were conducted under cryogenic conditions (77 K), and the shock pressure range achieved in the experiments was 4.8-25.8 GPa. The results show that alanine is oligomerized into peptides up to tripeptides due to the impact shock. The synthesized peptides were racemic, indicating that there was no enantioselective synthesis of peptides from racemic amino acids due to the impact shock. We also found that the yield of linear peptides was a magnitude higher than those of cyclic diketopiperazine. Furthermore, we estimated the amount of cometary-derived peptides to the early Earth based on two models (the Lunar Crating model and the Nice model) during the Late Heavy Bombardment (LHB) using our experimental data. The estimation based on the Lunar Crating model gave 3 × 109 mol of dialanine, 4 × 107 mol of trialanine, and 3 × 108 mol of alanine-diketopiperazine. Those based on the Nice model, in which the main impactor of LHB is comets, gave 6 × 1010 mol of dialanine, 1 × 109 mol of trialanine, and 8 × 109 mol of alanine-diketopiperazine. The estimated amounts were comparable to those originating from terrestrial sources (Cleaves, H.J., Aubrey, A.D., Bada, J.L. [2009]. Orig. Life Evol. Biosph. 39, 109-126). Our results indicate that comet impacts played an important role in chemical evolution as a supplier of linear peptides, which are important for further chemical evolution on the early Earth. Our study also highlights the importance of icy satellites, which were formed by comet accumulation, as prime targets for missions searching for extraterrestrial life.

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.

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

Tumor-targeting peptides from combinatorial libraries*

PubMed Central

Liu, Ruiwu; Li, Xiaocen; Xiao, Wenwu; Lam, Kit S.

2018-01-01

Cancer is one of the major and leading causes of death worldwide. Two of the greatest challenges infighting cancer are early detection and effective treatments with no or minimum side effects. Widespread use of targeted therapies and molecular imaging in clinics requires high affinity, tumor-specific agents as effective targeting vehicles to deliver therapeutics and imaging probes to the primary or metastatic tumor sites. Combinatorial libraries such as phage-display and one-bead one-compound (OBOC) peptide libraries are powerful approaches in discovering tumor-targeting peptides. This review gives an overview of different combinatorial library technologies that have been used for the discovery of tumor-targeting peptides. Examples of tumor-targeting peptides identified from each combinatorial library method will be discussed. Published tumor-targeting peptide ligands and their applications will also be summarized by the combinatorial library methods and their corresponding binding receptors. PMID:27210583

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

PubMed Central

Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

2011-01-01

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, as this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referred to as Statistical Tools for AMT tag Confidence (STAC). STAC additionally provides a Uniqueness Probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download as both a command line and a Windows graphical application. PMID:21692516

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

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.

CART PEPTIDE IN THE NUCLEUS ACCUMBENS REGULATES PSYCHOSTIMULANTS: CORRELATIONS BETWEEN PSYCHOSTIMULANT AND CART PEPTIDE EFFECTS

PubMed Central

JOB, MARTIN O.; KUHAR, MICHAEL J.

2017-01-01

In this study, we reexamined the effect of CART peptide on psychostimulant (PS)-induced locomotor activity (LMA) in individual rats. The Methods utilized were as previously published. The PS-induced LMA was defined as the distance traveled after PS administration (intraperitoneal), and the CART peptide effect was defined as the change in the PS-induced activity after bilateral intra-NAc administration of CART peptide. The experiments included both male and female Sprague-Dawley rats, and varying the CART peptide dose and the PS dose. While the average effect of CART peptide was to inhibit PS-induced LMA, the effect of CART peptide on individual PS treated animals was not always inhibitory and sometimes even produced an increase or no change in PS-induced LMA. Upon further analysis, we observed a linear correlation, reported for the first time, between the magnitude of PS-induced LMA and the CART peptide effect. Because CART peptide inhibits PS-induced LMA when it is large, and increases PS-induced LMA when it is small, the peptide can be considered a homeostatic regulator of dopamine (DA)-induced LMA, which supports our earlier homeostatic hypothesis. PMID:28215744

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

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.

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

Tidbits for the synthesis of bis(2-sulfanylethyl)amido (SEA) polystyrene resin, SEA peptides and peptide thioesters.

PubMed

Ollivier, Nathalie; Raibaut, Laurent; Blanpain, Annick; Desmet, Rémi; Dheur, Julien; Mhidia, Reda; Boll, Emmanuelle; Drobecq, Hervé; Pira, Silvain L; Melnyk, Oleg

2014-02-01

Protein total chemical synthesis enables the atom-by-atom control of the protein structure and therefore has a great potential for studying protein function. Native chemical ligation of C-terminal peptide thioesters with N-terminal cysteinyl peptides and related methodologies are central to the field of protein total synthesis. Consequently, methods enabling the facile synthesis of peptide thioesters using Fmoc-SPPS are of great value. Herein, we provide a detailed protocol for the preparation of bis(2-sulfanylethyl)amino polystyrene resin as a starting point for the synthesis of C-terminal bis(2-sulfanylethyl)amido peptides and of peptide thioesters derived from 3-mercaptopropionic acid. Copyright © 2013 European Peptide Society and John Wiley & Sons, Ltd.

Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB.

PubMed

Garton, Michael; Nim, Satra; Stone, Tracy A; Wang, Kyle Ethan; Deber, Charles M; Kim, Philip M

2018-02-13

Biologics are a rapidly growing class of therapeutics with many advantages over traditional small molecule drugs. A major obstacle to their development is that proteins and peptides are easily destroyed by proteases and, thus, typically have prohibitively short half-lives in human gut, plasma, and cells. One of the most effective ways to prevent degradation is to engineer analogs from dextrorotary (D)-amino acids, with up to 10 5 -fold improvements in potency reported. We here propose a general peptide-engineering platform that overcomes limitations of previous methods. By creating a mirror image of every structure in the Protein Data Bank (PDB), we generate a database of ∼2.8 million D-peptides. To obtain a D-analog of a given peptide, we search the (D)-PDB for similar configurations of its critical-"hotspot"-residues. As a proof of concept, we apply our method to two peptides that are Food and Drug Administration approved as therapeutics for diabetes and osteoporosis, respectively. We obtain D-analogs that activate the GLP1 and PTH1 receptors with the same efficacy as their natural counterparts and show greatly increased half-life. Copyright © 2018 the Author(s). Published by PNAS.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

PubMed

Diller, David J; Swanson, Jon; Bayden, Alexander S; Jarosinski, Mark; Audie, Joseph

2015-01-01

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

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.

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.

Antioxidant Peptides Identified from Ovotransferrin by the ORAC Method Did Not Show Anti-Inflammatory and Antioxidant Activities in Endothelial Cells.

PubMed

Jahandideh, Forough; Chakrabarti, Subhadeep; Davidge, Sandra T; Wu, Jianping

2016-01-13

Oxygen radical absorbance capacity (ORAC) is a widely used method of measuring antioxidant capacities of various antioxidant components. Surprisingly, 16 antioxidant peptides previously identified from egg protein ovotransferrin using the ORAC method did not show any anti-inflammatory and antioxidant activities in cells. After simulated gastro-intestinal digestion (GID), several peptide digests significantly reduced the expression of tumor necrosis factor-α (TNF-α)-induced pro-inflammatory intercellular cell adhesion molecule-1 (ICAM-1) by 65.7 ± 10.4% and vascular cell adhesion molecule-1 (VCAM-1) by 53.5 ± 9.6% to 61.0 ± 14.5%, but only GWNI reduced TNF-α-activated superoxide generation by 71.0 ± 12.9% when tested with dihydroethidium (DHE) assay. Mass spectrometer analysis identified two new peptides, GWN and GW, in the GWNI digest; however, only GW reduced TNF-α-induced VCAM-1 expression (64.3 ± 20.6%) significantly compared to the TNF-α treated cells. Our study suggested that ORAC lacked biological relevance in assessing bioactive peptides.

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

Tumor-targeting peptides from combinatorial libraries.

PubMed

Liu, Ruiwu; Li, Xiaocen; Xiao, Wenwu; Lam, Kit S

2017-02-01

Cancer is one of the major and leading causes of death worldwide. Two of the greatest challenges in fighting cancer are early detection and effective treatments with no or minimum side effects. Widespread use of targeted therapies and molecular imaging in clinics requires high affinity, tumor-specific agents as effective targeting vehicles to deliver therapeutics and imaging probes to the primary or metastatic tumor sites. Combinatorial libraries such as phage-display and one-bead one-compound (OBOC) peptide libraries are powerful approaches in discovering tumor-targeting peptides. This review gives an overview of different combinatorial library technologies that have been used for the discovery of tumor-targeting peptides. Examples of tumor-targeting peptides identified from each combinatorial library method will be discussed. Published tumor-targeting peptide ligands and their applications will also be summarized by the combinatorial library methods and their corresponding binding receptors. Copyright © 2017. Published by Elsevier B.V.

Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

PubMed

Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V

2015-05-20

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

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.

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.

Gastrin Receptor-Avid Peptide Conjugates

DOEpatents

Hoffman, Timothy J.; Volkert, Wynn A.; Li, Ning; Sieckman, Gary; Higginbotham, Chrys-Ann

2005-07-26

A compound for use as a therapeutic or diagnostic radiopharmaceutical includes a group capable of complexing a medically useful metal attached to a moiety which is capable of binding to a gastrin releasing peptide receptor. A method for treating a subject having a neoplastic disease includes administering to the subject an effective amount of a radiopharmaceutical having a metal chelated with a chelating group attached to a moiety capable of binding to a gastrin releasing peptide receptor expressed on tumor cells with subsequent internalization inside of the cell. A method of forming a therapeutic or diagnostic compound includes reacting a metal synthon with a chelating group covalently linked with a moiety capable of binding a gastrin releasing peptide receptor.

Gastrin receptor-avid peptide conjugates

DOEpatents

Hoffman, Timothy J.; Volkert, Wynn A.; Li, Ning; Sieckman, Gary; Higginbotham, C. A.

2001-01-01

A compound for use as a therapeutic or diagnostic radiopharmaceutical includes a group capable of complexing a medically useful metal attached to a moiety which is capable of binding to a gastrin releasing peptide receptor. A method for treating a subject having a neoplastic disease includes administering to the subject an effective amount of a radiopharmaceutical having a metal chelated with a chelating group attached to a moiety capable of binding to a gastrin releasing peptide receptor expressed on tumor cells with subsequent internalization inside of the cell. A method of forming a therapeutic or diagnostic compound includes reacting a metal synthon with a chelating group covalently linked with a moiety capable of binding a gastrin releasing peptide receptor.

Gastrin receptor-avid peptide conjugates

DOEpatents

Hoffman, Timothy J.; Volkert, Wynn A.; Sieckman, Gary; Smith, Charles J.; Gali, Hariprasad

2006-06-13

A compound for use as a therapeutic or diagnostic radiopharmaceutical includes a group capable of complexing a medically useful metal attached to a moiety which is capable of binding to a gastrin releasing peptide receptor. A method for treating a subject having a neoplastic disease includes administering to the subject an effective amount of a radiopharmaceutical having a metal chelated with a chelating group attached to a-moiety capable of binding to a gastrin releasing peptide receptor expressed on tumor cells with subsequent internalization inside of the cell. A method of forming a therapeutic or diagnostic compound includes reacting a metal synthon with a chelating group covalently linked with a moiety capable of binding a gastrin releasing peptide receptor.

Gastrin receptor-avid peptide conjugates

DOEpatents

Hoffman, Timothy J.; Volkert, Wynn A.; Li, Ning; Sieckman, Gary; Higginbotham, Chrys-Ann

2006-12-12

A compound for use as a therapeutic or diagnostic radiopharmaceutical includes a group capable of complexing a medically useful metal attached to a moiety which is capable of binding to a gastrin releasing peptide receptor. A method for treating a subject having a neoplastic disease includes administering to the subject an effective amount of a radiopharmaceutical having a metal chelated with a chelating group attached to a moiety capable of binding to a gastrin releasing peptide receptor expressed on tumor cells with subsequent internalization inside of the cell. A method of forming a therapeutic or diagnostic compound includes reacting a metal synthon with a chelating group covalently linked with a moiety capable of binding a gastrin releasing peptide receptor.

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

Species Identification of Bovine, Ovine and Porcine Type 1 Collagen; Comparing Peptide Mass Fingerprinting and LC-Based Proteomics Methods.

PubMed

Buckley, Mike

2016-03-24

Collagen is one of the most ubiquitous proteins in the animal kingdom and the dominant protein in extracellular tissues such as bone, skin and other connective tissues in which it acts primarily as a supporting scaffold. It has been widely investigated scientifically, not only as a biomedical material for regenerative medicine, but also for its role as a food source for both humans and livestock. Due to the long-term stability of collagen, as well as its abundance in bone, it has been proposed as a source of biomarkers for species identification not only for heat- and pressure-rendered animal feed but also in ancient archaeological and palaeontological specimens, typically carried out by peptide mass fingerprinting (PMF) as well as in-depth liquid chromatography (LC)-based tandem mass spectrometric methods. Through the analysis of the three most common domesticates species, cow, sheep, and pig, this research investigates the advantages of each approach over the other, investigating sites of sequence variation with known functional properties of the collagen molecule. Results indicate that the previously identified species biomarkers through PMF analysis are not among the most variable type 1 collagen peptides present in these tissues, the latter of which can be detected by LC-based methods. However, it is clear that the highly repetitive sequence motif of collagen throughout the molecule, combined with the variability of the sites and relative abundance levels of hydroxylation, can result in high scoring false positive peptide matches using these LC-based methods. Additionally, the greater alpha 2(I) chain sequence variation, in comparison to the alpha 1(I) chain, did not appear to be specific to any particular functional properties, implying that intra-chain functional constraints on sequence variation are not as great as inter-chain constraints. However, although some of the most variable peptides were only observed in LC-based methods, until the range of

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.

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

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

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.

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.

Peptide synthesis in early earth hydrothermal systems

USGS Publications Warehouse

Lemke, K.H.; Rosenbauer, R.J.; Bird, D.K.

2009-01-01

We report here results from experiments and thermodynamic calculations that demonstrate a rapid, temperature-enhanced synthesis of oligopeptides from the condensation of aqueous glycine. Experiments were conducted in custom-made hydrothermal reactors, and organic compounds were characterized with ultraviolet-visible procedures. A comparison of peptide yields at 260??C with those obtained at more moderate temperatures (160??C) gives evidence of a significant (13 kJ ?? mol-1) exergonic shift. In contrast to previous hydrothermal studies, we demonstrate that peptide synthesis is favored in hydrothermal fluids and that rates of peptide hydrolysis are controlled by the stability of the parent amino acid, with a critical dependence on reactor surface composition. From our study, we predict that rapid recycling of product peptides from cool into near-supercritical fluids in mid-ocean ridge hydrothermal systems will enhance peptide chain elongation. It is anticipated that the abundant hydrothermal systems on early Earth could have provided a substantial source of biomolecules required for the origin of life. Astrobiology 9, 141-146. ?? 2009 Mary Ann Liebert, Inc. 2009.

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.

Comparison of binding energies of SrcSH2-phosphotyrosyl peptides with structure-based prediction using surface area based empirical parameterization.

PubMed Central

Henriques, D. A.; Ladbury, J. E.; Jackson, R. M.

2000-01-01

The prediction of binding energies from the three-dimensional (3D) structure of a protein-ligand complex is an important goal of biophysics and structural biology. Here, we critically assess the use of empirical, solvent-accessible surface area-based calculations for the prediction of the binding of Src-SH2 domain with a series of tyrosyl phosphopeptides based on the high-affinity ligand from the hamster middle T antigen (hmT), where the residue in the pY+ 3 position has been changed. Two other peptides based on the C-terminal regulatory site of the Src protein and the platelet-derived growth factor receptor (PDGFR) are also investigated. Here, we take into account the effects of proton linkage on binding, and test five different surface area-based models that include different treatments for the contributions to conformational change and protein solvation. These differences relate to the treatment of conformational flexibility in the peptide ligand and the inclusion of proximal ordered solvent molecules in the surface area calculations. This allowed the calculation of a range of thermodynamic state functions (deltaCp, deltaS, deltaH, and deltaG) directly from structure. Comparison with the experimentally derived data shows little agreement for the interaction of SrcSH2 domain and the range of tyrosyl phosphopeptides. Furthermore, the adoption of the different models to treat conformational change and solvation has a dramatic effect on the calculated thermodynamic functions, making the predicted binding energies highly model dependent. While empirical, solvent-accessible surface area based calculations are becoming widely adopted to interpret thermodynamic data, this study highlights potential problems with application and interpretation of this type of approach. There is undoubtedly some agreement between predicted and experimentally determined thermodynamic parameters: however, the tolerance of this approach is not sufficient to make it ubiquitously applicable

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

Screening and Identification of Peptides Specifically Targeted to Gastric Cancer Cells from a Phage Display Peptide Library

PubMed

Sahin, Deniz; Taflan, Sevket Onur; Yartas, Gizem; Ashktorab, Hassan; Smoot, Duane T

2018-04-25

Background: Gastric cancer is the second most common cancer among the malign cancer types. Inefficiency of traditional techniques both in diagnosis and therapy of the disease makes the development of alternative and novel techniques indispensable. As an alternative to traditional methods, tumor specific targeting small peptides can be used to increase the efficiency of the treatment and reduce the side effects related to traditional techniques. The aim of this study is screening and identification of individual peptides specifically targeted to human gastric cancer cells using a phage-displayed peptide library and designing specific peptide sequences by using experimentally-eluted peptide sequences. Methods: Here, MKN-45 human gastric cancer cells and HFE-145 human normal gastric epithelial cells were used as the target and control cells, respectively. 5 rounds of biopannning with a phage display 12-peptide library were applied following subtraction biopanning with HFE-145 control cells. The selected phage clones were established by enzyme-linked immunosorbent assay and immunofluorescence detection. We first obtain random phage clones after five biopanning rounds, determine the binding levels of each individual clone. Then, we analyze the frequencies of each amino acid in best binding clones to determine positively overexpressed amino acids for designing novel peptide sequences. Results: DE532 (VETSQYFRGTLS) phage clone was screened positive, showing specific binding on MKN-45 gastric cancer cells. DE-Obs (HNDLFPSWYHNY) peptide, which was designed by using amino acid frequencies of experimentally selected peptides in the 5th round of biopanning, showed specific binding in MKN-45 cells. Conclusion: Selection and characterization of individual clones may give us specifically binding peptides, but more importantly, data extracted from eluted phage clones may be used to design theoretical peptides with better binding properties than even experimentally selected ones

Measuring peptide translocation into large unilamellar vesicles.

PubMed

Spinella, Sara A; Nelson, Rachel B; Elmore, Donald E

2012-01-27

There is an active interest in peptides that readily cross cell membranes without the assistance of cell membrane receptors(1). Many of these are referred to as cell-penetrating peptides, which are frequently noted for their potential as drug delivery vectors(1-3). Moreover, there is increasing interest in antimicrobial peptides that operate via non-membrane lytic mechanisms(4,5), particularly those that cross bacterial membranes without causing cell lysis and kill cells by interfering with intracellular processes(6,7). In fact, authors have increasingly pointed out the relationship between cell-penetrating and antimicrobial peptides(1,8). A firm understanding of the process of membrane translocation and the relationship between peptide structure and its ability to translocate requires effective, reproducible assays for translocation. Several groups have proposed methods to measure translocation into large unilamellar lipid vesicles (LUVs)(9-13). LUVs serve as useful models for bacterial and eukaryotic cell membranes and are frequently used in peptide fluorescent studies(14,15). Here, we describe our application of the method first developed by Matsuzaki and co-workers to consider antimicrobial peptides, such as magainin and buforin II(16,17). In addition to providing our protocol for this method, we also present a straightforward approach to data analysis that quantifies translocation ability using this assay. The advantages of this translocation assay compared to others are that it has the potential to provide information about the rate of membrane translocation and does not require the addition of a fluorescent label, which can alter peptide properties(18), to tryptophan-containing peptides. Briefly, translocation ability into lipid vesicles is measured as a function of the Foster Resonance Energy Transfer (FRET) between native tryptophan residues and dansyl phosphatidylethanolamine when proteins are associated with the external LUV membrane (Figure 1). Cell

Species Identification of Archaeological Skin Objects from Danish Bogs: Comparison between Mass Spectrometry-Based Peptide Sequencing and Microscopy-Based Methods

PubMed Central

Brandt, Luise Ørsted; Schmidt, Anne Lisbeth; Mannering, Ulla; Sarret, Mathilde; Kelstrup, Christian D.; Olsen, Jesper V.; Cappellini, Enrico

2014-01-01

Denmark has an extraordinarily large and well-preserved collection of archaeological skin garments found in peat bogs, dated to approximately 920 BC – AD 775. These objects provide not only the possibility to study prehistoric skin costume and technologies, but also to investigate the animal species used for the production of skin garments. Until recently, species identification of archaeological skin was primarily performed by light and scanning electron microscopy or the analysis of ancient DNA. However, the efficacy of these methods can be limited due to the harsh, mostly acidic environment of peat bogs leading to morphological and molecular degradation within the samples. We compared species assignment results of twelve archaeological skin samples from Danish bogs using Mass Spectrometry (MS)-based peptide sequencing, against results obtained using light and scanning electron microscopy. While it was difficult to obtain reliable results using microscopy, MS enabled the identification of several species-diagnostic peptides, mostly from collagen and keratins, allowing confident species discrimination even among taxonomically close organisms, such as sheep and goat. Unlike previous MS-based methods, mostly relying on peptide fingerprinting, the shotgun sequencing approach we describe aims to identify the complete extracted ancient proteome, without preselected specific targets. As an example, we report the identification, in one of the samples, of two peptides uniquely assigned to bovine foetal haemoglobin, indicating the production of skin from a calf slaughtered within the first months of its life. We conclude that MS-based peptide sequencing is a reliable method for species identification of samples from bogs. The mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium with the dataset identifier PXD001029. PMID:25260035

A method for the 32P labeling of peptides or peptide nucleic acid oligomers

NASA Technical Reports Server (NTRS)

Kozlov, I. A.; Nielsen, P. E.; Orgel, L. E.; Bada, J. L. (Principal Investigator)

1998-01-01

A novel approach to the radioactive labeling of peptides and PNA oligomers is described. It is based on the conjugation of a deoxynucleoside 3'-phosphate with the terminal amine of the substrate, followed by phosphorylation of the 5'-hydroxyl group of the nucleotide using T4 polynucleotide kinase and [gamma-32P]ATP.

Amino acids and peptides. XXXII: A bifunctional poly(ethylene glycol) hybrid of fibronectin-related peptides.

PubMed

Maeda, M; Izuno, Y; Kawasaki, K; Kaneda, Y; Mu, Y; Tsutsumi, Y; Lem, K W; Mayumi, T

1997-12-18

An amino acid type poly(ethylene glycol) (aaPPEG) was prepared and its application to a drug carrier was examined. The peptides, Arg-Gly-Asp (RGD) and Glu-Ile-Leu-Asp-Val (EILDV) which were reported as active fragments of Fibronectin (a cell adhesion protein), were conjugated with aaPEG (molecular weight, 10,000). The hybrid, RGD-aaPEG-EILDV, was prepared by a combination of the solid-phase method and the solution method. Antiadhesive activity of the peptides was not lost by its hybrid formation with the large aaPEG molecule. A mixture of RGD (0.43 mmol) and EILDV (0.43 mmol) did not demonstrate an antiadhesive effect, but the hybrid containing 0.43 mmol of each peptide did exhibit this effect.

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.

Protein quantification using a cleavable reporter peptide.

PubMed

Duriez, Elodie; Trevisiol, Stephane; Domon, Bruno

2015-02-06

Peptide and protein quantification based on isotope dilution and mass spectrometry analysis are widely employed for the measurement of biomarkers and in system biology applications. The accuracy and reliability of such quantitative assays depend on the quality of the stable-isotope labeled standards. Although the quantification using stable-isotope labeled peptides is precise, the accuracy of the results can be severely biased by the purity of the internal standards, their stability and formulation, and the determination of their concentration. Here we describe a rapid and cost-efficient method to recalibrate stable isotope labeled peptides in a single LC-MS analysis. The method is based on the equimolar release of a protein reference peptide (used as surrogate for the protein of interest) and a universal reporter peptide during the trypsinization of a concatenated polypeptide standard. The quality and accuracy of data generated with such concatenated polypeptide standards are highlighted by the quantification of two clinically important proteins in urine samples and compared with results obtained with conventional stable isotope labeled reference peptides. Furthermore, the application of the UCRP standards in complex samples is described.

N-Terminal Pro-B-type Natriuretic Peptide Is Useful to Predict Cardiac Complications Following Lung Resection Surgery

PubMed Central

Lee, Chang Young; Bae, Mi Kyung; Lee, Jin Gu; Kim, Kwan-Wook; Park, In Kyu

2011-01-01

Background Cardiovascular complications are major causes of morbidity and mortality following non-cardiac thoracic operations. Recent studies have demonstrated that elevation of N-Terminal Pro-B-type natriuretic peptide (NT-proBNP) levels can predict cardiac complications following non-cardiac major surgery as well as cardiac surgery. However, there is little information on the correlation between lung resection surgery and NT-proBNP levels. We evaluated the role of NT-proBNP as a potential marker for the risk stratification of cardiac complications following lung resection surgery. Material and Methods Prospectively collected data of 98 patients, who underwent elective lung resection from August 2007 to February 2008, were analyzed. Postoperative adverse cardiac events were categorized as myocardial injury, ECG evidence of ischemia or arrhythmia, heart failure, or cardiac death. Results Postoperative cardiac complications were documented in 9 patients (9/98, 9.2%): Atrial fibrillation in 3, ECG-evidenced ischemia in 2 and heart failure in 4. Preoperative median NT-proBNP levels was significantly higher in patients who developed postoperative cardiac complications than in the rest (200.2 ng/L versus 45.0 ng/L, p=0.009). NT-proBNP levels predicted adverse cardiac events with an area under the receiver operating characteristic curve of 0.76 [95% confidence interval (CI) 0.545~0.988, p=0.01]. A preoperative NT-proBNP value of 160 ng/L was found to be the best cut-off value for detecting postoperative cardiac complication with a positive predictive value of 0.857 and a negative predictive value of 0.978. Other factors related to cardiac complications by univariate analysis were a higher American Society of Anesthesiologists grade, a higher NYHA functional class and a history of hypertension. In multivariate analysis, however, high preoperative NT-proBNP level (>160 ng/L) only remained significant. Conclusion An elevated preoperative NT-proBNP level is identified as an

Rainfall prediction with backpropagation method

NASA Astrophysics Data System (ADS)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

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.

[BIOLOGICAL ACTIVITY OF ANTIMICROBIAL PEPTIDES FROM CHICKENS THROMBOCYTES].

PubMed

Sycheva, M V; Vasilchenko, A S; Rogozhin, E A; Pashkova, T M; Popova, L P; Kartashova, O L

2016-01-01

Isolation and study of biological activity of antimicrobial peptides from chickens thrombocytes. Peptides from chickens thrombocytes, obtained by reverse-phase high-performance liquid chromatography method with stepped and linear gradients of concentration increase of the organic solvent were used in the study. Their antimicrobial activity was determined by microtitration method in broth; mechanism of biological effect--by using fluorescent spectroscopy method with DNA-tropic dyes. Individual fractions of peptides were isolated from chickens thrombocytes, that possess antimicrobial activity against Staphylococcus aureus P209 and Escherichia coli K12. A disruption of integrity of barrier structures of microorganisms under the effect of thrombocyte antimicrobial peptides and predominance of cells with damaged membrane in the population of E. coli was established. The data obtained on antimicrobial activity and mechanism of bactericidal effect of the peptide fractions from chickens thrombocytes isolated for the first time expand the understanding of functional properties of chickens thrombocytes and open a perspective for their further study with the aim of use as antimicrobial means.

Biomimetic graphene sensors: functionalizing graphene with peptides

NASA Astrophysics Data System (ADS)

Ishigami, Masa; Nyon Kim, Sang; Naik, Rajesh; Tatulian, Suren A.; Katoch, Jyoti

2012-02-01

Non-covalent biomimetic functionalization of graphene using peptides is one of more promising methods for producing novel sensors with high sensitivity and selectivity. Here we combine atomic force microscopy, Raman spectroscopy, and attenuated total reflection Fourier transform infrared spectroscopy to investigate peptide binding to graphene and graphite. We choose to study a dodecamer peptide identified with phage display to possess affinities for graphite and we find that the peptide forms a complex mesh-like structure upon adsorption on graphene. Moreover, optical spectroscopy reveals that the peptide binds non-covalently to graphene and possesses an optical signature of an ?-helical conformation on graphene.

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.

Peptide-Like Molecules (PLMs): A Journey from Peptide Bond Isosteres to Gramicidin S Mimetics and Mitochondrial Targeting Agents

PubMed Central

Wipf, Peter; Xiao, Jingbo; Stephenson, Corey R. J.

2010-01-01

Peptides are natural ligands and substrates for receptors and enzymes and exhibit broad physiological effects. However, their use as therapeutic agents often suffers from poor bioavailability and insufficient membrane permeability. The success of peptide mimicry hinges on the ability of bioisosteres, in particular peptide bond replacements, to adopt suitable secondary structures relevant to peptide strands and position functional groups in equivalent space. This perspective highlights past and ongoing studies in our group that involve new methods development as well as specific synthetic library preparations and applications in chemical biology, with the goal to enhance the use of alkene and cyclopropane peptide bond isosteres. PMID:20725595

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.

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

A Cocoa Peptide Protects Caenorhabditis elegans from Oxidative Stress and β-Amyloid Peptide Toxicity

PubMed Central

Martorell, Patricia; Bataller, Esther; Llopis, Silvia; Gonzalez, Núria; Álvarez, Beatriz; Montón, Fernando; Ortiz, Pepa; Ramón, Daniel; Genovés, Salvador

2013-01-01

Background Cocoa and cocoa-based products contain different compounds with beneficial properties for human health. Polyphenols are the most frequently studied, and display antioxidant properties. Moreover, protein content is a very interesting source of antioxidant bioactive peptides, which can be used therapeutically for the prevention of age-related diseases. Methodology/Principal Findings A bioactive peptide, 13L (DNYDNSAGKWWVT), was obtained from a hydrolyzed cocoa by-product by chromatography. The in vitro inhibition of prolyl endopeptidase (PEP) was used as screening method to select the suitable fraction for peptide identification. Functional analysis of 13L peptide was achieved using the transgenic Caenorhabditis elegans strain CL4176 expressing the human Aβ1–42 peptide as a pre-clinical in vivo model for Alzheimer's disease. Among the peptides isolated, peptide 13L (1 µg/mL) showed the highest antioxidant activity (P≤0.001) in the wild-type strain (N2). Furthermore, 13L produced a significant delay in body paralysis in strain CL4176, especially in the 24–47 h period after Aβ1–42 peptide induction (P≤0.0001). This observation is in accordance with the reduction of Aβ deposits in CL4176 by western blot. Finally, transcriptomic analysis in wild-type nematodes treated with 13L revealed modulation of the proteosomal and synaptic functions as the main metabolic targets of the peptide. Conclusions/Significance These findings suggest that the cocoa 13L peptide has antioxidant activity and may reduce Aβ deposition in a C. elegans model of Alzheimer's disease; and therefore has a putative therapeutic potential for prevention of age-related diseases. Further studies in murine models and humans will be essential to analyze the effectiveness of the 13L peptide in higher animals. PMID:23675471