Science.gov

Sample records for peptides prediction methods

  1. Machine Learning Methods for Predicting HLA–Peptide Binding Activity

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Shi, Leming; Tong, Weida; Mendrick, Donna L.; Hong, Huixiao

    2015-01-01

    As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA–peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA–peptide binding prediction. We also summarize the descriptors based on which the HLA–peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA–peptide binding prediction method based on network analysis. PMID:26512199

  2. Method for predicting peptide detection in mass spectrometry

    DOEpatents

    Kangas, Lars [West Richland, WA; Smith, Richard D [Richland, WA; Petritis, Konstantinos [Richland, WA

    2010-07-13

    A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

  3. Improved Methods for Classification, Prediction and Design of Antimicrobial Peptides

    PubMed Central

    Wang, Guangshun

    2015-01-01

    Peptides with diverse amino acid sequences, structures and functions are essential players in biological systems. The construction of well-annotated databases not only facilitates effective information management, search and mining, but also lays the foundation for developing and testing new peptide algorithms and machines. The antimicrobial peptide database (APD) is an original construction in terms of both database design and peptide entries. The host defense antimicrobial peptides (AMPs) registered in the APD cover the five kingdoms (bacteria, protists, fungi, plants, and animals) or three domains of life (bacteria, archaea, and eukaryota). This comprehensive database (http://aps.unmc.edu/AP) provides useful information on peptide discovery timeline, nomenclature, classification, glossary, calculation tools, and statistics. The APD enables effective search, prediction, and design of peptides with antibacterial, antiviral, antifungal, antiparasitic, insecticidal, spermicidal, anticancer activities, chemotactic, immune modulation, or anti-oxidative properties. A universal classification scheme is proposed herein to unify innate immunity peptides from a variety of biological sources. As an improvement, the upgraded APD makes predictions based on the database-defined parameter space and provides a list of the sequences most similar to natural AMPs. In addition, the powerful pipeline design of the database search engine laid a solid basis for designing novel antimicrobials to combat resistant superbugs, viruses, fungi or parasites. This comprehensive AMP database is a useful tool for both research and education. PMID:25555720

  4. Flexible Backbone Methods for Predicting and Designing Peptide Specificity.

    PubMed

    Ollikainen, Noah

    2017-01-01

    Protein-protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it's "peptide specificity," is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein-peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.

  5. Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

    DOEpatents

    Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

    2006-11-14

    A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

  6. [A plain method of prediction of visibility of peptides in mass spectrometry with electrospray ionization].

    PubMed

    Rybina, A V; Skvortsov, V S; Kopylov, A T; Zgoda, V G

    2014-01-01

    A new method for screening of essential peptides for protein detection and quantification analysis in the direct positive electrospray mass spectrometry has been proposed. Our method is based on the prediction of the normalized abundance of the mass spectrometric peaks using a linear regression model. This method has the following limitations: (i) selected peptides should be taken so that at pH 2.5 the tested peptides must be presented mainly as the 2+ and 3+ ions; (ii) only peptides having C-terminal lysine or arginine residues are considered. The amino acid composition of the peptide, the peptide concentration, the ratio of the polar surface of peptide to common surface and ratio of the polar volume to common volume are used as independent variables in equation. Several combinations of variables were considered and the best linear regression model had a determination coefficient in leave-one-out validation procedure equal 0.54. This model confidently discriminates peptides with high response ability and peptides with low response ability, and therefore it allows to select only the most promising peptides. This screening method, a plain and fast, can be successfully applied to reduce the list of observed peptides.

  7. High-order neural networks and kernel methods for peptide-MHC binding prediction.

    PubMed

    Kuksa, Pavel P; Min, Martin Renqiang; Dugar, Rishabh; Gerstein, Mark

    2015-11-15

    Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding. The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25-40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding. There is no associated distributable software. renqiang@nec-labs.com or mark.gerstein@yale.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  8. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods.

    PubMed

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

    2011-04-13

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

  9. A consensus method for the prediction of 'aggregation-prone' peptides in globular proteins.

    PubMed

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

    2013-01-01

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

  10. MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions

    PubMed Central

    2014-01-01

    Background Computational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides. Methods We develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions. Results Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL

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

    PubMed Central

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

    2014-01-01

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

  12. In vitro and in vivo activities of antimicrobial peptides developed using an amino acid-based activity prediction method.

    PubMed

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

    2014-09-01

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

  13. Predicting protein-peptide interactions from scratch

    NASA Astrophysics Data System (ADS)

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin; Zou lab Team

    Protein-peptide interactions play an important role in many cellular processes. The ability to predict protein-peptide complex structures is valuable for mechanistic investigation and therapeutic development. Due to the high flexibility of peptides and lack of templates for homologous modeling, predicting protein-peptide complex structures is extremely challenging. Recently, we have developed a novel docking framework for protein-peptide structure prediction. Specifically, given the sequence of a peptide and a 3D structure of the protein, initial conformations of the peptide are built through protein threading. Then, the peptide is globally and flexibly docked onto the protein using a novel iterative approach. Finally, the sampled modes are scored and ranked by a statistical potential-based energy scoring function that was derived for protein-peptide interactions from statistical mechanics principles. Our docking methodology has been tested on the Peptidb database and compared with other protein-peptide docking methods. Systematic analysis shows significantly improved results compared to the performances of the existing methods. Our method is computationally efficient and suitable for large-scale applications. Nsf CAREER Award 0953839 (XZ) NIH R01GM109980 (XZ).

  14. Toward structure prediction of cyclic peptides.

    PubMed

    Yu, Hongtao; Lin, Yu-Shan

    2015-02-14

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

  15. [A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model].

    PubMed

    Zhang, Jiyang; Zhang, Daibing; Zhang, Wei; Xie, Hongwei

    2012-09-01

    The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy.

  16. Chapter 8. Methods for in silico prediction of microbial polyketide and nonribosomal peptide biosynthetic pathways from DNA sequence data.

    PubMed

    Bachmann, Brian O; Ravel, Jacques

    2009-01-01

    Fore-knowledge of the secondary metabolic potential of cultivated and previously uncultivated microorganisms can potentially facilitate the process of natural product discovery. By combining sequence-based knowledge with biochemical precedent, translated gene sequence data can be used to rapidly derive structural elements encoded by secondary metabolic gene clusters from microorganisms. These structural elements provide an estimate of the secondary metabolic potential of a given organism and a starting point for identification of potential lead compounds in isolation/structure elucidation campaigns. The accuracy of these predictions for a given translated gene sequence depends on the biochemistry of the metabolite class, similarity to known metabolite gene clusters, and depth of knowledge concerning its biosynthetic machinery. This chapter introduces methods for prediction of structural elements for two well-studied classes: modular polyketides and nonribosomally encoded peptides. A bioinformatics tool is presented for rapid preliminary analysis of these modular systems, and prototypical methods for converting these analyses into substructural elements are described.

  17. Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201.

    PubMed

    Doytchinova, Irini A; Blythe, Martin J; Flower, Darren R

    2002-01-01

    A method has been developed for prediction of binding affinities between proteins and peptides. We exemplify the method through its application to binding predictions of peptides with affinity to major histocompatibility complex class I molecule HLA-A*0201. The method is named "additive" because it is based on the assumption that the binding affinity of a peptide could be presented as a sum of the contributions of the amino acids at each position and the interactions between them. The amino acid contributions and the contributions of the interactions between adjacent side chains and every second side chain were derived using a partial least squares (PLS) statistical methodology using a training set of 420 experimental IC50 values. The predictive power of the method was assessed using rigorous cross-validation and using an independent test set of 89 peptides. The mean value of the residuals between the experimental and predicted pIC50 values was 0.508 for this test set. The additive method was implemented in a program for rapid T-cell epitope search. It is universal and can be applied to any peptide-protein interaction where binding data is known.

  18. [High performance liquid chromatography of peptide bioregulators, their fragments and derivatives. III. Regularities of sorption, prediction of retention and analysis of peptides by a reversed phase HPLC method].

    PubMed

    Grigor'eva, V D; Shatts, V D

    1989-08-01

    Parameters of statistical models of fully or partially protected peptides' retention on Zorbax ODS and Silasorb C18 have been compared. The proposed model can be used for non-protected linear and cyclic peptides. Special increments have to be introduced in calculation of hydrophobicity of these peptides.

  19. On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values

    PubMed Central

    Thompson, Sarah J; Hattotuwagama, Channa K; Holliday, John D; Flower, Darren R

    2006-01-01

    Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r2 values, with ALogP being the most effective (r 2 = 0.822) and MLogP the least (r2 = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides – ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides ­ PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides ­ QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides ­ LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted PMID:17597897

  20. Machine learning study for the prediction of transdermal peptide.

    PubMed

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

    2011-04-01

    In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

  1. Empirical prediction of peptide octanol-water partition coefficients

    PubMed Central

    Hattotuwagama, Channa K; Flower, Darren R

    2006-01-01

    Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r2 = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability. PMID:17597903

  2. 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.

  3. Synthesis of peptide analogues using the multipin peptide synthesis method.

    PubMed

    Valerio, R M; Benstead, M; Bray, A M; Campbell, R A; Maeji, N J

    1991-08-15

    Modification of the multipin peptide synthesis method which allows the simultaneous synthesis of large numbers of different peptide analogues is described. Peptides were assembled on polyethylene pins derivatized with a 4-(beta-alanyloxymethyl)benzoate (beta-Ala-HMB) handle. For comparative purposes, peptides were also assembled on the diketopiperazine-forming handle N epsilon-(beta-alanyl)lysylprolyloxylactate. In model studies it was demonstrated that beta-Ala-HMB-linked peptides were cleaved from polyethylene pins with dilute sodium hydroxide or 4% methylamine/water to yield analogues with beta-Ala-free acid (beta-Ala-CO2H) and beta-Ala-methylamide (beta-Ala-CONHCH3), respectively. To assess the suitability of this approach for T-cell determinant analysis, analogues of a known T-cell determinant were synthesized with the various C-terminal endings. Peptides were characterized by amino acid analysis and fast atom bombardment-mass spectrometry. HPLC of the crude cleaved peptides indicated that 22 of the 24 peptides were greater than 95% pure. These crude peptide solutions were nontoxic in sensitive cell culture assays without further purification. All three cleavage procedures gave comparable activities in T-cell proliferation assays. These results demonstrate the potential of the multipin peptide synthesis method for the production of large numbers of different peptide analogues.

  4. A new fingerprint to predict nonribosomal peptides activity

    NASA Astrophysics Data System (ADS)

    Abdo, Ammar; Caboche, Ségolène; Leclère, Valérie; Jacques, Philippe; Pupin, Maude

    2012-10-01

    Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy (>93 %). Also a high recall rate (>82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database.

  5. Prediction of drift time in ion mobility-mass spectrometry based on Peptide molecular weight.

    PubMed

    Wang, Bing; Valentine, Steve; Plasencia, Manolo; Zhang, Xiang

    2010-09-01

    A computational model is introduced for predicting peptide drift time in ion mobility-mass spectrometry (IMMS). Each peptide was represented using a numeric descriptor: molecular weight. A simple linear regression predictor was constructed for peptides drift time prediction. Three datasets with different charge state assignments were used for the model training and testing. The dataset one contains 212 singly charged peptides, dataset two has 306 doubly charged peptides, and dataset three contains 77 triply charged peptides. Our proposed method achieved a prediction accuracy of 86.3%, 72.6%, and 59.7% for the dataset one, two and three, respectively. Peptide drift time prediction in IMMS will improve the confidence of peptide identifications by limiting the peptide search space during MS/MS database searching and therefore, reducing false discovery rate (FDR) of protein identification.

  6. Methods for peptide identification by spectral comparison

    PubMed Central

    Liu, Jian; Bell, Alexander W; Bergeron, John JM; Yanofsky, Corey M; Carrillo, Brian; Beaudrie, Christian EH; Kearney, Robert E

    2007-01-01

    Background Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies. Results This paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency. Conclusion Our results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification. PMID:17227583

  7. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information

    SciTech Connect

    Petritis, Konstantinos; Kangas, Lars J.; Yan, Bo; Monroe, Matthew E.; Strittmatter, Eric F.; Qian, Weijun; Adkins, Joshua N.; Moore, Ronald J.; Xu, Ying; Lipton, Mary S.; Camp, David G.; Smith, Richard D.

    2006-07-15

    We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using {approx}345,000 non-redundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of {approx}1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis et al., Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.

  8. Automated benchmarking of peptide-MHC class I binding predictions.

    PubMed

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

    2015-07-01

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

  9. Automated benchmarking of peptide-MHC class I binding predictions

    PubMed Central

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

    2015-01-01

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

  10. PQuad: Visualization of Predicted Peptides and Proteins

    SciTech Connect

    Havre, Susan L.; Singhal, Mudita; Payne, Deborah A.; Webb-Robertson, Bobbie-Jo M.

    2004-10-10

    New high-throughput proteomic techniques generate data faster than biologist and bioinformaticists can analyze it. Yet, hidden within this massive and complex data are answers to basic questions about how cells function to support life or respond to disease. Now biologists can take a global or systems approach studying not one or two proteins at a time but whole proteomes comprising all the proteins in a cell. However, the tremendous size and complexity of the high-throughput experiment data make it difficult to process and interpret. Visualization provides powerful analysis capabilities for such enormous and complex data. In this paper, we introduce a novel interactive visualization, PQuad (Peptide Permutation and Protein Prediction), designed for the visual analysis of peptides (protein fragments) identified from high-throughput data. PQuad depicts the experiment peptides in the context of their parent protein and DNA, thereby integrating proteomic and genomic information. A wrapped line metaphor is applied across key resolutions of the data, from a compressed view of an entire chromosome to the actual nucleotide sequence. PQuad provides a difference visualization for comparing peptides from different experimental conditions. We describe the requirements for such a visual analysis tool, the design decisions, and the novel aspects of PQuad.

  11. Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra.

    PubMed

    Gay, Steven; Binz, Pierre-Alain; Hochstrasser, Denis F; Appel, Ron D

    2002-10-01

    Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into

  12. Prediction of bioactive peptides using artificial neural networks.

    PubMed

    Andreu, David; Torrent, Marc

    2015-01-01

    Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.

  13. Predicting protein-ligand and protein-peptide interfaces

    NASA Astrophysics Data System (ADS)

    Bertolazzi, Paola; Guerra, Concettina; Liuzzi, Giampaolo

    2014-06-01

    The paper deals with the identification of binding sites and concentrates on interactions involving small interfaces. In particular we focus our attention on two major interface types, namely protein-ligand and protein-peptide interfaces. As concerns protein-ligand binding site prediction, we classify the most interesting methods and approaches into four main categories: (a) shape-based methods, (b) alignment-based methods, (c) graph-theoretic approaches and (d) machine learning methods. Class (a) encompasses those methods which employ, in some way, geometric information about the protein surface. Methods falling into class (b) address the prediction problem as an alignment problem, i.e. finding protein-ligand atom pairs that occupy spatially equivalent positions. Graph theoretic approaches, class (c), are mainly based on the definition of a particular graph, known as the protein contact graph, and then apply some sophisticated methods from graph theory to discover subgraphs or score similarities for uncovering functional sites. The last class (d) contains those methods that are based on the learn-from-examples paradigm and that are able to take advantage of the large amount of data available on known protein-ligand pairs. As for protein-peptide interfaces, due to the often disordered nature of the regions involved in binding, shape similarity is no longer a determining factor. Then, in geometry-based methods, geometry is accounted for by providing the relative position of the atoms surrounding the peptide residues in known structures. Finally, also for protein-peptide interfaces, we present a classification of some successful machine learning methods. Indeed, they can be categorized in the way adopted to construct the learning examples. In particular, we envisage three main methods: distance functions, structure and potentials and structure alignment.

  14. A regularized method for peptide quantification.

    PubMed

    Yang, Chao; Yang, Can; Yu, Weichuan

    2010-05-07

    Peptide abundance estimation is generally the first step in protein quantification. In peptide abundance estimation, peptide overlapping and peak intensity variation are two challenges. The main objective of this paper is to estimate peptide abundance by taking advantage of peptide isotopic distribution and smoothness of peptide elution profile. Our method proposes to solve the peptide overlapping problem and provides a way to control the variance of estimation. We compare our method with a commonly used method on simulated data sets and two real data sets of standard protein mixtures. The results show that our method achieves more accurate estimation of peptide abundance on different samples. In our method, there is a variance-related parameter. Considering the well-known trade-off between the variance and the bias of estimation, we should not only focus on reducing the variance in real applications. A suggestion about parameter selection is given based on the discussion of variance and bias. Matlab source codes and detailed experimental results are available at http://bioinformatics.ust.hk/PeptideQuant/peptidequant.htm.

  15. Predicting and Improving the Membrane Permeability of Peptidic Small Molecules

    PubMed Central

    Rafi, Salma B.; Hearn, Brian R.; Vedantham, Punitha; Jacobson, Matthew P.; Renslo, Adam R.

    2012-01-01

    We evaluate experimentally and computationally the membrane permeability of matched sets of peptidic small molecules bearing natural or bioisosteric unnatural amino acids. We find that the intentional introduction of hydrogen bond acceptor-donor pairs in such molecules can improve membrane permeability while retaining or improving other favorable drug-like properties. We employ an all-atom force-field based method to calculate changes in free energy associated with the transfer of the peptidic molecules from water to membrane. This computational method correctly predicts rank-order experimental permeability trends within congeneric series and is much more predictive than calculations (e.g. clogP) that do not consider three-dimensional conformation. PMID:22394492

  16. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server.

    PubMed

    Käll, Lukas; Krogh, Anders; Sonnhammer, Erik L L

    2007-07-01

    When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hence this is an important issue in protein annotation. To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.

  17. 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

  18. Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity.

    PubMed

    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 (> 20 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.

  19. Discovering new in silico tools for antimicrobial peptide prediction.

    PubMed

    Torrent, Marc; Nogués, M Victòria; Boix, Ester

    2012-08-01

    Antimicrobial peptides (AMPs) are important effectors of the innate immune system and play a vital role in the prevention of infections. Due to the increased emergence of new antibiotic-resistant bacteria, new drugs are constantly under investigation. AMPs in particular are recognized as promising candidates because of their modularity and wide antimicrobial spectrum. However, the mechanisms of action of AMPs, as well as their structure-activity relationships, are not completely understood. AMPs display no conserved three-dimensional structure and poor sequence conservation, which hinders rational design. Several bioinformatics tools have been developed to generate new templates with appealing antimicrobial properties with the aim of finding highly active peptide compounds with low cytotoxicity. The current tools reviewed here allow for the prediction and design of new active peptides with reasonable accuracy. However, a reliable method to assess the antimicrobial activity of AMPs has not yet been developed. The standardization of procedures to experimentally evaluate the antimicrobial activity of AMPs, together with the constant growth of current well-established databases, may allow for the future development of new bioinformatics tools to accurately predict antimicrobial activity.

  20. Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach

    NASA Astrophysics Data System (ADS)

    Zeng, Jun; Treutlein, Herbert R.; Rudy, George B.

    2001-06-01

    Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].

  1. 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

  2. Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.

    PubMed

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin

    2016-10-04

    Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. 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

  4. Predicting peptide binding sites on protein surfaces by clustering chemical interactions.

    PubMed

    Yan, Chengfei; Zou, Xiaoqin

    2015-01-05

    Short peptides play important roles in cellular processes including signal transduction, immune response, and transcription regulation. Correct identification of the peptide binding site on a given protein surface is of great importance not only for mechanistic investigation of these biological processes but also for therapeutic development. In this study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the 20 standard amino acids as probes to globally scan the protein surface. The poses forming good chemical interactions with the protein are identified, followed by clustering with the density-based spatial clustering of applications with noise technique. Finally, these clusters are ranked based on their sizes. The cluster with the largest size is predicted as the putative binding site. Assessment of ACCLUSTER was performed on a diverse test set of 251 nonredundant protein-peptide complexes. The results were compared with the performance of POCASA, a pocket detection method for ligand binding site prediction. Peptidb, another protein-peptide database that contains both bound structures and unbound or homologous structures was used to test the robustness of ACCLUSTER. The performance of ACCLUSTER was also compared with PepSite2 and PeptiMap, two recently developed methods developed for identifying peptide binding sites. The results showed that ACCLUSTER is a promising method for peptide binding site prediction. Additionally, ACCLUSTER was also shown to be applicable to nonpeptide ligand binding site prediction. © 2014 Wiley Periodicals, Inc.

  5. Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test.

    PubMed

    Hajisharifi, Zohre; Piryaiee, Moien; Mohammad Beigi, Majid; Behbahani, Mandana; Mohabatkar, Hassan

    2014-01-21

    Cancer is an important reason of death worldwide. Traditional cytotoxic therapies, such as radiation and chemotherapy, are expensive and cause severe side effects. Currently, design of anticancer peptides is a more effective way for cancer treatment. So there is a need to develop a computational method for predicting the anticancer peptides. In the present study, two methods have been developed to predict these peptides using support vector machine (SVM) as a powerful machine learning algorithm. Classifiers have been applied based on the concept of Chou's pseudo-amino acid composition (PseAAC) and local alignment kernel. Since a number of HIV-1 proteins have cytotoxic effect, therefore we predicted the anticancer effect of HIV-1 p24 protein with these methods. After the prediction, mutagenicity of 2 anticancer peptides and 2 non-anticancer peptides was investigated by Ames test. Our results show that, the accuracy and the specificity of local alignment kernel based method are 89.7% and 92.68%, respectively. The accuracy and specificity of PseAAC-based method are 83.82% and 85.36%, respectively. By computational analysis, out of 22 peptides of p24 protein, 4 peptides are anticancer and 18 are non-anticancer. In the Ames test results, it is clear that anticancer peptides (ARP788.8 and ARP788.21) are not mutagenic. Therefore the results demonstrate that the described computation methods are useful to identify potential anticancer peptides, which are worthy of further experimental validation and 2 peptides (ARP788.8 and ARP788.21) of HIV-1 p24 protein can be used as new anticancer candidates without mutagenicity.

  6. 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.

  7. Prediction of HLA-A2 binding peptides using Bayesian network.

    PubMed

    Astakhov, Vadim; Cherkasov, Artem

    2005-10-11

    Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99 % accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets.

  8. Prediction of HLA-A2 binding peptides using Bayesian network

    PubMed Central

    Astakhov, Vadim; Cherkasov, Artem

    2005-01-01

    Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99 % accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets. PMID:17597855

  9. Models to predict intestinal absorption of therapeutic peptides and proteins.

    PubMed

    Antunes, Filipa; Andrade, Fernanda; Ferreira, Domingos; Nielsen, Hanne Morck; Sarmento, Bruno

    2013-01-01

    Prediction of human intestinal absorption is a major goal in the design, optimization, and selection of drugs intended for oral delivery, in particular proteins, which possess intrinsic poor transport across intestinal epithelium. There are various techniques currently employed to evaluate the extension of protein absorption in the different phases of drug discovery and development. Screening protocols to evaluate protein absorption include a range of preclinical methodologies like in silico, in vitro, in situ, ex vivo and in vivo. It is the careful and critical use of these techniques that can help to identify drug candidates, which most probably will be well absorbed from the human intestinal tract. It is well recognized that the human intestinal permeability cannot be accurately predicted based on a single preclinical method. However, the present social and scientific concerns about the animal well care as well as the pharmaceutical industries need for rapid, cheap and reliable models predicting bioavailability give reasons for using methods providing an appropriate correlation between results of in vivo and in vitro drug absorption. The aim of this review is to describe and compare in silico, in vitro, in situ, ex vivo and in vivo methods used to predict human intestinal absorption, giving a special attention to the intestinal absorption of therapeutic peptides and proteins.

  10. Prediction and interpretation of the lipophilicity of small peptides

    NASA Astrophysics Data System (ADS)

    Visconti, Alessia; Ermondi, Giuseppe; Caron, Giulia; Esposito, Roberto

    2015-04-01

    Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict log D_{ {oct}} of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict log D_{ {oct}} of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.

  11. Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation.

    PubMed

    Sun, Pingping; Chen, Wenhan; Huang, Yanxin; Wang, Hongyan; Ma, Zhiqiang; Lv, Yinghua

    2011-06-16

    Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.

  12. Prediction and verification of novel peptide targets of protein tyrosine phosphatase 1B.

    PubMed

    Li, Xun; Köhn, Maja

    2016-08-01

    Phosphotyrosine peptides are useful starting points for inhibitor design and for the search for protein tyrosine phosphatase (PTP) phosphoprotein substrates. To identify novel phosphopeptide substrates of PTP1B, we developed a computational prediction protocol based on a virtual library of protein sequences with known phosphotyrosine sites. To these we applied sequence-based methods, biologically meaningful filters and molecular docking. Five peptides were selected for biochemical testing of their potential as PTP1B substrates. All five peptides were equally good substrates for PTP1B compared to a known peptide substrate whereas appropriate control peptides were not recognized, showing that our protocol can be used to identify novel peptide substrates of PTP1B.

  13. The Optimal Prediction method

    SciTech Connect

    Burin des Roziers, Thibaut

    1999-08-01

    The purpose of the work is to test and show how well the numerical method called Optima Prediction works. This method is relatively new and only a few experiment have been made. The authors first did a series of simple tests to see how the method behaves. In order to have a better understanding of the method, they then reproduced one of the main experiment which was done about Optimal Prediction by Kupferman. Once they obtained the same results that Kupferman had, they changed a few parameters to see how dependant the method was on this parameters. In this paper, they will present all the tests they made, the results they obtained and what they concluded about the method. Before talking about the experiments, they have to explain what is the Optimal Prediction method and how does it work. This will be done in the first section of this paper.

  14. 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.

  15. 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

  16. Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

    PubMed

    Ishikawa, Takeshi

    2016-10-01

    Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

  17. Prediction of MHC binding peptides and epitopes from alfalfa mosaic virus.

    PubMed

    Gomase, Virendra S; Kale, Karbhari V; Chikhale, Nandkishor J; Changbhale, Smruti S

    2007-08-01

    Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency.

  18. 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.

  19. 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.

  20. 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.

  1. Location predicting methods for UAVs

    NASA Astrophysics Data System (ADS)

    Peng, Xiaodong; Zhang, Yu

    2017-08-01

    Location prediction of unmanned aerial vehicle (UAV) is important for fighting with its enemy and ensuring its normal operation. This paper presents the motion model of UAVs and reduces the state space into 7 dimensions. The Bayesian Network, Markov Chain, Curve Fitting and Neural Network are introduced for designing predicting methods. Then Curve Fitting Predicting method, Markov Chain Predicting method, Bayesian Network Predicting method and Neural Network Predicting method are designed for UAVs. The simulation result shows that 1) Neural Network Predicting method has highest predicting accuracy; 2) Markov Chain Predicting method and Bayesian Network Predicting method methods have similar performance and both are better than Bayesian Network Predicting method methods; 3) Neural Network Predicting method is the first choice when predicting the locations of UAVs.

  2. Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra.

    PubMed

    Ji, Chao; Arnold, Randy J; Sokoloski, Kevin J; Hardy, Richard W; Tang, Haixu; Radivojac, Predrag

    2013-03-01

    Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

  3. 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.

  4. Peptide motif analysis predicts alphaviruses as triggers for rheumatoid arthritis.

    PubMed

    Hogeboom, Charissa

    2015-12-01

    Rheumatoid arthritis (RA) develops in response to both genetic and environmental factors. The strongest genetic determinant is HLA-DR, where polymorphisms within the P4 and P6 binding pockets confer elevated risk. However, low disease concordance across monozygotic twin pairs underscores the importance of an environmental factor, probably infectious. The goal of this investigation was to predict the microorganism most likely to interact with HLA-DR to trigger RA under the molecular mimicry hypothesis. A set of 185 structural proteins from viruses or intracellular bacteria was scanned for regions of sequence homology with a collagen peptide that binds preferentially to DR4; candidates were then evaluated against a motif required for T cell cross-reactivity. The plausibility of the predicted agent was evaluated by comparison of microbial prevalence patterns to epidemiological characteristics of RA. Peptides from alphavirus capsid proteins provided the closest fit. Variations in the P6 position suggest that the HLA binding preference may vary by species, with Ross River virus, Chikungunya virus, and Mayaro virus peptides binding preferentially to DR4, and peptides from Sindbis/Ockelbo virus showing stronger affinity to DR1. The predicted HLA preference is supported by epidemiological studies of post-infection chronic arthralgia. Parallels between the cytokine profiles of RA and chronic alphavirus infection are discussed.

  5. 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.

  6. 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

  7. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    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 charged 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.

  8. 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

  9. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    PubMed Central

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel C.; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-01-01

    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 charged 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. PMID:23499924

  10. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293

  11. 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.

  12. Motor degradation prediction methods

    SciTech Connect

    Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

    1996-12-01

    Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.

  13. Improvements to the TMSBr method of peptide resin deprotection and cleavage: application to large peptides.

    PubMed

    Sparrow, J T; Monera, O D

    1996-01-01

    The original trimethylsilyl bromide (TMSBr) method of peptide resin deprotection and cleavage has been modified for convenience and applicability to larger peptides. Equal amounts of a 66-residue test peptide resin were cleaved by the standard hydrogen fluoride (HF) procedure, the original TMSBr method and the modified TMSBr method. The peptide profile from the original TMSBr cleavage procedure showed multiple products and a lower overall yield. In contrast, the modified TMSBr procedure gave high yields of crude products comparable in purity to those obtained by HF cleavage.

  14. A novel fuzzy Fisher classifier for signal peptide prediction.

    PubMed

    Gao, Cui-Fang; Qiu, Zi-Xue; Wu, Xiao-Jun; Tian, Feng-Wei; Zhang, Hao; Chen, Wei

    2011-08-01

    Signal peptides recognition by bioinformatics approaches is particularly important for the efficient secretion and production of specific proteins. We concentrate on developing an integrated fuzzy Fisher clustering (IFFC) and designing a novel classifier based on IFFC for predicting secretory proteins. IFFC provides a powerful optimal discriminant vector calculated by fuzzy intra-cluster scatter matrix and fuzzy inter-cluster scatter matrix. Because the training samples and test samples are processed together in IFFC, it is convenient for users to employ their own specific samples of high reliability as training data if necessary. The cross-validation results on some existing datasets indicate that the fuzzy Fisher classifier is quite promising for signal peptide prediction.

  15. 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

  16. New method of iodine labelling of peptide hormones

    SciTech Connect

    Escher, E.

    1984-01-01

    Usually peptide hormones and related compounds are radioactively labelled with iodine on tyrosine residues of the peptide. However many peptide hormones do not contain tyrosine or the iodinated tyrosine interferes with the biological properties. In order to circumvent these and other problems, a general method is proposed which allows the introduction of iodine into the para-position of phenylalanine with a modified Sandmeyer procedure. This last-step modification together with HPLC purification permits the obtention of carrier-free and metabolically stable labelled products with maximal specific activity possible. The model has been carried out on several peptide-models like angiotensin II, endorphine and head activator peptide.

  17. Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines

    PubMed Central

    Tang, Zhi Qun; Lin, Hong Huang; Zhang, Hai Lei; Han, Lian Yi; Chen, Xin; Chen, Yu Zong

    2007-01-01

    Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented. PMID:20066123

  18. Multi-label Learning for Predicting the Activities of Antimicrobial Peptides.

    PubMed

    Wang, Pu; Ge, Ruiquan; Liu, Liming; Xiao, Xuan; Li, Ye; Cai, Yunpeng

    2017-05-19

    Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.

  19. 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.

  20. Prediction of Peptide and Protein Propensity for Amyloid Formation.

    PubMed

    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.

  1. 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

  2. Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy.

    PubMed

    Crotti, Sara; Enzo, Maria Vittoria; Bedin, Chiara; Pucciarelli, Salvatore; Maretto, Isacco; Del Bianco, Paola; Traldi, Pietro; Tasciotti, Ennio; Ferrari, Mauro; Rizzolio, Flavio; Toffoli, Giuseppe; Giordano, Antonio; Nitti, Donato; Agostini, Marco

    2015-08-01

    Preoperative chemoradiotherapy is worldwide accepted as a standard treatment for locally advanced rectal cancer. Current standard of treatment includes administration of ionizing radiation for 45-50.4 Gy in 25-28 fractions associated with 5-fluorouracil administration during radiation therapy. Unfortunately, 40% of patients have a poor or absent response and novel predictive biomarkers are demanding. For the first time, we apply a novel peptidomic methodology and analysis in rectal cancer patients treated with preoperative chemoradiotherapy. Circulating peptides (Molecular Weight <3 kDa) have been harvested from patients' plasma (n = 33) using nanoporous silica chip and analyzed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass spectrometer. Peptides fingerprint has been compared between responders and non-responders. Random Forest classification selected three peptides at m/z 1082.552, 1098.537, and 1104.538 that were able to correctly discriminate between responders (n = 16) and non-responders (n = 17) before therapy (T0) providing an overall accuracy of 86% and an area under the receiver operating characteristic (ROC) curve of 0.92. In conclusion, the nanoporous silica chip coupled to mass spectrometry method was found to be a realistic method for plasma-based peptide analysis and we provide the first list of predictive circulating biomarker peptides in rectal cancer patients underwent preoperative chemoradiotherapy.

  3. 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.

  4. 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.

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

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

    2016-01-01

    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. This algorithm 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. PMID:27558848

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

    SciTech Connect

    Luo, Heng; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

    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 algorithm 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.

  7. 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

  8. Detection of selective antibacterial peptides by the Polarity Profile method.

    PubMed

    Polanco, Carlos; Buhse, Thomas; Samaniego, José Lino; Castañón-González, Jorge Alberto

    2013-01-01

    Antimicrobial peptides occupy a prominent place in the production of pharmaceuticals, because of their effective contribution to the protection of the immune system against almost all types of pathogens. These peptides are thoroughly studied by computational methods designed to shed light on their main functions. In this paper, we propose a computational approach, named the Polarity Profile method that represents an improvement to the former Polarity Index method. The Polarity Profile method is very effective in detecting the subgroup of antibacterial peptides called selective cationic amphipathic antibacterial peptides (SCAAP) that show high toxicity towards bacterial membranes and exhibit almost zero toxicity towards mammalian cells. Our study was restricted to the peptides listed in the antimicrobial peptides database (APD2) of December 19, 2012. Performance of the Polarity Profile method is demonstrated through a comparison to the former Polarity Index method by using the same sets of peptides. The efficiency of the Polarity Profile method exceeds 85% taking into account the false positive and/or false negative peptides.

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

    PubMed Central

    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. PMID

  10. MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.

    PubMed

    Xu, Yichang; Luo, Cheng; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-01-01

    Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info .

  11. Prediction of Signal Peptide Cleavage Sites with Subsite-Coupled and Template Matching Fusion Algorithm.

    PubMed

    Zhang, Shao-Wu; Zhang, Ting-He; Zhang, Jun-Nan; Huang, Yufei

    2014-03-01

    Fast and effective prediction of signal peptides (SP) and their cleavage sites is of great importance in computational biology. The approaches developed to predict signal peptide can be roughly divided into machine learning based, and sliding windows based. In order to further increase the prediction accuracy and coverage of organism for SP cleavage sites, we propose a novel method for predicting SP cleavage sites called Signal-CTF that utilizes machine learning and sliding windows, and is designed for N-termial secretory proteins in a large variety of organisms including human, animal, plant, virus, bacteria, fungi and archaea. Signal-CTF consists of three distinct elements: (1) a subsite-coupled and regularization function with a scaled window of fixed width that selects a set of candidates of possible secretion-cleavable segment for a query secretory protein; (2) a sum fusion system that integrates the outcomes from aligning the cleavage site template sequence with each of the aforementioned candidates in a scaled window of fixed width to determine the best candidate cleavage sites for the query secretory protein; (3) a voting system that identifies the ultimate signal peptide cleavage site among all possible results derived from using scaled windows of different width. When compared with Signal-3L and SignalP 4.0 predictors, the prediction accuracy of Signal-CTF is 4-12 %, 10-25 % higher than that of Signal-3L for human, animal and eukaryote, and SignalP 4.0 for eukaryota, Gram-positive bacteria and Gram-negative bacteria, respectively. Comparing with PRED-SIGNAL and SignalP 4.0 predictors on the 32 archaea secretory proteins of used in Bagos's paper, the prediction accuracy of Signal-CTF is 12.5 %, 25 % higher than that of PRED-SIGNAL and SignalP 4.0, respectively. The predicting results of several long signal peptides show that the Signal-CTF can better predict cleavage sites for long signal peptides than SignalP, Phobius, Philius, SPOCTOPUS, Signal

  12. Prediction method abstracts

    SciTech Connect

    1994-12-31

    This conference was held December 4--8, 1994 in Asilomar, California. The purpose of this meeting was to provide a forum for exchange of state-of-the-art information concerning the prediction of protein structure. Attention if focused on the following: comparative modeling; sequence to fold assignment; and ab initio folding.

  13. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

    PubMed Central

    2012-01-01

    Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP) and orthology (OrthoMCL) were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups). In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples) or on experimental evidence already published (ApiLoc). Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug targets and proteins

  14. 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.

  15. 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.

  16. Analysis of proteins and peptides by electromigration methods in microchips.

    PubMed

    Štěpánová, Sille; Kašička, Václav

    2017-01-01

    This review presents the developments and applications of microchip electromigration methods in the separation and analysis of peptides and proteins in the period 2011-mid-2016. The developments in sample preparation and preconcentration, microchannel material, and surface treatment are described. Separations by various microchip electromigration methods (zone electrophoresis in free and sieving media, affinity electrophoresis, isotachophoresis, isoelectric focusing, electrokinetic chromatography, and electrochromatography) are demonstrated. Advances in detection methods are reported and novel applications in the areas of proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices, and determination of physicochemical parameters are shown.

  17. Narrow-range peptide isoelectric focusing as peptide prefractionation method prior to tandem mass spectrometry analysis.

    PubMed

    Pernemalm, Maria

    2013-01-01

    High sample complexity is one of the major challenges in mass spectrometry-based proteomics today. Despite massive improvement in instrumentation, sample prefractionation is still needed to reduce sample complexity and improve proteome coverage. Isoelectric focusing (IEF) has been traditionally used as a first-dimension protein separation technique in two-dimensional gel electrophoresis-based proteomics. Recently, peptide IEF has emerged as appealing alternative for anion exchange chromatography in multidimensional LC-MS/MS workflows. The rationale behind using narrow-range peptide isoelectric focusing as a prefractionation method prior to ms/ms is to reduce the complexity induced by tryptic digestion. This is done by selectively analyzing a sub-fraction of peptides with an acidic pI. The pI range is chosen as it has previously been shown that 96 % of human proteins have at least one tryptic peptide between pH 3.4 and 4.9. This ensures high proteome coverage while reducing the number of peptides with 2/3. In addition the focusing precision is optimal in this range. Therefore, by analyzing this sub-fraction of peptides the complexity of the sample can be reduced without significant loss of proteome coverage. As the theoretical pI of peptides can be calculated, the pI of the identified peptides can be used to validate the peptide sequence (identified peptides with pI outside the pH range 3.4-4.9 are more likely to be false positives). In addition, this approach is compatible with iTRAQ labelling as the different iTRAQ labels migrate similarly in IEF.

  18. Brain natriuretic peptide predicts mortality in the elderly.

    PubMed Central

    Wallén, T.; Landahl, S.; Hedner, T.; Nakao, K.; Saito, Y.

    1997-01-01

    OBJECTIVE: To study whether prospective measurements of circulating concentrations of brain natriuretic peptide (BNP) could predict mortality in the general elderly population. DESIGN AND SETTING: Circulating BNP was measured in a cohort of 85 year olds from the general population who were followed up prospectively for five years as part of a longitudinal population study, "70 year old people in Gothenburg, Sweden". PATIENTS: 541 subjects from the 85 year old population in Gothenburg. All subjects were investigated for the presence or absence of cardiovascular disorder such as congestive heart failure, ischaemic heart disease, hypertension, and atrial fibrillation. Venous plasma samples were obtained for BNP analysis. MAIN OUTCOME MEASURE: Overall mortality during the prospective follow up period. RESULTS: Circulating concentrations of BNP predicted five-year mortality in the total population (P < 0.001). In subjects with a known cardiovascular disorder, five-year mortality was correlated with increased BNP concentrations (P < 0.01). Increased BNP concentrations predicted five-year mortality in subjects without a defined cardiovascular disorder (P < 0.05). CONCLUSIONS: In an elderly population, measurements of BNP may add valuable prognostic information and may be used to predict mortality in the total population as well as in patients with known cardiovascular disorders. In subjects without any known cardiovascular disorder, BNP was a strong and independent predictor of total mortality. PMID:9093047

  19. Peptide reactivity assay using spectrophotometric method for high-throughput screening of skin sensitization potential of chemical haptens.

    PubMed

    Jeong, Yun Hyeok; An, Susun; Shin, Kyeho; Lee, Tae Ryong

    2013-02-01

    Haptens must react with cellular proteins to be recognized by antigen presenting cells. Therefore, monitoring reactivity of chemicals with peptide/protein has been considered an in vitro skin sensitization testing method. The reactivity of peptides with chemicals (peptide reactivity) has usually been monitored by chromatographic methods like HPLC or LC/MS, which are robust tools for monitoring common chemical reactions but are rather expensive and time consuming. Here, we examined the possibility of using spectrophotometric methods to monitor peptide reactivity. Two synthetic peptides, Ac-RWAACAA and Ac-RWAAKAA, were reacted with 48 chemicals (34 sensitizers and 14 non-sensitizers). Peptide reactivity was measured by monitoring unreacted peptides with UV-Vis spectrophotometer using 5,5'-dithiobis-2-nitrobenzoic acid as a detection reagent for the free thiol group of cysteine-containing peptide or fluorometer using fluorescamine™ as a detection reagent for the free amine group of lysine-containing peptide. Chemicals were categorized as sensitizers when they induced more than 10% depletion of cysteine-containing peptide or 20% depletion of lysine-containing peptide. The sensitivity, specificity, and accuracy of this method were 82.4%, 85.7%, and 83.3%, respectively. These results demonstrate that spectrophotometric methods can be easy, fast, and high-throughput screening tools for the prediction of the skin sensitization potential of chemical haptens.

  20. Seizure Prediction: Methods

    PubMed Central

    Carney, Paul R.; Myers, Stephen; Geyer, James D.

    2011-01-01

    Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. PMID:22078526

  1. Improved Methods for the Enrichment and Analysis of Glycated Peptides

    SciTech Connect

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

    2008-12-15

    Non-enzymatic 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 on-line wash of column-bound glycated peptides using 50 mM ammonium acetate. 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. In general, 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 glycated peptides identified by LC-MS/MS.

  2. 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

  3. 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.

  4. A comprehensive analysis of predicted HLA binding peptides of JE viral proteins specific to north Indian isolates.

    PubMed

    Sharma, Pawan; Saxena, Komal; Mishra, Sanjay; Kumar, Ajay

    2014-01-01

    Japanese encephalitis (JE), a viral disease has significantly increased worldwide especially, in the developing region due to challenges in immunization, vector control and lack of appropriate treatment methods. An effective, yet an expensive heat-killed vaccine is available for the disease. Therefore, the design and development of short peptide vaccine candidate is promising. We used immune-informatics methods to perform a comprehensive analysis of the entire JEV proteome of north Indian isolate to identify the conserved peptides binding known specific HLA alleles among the documented JEV genotypes 1, 2, 3, 4 and 5. The prediction analysis identified 102 class I (using propred I) and 118 class II (using propred) binding peptides at 4% threshold value. These predicted HLA allele binding peptides were further analyzed for potential conserved region using IEDB (an immune epitope database and analysis resource). This analysis shows that 78.81% of class II (in genotype 2) and 76.47% of HLA I (in genotype 3) bound peptides are conserved. The peptides IPIVSVASL, KGAQRLAAL, LAVFLICVL and FRTLFGGMS, VFLICVLTV, are top ranking with potential super antigenic property by binding to all HLA allele members of B7 and DR4 super-types, respectively. This data finds application in the design and development of short peptide vaccine candidates and diagnostic agents for JE following adequate validation and verification.

  5. Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

    PubMed Central

    Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.

    2014-01-01

    Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine

  6. Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

    PubMed

    Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

    2014-01-01

    Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery

  7. A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01.

    PubMed

    Pedersen, Lasse Eggers; Rasmussen, Michael; Harndahl, Mikkel; Nielsen, Morten; Buus, Søren; Jungersen, Gregers

    2016-02-01

    Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-1*04:01 and SLA-2*04:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-3*04:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01, a total of 71, 28, and 38% were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.

  8. Improved machine learning method for analysis of gas phase chemistry of peptides.

    PubMed

    Gehrke, Allison; Sun, Shaojun; Kurgan, Lukasz; Ahn, Natalie; Resing, Katheryn; Kafadar, Karen; Cios, Krzysztof

    2008-12-03

    Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.

  9. Improved machine learning method for analysis of gas phase chemistry of peptides

    PubMed Central

    Gehrke, Allison; Sun, Shaojun; Kurgan, Lukasz; Ahn, Natalie; Resing, Katheryn; Kafadar, Karen; Cios, Krzysztof

    2008-01-01

    Background Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. Results We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. Conclusion The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future. PMID:19055745

  10. Anticitrullinated protein/peptide antibody assays in early rheumatoid arthritis for predicting five year radiographic damage

    PubMed Central

    Meyer, O; Labarre, C; Dougados, M; Goupille, P.; Cantagrel, A; Dubois, A; Nicaise-Roland, P; Sibilia, J; Combe, B

    2003-01-01

    Objective: To study the value of antibodies to citrullinated proteins/peptides for predicting joint outcomes in patients with recent onset rheumatoid arthritis (RA). Methods: 191 patients with RA onset within the past year were followed up prospectively for five years. Serum samples obtained from 145 patients at baseline before disease modifying antirheumatic drug treatment were examined using three anticitrullinated protein/peptide antibody assays: antiperinuclear factor (APF) by indirect immunofluorescence (IIF), antikeratin antibodies (AKA) by IIF, and anti-cyclic citrullinated peptide (CCP) antibodies by enzyme linked immunosorbent assay (ELISA). Radiographs of the hands and feet taken at baseline and after three and five years were evaluated using Sharp scores modified by van der Heijde. Results:Anti-CCP ELISA was positive in 58.9% of patients. APF/anti-CCP agreement was 77%. The likelihood of a total Sharp score increase after five years was significantly greater among patients with anti-CCP antibodies (67%; odds ratio (OR) 2.5; 95% confidence interval (95% CI) 1.2 to 5.0) or APF (57%; OR 2.4; 95% CI 1.2 to 4.9) but not rheumatoid factor (RF; OR 0.7; 95% CI 0.3 to 1.5). Mean values for radiographic damage, erosion, and joint narrowing scores at the three times were significantly higher in patients with anti-CCP or APF than in those without. AKA did not significantly predict radiographic damage. In separate analyses of patients with and without RF, anti-CCP or APF was better than RF for predicting total joint damage and joint damage progression after five years. Conclusion: Antibodies to citrullinated proteins/peptides determined early in the course of RA by APF IIF or anti-CCP ELISA are good predictors of radiographic joint damage. Further studies of clinical, laboratory, and genetic parameters are needed to improve RA outcome prediction in clinical practice. PMID:12525380

  11. Computational analysis and structure predictions of CHH-related peptides from Litopenaeus vannamei.

    PubMed

    Nagaraju, G Purna Chandra; Kumari, N Siva; Prasad, G L V; Naik, B Reddya; Borst, D W

    2011-03-01

    The crustaceans produce several related peptides that belong to the crustacean hyperglycemic hormone (CHH) family. While these peptides have similar amino acid sequences, they have diverse biological functions that must arise, in part, from differences in the 3D shape of these peptides. However, it is generally accepted that peptides with a high degree of sequence similarity also have a similar 3-D structure. We used the solution structure of one peptide in the crustacean hyperglycemic hormone family, the molt-inhibiting hormone of the kuruma prawn (Marsupenaeus japonicus), to predict the shape of the five known peptides related to CHH in the Pacific white shrimp, Litopenaeus vannamei. The high similarity of the 3-D structures of these peptides suggests a common fold for the entire family. Nevertheless, minor differences in the shape of these peptides were observed, which may be the basis for their different biological properties.

  12. 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

  13. Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction.

    PubMed

    Shen, Yimin; Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2014-10-14

    Peptides and mini proteins have many biological and biomedical implications, which motivates the development of accurate methods, suitable for large-scale experiments, to predict their experimental or native conformations solely from sequences. In this study, we report PEP-FOLD2, an improved coarse grained approach for peptide de novo structure prediction and compare it with PEP-FOLD1 and the state-of-the-art Rosetta program. Using a benchmark of 56 structurally diverse peptides with 25-52 amino acids and a total of 600 simulations for each system, PEP-FOLD2 generates higher quality models than PEP-FOLD1, and PEP-FOLD2 and Rosetta generate near-native or native models for 95% and 88% of the targets, respectively. In the situation where we do not have any experimental structures at hand, PEP-FOLD2 and Rosetta return a near-native or native conformation among the top five best scored models for 80% and 75% of the targets, respectively. While the PEP-FOLD2 prediction rate is better than the ROSETTA prediction rate by 5%, this improvement is non-negligible because PEP-FOLD2 explores a larger conformational space than ROSETTA and consists of a single coarse-grained phase. Our results indicate that if the coarse-grained PEP-FOLD2 method is approaching maturity, we are not at the end of the game of mini-protein structure prediction, but this opens new perspectives for large-scale in silico experiments.

  14. Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity1

    PubMed Central

    Rasmussen, Michael; Fenoy, Emilio; Harndahl, Mikkel; Kristensen, Anne Bregnballe; Nielsen, Ida Kallehauge; Nielsen, Morten; Buus, Søren

    2016-01-01

    Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of antigens to cytotoxic T lymphocytes (CTL) and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, i.e. the binding affinity, yet it has been show that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared to binding affinity. Here, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop neural networks based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural networks predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at www.cbs.dtu.dk/services/NetMHCstabpan. PMID:27402703

  15. Prediction of Leymus arenarius (L.) antimicrobial peptides based on de novo transcriptome assembly.

    PubMed

    Slavokhotova, Anna A; Shelenkov, Andrey A; Odintsova, Tatyana I

    2015-10-01

    Leymus arenarius is a unique wild growing Poaceae plant exhibiting extreme tolerance to environmental conditions. In this study we for the first time performed whole-transcriptome sequencing of lymegrass seedlings using Illumina platform followed by de novo transcriptome assembly and functional annotation. Our goal was to identify transcripts encoding antimicrobial peptides (AMPs), one of the key components of plant innate immunity. Using the custom software developed for this study that predicted AMPs and classified them into families, we revealed more than 160 putative AMPs in lymegrass seedlings. We classified them into 7 families based on their cysteine motifs and sequence similarity. The families included defensins, thionins, hevein-like peptides, snakins, cyclotide, alfa-hairpinins and LTPs. This is the first communication about the presence of almost all known AMP families in trascriptomic data of a single plant species. Additionally, cysteine-rich peptides that potentially represent novel families of AMPs were revealed. We have confirmed by RT-PCR validation the presence of 30 transcripts encoding selected AMPs in lymegrass seedlings. In summary, the presented method of pAMP prediction developed by us can be applied for relatively fast and simple screening of novel components of plant immunity system and is well suited for whole-transcriptome or genome analysis of uncharacterized plants.

  16. Enrichment of phosphorylated peptides and proteins by selective precipitation methods.

    PubMed

    Rainer, Matthias; Bonn, Günther K

    2015-01-01

    Protein phosphorylation is one of the most prominent post-translational modifications involved in the regulation of cellular processes. Fundamental understanding of biological processes requires appropriate bioanalytical methods for selectively enriching phosphorylated peptides and proteins. Most of the commonly applied enrichment approaches include chromatographic materials including Fe(3+)-immobilized metal-ion affinity chromatography or metal oxides. In the last years, the introduction of several non-chromatographic isolation technologies has increasingly attracted the interest of many scientists. Such approaches are based on the selective precipitation of phosphorylated peptides and proteins by applying various metal cations. The excellent performance of precipitation-based enrichment methods can be explained by the absence of any stationary phase, resin or sorbent, which usually leads to unspecific binding. This review provides an overview of recently published methods for the selective precipitation of phosphorylated peptides and proteins.

  17. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides.

    PubMed

    Chou, Kuo-Chen; Shen, Hong-Bin

    2007-06-08

    We have developed an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences. It is a 2-layer predictor: the 1st-layer prediction engine is to identify a query protein as secretory or non-secretory; if it is secretory, the process will be automatically continued with the 2nd-layer prediction engine to further identify the cleavage site of its signal peptide. The new predictor is called Signal-CF, where C stands for "coupling" and F for "fusion", meaning that Signal-CF is formed by incorporating the subsite coupling effects along a protein sequence and by fusing the results derived from many width-different scaled windows through a voting system. Signal-CF is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-CF is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-CF/ or http://202.120.37.186/bioinf/Signal-CF/.

  18. Abundance-based Classifier for the Prediction of Mass Spectrometric Peptide Detectability Upon Enrichment (PPA)*

    PubMed Central

    Muntel, Jan; Boswell, Sarah A.; Tang, Shaojun; Ahmed, Saima; Wapinski, Ilan; Foley, Greg; Steen, Hanno; Springer, Michael

    2015-01-01

    The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, “Peptide Prediction with Abundance” (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set. PMID:25473088

  19. SANDPUMA: Ensemble Predictions of Nonribosomal Peptide Chemistry Reveals Biosynthetic Diversity across Actinobacteria.

    PubMed

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

    2017-06-19

    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 7,635 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 , marnix.medema@wur.nl. Supplementary data are available at Bioinformatics online.

  20. Identification of novel peptide biomarkers to predict safety and efficacy of cow's milk oral immunotherapy by peptide microarray.

    PubMed

    Martínez-Botas, J; Rodríguez-Álvarez, M; Cerecedo, I; Vlaicu, C; Diéguez, M C; Gómez-Coronado, D; Fernández-Rivas, M; de la Hoz, B

    2015-06-01

    Cow's milk oral immunotherapy (CM-OIT) is still an experimental treatment. The development of novel biomarkers to predict the safety and efficacy of CM-OIT is crucial to translate this treatment to common clinical practice. To analyse long-term changes in IgE and IgG4 epitope binding profile induced by CM-OIT to identify safety and efficacy biomarkers. We studied 25 CM-allergic children who underwent CM-OIT and seven non-treated CM-allergic children as controls. CM-OIT patients were classified as low, moderate, and high risk according to the number of allergic reactions (safety), time required to achieve desensitization (efficacy) and need of premedication. IgE and IgG4 peptide microarray immunoassay was performed using a library of overlapping peptides of CM proteins at baseline, after oral desensitization, and 6, 12, and 24 months of follow-up. Cow's milk oral immunotherapy induced a rapid increase of IgG4-binding epitopes and a slow decrease in IgE-binding epitopes. High-risk patients recognized a statistically significant higher number of IgE peptides in caseins at all the times studied. Similar but less pronounced changes were observed for IgG4-positive peptides. Clustering analysis grouped together the high-risk patients, and we identified 13 regions of caseins significantly differed between groups of patients. Bioinformatics analysis selected two sets of 16 IgE-binding peptides at baseline that predicted safety (R(2)  = 0.858) and efficacy (R(2)  = 0.732), respectively, of CM-OIT. We found two sets of IgE-binding peptides that can be used as novel biomarkers to predict the safety and efficacy of CM-OIT before starting treatment. © 2015 John Wiley & Sons Ltd.

  1. Utilization of the Monte Carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides.

    PubMed

    Toropova, Alla P; Toropov, Andrey A; Beeg, Marten; Gobbi, Marco; Salmona, Mario

    2017-05-24

    Traditional quantitative structure - property / activity relationships (QSPRs/QSARs) are based on representation of molecular structure by molecular graph or simplified molecular input-line entry system (SMILES). It is attractive idea to develop predictive models for large molecules in general and for peptides in particular. However, the representation of these molecules by molecular graph or SMILES is problematic owing to large size of these molecules. A possible alternative of SMILES is representation of peptides via sequence of abbreviations of amino acids. Models for hemolysis and cytotoxicity of peptides are suggested. These models are based on representation of the peptides by sequences of amino acids. Correlation weights, which are calculated for each amino acid using the Monte Carlo method are basis for quantitative sequence - activity relationships (QSAR) for antimicrobial peptides. The correlation weights are basis for optimal descriptors, which are correlated with experimental data for hemolysis and cytotoxicity. The basic hypothesis is that if optimal descriptors correlated with endpoints of peptides for the training set, also they should correlate with the endpoints for validation set. Checking up of correlations between the above-mentioned descriptors and antimicrobial activity of peptides (cytotoxicity or hemolysis) has shown that these models have good predictive potential. Suggested approach can be used as a tool to develop predictive models of biological activity of peptides as a mathematical function of sequences of amino acids. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. C-Peptide Levels Predict the Effectiveness of Dipeptidyl Peptidase-4 Inhibitor Therapy

    PubMed Central

    Demir, Sevin; Sargin, Mehmet

    2016-01-01

    Background. Our aim was to define the conditions that affect therapeutic success when dipeptidyl peptidase-4 (DPP-4) inhibitor is added to metformin monotherapy. Materials and Methods. We reviewed the medical records of 56 patients who had received DPP-4 inhibitor as an add-on to metformin monotherapy and evaluated their response in the first year of therapy. Fasting blood glucose (FBG), HbA1c, C-peptide, and weight of the patients were recorded at 3-month intervals during the first year of treatment. Results. Patients who added DPP-4 inhibitor to metformin monotherapy had significant weight loss (P = 0.004) and FBG and HbA1c levels were significantly lowered during the first 6 months (both P < 0.001). Baseline levels of C-peptide were predictive for success of the treatment (P = 0.02), even after correction for confounding factors, for example, age, gender, or BMI (P = 0.03). Duration of diabetes was not a predictor of response to treatment (P = 0.60). Conclusion. Our study demonstrates that in patients having inadequate glycemic control, the addition of a DPP-4 inhibitor as a second oral agent to metformin monotherapy provides better glycemic control, protects β-cell reserves, and does not cause weight gain. These effects depend on baseline C-peptide levels. PMID:27882332

  3. Fetal urinary peptides to predict postnatal outcome of renal disease in fetuses with posterior urethral valves (PUV).

    PubMed

    Klein, Julie; Lacroix, Chrystelle; Caubet, Cécile; Siwy, Justyna; Zürbig, Petra; Dakna, Mohammed; Muller, Françoise; Breuil, Benjamin; Stalmach, Angelique; Mullen, William; Mischak, Harald; Bandin, Flavio; Monsarrat, Bernard; Bascands, Jean-Loup; Decramer, Stéphane; Schanstra, Joost P

    2013-08-14

    Bilateral congenital abnormalities of the kidney and urinary tract (CAKUT), although are individually rare diseases, remain the main cause of chronic kidney disease in infants worldwide. Bilateral CAKUT display a wide spectrum of pre- and postnatal outcomes ranging from death in utero to normal postnatal renal function. Methods to predict these outcomes in utero are controversial and, in several cases, lead to unjustified termination of pregnancy. Using capillary electrophoresis coupled with mass spectrometry, we have analyzed the urinary proteome of fetuses with posterior urethral valves (PUV), the prototypic bilateral CAKUT, for the presence of biomarkers predicting postnatal renal function. Among more than 4000 fetal urinary peptide candidates, 26 peptides were identified that were specifically associated with PUV in 13 patients with early end-stage renal disease (ESRD) compared to 15 patients with absence of ESRD before the age of 2. A classifier based on these peptides correctly predicted postnatal renal function with 88% sensitivity and 95% specificity in an independent blinded validation cohort of 38 PUV patients, outperforming classical methods, including fetal urine biochemistry and fetal ultrasound. This study demonstrates that fetal urine is an important pool of peptides that can predict postnatal renal function and thus be used to make clinical decisions regarding pregnancy.

  4. 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.

  5. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

    PubMed Central

    2013-01-01

    Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. PMID:24564280

  6. 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/.

  7. A method to determine the ionization efficiency change of peptides caused by phosphorylation.

    PubMed

    Gao, Yuan; Wang, Yinsheng

    2007-11-01

    Quantitative assessment of post-translational modifications in proteins by mass spectrometry often requires the consideration of the alteration in ionization efficiency of peptides induced by the modification. Herein, we introduced a method to measure the relative ionization efficiencies of peptides using specifically designed unlabeled peptides. In our design, the peptide under study, in either the unmodified or modified form, is linked with an internal standard peptide via an enzyme cleavage site; thus, after enzymatic digestion, we could obtain readily a 1:1 ratio between the peptide under investigation and the internal standard peptide. The relative ionization efficiencies of the modified and unmodified peptides can then be calculated from the modification-induced change in the ratio of relative abundances of the ion of the peptide of interest over that of the internal standard peptide. We demonstrated the usefulness of the method by assessing the change in ionization efficiencies of four peptides introduced by phosphorylation.

  8. Peptide Suboptimal Conformation Sampling for the Prediction of Protein-Peptide Interactions.

    PubMed

    Lamiable, Alexis; Thévenet, Pierre; Eustache, Stephanie; Saladin, Adrien; Moroy, Gautier; Tuffery, Pierre

    2017-01-01

    The blind identification of candidate patches of interaction on the protein surface is a difficult task that can hardly be accomplished without a heuristic or the use of simplified representations to speed up the search. The PEP-SiteFinder protocol performs a systematic blind search on the protein surface using a rigid docking procedure applied to a limited set of peptide suboptimal conformations expected to approximate satisfactorily the conformation of the peptide in interaction. All steps rely on a coarse-grained representation of the protein and the peptide. While simple, such a protocol can help to infer useful information, assuming a critical analysis of the results. Moreover, such a protocol can be extended to a semi-flexible protocol where the suboptimal conformations are directly folded in the vicinity of the receptor.

  9. An Evaluation on Different Machine Learning Algorithms for Classification and Prediction of Antifungal Peptides.

    PubMed

    Mousavizadegan, Maryam; Mohabatkar, Hassan

    2016-01-01

    Fungi are an emerging threat in medicine and agriculture and current therapeutics have proved to be insufficient and toxic. This has led to an increased interest in peptide-based therapeutics, especially antifungal peptides (AFPs), being safer and more effective drug candidates against fungal threats. However, screening for peptides with antifungal activity is costly and timeconsuming. However, by using computational techniques, we can overcome these restricting factors. The aim of the present study is to compare different machine learning algorithms in combination with Chou's pseudo amino acid composition in classifying and predicting AFPs to represent a precise model for AFP prediction. Five different machine learning algorithms frequently used for classification of biological data were used and their performance was evaluated and compared based on their accuracy, sensitivity, specificity and Matthew's correlation coefficient. The two algorithms with the best performance were then further verified with an independent test dataset. SVM and Bagged-C4.5 classifiers had the highest performance results among the five algorithms. Further validations showed that the model generated using SVM can be employed for precise classification and prediction of antifungal peptides. All the performance values of this model were above 80%, making the classifier highly accurate and trustable. Using computational approaches, especially data mining techniques, we can develop a precise prediction model for antifungal peptides. The model developed in this study using SVM can be considered a powerful tool for the prediction of antifungal peptides, which can be the first step in synthesis and discovery of novel fungi targeting agents.

  10. 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.

  11. 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.

  12. Gaussian process: a promising approach for the modeling and prediction of Peptide binding affinity to MHC proteins.

    PubMed

    Ren, Yanrong; Chen, Xiaolin; Feng, Ming; Wang, Qiang; Zhou, Peng

    2011-07-01

    On the basis of Bayesian probabilistic inference, Gaussian process (GP) is a powerful machine learning method for nonlinear classification and regression, but has only very limited applications in the new areas of computational vaccinology and immunoinformatics. In the current work, we present a paradigmatic study of using GP regression technique to quantitatively model and predict the binding affinities of over 7000 immunodominant peptide epitopes to six types of human major histocompatibility complex (MHC) proteins. In this procedure, the sequence patterns of diverse peptides are characterized quantitatively and the resulting variables are then correlated with the experimentally measured affinities between different MHC and their peptide ligands, by using a linearity- and nonlinearity-hybrid GP approach. We also make systematical comparisons between the GP and two sophisticated modeling methods as partial least square (PLS) regression and support vector machine (SVM) with respect to their fitting ability, predictive power and generalization capability. The results suggest that GP could be a new and effective tool for the modeling and prediction of MHC-peptide interactions and would be promising in the field of computer-aided vaccine design (CAVD).

  13. GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies.

    PubMed

    Thangakani, A Mary; Kumar, Sandeep; Nagarajan, R; Velmurugan, D; Gromiha, M Michael

    2014-07-15

    Distinguishing between amyloid fibril-forming and amorphous β-aggregating aggregation-prone regions (APRs) in proteins and peptides is crucial for designing novel biomaterials and improved aggregation inhibitors for biotechnological and therapeutic purposes. Adjacent and alternate position residue pairs in hexapeptides show distinct preferences for occurrence in amyloid fibrils and amorphous β-aggregates. These observations were converted into energy potentials that were, in turn, machine learned. The resulting tool, called Generalized Aggregation Proneness (GAP), could successfully distinguish between amyloid fibril-forming and amorphous β-aggregating hexapeptides with almost 100 percent accuracies in validation tests performed using non-redundant datasets. Accuracies of the predictions made by GAP are significantly improved compared with other methods capable of predicting either general β-aggregation or amyloid fibril-forming APRs. This work demonstrates that amino acid side chains play important roles in determining the morphological fate of β-mediated aggregates formed by short peptides. http://www.iitm.ac.in/bioinfo/GAP/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis.

    PubMed

    Yu, Ke; Cheng, Yiyu

    2007-02-15

    Three machine learning techniques including back propagation artificial neural network (BP-ANN), radial basis function artificial neural network (RBF-ANN) and support vector regression (SVR) were applied to predicting the peptide mobility in capillary zone electrophoresis through the development of quantitative structure-mobility relationship (QSMR) models. A data set containing 102 peptides with a large range of size, charge and hydrophobicity was used as a typical study. The optimal modeling parameters of the models were determined by grid-searching approach using 10-fold cross-validation. The predicted results were compared with that obtained by the multiple linear regression (MLR) method. The results showed that the relative standard errors (R.S.E.) of the developed models for the test set obtained by MLR, BP-ANN, RBF-ANN and SVR were 11.21%, 7.47%, 5.79% and 5.75%, respectively, while the R.S.E.s for the external validation set were 11.18%, 7.87%, 7.54% and 7.18%, respectively. The better generalization ability of the QSMR models developed by machine learning techniques over MLR was exactly presented. It was shown that the machine learning techniques were effective for developing the accurate and relaible QSMR models.

  15. New Methods for Labeling RGD Peptides with Bromine-76

    PubMed Central

    Lang, Lixin; Li, Weihua; Jia, Hong-Mei; Fang, De-Cai; Zhang, Shushu; Sun, Xilin; Zhu, Lei; Ma, Ying; Shen, Baozhong; Kiesewetter, Dale O.; Niu, Gang; Chen, Xiaoyuan

    2011-01-01

    Direct bromination of the tyrosine residues of peptides and antibodies with bromine-76, to create probes for PET imaging, has been reported. For peptides that do not contain tyrosine residues, however, a prosthetic group is required to achieve labeling via conjugation to other functional groups such as terminal α-amines or lysine ε-amines. The goal of this study was to develop new strategies for labeling small peptides with Br-76 using either a direct labeling method or a prosthetic group, depending on the available functional group on the peptides. A new labeling agent, N-succinimidyl-3-[76Br]bromo-2,6-dimethoxybenzoate ([76Br]SBDMB) was prepared for cyclic RGD peptide labeling. N-succinimidyl-2, 6-dimethoxybenzoate was also used to pre-attach a 2, 6-dimethoxybenzoyl (DMB) moiety to the peptide, which could then be labeled with Br-76. A competitive cell binding assay was performed to determine the binding affinity of the brominated peptides. PET imaging of U87MG human glioblastoma xenografted mice was performed using [76Br]-BrE[c(RGDyK)]2 and [76Br]-BrDMB-E[c(RGDyK)]2. An ex vivo biodistribution assay was performed to confirm PET quantification. The mechanisms of bromination reaction between DMB-c(RGDyK) and the brominating agent CH3COOBr were investigated with the SCRF-B3LYP/6-31G* method with the Gaussian 09 program package. The yield for direct labeling of c(RGDyK) and E[c(RGDyK)]2 using chloramine-T and peracetic acid at ambient temperature was greater than 50%. The yield for [76Br]SBDMB was over 60% using peracetic acid. The conjugation yields for labeling c(RGDfK) and c(RGDyK) were over 70% using the prosthetic group at room temperature. Labeling yield for pre-conjugated peptides was over 60%. SDMB conjugation and bromination did not affect the binding affinity of the peptides with integrin receptors. Both [76Br]Br-E[c(RGDyK)]2 and [76Br]BrDMB-E[c(RGDyK)]2 showed high tumor uptake in U87MG tumor bearing mice. The specificity of the imaging tracers was

  16. Hydrogen exchange-mass spectrometry measures stapled peptide conformational dynamics and predicts pharmacokinetic properties.

    PubMed

    Shi, Xiangguo Eric; Wales, Thomas E; Elkin, Carl; Kawahata, Noriyuki; Engen, John R; Annis, D Allen

    2013-12-03

    Peptide drugs have traditionally suffered from poor pharmacokinetic properties due to their conformational flexibility and the interaction of proteases with backbone amide bonds. "Stapled Peptides" are cyclized using an all-hydrocarbon cross-linking strategy to reinforce their α-helical conformation, yielding improved protease resistance and drug-like properties. Here we demonstrate that hydrogen exchange-mass spectrometry (HX-MS) effectively probes the conformational dynamics of Stapled Peptides derived from the survivin-borealin protein-protein interface and predicts their susceptibility to proteolytic degradation. In Stapled Peptides, amide exchange was reduced by over five orders-of-magnitude versus the native peptide sequence depending on staple placement. Furthermore, deuteration kinetics correlated directly with rates of proteolysis to reveal the optimal staple placement for improved drug properties.

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

    PubMed Central

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

    2016-01-01

    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. PMID:27934901

  18. 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

  19. A Fourier transformation based method to mine peptide space for antimicrobial activity.

    PubMed

    Nagarajan, Vijayaraj; Kaushik, Navodit; Murali, Beddhu; Zhang, Chaoyang; Lakhera, Sanyogita; Elasri, Mohamed O; Deng, Youping

    2006-09-26

    Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides. We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides. The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides.

  20. Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.

    PubMed

    Heo, Lim; Lee, Hasup; Baek, Minkyung; Seok, Chaok

    2016-01-01

    We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

  1. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides

    PubMed Central

    Sharma, Arun; Gupta, Pooja; Kumar, Rakesh; Bhardwaj, Anshu

    2016-01-01

    Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/. PMID:26912180

  2. Computer-predicted peptides that mimic discontinuous epitopes on the A2 domain of factor VIII.

    PubMed

    Lebreton, A; Simon, N; Moreau, V; Demolombe, V; Cayzac, C; Nguyen, C; Schved, J F; Granier, C; Lavigne-Lissalde, G

    2015-05-01

    Development of antibodies (Abs) against factor VIII (FVIII) is a severe complication of haemophilia A treatment. Recent publications suggest that domain specificity of anti-FVIII antibodies, particularly during immune tolerance induction (ITI), might be related to the outcome of the treatment. Obtaining suitable tools for a fine mapping of discontinuous epitopes could thus be helpful. The aim of this study was to map discontinuous epitopes on FVIII A2 domain using a new epitope prediction functionality of the PEPOP bioinformatics tool and a peptide inhibition assay based on the Luminex technology. We predicted, selected and synthesized 40 peptides mimicking discontinuous epitopes on the A2 domain of FVIII. A new inhibition assays using Luminex technology was performed to identify peptides able to inhibit the binding of anti-A2 Abs to A2 domain. We identified two peptides (IFKKLYHVWTKEVG and LYSRRLPKGVKHFD) able to block the binding of anti-A2 allo-antibodies to this domain. The three-dimensional representation of these two peptides on the A2 domain revealed that they are localized on a limited region of A2. We also confirmed that residues 484-508 of the A2 domain define an antigenic site. We suggest that dissection of the antibody response during ITI using synthetic peptide epitopes could provide important information for the management of patients with inhibitors.

  3. Syndecan-4 as a biomarker to predict clinical outcome for glioblastoma multiforme treated with WT1 peptide vaccine

    PubMed Central

    Takashima, Satoshi; Oka, Yoshihiro; Fujiki, Fumihiro; Morimoto, Soyoko; Nakajima, Hiroko; Nakae, Yoshiki; Nakata, Jun; Nishida, Sumiyuki; Hosen, Naoki; Tatsumi, Naoya; Mizuguchi, Kenji; Hashimoto, Naoya; Oji, Yusuke; Tsuboi, Akihiro; Kumanogoh, Atsushi; Sugiyama, Haruo

    2016-01-01

    Aim: In cancer immunotherapy, biomarkers are important for identification of responsive patients. This study was aimed to find biomarkers that predict clinical outcome of WT1 peptide vaccination. Materials & methods: Candidate genes that were expressed differentially between long- and short-term survivors were identified by cDNA microarray analysis of peripheral blood mononuclear cells that were extracted from 30 glioblastoma patients (discovery set) prior to vaccination and validated by quantitative RT-PCR using discovery set and different 23 patients (validation set). Results: SDC-4 mRNA expression levels distinguished between the long- and short-term survivors: 1-year survival rates were 64.0 and 18.5% in SDC4-low and -high patients, respectively. Conclusion: SDC-4 is a novel predictive biomarker for the efficacy of WT1 peptide vaccine. PMID:28116121

  4. Prediction of therapeutic peptides by incorporating q-Wiener index into Chou's general PseAAC.

    PubMed

    Xu, Chunrui; Ge, Li; Zhang, Yusen; Dehmer, Matthias; Gutman, Ivan

    2017-09-25

    As therapeutic peptides have been taken into consideration in disease therapy in recent years, many biologists spent time and labor to verify various functional peptides from a large number of peptide sequences. In order to reduce the workload and increase the efficiency of identification of functional proteins, we propose a sequence-based model, q-FP (functional peptide prediction based on the q-Wiener Index), capable of recognizing potentially functional proteins. We extract three types of features by mixing graphic representation and statistical indices based on the q-Wiener index and physicochemical properties of amino acids. Our support-vector-machine-based model achieves an accuracy of 96.71%, 92.52%, 98.40%, and 91.40% for anticancer, virulent, and allergenic proteins datasets, respectively, by using 5-fold cross validation. Copyright © 2017. Published by Elsevier Inc.

  5. 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

  6. Predicting retention time shifts associated with variation of the gradient slope in peptide RP-HPLC.

    PubMed

    Spicer, Vic; Grigoryan, Marine; Gotfrid, Alexander; Standing, Kenneth G; Krokhin, Oleg V

    2010-12-01

    We have developed a sequence-specific model for predicting slopes (S) in the fundamental equation of linear solvent strength theory for the reversed-phase HPLC separation of tryptic peptides detected in a typical bottom-up-proteomics experiment. These slopes control the variation in the separation selectivity observed when the physical parameters of chromatographic separation, such as gradient slope, flow rate, and column size are altered. For example, with the use of an arbitrarily chosen set of tryptic peptides with a 4-times difference in the gradient slope between two experiments, the R(2)-value of correlation between the observed retention times of identical species decreases to ~0.993-0.996 (compared to a theoretical value of ~1.00). The observed retention time shifts associated with variations of the gradient slope can be predicted a priori using the approach described here. The proposed model is based on our findings for a set of synthetic species (Vu, H.; Spicer, V.; Gotfrid, A.; Krokhin, O. V. J. Chromatogr., A, 2010, 1217, 489-497), which postulate that slopes S can be predicted taking into account simultaneously peptide length, charge, and hydrophobicity. Here we extend this approach using an extensive set of real tryptic peptides. We developed the procedure to accurately measure S-values in nano-RP HPLC MS experiments and introduced sequence-specific corrections for a more accurate prediction of the slopes S. A correlation of ~0.95 R(2)-value between the predicted and experimental S-values was demonstrated. Predicting S-values and calculating the expected retention time shifts when the physical parameters of separation like gradient slope are altered will facilitate a more accurate application of peptide retention prediction protocols, aid in the transfer of scheduled MRM (SRM) procedures between LC systems, and increase the efficiency of interlaboratory data collection and comparison.

  7. A Priori Intrinsic PTM Size Parameters for Predicting the Ion Mobilities of Modified Peptides

    NASA Astrophysics Data System (ADS)

    Kaszycki, Julia L.; Shvartsburg, Alexandre A.

    2017-02-01

    The rising profile of ion mobility spectrometry (IMS) in proteomics has driven the efforts to predict peptide cross-sections. In the simplest approach, these are derived by adding the contributions of all amino acid residues and post-translational modifications (PTMs) defined by their intrinsic size parameters (ISPs). We show that the ISPs for PTMs can be calculated from properties of constituent atoms, and introduce the "impact scores" that govern the shift of cross-sections from the central mass-dependent trend for unmodified peptides. The ISPs and scores tabulated for 100 more common PTMs enable predicting the domains for modified peptides in the IMS/MS space that would guide subproteome investigations.

  8. Prediction of Impending Type 1 Diabetes through Automated Dual-Label Measurement of Proinsulin:C-Peptide Ratio

    PubMed Central

    Balti, Eric V.; Keymeulen, Bart; Gillard, Pieter; Lapauw, Bruno; De Block, Christophe; Abrams, Pascale; Weber, Eric; Vermeulen, Ilse; De Pauw, Pieter; Pipeleers, Daniël; Weets, Ilse; Gorus, Frans K.

    2016-01-01

    Background The hyperglycemic clamp test, the gold standard of beta cell function, predicts impending type 1 diabetes in islet autoantibody-positive individuals, but the latter may benefit from less invasive function tests such as the proinsulin:C-peptide ratio (PI:C). The present study aims to optimize precision of PI:C measurements by automating a dual-label trefoil-type time-resolved fluorescence immunoassay (TT-TRFIA), and to compare its diagnostic performance for predicting type 1 diabetes with that of clamp-derived C-peptide release. Methods Between-day imprecision (n = 20) and split-sample analysis (n = 95) were used to compare TT-TRFIA (AutoDelfia, Perkin-Elmer) with separate methods for proinsulin (in-house TRFIA) and C-peptide (Elecsys, Roche). High-risk multiple autoantibody-positive first-degree relatives (n = 49; age 5–39) were tested for fasting PI:C, HOMA2-IR and hyperglycemic clamp and followed for 20–57 months (interquartile range). Results TT-TRFIA values for proinsulin, C-peptide and PI:C correlated significantly (r2 = 0.96–0.99; P<0.001) with results obtained with separate methods. TT-TRFIA achieved better between-day %CV for PI:C at three different levels (4.5–7.1 vs 6.7–9.5 for separate methods). In high-risk relatives fasting PI:C was significantly and inversely correlated (rs = -0.596; P<0.001) with first-phase C-peptide release during clamp (also with second phase release, only available for age 12–39 years; n = 31), but only after normalization for HOMA2-IR. In ROC- and Cox regression analysis, HOMA2-IR-corrected PI:C predicted 2-year progression to diabetes equally well as clamp-derived C-peptide release. Conclusions The reproducibility of PI:C benefits from the automated simultaneous determination of both hormones. HOMA2-IR-corrected PI:C may serve as a minimally invasive alternative to the more tedious hyperglycemic clamp test. PMID:27907006

  9. Prediction of Protein-Peptide Interactions: Application of the XPairIT to Anthrax Lethal Factor and Substrates

    DTIC Science & Technology

    2013-09-01

    Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates by Margaret M. Hurley and...Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates Margaret M. Hurley and Michael S. Sellers Weapons and...Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates 5a. CONTRACT NUMBER ORAUW911QX-04-C

  10. 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…

  11. 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…

  12. Lipid Tail Protrusion in Simulations Predicts Fusogenic Activity of Influenza Fusion Peptide Mutants and Conformational Models

    PubMed Central

    Larsson, Per; Kasson, Peter M.

    2013-01-01

    Fusion peptides from influenza hemagglutinin act on membranes to promote membrane fusion, but the mechanism by which they do so remains unknown. Recent theoretical work has suggested that contact of protruding lipid tails may be an important feature of the transition state for membrane fusion. If this is so, then influenza fusion peptides would be expected to promote tail protrusion in proportion to the ability of the corresponding full-length hemagglutinin to drive lipid mixing in fusion assays. We have performed molecular dynamics simulations of influenza fusion peptides in lipid bilayers, comparing the X-31 influenza strain against a series of N-terminal mutants. As hypothesized, the probability of lipid tail protrusion correlates well with the lipid mixing rate induced by each mutant. This supports the conclusion that tail protrusion is important to the transition state for fusion. Furthermore, it suggests that tail protrusion can be used to examine how fusion peptides might interact with membranes to promote fusion. Previous models for native influenza fusion peptide structure in membranes include a kinked helix, a straight helix, and a helical hairpin. Our simulations visit each of these conformations. Thus, the free energy differences between each are likely low enough that specifics of the membrane environment and peptide construct may be sufficient to modulate the equilibrium between them. However, the kinked helix promotes lipid tail protrusion in our simulations much more strongly than the other two structures. We therefore predict that the kinked helix is the most fusogenic of these three conformations. PMID:23505359

  13. Lipid tail protrusion in simulations predicts fusogenic activity of influenza fusion peptide mutants and conformational models.

    PubMed

    Larsson, Per; Kasson, Peter M

    2013-01-01

    Fusion peptides from influenza hemagglutinin act on membranes to promote membrane fusion, but the mechanism by which they do so remains unknown. Recent theoretical work has suggested that contact of protruding lipid tails may be an important feature of the transition state for membrane fusion. If this is so, then influenza fusion peptides would be expected to promote tail protrusion in proportion to the ability of the corresponding full-length hemagglutinin to drive lipid mixing in fusion assays. We have performed molecular dynamics simulations of influenza fusion peptides in lipid bilayers, comparing the X-31 influenza strain against a series of N-terminal mutants. As hypothesized, the probability of lipid tail protrusion correlates well with the lipid mixing rate induced by each mutant. This supports the conclusion that tail protrusion is important to the transition state for fusion. Furthermore, it suggests that tail protrusion can be used to examine how fusion peptides might interact with membranes to promote fusion. Previous models for native influenza fusion peptide structure in membranes include a kinked helix, a straight helix, and a helical hairpin. Our simulations visit each of these conformations. Thus, the free energy differences between each are likely low enough that specifics of the membrane environment and peptide construct may be sufficient to modulate the equilibrium between them. However, the kinked helix promotes lipid tail protrusion in our simulations much more strongly than the other two structures. We therefore predict that the kinked helix is the most fusogenic of these three conformations.

  14. Quantitative evaluation of peptide-extraction methods by HPLC-triple-quad MS-MS.

    PubMed

    Du, Yan; Wu, Dapeng; Wu, Qian; Guan, Yafeng

    2015-02-01

    In this study, the efficiency of five peptide-extraction methods—acetonitrile (ACN) precipitation, ultrafiltration, C18 solid-phase extraction (SPE), dispersed SPE with mesoporous carbon CMK-3, and mesoporous silica MCM-41—was quantitatively investigated. With 28 tryptic peptides as target analytes, these methods were evaluated on the basis of recovery and reproducibility by using high-performance liquid chromatography-triple-quad tandem mass spectrometry in selected-reaction-monitoring mode. Because of the distinct extraction mechanisms of the methods, their preferences for extracting peptides of different properties were revealed to be quite different, usually depending on the pI values or hydrophobicity of peptides. When target peptides were spiked in bovine serum albumin (BSA) solution, the extraction efficiency of all the methods except ACN precipitation changed significantly. The binding of BSA with target peptides and nonspecific adsorption on adsorbents were believed to be the ways through which BSA affected the extraction behavior. When spiked in plasma, the performance of all five methods deteriorated substantially, with the number of peptides having recoveries exceeding 70% being 15 for ACN precipitation, and none for the other methods. Finally, the methods were evaluated in terms of the number of identified peptides for extraction of endogenous plasma peptides. Only ultrafiltration and CMK-3 dispersed SPE performed differently from the quantitative results with target peptides, and the wider distribution of the properties of endogenous peptides was believed to be the main reason.

  15. 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.

  16. Predictive sensor method and apparatus

    NASA Technical Reports Server (NTRS)

    Cambridge, Vivien J.; Koger, Thomas L.

    1993-01-01

    A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors.

  17. Predictive sensor method and apparatus

    NASA Technical Reports Server (NTRS)

    Nail, William L. (Inventor); Koger, Thomas L. (Inventor); Cambridge, Vivien (Inventor)

    1990-01-01

    A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.

  18. Prediction of ozone concentrations using nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Abd Hamid, Nor Zila; Md Noorani, Mohd Salmi; Juneng, Liew; Latif, Mohd Talib

    2013-04-01

    Prediction of ozone (O3) is very important because O3 gives effects on human health, human activities and more. Nonlinear prediction method, a method which was developed based on the idea comes from chaos theory is used to predict the concentrations of O3. There are two steps in the nonlinear prediction method. First is the reconstruction of the observed data from the form of a one-dimensional to multi-dimensional phase space. Second is the prediction of the reconstructed phase space through local linear approximation method. Hourly O3 concentrations observed in Shah Alam city located in the state of Selangor, Malaysia were studied. Predictions found in a close agreement with those observed ones. The value of the correlation coefficient obtained in this study is 0.9097. This demonstrates the suitability of the nonlinear prediction method to predict the hourly concentrations of O3. At the end of this paper, suggestions were made for better prediction in the future.

  19. PEP-FOLD: an online resource for de novo peptide structure prediction

    PubMed Central

    Maupetit, Julien; Derreumaux, Philippe; Tuffery, Pierre

    2009-01-01

    Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9–23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6 Å Cα root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD PMID:19433514

  20. PEP-FOLD: an online resource for de novo peptide structure prediction.

    PubMed

    Maupetit, Julien; Derreumaux, Philippe; Tuffery, Pierre

    2009-07-01

    Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9-23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6 A Calpha root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD.

  1. 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.

  2. 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

  3. Predicting the effects of amino acid replacements in peptide hormones on their binding affinities for class B GPCRs and application to the design of secretin receptor antagonists

    NASA Astrophysics Data System (ADS)

    Te, Jerez A.; Dong, Maoqing; Miller, Laurence J.; Bordner, Andrew J.

    2012-07-01

    Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental ΔΔG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within the segment binding the receptor N-terminal domain. Amino acid replacements predicted to most enhance receptor binding were then experimentally tested by competition-binding assays. We found two residue changes that improved binding affinities by almost one log unit. Furthermore, a peptide combining both of these favorable modifications resulted in an almost two log unit improvement in binding affinity, demonstrating the approximately additive effect of these changes on binding. In order to further investigate possible physical effects of these residue changes on receptor binding affinity, molecular dynamics simulations were performed on representatives of the successful peptide analogues (namely A17I, G25R, and A17I/G25R) in bound and

  4. Polyvalent display and packing of peptides and proteins on semiconductor quantum dots: predicted versus experimental results.

    PubMed

    Prasuhn, Duane E; Deschamps, Jeffrey R; Susumu, Kimihiro; Stewart, Michael H; Boeneman, Kelly; Blanco-Canosa, Juan B; Dawson, Philip E; Medintz, Igor L

    2010-02-22

    Quantum dots (QDs) are loaded with a series of peptides and proteins of increasing size, including a <20 residue peptide, myoglobin, mCherry, and maltose binding protein, which together cover a range of masses from <2.2 to approximately 44 kDa. Conjugation to the surface of dihydrolipoic acid-functionalized QDs is facilitated by polyhistidine metal affinity coordination. Increasing ratios of dye-labeled peptides and proteins are self-assembled to the QDs and then the bioconjugates are separated and analyzed using agarose gel electrophoresis. Fluorescent visualization of both conjugated and unbound species allows determination of an experimentally derived maximum loading number. Molecular modeling utilizing crystallographic coordinates or space-filling structures of the peptides and proteins also allow the predicted maximum loadings to the QDs to be estimated. Comparison of the two sets of results provides insight into the nature of the QD surface and reflects the important role played by the nanoparticle's hydrophilic solubilizing surface ligands. It is found that for the larger protein molecules steric hindrance is the major packing constraint. In contrast, for the smaller peptides, the number of available QD binding sites is the principal determinant. These results can contribute towards an overall understanding of how to engineer designer bioconjugates for both QDs and other nanoparticle materials.

  5. Interim prediction method for jet noise

    NASA Technical Reports Server (NTRS)

    Stone, J. R.

    1974-01-01

    A method is provided for predicting jet noise for a wide range of nozzle geometries and operating conditions of interest for aircraft engines. Jet noise theory, data and existing prediction methods was reviewed, and based on this information a interim method of jet noise prediction is proposed. Problem areas are idenified where further research is needed to improve the prediction method. This method predicts only the noise generated by the exhaust jets mixing with the surrounding air and does not include other noises emanating from the engine exhaust, such as combustion and machinery noise generated inside the engine (i.e., core noise). It does, however, include thrust reverser noise. Prediction relations are provided for conical nozzles, plug nozzles, coaxial nozzles and slot nozzles.

  6. An improved method for utilization of peptide substrates for antibody characterization and enzymatic assays.

    PubMed

    Ghosh, Inca; Sun, Luo; Evans, Thomas C; Xu, Ming-Qun

    2004-10-01

    Synthetic peptides have become an important tool in antibody production and enzyme characterization. The small size of peptides, however, has hindered their use in assays systems, such as Western blots, and as immunogens. Here, we present a facile method to improve the properties of peptides for multiple applications by ligating the peptides to intein-generated carrier proteins. The stoichiometric ligation of peptide and carrier achieved by intein-mediated protein ligation (IPL) results in the ligation product migrating as a single band on a SDS-PAGE gel. The carrier proteins, HhaI methylase (M.HhaI) and maltose-binding protein (MBP), were ligated to various peptides; the ligated carrier-peptide products gave sharp, reproducible bands when used as positive controls for antibodies raised against the same peptides during Western blot analysis. We further show that ligation of the peptide antigens to a different thioester-tagged carrier protein, paramyosin, produced immunogens for the production of antisera in rabbits or mice. Furthermore, we demonstrate the generation of a substrate for enzymatic assays by ligating a peptide containing the phosphorylation site for Abl protein tyrosine kinase to a carrier protein. This carrier-peptide protein was used as a kinase substrate that could easily be tested for phosphorylation using a phosphotyrosine antibody in Western blot analysis. These techniques do not require sophisticated equipment, reagents, or skills thereby providing a simple method for research and development.

  7. Methods to follow intracellular trafficking of cell-penetrating peptides.

    PubMed

    Pärnaste, Ly; Arukuusk, Piret; Zagato, Elisa; Braeckmans, Kevin; Langel, Ülo

    2016-01-01

    Cell-penetrating peptides (CPPs) are efficient vehicles to transport bioactive molecules into the cells. Despite numerous studies the exact mechanism by which CPPs facilitate delivery of cargo to its intracellular target is still debated. The current work presents methods that can be used for tracking CPP/pDNA complexes through endosomal transport and show the role of endosomal transport in the delivery of cargo. Separation of endosomal vesicles by differential centrifugation enables to pinpoint the localization of delivered cargo without labeling it and gives important quantitative information about pDNA trafficing in certain endosomal compartments. Single particle tracking (SPT) allows following individual CPP/cargo complex through endosomal path in live cells, using fluoresently labled cargo and green fluoresent protein expressing cells. These two different methods show similar results about tested NickFect/pDNA complexes intracellular trafficing. NF51 facilitates rapid internalization of complexes into the cells, prolongs their stay in early endosomes and promotes release to cytosol. NF1 is less capable to induce endosomal release and higher amount of complexes are routed to lysosomes for degradation. Our findings offer potential delivery vector for in vivo applications, NF51, where endosomal entrapment has been allayed. Furthermore, these methods are valuable tools to study other CPP-based delivery systems.

  8. Empirical methods for identifying specific peptide-protein interactions for smart reagent development

    NASA Astrophysics Data System (ADS)

    Kogot, Joshua M.; Sarkes, Deborah A.; Stratis-Cullum, Dimitra N.; Pellegrino, Paul M.

    2012-06-01

    The current state of the art in the development of antibody alternatives is fraught with difficulties including mass production, robustness, and overall cost of production. The isolation of synthetic alternatives using peptide libraries offers great potential for recognition elements that are more stable and have improved binding affinity and target specificity. Although recent advances in rapid and automated discovery and synthetic library engineering continue to show promise for this emerging science, there remains a critical need for an improved fundamental understanding of the mechanisms of recognition. To better understand the fundamental mechanisms of binding, it is critical to be able to accurately assess binding between peptide reagents and protein targets. The development of empirical methods to analyze peptide-protein interactions is often overlooked, since it is often assumed that peptides can easily substitute for antibodies in antibody-derived immunoassays. The physico-chemical difference between peptides and antibodies represents a major challenge for developing peptides in standard immunoassays as capture or detection reagents. Analysis of peptide presents a unique challenge since the peptide has to be soluble, must be capable of target recognition, and capable of ELISA plate or SPR chip binding. Incorporating a plate-binding, hydrophilic peptide fusion (PS-tag) improves both the solubility and plate binding capability in a direct peptide ELISA format. Secondly, a solution based methods, affinity capillary electrophoresis (ACE) method is presented as a solution-based, affinity determination method that can be used for determining both the association constants and binding kinetics.

  9. Exploiting heterogeneous features to improve in silico prediction of peptide status – amyloidogenic or non-amyloidogenic

    PubMed Central

    2011-01-01

    Background Prediction of short stretches in protein sequences capable of forming amyloid-like fibrils is important in understanding the underlying cause of amyloid illnesses thereby aiding in the discovery of sequence-targeted anti-aggregation pharmaceuticals. Due to the constraints of experimental molecular techniques in identifying such motif segments, it is highly desirable to develop computational methods to provide better and affordable in silico predictions. Results Accurate in silico prediction techniques of amyloidogenic peptide regions rely on the cooperation between informative features and classifier design. In this research article, we propose one such efficient fibril prediction implementation exploiting heterogeneous features based on bio-physio-chemical (BPC) properties, auto-correlation function of carefully selected amino acid indices and atomic composition within a protein fragment of amino acids in a window. In an attempt to get an optimal number of BPC features, an evolutionary Support Vector Machine (SVM) integrating a novel implementation of hybrid Genetic Algorithm termed Memetic Algorithm and SVM is utilized. Five prediction modules designed using Artificial Neural Network (ANN) models are trained with independent and integrated features in order to validate the fibril forming motifs. The results provide evidence that incorporating new feature namely auto-correlation function besides BPC, attempt to strengthen the sequence interaction effect in forming the feature vector thereby obtaining better prediction quality in terms of sensitivity, specificity, Mathews Correlation Coefficient and Area under the Receiver Operating Characteristics curve. Conclusion A significant improvement in performance is observed by introducing features like auto-correlation function that maintains sequence order effect, in addition to the conventional BPC properties selected through a novel optimization strategy to predict the peptide status – amyloidogenic or

  10. Protein Residue Contacts and Prediction Methods.

    PubMed

    Adhikari, Badri; Cheng, Jianlin

    2016-01-01

    In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated.

  11. 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.

  12. A novel method to identify and characterise peptide mimotopes of heat shock protein 70-associated antigens.

    PubMed

    Arnaiz, Blanca; Madrigal-Estebas, Laura; Todryk, Stephen; James, Tharappel C; Doherty, Derek G; Bond, Ursula

    2006-04-08

    The heat shock protein, Hsp70, has been shown to play an important role in tumour immunity. Vaccination with Hsp70-peptide complexes (Hsp70-PCs), isolated from autologous tumour cells, can induce protective immune responses. We have developed a novel method to identify synthetic mimic peptides of Hsp70-PCs and to test their ability to activate T-cells. Peptides (referred to as "recognisers") that bind to Hsp70-PCs from the human breast carcinoma cell line, MDA-MB-231, were identified by bio-panning a random peptide M13 phage display library. Synthetic recogniser peptides were subsequently used as bait in a reverse bio-panning experiment to identify potential Hsp70-PC mimic peptides. The ability of the recogniser and mimic peptides to prime human lymphocyte responses against tumour cell antigens was tested by stimulating lymphocytes with autologous peptide-loaded monocyte-derived dendritic cells (DCs). Priming and subsequent stimulation with either the recogniser or mimic peptide resulted in interferon-gamma (IFN-gamma) secretion by the lymphocytes. Furthermore, DCs loaded with Hsp70, Hsp70-PC or the recogniser or the mimic peptide primed the lymphocytes to respond to soluble extracts from breast cells. These results highlight the potential application of synthetic peptide-mimics of Hsp70-PCs, as modulators of the immune response against tumours.

  13. Aortic stiffness and plasma brain natriuretic peptide predicts mortality in acute ischemic stroke.

    PubMed

    Biteker, Murat; Özden, Temel; Dayan, Akın; Tekkeşin, Ahmet İlker; Mısırlı, Cemile Handan

    2015-07-01

    The study aimed to evaluate the prognostic role and discriminative power of aortic stiffness and plasma brain natriuretic peptide levels in a cohort of patients hospitalized for acute ischemic stroke. Three hundred and ten consecutive patients aged 50 years and older with a first episode of acute ischemic stroke were prospectively evaluated. All patients were admitted to the hospital within 24 h of the onset of stroke symptoms. The type of acute ischemic stroke was classified according to the Trial of Org 10172 in Acute Stroke Treatment classification. Blood samples were taken for measurement of brain natriuretic peptide levels at admission. Aortic stiffness indices, aortic strain and distensibility, were calculated from the aortic diameters measured by transthoracic echocardiography. The patients were followed for one-year or until death, whichever came first. Death occurred in 51 (16·5%) patients. On multivariate logistic regression analysis, National Institutes of Health Stroke Scale score >13, diabetes, brain natriuretic peptide >235 pg/mL, aortic distensibility, and aortic strain were associated with all-cause mortality. The optimal cutoff level of brain natriuretic peptide to distinguish the deceased group from the survival group was 235 pg/mL (sensitivity 71·0% and specificity 63·0%) and to distinguish cardioembolic stroke from noncardioembolic stroke was 155 pg/mL (sensitivity 81% and specificity 63%). Aortic stiffness and brain natriuretic peptide predict mortality in patients with first-ever acute ischemic stroke. Brain natriuretic peptide also differentiates cardioembolic stroke from noncardioembolic stroke. © 2013 The Authors. International Journal of Stroke © 2013 World Stroke Organization.

  14. Signal peptide prediction based on analysis of experimentally verified cleavage sites

    PubMed Central

    Zhang, Zemin; Henzel, William J.

    2004-01-01

    A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data. PMID:15340161

  15. Incremental predictive value of natriuretic peptides for prognosis in the chronic stable heart failure population: a systematic review.

    PubMed

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; Oremus, Mark; McKelvie, Robert; Ali, Usman; Brown, Judy A; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The aim of this study was to determine whether measurement of natriuretic peptides independently adds incremental predictive value for mortality and morbidity in patients with chronic stable heart failure (CSHF). We electronically searched Medline®, Embase™, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL from 1989 to June 2012. We also searched reference lists of included articles, systematic reviews, and the gray literature. Studies were screened for eligibility criteria and assessed for methodological quality. Data were extracted on study design, population demographics, assay cutpoints, prognostic risk prediction model covariates, statistical methods, outcomes, and results. One hundred and eighty-three studies were identified as prognostic in the systematic review. From these, 15 studies (all NT-proBNP) considered incremental predictive value in CSHF subjects. Follow-up varied from 12 to 37 months. All studies presented at least one estimate of incremental predictive value of NT-proBNP relative to the base prognostic model. Using discrimination or likelihood statistics, these studies consistently showed that NT-proBNP increased model performance. Three studies used re-classification and model validation computations to establish incremental predictive value; these studies showed less consistency with respect to added value. Although there were differences in the base risk prediction models, assay cutpoints, and lengths of follow-up, there was consistency in NT-proBNP adding incremental predictive value for prognostic models in chronic stable CSHF patients. The limitations in the literature suggest that studies designed to evaluate prognostic models should be undertaken to evaluate the incremental value of natriuretic peptide as a predictor of mortality and morbidity in CSHF.

  16. Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity.

    PubMed

    Kleandrova, Valeria V; Ruso, Juan M; Speck-Planche, Alejandro; Dias Soeiro Cordeiro, M Natália

    2016-08-08

    Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

  17. 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.

  18. Histidine-containing peptide catalysts developed by a facile library screening method.

    PubMed

    Akagawa, Kengo; Sakai, Nobutaka; Kudo, Kazuaki

    2015-02-02

    Although peptide catalysts have a high potential for the use as organocatalysts, the optimization of peptide sequences is laborious and time-consuming. To address this issue, a facile screening method for finding efficient aminocatalysts from a peptide library has been developed. In the screening for the Michael addition of a malonate to an enal, a dye-labeled product is immobilized on resin-bound peptides through reductive amination to visualize active catalysts. This procedure allows for the monitoring of the reactivity of entire peptides without modifying the resin beads beforehand. Peptides containing histidine at an appropriate position were identified by this method. A novel function of the histidyl residue, which enhances the binding of a substrate to the catalyst by capturing an iminium intermediate, was indicated.

  19. Methods and protocols of modern solid phase Peptide synthesis.

    PubMed

    Amblard, Muriel; Fehrentz, Jean-Alain; Martinez, Jean; Subra, Gilles

    2006-07-01

    The purpose of this article is to delineate strategic considerations and provide practical procedures to enable non-experts to synthesize peptides with a reasonable chance of success. This article is not encyclopedic but rather devoted to the Fmoc/tBu approach of solid phase peptide synthesis (SPPS), which is now the most commonly used methodology for the production of peptides. The principles of SPPS with a review of linkers and supports currently employed are presented. Basic concepts for the different steps of SPPS such as anchoring, deprotection, coupling reaction and cleavage are all discussed along with the possible problem of aggregation and side-reactions. Essential protocols for the synthesis of fully deprotected peptides are presented including resin handling, coupling, capping, Fmoc-deprotection, final cleavage and disulfide bridge formation.

  20. 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.

  1. Methods for studying transmembrane peptides in bicelles: consequences of hydrophobic mismatch and peptide sequence

    NASA Astrophysics Data System (ADS)

    Whiles, Jennifer A.; Glover, Kerney J.; Vold, Regitze R.; Komives, Elizabeth A.

    2002-09-01

    We have shown that bicelles prepared from dilauryl phosphatidylcholine (DLPC) and dipalmitoyl phosphatidylcholine (DPPC) align in a magnetic field under conditions similar to the more common dimyristoyl phosphatidylcholine (DMPC) bicelles. In addition, a model transmembrane peptide, P16, with a hydrophobic stretch of 24 Å, and specific alanine-d 3 labels, was incorporated into all of the different bicelles. The long-chain phospholipid (DLPC, DMPC, or DPPC) remained unperturbed upon incorporation of the peptide while the quadrupolar splitting of the short-chain phospholipid along the bicelle rim increased by varying degrees in the different bicelle systems. The change in quadrupolar splitting of the short-chain phospholipids was attributed to changes in either fluidity of the planar region of the bicelle or differences in overall lipid packing. When the hydrophobic stretch of the bilayer was 22.8 (DMPC) or 26.3 Å (DPPC), the peptide tilt was found to be transmembrane (33-35° with respect to the bicelle normal). When the hydrophobic stretch of the bilayer was 19.5 Å (DLPC), the peptide quadrupolar splittings suggested a loss of transmembrane orientation. When tryptophan was incorporated in the middle of the transmembrane region, the transmembrane orientation was also lost.

  2. Screening Preoperative Peptide Biomarkers for Predicting Postoperative Myocardial Infarction after Coronary Artery Bypass Grafting

    PubMed Central

    Jiang, Zhibin; Hu, Ping; Liu, Jianxin; Wang, Dianjun; Jin, Longyu; Hong, Chao

    2014-01-01

    Postoperative myocardial infarction (PMI) is one of the most serious complications of cardiac surgeries. No preoperative biomarker is currently available for predicting PMI after cardiac surgeries. In the present study, we used a phage display peptide library to screen potential preoperative peptide biomarkers for predicting PMI after coronary artery bypass grafting (CABG) surgery. Twenty patients who developed PMI after CABG and 20 age-, sex-, and body mass index-matched patients without PMI after CABG were enrolled as a discovery cohort. Another 50 patients who developed PMI after CABG and 50 randomly selected patients without PMI after CABG were enrolled as a validation cohort to validate the potential peptide biomarkers identified in the discovery cohort. Fifty randomly selected healthy volunteers were also enrolled in the validation phase as a healthy control group. In the discovery/screening phase, 17 out of 20 randomly selected phage clones exhibited specific reaction with purified sera IgG from the PMI group, among which 11 came from the same phage clone with inserted peptide sequence GVIMVIAVSCVF (named PMI-1). In the validation phase, phage ELISA showed that serum IgG from 90% of patients in the PMI group had a positive reaction with PMI-1; in contrast, only 14% and 6% of patients in the non-PMI group and the healthy control group had a positive reaction with PMI-1, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the PMI-1 phage clone to preoperatively identify patients who would develop PMI after CABG were 90.0%, 86.0%, 86.5, 89.5% and 88.0%, respectively. The absorbance value of the PMI-1 phage clone showed statistically significant correlation with the peak postoperative serum cardiac troponin I level (r = 0.349, p = 0.012) in the PMI group. In conclusion, we for the first time identified a mimic peptide (PMI-1) with high validity in preoperative prediction of PMI after CABG. PMID

  3. Protein structure prediction using hybrid AI methods

    SciTech Connect

    Guan, X.; Mural, R.J.; Uberbacher, E.C.

    1993-11-01

    This paper describes a new approach for predicting protein structures based on Artificial Intelligence methods and genetic algorithms. We combine nearest neighbor searching algorithms, neural networks, heuristic rules and genetic algorithms to form an integrated system to predict protein structures from their primary amino acid sequences. First we describe our methods and how they are integrated, and then apply our methods to several protein sequences. The results are very close to the real structures obtained by crystallography. Parallel genetic algorithms are also implemented.

  4. 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites.

    PubMed

    Concu, R; Dea-Ayuela, M A; Perez-Montoto, L G; Prado-Prado, F J; Uriarte, E; Bolás-Fernández, F; Podda, G; Pazos, A; Munteanu, C R; Ubeira, F M; González-Díaz, H

    2009-12-01

    The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a new and fast Markov chain model (MCM) to predict the enzyme classification (EC) number. We used both linear discriminant analysis (LDA) and/or artificial neural networks (ANN) in order to compare linear vs. non-linear classifiers. The LDA model found is very simple (three variables) and at the same time is able to predict the first EC number with an overall accuracy of 79% for a data set of 4755 proteins (859 enzymes and 3896 non-enzymes) divided into both training and external validation series. In addition, the best non-linear ANN model is notably more complex but has an overall accuracy of 98.85%. It is important to emphasize that this method may help us to predict not only new enzyme proteins but also to select peptide candidates found on the peptide mass fingerprints (PMFs) of new proteins that may improve enzyme activity. In order to illustrate the use of the model in this regard, we first report the 2D electrophoresis (2DE) and MADLI-TOF mass spectra characterization of the PMF of a new possible malate dehydrogenase sequence from Leishmania infantum. Next, we used the models to predict the contribution to a specific enzyme action of 30 peptides found in the PMF of the new protein. We implemented the present model in a server at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php). This free on-line tool is based on PHP/HTML/Python and MARCH-INSIDE routines. 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.

  5. Clinical Value of Natriuretic Peptides in Predicting Time to Dialysis in Stage 4 and 5 Chronic Kidney Disease Patients

    PubMed Central

    Sundqvist, Sofia; Larson, Thomas; Cauliez, Bruno; Bauer, Fabrice; Dumont, Audrey; Le Roy, Frank; Hanoy, Mélanie; Fréguin-Bouilland, Caroline; Godin, Michel

    2016-01-01

    Background Anticipating the time to renal replacement therapy (RRT) in chronic kidney disease (CKD) patients is an important but challenging issue. Natriuretic peptides are biomarkers of ventricular dysfunction related to poor outcome in CKD. We comparatively investigated the value of B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) as prognostic markers for the risk of RRT in stage 4 and 5 CKD patients, and in foretelling all-cause mortality and major cardiovascular events within a 5-year follow-up period. Methods Baseline plasma BNP (Triage, Biosite) and NT-proBNP (Elecsys, Roche) were measured at inclusion. Forty-three patients were followed-up during 5 years. Kaplan-Meier analysis, with log-rank testing and hazard ratios (HR), were calculated to evaluate survival without RRT, cardiovascular events or mortality. The independent prognostic value of the biomarkers was estimated in separate Cox multivariate analysis, including estimated glomerular filtration rate (eGFR), creatininemia and comorbidities. Results During the first 12-month follow-up period, 16 patients started RRT. NT-proBNP concentration was higher in patients who reached endpoint (3221 ng/L vs 777 ng/L, p = 0.02). NT-proBNP concentration > 1345 ng/L proved significant predictive value on survival analysis for cardiovascular events (p = 0.04) and dialysis within 60 months follow-up (p = 0.008). BNP concentration > 140 ng/L was an independent predictor of RRT after 12 months follow-up (p<0.005), and of significant predictive value for initiation of dialysis within 60 months follow-up. Conclusions Our results indicate a prognostic value for BNP and NT-proBNP in predicting RRT in stage 4 and 5 CKD patients, regarding both short- and long-term periods. NT-proBNP also proved a value in predicting cardiovascular events. Natriuretic peptides could be useful predictive biomarkers for therapeutic guidance in CKD. PMID:27548064

  6. Folding peptides and proteins with all-atom physics: methods and applications

    NASA Astrophysics Data System (ADS)

    Shell, M. Scott

    2008-03-01

    Computational methods offer powerful tools for investigating proteins and peptides at the molecular-level; however, it has proven challenging to reproduce the long time scale folding processes of these molecules at a level that is both faithful to the atomic driving forces and attainable with modern commodity cluster computing. Alternatively, the past decade has seen significant progress in using bioinformatics-based approaches to infer the three dimensional native structures of proteins, drawing upon extensive knowledge databases of known protein structures [1]. These methods work remarkably well when a homologous protein can be found to provide a structural template for a candidate sequence. However, in cases where homology to database proteins is low, where the folding pathway is of interest, or where conformational flexibility is substantial---as in many emerging protein and peptide technologies---bioinformatics methods perform poorly. There is therefore great interest in seeing purely physics-based approaches succeed. We discuss a purely physics-based, database-free folding method, relying on proper thermal sampling (replica exchange molecular dynamics) and molecular potential energy functions. In order to surmount the tremendous computational demands of all-atom folding simulations, our approach implements a conformational search strategy based on a putative protein folding mechanism called zipping and assembly [2-4]. That is, we explicitly seek out potential folding pathways inferred from short simulations, and iteratively pursue all such routes by coaxing a polypeptide chain along them. The method is called the Zipping and Assembly Method (ZAM) and it works in two parts: (1) the full polypeptide chain is broken into small fragments that are first simulated independently and then successively re-assembled into larger segments with further sampling, and (2) consistently stable structure in fragments is detected and locked into place, in order to avoid re

  7. Signal-BNF: a Bayesian network fusing approach to predict signal peptides.

    PubMed

    Zheng, Zhi; Chen, Youying; Chen, Liping; Guo, Gongde; Fan, Yongxian; Kong, Xiangzeng

    2012-01-01

    A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.

  8. Signal-BNF: A Bayesian Network Fusing Approach to Predict Signal Peptides

    PubMed Central

    Zheng, Zhi; Chen, Youying; Chen, Liping; Guo, Gongde; Fan, Yongxian; Kong, Xiangzeng

    2012-01-01

    A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells. PMID:23118510

  9. Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features.

    PubMed

    Zhang, Yi-Ze; Shen, Hong-Bin

    2017-04-24

    Signal peptides play key roles in targeting and translocation of integral membrane proteins and secretory proteins. However, signal peptides present several challenges for automatic prediction methods. One challenge is that it is difficult to discriminate signal peptides from transmembrane helices, as both the H-region of the peptides and the transmembrane helices are hydrophobic. Another is that it is difficult to identify the cleavage site between signal peptides and mature proteins, as cleavage motifs or patterns are still unclear for most proteins. To solve these problems and further enhance automatic signal peptide recognition, we report a new Signal-3L 2.0 predictor. Our new model is constructed with a hierarchical protocol, where it first determines the existence of a signal peptide. For this, we propose a new residue-domain cross-level feature-driven approach, and we demonstrate that protein functional domain information is particularly useful for discriminating between the transmembrane helices and signal peptides as they perform different functions. Next, in order to accurately identify the unique signal peptide cleavage sites along the sequence, we designed a top-down approach where a subset of potential cleavage sites are screened using statistical learning rules, and then a final unique site is selected according to its evolution conservation score. Because this mixed approach utilizes both statistical learning and evolution analysis, it shows a strong capacity for recognizing cleavage sites. Signal-3L 2.0 has been benchmarked on multiple data sets, and the experimental results have demonstrated its accuracy. The online server is available at www.csbio.sjtu.edu.cn/bioinf/Signal-3L/ .

  10. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins.

    PubMed

    Neto, Armando de Menezes; Alvarenga, Denise A; Rezende, Antônio M; Resende, Sarah S; Ribeiro, Ricardo de Souza; Fontes, Cor J F; Carvalho, Luzia H; de Brito, Cristiana F Alves

    2012-11-15

    Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Signal peptide (SignalP) and orthology (OrthoMCL) were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups). In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples) or on experimental evidence already published (ApiLoc). The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug targets and proteins involved in host/parasite interactions

  11. A novel peptide designated PYLa and its precursor as predicted from cloned mRNA of Xenopus laevis skin.

    PubMed

    Hoffmann, W; Richter, K; Kreil, G

    1983-01-01

    A variety of peptides closely related to mammalian hormones and neurotransmitters are secreted from amphibian skin. Using cDNA clones of mRNA isolated from skin of Xenopus laevis, we have been searching for precursors of some of these constituents. Here we present the sequences of parts of cloned mRNAs which code for precursors of a novel peptide. In the predicted polypeptides, pairs of basic residues flank a sequence of 25 amino acids terminating with glycine, the signal for the formation of a terminal amide. The predicted final product liberated from these precursors would be a peptide comprised of 24 amino acids starting with tyrosine and ending with leucine amide, which has therefore been designated PYLa. This peptide can form an amphipathic helix similar to that found in peptides with cytotoxic, bacteriostatic and/or lytic properties.

  12. Numerical Methods for Explosion Plume Predictions

    DTIC Science & Technology

    1993-03-12

    AD-A262 343 6 NAVSWC TR 91-718 A -22~4..v~w•T ,,-., I II It ill/111111 ti(. NUMERICAL METHODS FOR EXPLOSION PLUME PREDICTIONS BY W.G. SZYMCZAK AND A...METHODS FOR EXPLOSION PLUME PREDICTIONS BY W. G. SZYMCZAK AND A. B. WARDLAW RESEARCH AND TECHNOLOGY DEPARTMENT 12 MARCH 1993 Approved for public release...2 TABLES Table Page 3-1 SHALLOW DEPTH EXPLOSION BUBBLE INITIAL DATA

  13. Modern Prediction Methods for Turbomachine Performance

    DTIC Science & Technology

    1976-01-01

    Techniques. In Distortion Induced Engine Instability. AGARD LS-72. October 1974. Paper 5. 107. Erdos , John, Alznor, Ldgar, Kalben, Paul , McNally...subject of Modern Prediction Methods for Turbo- machine Performance, is sponsored by the Propulsion and Energetics Panel of AGARD and implemented by...the Consultan’. and Exechange Programme. Propulsion system development costs may be significantly reduced by improvement of methods tor prediction of

  14. A simplified method for peptide de novo sequencing using (18)O labeling.

    PubMed

    Voráĉ, Aleš; Sedo, Ondrej; Havliš, Jan; Zdráhal, Zbyněk

    2014-01-01

    Incorporation of an (18)O atom into a peptide C-terminus by proteolytic cleavage in the presence of H2(18)O is one of the most effective ways of enhancing tandem mass spectrometry (MS/MS)-based de novo sequencing. Incorporation is usually accomplished by procedures including vacuum-assisted drying of tryptic peptides extracted from gels, their subsequent reconstitution in a H2(16)O/H2(18)O mixture and re-treatment with trypsin. In the present work, we propose a simplified procedure for (18)O incorporation into tryptic peptides by adding H2(18)O and trypsin to the original digest solution. In comparison to published methods, the proposed protocol for peptide de novo sequencing brings significant advantages in analysis and workflow with no deterioration in method performance. We show that labeling by this simplified method leads to a highlighting of the y-ion fragment series in the peptide matrix-assisted laser desorption/ionization (MALDI)- MS/MS data, which facilitates MS/MS data interpretation. We also prove that eliminating acid extraction of peptides from gels does not result in a decrease in sequence coverage or a qualitative loss of particular peptides detectable by MALDI-MS. The method was examined by MALDI-MS/MS on bovine serum albumin and recombinant histidine kinase CKI1 from Arabidopsis thaliana, and was verified by de novo sequencing of tryptic peptides originating from Apodemus sylvaticus salivary proteins.

  15. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  16. Hierarchical ensemble methods for protein function prediction.

    PubMed

    Valentini, Giorgio

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware "flat" prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.

  17. 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.

  18. Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions.

    PubMed

    Schindler, Christina; Zacharias, Martin

    2017-01-01

    Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

  19. A continuous peptide epitope reacting with pandemic influenza AH1N1 predicted by bioinformatic approaches.

    PubMed

    Carrillo-Vazquez, Jonathan P; Correa-Basurto, José; García-Machorro, Jazmin; Campos-Rodríguez, Rafael; Moreau, Violaine; Rosas-Trigueros, Jorge L; Reyes-López, Cesar A; Rojas-López, Marlon; Zamorano-Carrillo, Absalom

    2015-09-01

    Computational identification of potential epitopes with an immunogenic capacity challenges immunological research. Several methods show considerable success, and together with experimental studies, the efficiency of the algorithms to identify potential peptides with biological activity has improved. Herein, an epitope was designed by combining bioinformatics, docking, and molecular dynamics simulations. The hemagglutinin protein of the H1N1 influenza pandemic strain served as a template, owing to the interest of obtaining a scheme of immunization. Afterward, we performed enzyme-linked immunosorbent assay (ELISA) using the epitope to analyze if any antibodies in human sera before and after the influenza outbreak in 2009 recognize this peptide. Also, a plaque reduction neutralization test induced by virus-neutralizing antibodies and the IgG determination showed the biological activity of this computationally designed peptide. The results of the ELISAs demonstrated that the serum of both prepandemic and pandemic recognized the epitope. Moreover, the plaque reduction neutralization test evidenced the capacity of the designed peptide to neutralize influenza virus in Madin-Darby canine cells. Copyright © 2015 John Wiley & Sons, Ltd.

  20. 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

  1. Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.

    PubMed

    Chang, Kuan Y; Lin, Tung-pei; Shih, Ling-Yi; Wang, Chien-Kuo

    2015-01-01

    Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

  2. Coupled rotor/airframe vibration prediction methods

    NASA Technical Reports Server (NTRS)

    Staley, J. A.; Sciarra, J. J.

    1974-01-01

    The problems of airframe structural dynamic representation and effects of coupled rotor/airframe vibration are discussed. Several finite element computer programs (including NASTRAN) and methods for idealization and computation of airframe natural modes and frequencies and forced response are reviewed. Methods for obtaining a simultaneous rotor and fuselage vibratory response, determining effectiveness of vibration control devices, and energy methods for structural optimization are also discussed. Application of these methods is shown for the vibration prediction of the model 347 helicopter.

  3. 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.

  4. Methods for Predicting Submersible Hydrodynamic Characteristics

    DTIC Science & Technology

    1978-07-01

    TO PREDICT COMPLETE CONFIGURATION CHARACTERISTICS In this section the previously developed methods are combined to determine... characteristics of individual vehicle components (bodies, tails ), and with their mutual interactions when combined into complete configurations . Each method is...approach used successfully in mis- sile aerodynamics , a set of models was built and tested to obtain systematic data over relevant ranges of geometry

  5. Random vibration ESS adequacy prediction method

    NASA Astrophysics Data System (ADS)

    Lambert, Ronald G.

    Closed form analytical expressions have been derived and are used as part of the proposed method to quantitatively predict the adequacy of the random vibration portion of an Environmental Stress Screen (ESS) to meet its main objective for screening typical avionics electronic assemblies for workmanship defects without consuming excessive useful life. This method is limited to fatigue related defects (including initial damage/Fracture Mechanics effects) and requires defect fatigue and service environment parameter values. Examples are given to illustrate the method.

  6. A novel method to measure HLA-DM-susceptibility of peptides bound to MHC class II molecules based on peptide binding competition assay and differential IC(50) determination.

    PubMed

    Yin, Liusong; Stern, Lawrence J

    2014-04-01

    HLA-DM (DM) functions as a peptide editor that mediates the exchange of peptides loaded onto MHCII molecules by accelerating peptide dissociation and association kinetics. The relative DM-susceptibility of peptides bound to MHCII molecules correlates with antigen presentation and immunodominance hierarchy, and measurement of DM-susceptibility has been a key effort in this field. Current assays of DM-susceptibility, based on differential peptide dissociation rates measured for individually labeled peptides over a long time base, are difficult and cumbersome. Here, we present a novel method to measure DM-susceptibility based on peptide binding competition assays performed in the presence and absence of DM, reported as a delta-IC(50) (change in 50% inhibition concentration) value. We simulated binding competition reactions of peptides with various intrinsic and DM-catalyzed kinetic parameters and found that under a wide range of conditions the delta-IC(50) value is highly correlated with DM-susceptibility as measured in off-rate assay. We confirmed experimentally that DM-susceptibility measured by delta-IC(50) is comparable to that measured by traditional off-rate assay for peptides with known DM-susceptibility hierarchy. The major advantage of this method is that it allows simple, fast and high throughput measurement of DM-susceptibility for a large set of unlabeled peptides in studies of the mechanism of DM action and for identification of CD4+ T cell epitopes.

  7. 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.

  8. 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.

  9. 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.

  10. Chemical methods for producing disulfide bonds in peptides and proteins to study folding regulation.

    PubMed

    Okumura, Masaki; Shimamoto, Shigeru; Hidaka, Yuji

    2014-04-01

    Disulfide bonds play a critical role in the folding of secretory and membrane proteins. Oxidative folding reactions of disulfide bond-containing proteins typically require several hours or days, and numerous misbridged disulfide isomers are often observed as intermediates. The rate-determining step in refolding is thought to be the disulfide-exchange reaction from nonnative to native disulfide bonds in folding intermediates, which often precipitate during the refolding process because of their hydrophobic properties. To overcome this, chemical additives or a disulfide catalyst, protein disulfide isomerase (PDI), are generally used in refolding experiments to regulate disulfide-coupled peptide and protein folding. This unit describes such methods in the context of the thermodynamic and kinetic control of peptide and protein folding, including (1) regulation of disulfide-coupled peptides and protein folding assisted by chemical additives, (2) reductive unfolding of disulfide-containing peptides and proteins, and (3) regulation of disulfide-coupled peptide and protein folding using PDI.

  11. Air oxidation method employed for the disulfide bond formation of natural and synthetic peptides.

    PubMed

    Calce, Enrica; Vitale, Rosa Maria; Scaloni, Andrea; Amodeo, Pietro; De Luca, Stefania

    2015-08-01

    Among the available protocols, chemically driven approaches to oxidize cysteine may not be required for molecules that, under the native-like conditions, naturally fold in conformations ensuring an effective pairing of the right disulfide bridge pattern. In this contest, we successfully prepared the distinctin, a natural heterodimeric peptide, and some synthetic cyclic peptides that are inhibitors of the CXCR4 receptor. In the first case, the air oxidation reaction allowed to connect two peptide chains via disulfide bridge, while in the second case allowed the cyclization of rationally designed peptides by an intramolecular disulfide bridge. Computational approaches helped to either drive de-novo design or suggest structural modifications and optimal oxidization protocols for disulfide-containing molecules. They are able to both predict and to rationalize the propensity of molecules to spontaneously fold in suitable conformations to achieve the right disulfide bridges.

  12. 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.

  13. A peptide N-terminal protection strategy for comprehensive glycoproteome analysis using hydrazide chemistry based method.

    PubMed

    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

    2015-05-11

    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.

  14. 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.

  15. Design of a predicted MHC restricted short peptide immunodiagnostic and vaccine candidate for Fowl adenovirus C in chicken infection.

    PubMed

    Valdivia-Olarte, Hugo; Requena, David; Ramirez, Manuel; Saravia, Luis E; Izquierdo, Ray; Falconi-Agapito, Francesca; Zavaleta, Milagros; Best, Iván; Fernández-Díaz, Manolo; Zimic, Mirko

    2015-01-01

    Fowl adenoviruses (FAdVs) are the ethiologic agents of multiple pathologies in chicken. There are five different species of FAdVs grouped as FAdV-A, FAdV-B, FAdV-C, FAdV-D, and FAdV-E. It is of interest to develop immunodiagnostics and vaccine candidate for Peruvian FAdV-C in chicken infection using MHC restricted short peptide candidates. We sequenced the complete genome of one FAdV strain isolated from a chicken of a local farm. A total of 44 protein coding genes were identified in each genome. We sequenced twelve Cobb chicken MHC alleles from animals of different farms in the central coast of Peru, and subsequently determined three optimal human MHC-I and four optimal human MHC-II substitute alleles for MHC-peptide prediction. The potential MHC restricted short peptide epitope-like candidates were predicted using human specific (with determined suitable chicken substitutes) NetMHC MHC-peptide prediction model with web server features from all the FAdV genomes available. FAdV specific peptides with calculated binding values to known substituted chicken MHC-I and MHC-II were further filtered for diagnostics and potential vaccine epitopes. Promiscuity to the 3/4 optimal human MHC-I/II alleles and conservation among the available FAdV genomes was considered in this analysis. The localization on the surface of the protein was considered for class II predicted peptides. Thus, a set of class I and class II specific peptides from FAdV were reported in this study. Hence, a multiepitopic protein was built with these peptides, and subsequently tested to confirm the production of specific antibodies in chicken.

  16. Design of a predicted MHC restricted short peptide immunodiagnostic and vaccine candidate for Fowl adenovirus C in chicken infection

    PubMed Central

    Valdivia-Olarte, Hugo; Requena, David; Ramirez, Manuel; Saravia, Luis E; Izquierdo, Ray; Falconi-Agapito, Francesca; Zavaleta, Milagros; Best, Iván; Fernández-Díaz, Manolo; Zimic, Mirko

    2015-01-01

    Fowl adenoviruses (FAdVs) are the ethiologic agents of multiple pathologies in chicken. There are five different species of FAdVs grouped as FAdV-A, FAdV-B, FAdV-C, FAdV-D, and FAdV-E. It is of interest to develop immunodiagnostics and vaccine candidate for Peruvian FAdV-C in chicken infection using MHC restricted short peptide candidates. We sequenced the complete genome of one FAdV strain isolated from a chicken of a local farm. A total of 44 protein coding genes were identified in each genome. We sequenced twelve Cobb chicken MHC alleles from animals of different farms in the central coast of Peru, and subsequently determined three optimal human MHC-I and four optimal human MHC-II substitute alleles for MHC-peptide prediction. The potential MHC restricted short peptide epitope-like candidates were predicted using human specific (with determined suitable chicken substitutes) NetMHC MHC-peptide prediction model with web server features from all the FAdV genomes available. FAdV specific peptides with calculated binding values to known substituted chicken MHC-I and MHC-II were further filtered for diagnostics and potential vaccine epitopes. Promiscuity to the 3/4 optimal human MHC-I/II alleles and conservation among the available FAdV genomes was considered in this analysis. The localization on the surface of the protein was considered for class II predicted peptides. Thus, a set of class I and class II specific peptides from FAdV were reported in this study. Hence, a multiepitopic protein was built with these peptides, and subsequently tested to confirm the production of specific antibodies in chicken. PMID:26664030

  17. Soft Computing Methods for Disulfide Connectivity Prediction

    PubMed Central

    Márquez-Chamorro, Alfonso E.; Aguilar-Ruiz, Jesús S.

    2015-01-01

    The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods. PMID:26523116

  18. The investigation of the secondary structures of various peptide sequences of β-casein by the multicanonical simulation method

    NASA Astrophysics Data System (ADS)

    Yaşar, F.; Çelik, S.; Köksel, H.

    2006-05-01

    The structural properties of Arginine-Glutamic acid-Leucine-Glutamic acid-Glutamic acid-Leucine-Asparagine-Valine-Proline-Glycine (RELEELNVPG, in one letter code), Glutamic acid-Glutamic acid-Glutamine-Glutamine-Glutamine-Threonine-Glutamic acid (EEQQQTE) and Glutamic acid-Aspartic acid-Glutamic acid-Leucine-Glutamine-Aspartic acid-Lysine-Isoleucine (EDELQDKI) peptide sequences of β-casein were studied by three-dimensional molecular modeling. In this work, the three-dimensional conformations of each peptide from their primary sequences were obtained by multicanonical simulations. With using major advantage of this simulation technique, Ramachandran plots were prepared and analysed to predict the relative occurrence probabilities of β-turn, γ-turn and helical structures. Structural predictions of these sequences of β-casein molecule indicate the presence of high level of helical structures and βIII-turns. The occurrence probabilities of inverse and classical β-turns were low. The probability of helical structure of each sequence significantly decreased when the temperature increased. Our results show these peptides have highly helical structure and better agreement with the results of spectroscopic techniques and other prediction methods.

  19. GECluster: a novel protein complex prediction method.

    PubMed

    Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun

    2014-07-04

    Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein-protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analysis of protein complex evolution has been poorly studied, partly due to the lack of high-precision protein complex datasets. In this paper, a combined PPI network is introduced to predict protein complexes which integrate both GO and expression value of relevant protein-coding genes. A novel protein complex prediction method GECluster (Gene Expression Cluster) was proposed based on a seed node expansion strategy, in which a combined PPI network was utilized. GECluster was applied to a training combined PPI network and it predicted more credible complexes than peer methods. The results indicate that using a combined PPI network can efficiently improve protein complex prediction accuracy. In order to study protein complex evolution within cells due to changes in the living environment surrounding cells, GECluster was applied to seven combined PPI networks constructed using the data of a test set including yeast response to stress throughout a wine fermentation process. Our results showed that with the rise of alcohol concentration, protein complexes within yeast cells gradually evolve from one state to another. Besides this, the number of core and attachment proteins within a protein complex both changed significantly.

  20. Destabilization of a model membrane by a predicted fusion peptide of fertilin α

    NASA Astrophysics Data System (ADS)

    Schanck, A.; Brasseur, R.; Peuvot, J.

    1998-02-01

    The subunit of the guinea pig fertilin (previously known as PH-30, an integral membrane protein involved in sperm-egg binding and fusion) is predicted to be a potential fusion protein. The structure of this putative fusion protein was analysed by molecular modeling and we have found a peptidic sequence of 17 residues (D83{-P99}) organized in helix that inserts obliquely in lipid phases. The effect of this synthesized peptide was studied on a model membrane by 31P NMR and light scattering. It appears to increase the size of lipid vesicles and induces structural modifications. We interpret these observations as a destabilization of the lipid organization by this peptide because of its tilted insertion in phospholipid layers. This destabilization could favor membrane fusion. La sous-unité α de la fertiline du cochon d'inde (précédemment appelée PH-30, une protéine membranaire impliquée dans la liaison et la fusion ovule-spermatozoïde) est prédite comme étant une protéine de fusion potentielle. Nous avons analysé la structure de cette protéine par modélisation moléculaire et nous avons trouvé une séquence peptidique de 17 résidus (D83 P99) organisée en hélice qui s'insère de façon oblique dans une phase lipidique. L'effet de ce peptide synthétique a été étudié sur membrane modèle par RMN du 31P et par diffusion de la lumière. Il provoque une augmentation de taille de vésicules lipidiques et induit des modifications structurales. Nous interprétons ces observations en termes de déstabilisation de l'organisation lipidique par ce peptide à cause de son insertion oblique dans la couche lipidique. Cette déstabilisation pourrait favoriser la fusion membranaire.

  1. Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

    PubMed

    Fagerberg, Theres; Zoete, Vincent; Viatte, Sebastien; Baumgaertner, Petra; Alves, Pedro M; Romero, Pedro; Speiser, Daniel E; Michielin, Olivier

    2013-01-01

    The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the

  2. On some methods for assessing earthquake predictions

    NASA Astrophysics Data System (ADS)

    Molchan, G.; Romashkova, L.; Peresan, A.

    2017-09-01

    A regional approach to the problem of assessing earthquake predictions inevitably faces a deficit of data. We point out some basic limits of assessment methods reported in the literature, considering the practical case of the performance of the CN pattern recognition method in the prediction of large Italian earthquakes. Along with the classical hypothesis testing, a new game approach, the so-called parimutuel gambling (PG) method, is examined. The PG, originally proposed for the evaluation of the probabilistic earthquake forecast, has been recently adapted for the case of 'alarm-based' CN prediction. The PG approach is a non-standard method; therefore it deserves careful examination and theoretical analysis. We show that the PG alarm-based version leads to an almost complete loss of information about predicted earthquakes (even for a large sample). As a result, any conclusions based on the alarm-based PG approach are not to be trusted. We also show that the original probabilistic PG approach does not necessarily identifies the genuine forecast correctly among competing seismicity rate models, even when applied to extensive data.

  3. Novel hyperspectral prediction method and apparatus

    NASA Astrophysics Data System (ADS)

    Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

    2009-05-01

    Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

  4. A Method for Selective Enrichment and Analysis of Nitrotyrosine-Containing Peptides in Complex Proteome Samples

    SciTech Connect

    Zhang, Qibin; Qian, Weijun; Knyushko, Tanya V.; Clauss, Therese RW; Purvine, Samuel O.; Moore, Ronald J.; Sacksteder, Colette A.; Chin, Mark H.; Smith, Desmond J.; Camp, David G.; Bigelow, Diana J.; Smith, Richard D.

    2007-06-01

    Elevated levels of protein tyrosine nitration have been found in various neurodegenerative diseases and aging related pathologies; however, the lack of an efficient enrichment method has prevented the analysis of this important low level protein modification. We have developed an efficient method for specific enrichment of nitrotyrosine containing peptides that permits nitrotyrosine peptides and specific nitration sites to be unambiguously identified with LC-MS/MS. The method is based on the derivatization of nitrotyrosine into free sulfhydryl groups followed by high efficiency enrichment of sulfhydryl-containing peptides with thiopropyl sepharose beads. The derivatization process starts with acetylation with acetic anhydride to block all primary amines, followed by reduction of nitrotyrosine to aminotyrosine, then derivatization of aminotyrosine with N-Succinimidyl S-Acetylthioacetate (SATA), and finally deprotecting of S-acetyl on SATA to form free sulfhydryl groups. This method was evaluated using nitrotyrosine containing peptides, in-vitro nitrated human histone 1.2, and bovine serum albumin (BSA). 91% and 62% of the identified peptides from enriched histone and BSA samples were nitrotyrosine derivatized peptides, respectively, suggesting relative high specificity of the enrichment method. The application of this method to in-vitro nitrated mouse brain homogenate resulted in 35% of identified peptides containing nitrotyrosine (compared to only 5.9% observed from the global analysis of unenriched sample), and a total of 150 unique nitrated peptides covering 102 proteins were identified with a false discovery rate estimated at 3.3% from duplicate LC-MS/MS analyses of a single enriched sample.

  5. 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.

  6. A robust method of determination of high concentrations of peptides and proteins.

    PubMed

    Levashov, Pavel A; Sutherland, Duncan S; Besenbacher, Flemming; Shipovskov, Stepan

    2009-12-01

    In this paper, we pioneer application of a unique method of protein determination by coloring peptide bonds for analysis of a variety of biomolecules with different grades of purity (e.g., oligopeptides, membrane, and glycol proteins). We demonstrated that the calibration curve for all studied molecules is universal and linear within 0.1 to 1.2mg protein content range. The assay thus can be used to analyze peptides without preliminary dilutions and calibration in up to 1g/ml solutions of peptides, which is crucial for many biotechnological processes, such as development of coatings, scaffolds, and biocompatible materials.

  7. 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

  8. Mass spectrometry methods for predicting antibiotic resistance.

    PubMed

    Charretier, Yannick; Schrenzel, Jacques

    2016-10-01

    Developing elaborate techniques for clinical applications can be a complicated process. Whole-cell MALDI-TOF MS revolutionized reliable microorganism identification in clinical microbiology laboratories and is now replacing phenotypic microbial identification. This technique is a generic, accurate, rapid, and cost-effective growth-based method. Antibiotic resistance keeps emerging in environmental and clinical microorganisms, leading to clinical therapeutic challenges, especially for Gram-negative bacteria. Antimicrobial susceptibility testing is used to reliably predict antimicrobial success in treating infection, but it is inherently limited by the need to isolate and grow cultures, delaying the application of appropriate therapies. Antibiotic resistance prediction by growth-independent methods is expected to reduce the turnaround time. Recently, the potential of next-generation sequencing and microarrays in predicting microbial resistance has been demonstrated, and this review evaluates the potential of MS in this field. First, technological advances are described, and the possibility of predicting antibiotic resistance by MS is then illustrated for three prototypical human pathogens: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Clearly, MS methods can identify antimicrobial resistance mediated by horizontal gene transfers or by mutations that affect the quantity of a gene product, whereas antimicrobial resistance mediated by target mutations remains difficult to detect.

  9. Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

    PubMed Central

    Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka

    2011-01-01

    Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. PMID:22272081

  10. Midregional pro-atrial natriuretic peptide and procalcitonin improve survival prediction in VAP.

    PubMed

    Boeck, L; Eggimann, P; Smyrnios, N; Pargger, H; Thakkar, N; Siegemund, M; Marsch, S; Rakic, J; Tamm, M; Stolz, D

    2011-03-01

    Ventilator-associated pneumonia (VAP) affects mortality, morbidity and cost of critical care. Reliable risk estimation might improve end-of-life decisions, resource allocation and outcome. Several scoring systems for survival prediction have been established and optimised over the last decades. Recently, new biomarkers have gained interest in the prognostic field. We assessed whether midregional pro-atrial natriuretic peptide (MR-proANP) and procalcitonin (PCT) improve the predictive value of the Simplified Acute Physiologic Score (SAPS) II and Sequential Related Organ Failure Assessment (SOFA) in VAP. Specified end-points of a prospective multinational trial including 101 patients with VAP were analysed. Death <28 days after VAP onset was the primary end-point. MR-proANP and PCT were elevated at the onset of VAP in nonsurvivors compared with survivors (p = 0.003 and p = 0.017, respectively) and their slope of decline differed significantly (p = 0.018 and p = 0.039, respectively). Patients with the highest MR-proANP quartile at VAP onset were at increased risk for death (log rank p = 0.013). In a logistic regression model, MR-proANP was identified as the best predictor of survival. Adding MR-proANP and PCT to SAPS II and SOFA improved their predictive properties (area under the curve 0.895 and 0.880). We conclude that the combination of two biomarkers, MR-proANP and PCT, improve survival prediction of clinical severity scores in VAP.

  11. A manual sequence method of peptides and phosphopeptides using 4-(1'-cyanoisoindolyl)phenylisothiocyanate.

    PubMed

    Shibata, Takayuki; Wainaina, Moses N; Miyoshi, Takayuki; Kabashima, Tsutomu; Kai, Masaaki

    2011-06-17

    A method for sequence analysis and identification of phosphoamino acids in peptides based on high performance liquid chromatography (HPLC) is described. The peptides were derivatized with an Edman type reagent, 4-(1'-cyanoisoindolyl)phenylisothiocyanate (CIPIC) and subsequently cleaved to generate stable and fluorescent 4-(1'-cyanoisoindolyl)phenylthiazolinone (CIP-TZ)-amino acids. Several experimental factors that affected derivatization on membranes were examined. Under the optimized conditions, the CIP-TZ derivatives of Try(p), Thr(p) and Ser(p) were obtained and separated from their parent amino acids with baseline resolution using an isocratic elution system. Up to the 4th residue of phosphorylated pentapeptides was successfully identified, whereas phosphoamino acid residues could not be detected by the conventional procedure using phenylisothiocyanate (PITC). The results demonstrated the potential of CIPIC as a derivatization reagent for peptide sequencing and the applicability of the method for the study and identification of phosphoamino acids in peptides.

  12. Discovery of peptidic miR-21 processing inhibitor by mirror image phage display: A novel method to generate RNA binding D-peptides.

    PubMed

    Sakamoto, Kotaro; Otake, Kentaro; Umemoto, Tadashi

    2017-02-15

    A novel method to generate RNA binding D-peptide has been developed. To achieve the screening method, phage display was applied to "Mirrored" RNA (L-enantiomer of RNA). We have selected pre-miR21 as an initial screening target to demonstrate the method. The mirrored pre-miR-21 binding peptide sequences were successfully obtained, and were chemically synthesized using D-amino acids. D-peptide is expected to have favorable properties as a drug candidate such as protease resistance and low immunogenicity. As a result of binding evaluation of the D-peptide to pre-miR-21, the EC50 value was 440nM. In addition, the D-peptide possessed inhibition activity to miR-21 processing.

  13. Predicting abrasive wear with coupled Lagrangian methods

    NASA Astrophysics Data System (ADS)

    Beck, Florian; Eberhard, Peter

    2015-05-01

    In this paper, a mesh-less approach for the simulation of a fluid with particle loading and the prediction of abrasive wear is presented. We are using the smoothed particle hydrodynamics (SPH) method for modeling the fluid and the discrete element method (DEM) for the solid particles, which represent the loading of the fluid. These Lagrangian methods are used to describe heavily sloshing fluids with their free surfaces as well as the interface between the fluid and the solid particles accurately. A Reynolds-averaged Navier-Stokes equations model is applied for handling turbulences. We are predicting abrasive wear on the boundary geometry with two different wear models taking cutting and deformation mechanisms into account. The boundary geometry is discretized with special DEM particles. In doing so, it is possible to use the same particle type for both the calculation of the boundary conditions for the SPH method as well as the DEM and for predicting the abrasive wear. After a brief introduction to the SPH method and the DEM, the handling of the boundary and the coupling of the fluid and the solid particles are discussed. Then, the applied wear models are presented and the simulation scenarios are described. The first numerical experiment is the simulation of a fluid with loading which is sloshing inside a tank. The second numerical experiment is the simulation of the impact of a free jet with loading to a simplified pelton bucket. We are especially investigating the wear patterns inside the tank and the bucket.

  14. 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.

  15. Artificial neural network intelligent method for prediction

    NASA Astrophysics Data System (ADS)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  16. Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.

    PubMed

    González-Díaz, Humberto; Pérez-Montoto, Lázaro G; Ubeira, Florencio M

    2014-01-01

    Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

  17. 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.

  18. Prediction of radiological outcome in early rheumatoid arthritis in clinical practice: role of antibodies to citrullinated peptides (anti-CCP)

    PubMed Central

    Forslind, K; Ahlmen, M; Eberhardt, K; Hafstrom, I; Svensson, B

    2004-01-01

    Objective: To investigate the role of anti-cyclic citrullinated peptide antibody (anti-CCP) for the prediction of radiological outcome in patients with early rheumatoid arthritis. Methods: Anti-CCP was assessed at baseline in 379 patients with early rheumatoid arthritis (disease duration <1 year). Radiological joint damage and progression were assessed by Larsen score after two years of follow up (end point) and used as outcome variables. The prognostic value of anti-CCP and other demographic and disease related baseline variables were assessed by univariate and multivariate analyses, including calculation of odds ratios (OR), predictive values, and multiple logistic regression models. Results: The presence of anti-CCP was associated with significantly higher Larsen score both at baseline and at end point. Univariate predictor analysis showed that anti-CCP had the highest significant OR for radiological joint damage and progression after baseline Larsen score, followed by rheumatoid factor, erythrocyte sedimentation rate (ESR), C reactive protein, age, smoking status, and sex. In stepwise multiple regression analyses, baseline Larsen score, anti-CCP, and ESR were selected as significant independent predictors of the radiological outcomes. Conclusions: There is good evidence for an association of anti-CCP with radiological joint changes in rheumatoid arthritis. Anti-CCP is an independent predictor of radiological damage and progression. Though prediction in early rheumatoid arthritis is still far from perfect, the use of anti-CCP in clinical practice should make it easier for rheumatologists to reach judicious treatment decisions. PMID:15308518

  19. Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.

    PubMed

    Schmidler, Scott C; Lucas, Joseph E; Oas, Terrence G

    2007-12-01

    Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined parameters, or largely empirical statistical models obtained by extracting parameters from large datasets. In this work, we demonstrate a merger of these two approaches using Bayesian statistics. We adopt a common biophysical model for local protein folding and peptide configuration, the helix-coil model. The parameters of this model are estimated by statistical fitting to a large dataset, using prior distributions based on experimental data. L(1)-norm shrinkage priors are applied to induce sparsity among the estimated parameters, resulting in a significantly simplified model. Formal statistical procedures for evaluating support in the data for previously proposed model extensions are presented. We demonstrate the advantages of this approach including improved prediction accuracy and quantification of prediction uncertainty, and discuss opportunities for statistical design of experiments. Our approach yields a 39% improvement in mean-squared predictive error over the current best algorithm for this problem. In the process we also provide an efficient recursive algorithm for exact calculation of ensemble helicity including sidechain interactions, and derive an explicit relation between homo- and heteropolymer helix-coil theories and Markov chains and (non-standard) hidden Markov models respectively, which has not appeared in the literature previously.

  20. 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.

  1. Estimation of angiotensin peptides in biological samples by LC/MS method.

    PubMed

    Ali, Quaisar; Wu, Yonnie; Nag, Sourashish; Hussain, Tahir

    2014-01-21

    The low abundance of angiotensin peptides in biological tissues such as the kidney cortex, adipose tissue, urine and plasma makes their detection and quantification a challenge. A few available methods used to quantify these peptides involve lengthy processes of sample preparation and are hardly quantitative. Here, we report a mass spectrometry approach for quantifying angiotensin peptides [Ang II, Ang-(1-7)] in the kidney cortex, epididymal white adipose tissue (eWAT), urine and plasma of male mice. Tissue homogenates, urine and plasma samples were solid-phase extracted with C18 Sep-Pak cartridges and eluted off proteinaceous compounds. These extracted peptide samples were separated on C18 column with a linear acetonitrile gradient and detected by Q-ToF mass analyzer in ESI+-MS ion mode based on their retention time, accurate mass measurement of peptides, the isotope pattern of doubly charged molecular ion, and quantitation of peak area (or ion count) when referencing to the angiotensin peptide standards. The lower limit of quantitation for each angiotensin peptide was 10 pgmg(-1) with the percent recovery at 100.6%. The intra-batch precision for Ang-(1-7) and Ang II were 24.0 and 12.7%, accuracy 84.0-123.0% and 100.2-116.0% respectively. Using this method, we determined the levels of Ang II and Ang-(1-7) in the kidney cortex, eWAT, urine and plasma. Quantification of angiotensin peptides could help target subtle therapeutics changes against pathophysiological conditions such as obesity, kidney disease and hypertension.

  2. 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®

  3. In silico methods to predict drug toxicity.

    PubMed

    Roncaglioni, Alessandra; Toropov, Andrey A; Toropova, Alla P; Benfenati, Emilio

    2013-10-01

    This review describes in silico methods to characterize the toxicity of pharmaceuticals, including tools which predict toxicity endpoints such as genotoxicity or organ-specific models, tools addressing ADME processes, and methods focusing on protein-ligand docking binding. These in silico tools are rapidly evolving. Nowadays, the interest has shifted from classical studies to support toxicity screening of candidates, toward the use of in silico methods to support the expert. These methods, previously considered useful only to provide a rough, initial estimation, currently have attracted interest as they can assist the expert in investigating toxic potential. They provide the expert with safety perspectives and insights within a weight-of-evidence strategy. This represents a shift of the general philosophy of in silico methodology, and it is likely to further evolve especially exploiting links with system biology. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1989-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.

  5. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1989-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.

  6. Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.

    PubMed

    Kumaran Nair, Smitha Sunil; Subba Reddy, N V; Hareesha, K S

    2012-09-01

    It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.

  7. Increasing B-type natriuretic peptide levels predict mortality in unselected haemodialysis patients.

    PubMed

    Breidthardt, Tobias; Kalbermatter, Stefan; Socrates, Thenral; Noveanu, Markus; Klima, Theresia; Mebazaa, Alexandre; Mueller, Christian; Kiss, Denes

    2011-08-01

    Cardiac disease is the major cause of death in patients undergoing chronic haemodialysis. Recent studies have found that B-type natriuretic peptide (BNP) levels accurately reflect the cardiovascular burden of dialysis patients. However, the prognostic potential of BNP measurements in dialysis patients remains unknown. The study included 113 chronic dialysis patients who were prospectively followed up. Levels of BNP were measured at baseline and every 6 months thereafter. The potential of baseline BNP and annual BNP changes to predict all-cause and cardiac mortality were assessed as endpoints. Median follow-up was 735 (354-1459) days; 35 (31%) patients died, 17 (15%) of them from cardiac causes. Baseline BNP levels were similar among survivors and non-survivors, and failed to predict all-cause and cardiac death. Cardiac death was preceded by a marked increase in BNP levels. In survivors BNP levels remained stable [median change: +175% (+20-+384%) vs. -14% (-35-+35%) over the 18 months preceding either death or the end of follow-up, P< 0.001]. Hence, annual BNP changes adequately predicted all-cause and cardiac death in the subsequent year {AUC(all-cause) = 0.70 [SD 0.05, 95% CI (0.60-0.81)]; AUC(cardiac) = 0.82 [SD 0.04, 95%CI (0.73-0.90)]}. A BNP increase of 40% provided the best cut-off level. Cox regression analysis confirmed that annual increases over 40% were associated with a seven-fold increased risk for all-cause and cardiac death. Annual BNP increases above 40% predicted all-cause and cardiac death in the subsequent year. Hence, serially measuring BNP levels may present a novel tool for risk stratification and treatment guidance of end-stage renal disease patients on chronic dialysis.

  8. Airframe Noise Prediction Using the Sngr Method

    NASA Astrophysics Data System (ADS)

    Chen, Rongqian; Wu, Yizhao; Xia, Jian

    In this paper, the Stochastic Noise Generation and Radiation method (SNGR) is used to predict airframe noise. The SNGR method combines a stochastic model with Computational Fluid Dynamics (CFD), and it can give acceptable noise results while the computation cost is relatively low. In the method, the time-averaged mean flow field is firstly obtained by solving Reynolds Averaged Navier-Stokes equations (RANS), and a stochastic velocity is generated based on the obtained information. Then the turbulent field is used to generate the source for the Acoustic Perturbation Equations (APEs) that simulate the noise propagation. For numerical methods, timeaveraged RANS equations are solved by finite volume method, and the turbulent model is K - ɛ model; APEs are solved by finite difference method, and the numerical scheme is the Dispersion-Relation-Preserving (DRP) scheme, with explicit optimized 5-stage Rung-Kutta scheme time step. In order to test the APE solver, propagation of a Gaussian pulse in a uniform mean flow is firstly simulated and compared with the analytical solution. Then, using the method, the trailing edge noise of NACA0012 airfoil is calculated. The results are compared with reference data, and good agreements are demonstrated.

  9. Computational approach for designing tumor homing peptides

    PubMed Central

    Sharma, Arun; Kapoor, Pallavi; Gautam, Ankur; Chaudhary, Kumardeep; Kumar, Rahul; Chauhan, Jagat Singh; Tyagi, Atul; Raghava, Gajendra P. S.

    2013-01-01

    Tumor homing peptides are small peptides that home specifically to tumor and tumor associated microenvironment i.e. tumor vasculature, after systemic delivery. Keeping in mind the huge therapeutic importance of these peptides, we have made an attempt to analyze and predict tumor homing peptides. It was observed that certain types of residues are preferred in tumor homing peptides. Therefore, we developed support vector machine based models for predicting tumor homing peptides using amino acid composition and binary profiles of peptides. Amino acid composition, dipeptide composition and binary profile-based models achieved a maximum accuracy of 86.56%, 82.03%, and 84.19% respectively. These methods have been implemented in a user-friendly web server, TumorHPD. We anticipate that this method will be helpful to design novel tumor homing peptides. TumorHPD web server is freely accessible at http://crdd.osdd.net/raghava/tumorhpd/. PMID:23558316

  10. [A method of determining fragments of peptide bioregulators responsible for interaction with receptors].

    PubMed

    Golubovich, V P; Drboglav, V V

    1989-01-01

    A method for investigating the relationship between chemical structure of peptide molecules and their biological activity is suggested. It is based on a few statistical algorithms which are described. The results of the method testing on the thyrotropin-releasing hormone analogs are presented.

  11. Computational predictive methods for fracture and fatigue

    NASA Technical Reports Server (NTRS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-01-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  12. Computational predictive methods for fracture and fatigue

    NASA Astrophysics Data System (ADS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-09-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  13. B-Type Natriuretic Peptide Levels Predict Ventricular Arrhythmia Post Left Ventricular Assist Device Implantation.

    PubMed

    Hellman, Yaron; Malik, Adnan S; Lin, Hongbo; Shen, Changyu; Wang, I-Wen; Wozniak, Thomas C; Hashmi, Zubair A; Pickrell, Jeanette; Jani, Milena; Caccamo, Marco A; Gradus-Pizlo, Irmina; Hadi, Azam

    2015-12-01

    B-type natriuretic peptide (BNP) levels have been shown to predict ventricular arrhythmia (VA) and sudden death in patients with heart failure. We sought to determine whether BNP levels before left ventricular assist device (LVAD) implantation can predict VA post LVAD implantation in advanced heart failure patients. We conducted a retrospective study consisting of patients who underwent LVAD implantation in our institution during the period of May 2009-March 2013. The study was limited to patients receiving a HeartMate II or HeartWare LVAD. Acute myocardial infarction patients were excluded. We compared between the patients who developed VA within 15 days post LVAD implantation to the patients without VA. A total of 85 patients underwent LVAD implantation during the study period. Eleven patients were excluded (five acute MI, four without BNP measurements, and two discharged earlier than 13 days post LVAD implantation). The incidence of VA was 31%, with 91% ventricular tachycardia (VT) and 9% ventricular fibrillation. BNP remained the single most powerful predictor of VA even after adjustment for other borderline significant factors in a multivariate logistic regression model (P < 0.05). BNP levels are a strong predictor of VA post LVAD implantation, surpassing previously described risk factors such as age and VT in the past.

  14. BactPepDB: a database of predicted peptides from a exhaustive survey of complete prokaryote genomes

    PubMed Central

    Rey, Julien; Deschavanne, Patrick; Tuffery, Pierre

    2014-01-01

    With the recent progress in complete genome sequencing, mining the increasing amount of genomic information available should in theory provide the means to discover new classes of peptides. However, annotation pipelines often do not consider small reading frames likely to be expressed. BactPepDB, available online at http://bactpepdb.rpbs.univ-paris-diderot.fr, is a database that aims at providing an exhaustive re-annotation of all complete prokaryotic genomes—chromosomal and plasmid DNA—available in RefSeq for coding sequences ranging between 10 and 80 amino acids. The identified peptides are classified as (i) previously identified in RefSeq, (ii) entity-overlapping (intragenic) or intergenic, and (iii) potential pseudogenes—intergenic sequences corresponding to a portion of a previously annotated larger gene. Additional information is related to homologs within order, predicted signal sequence, transmembrane segments, disulfide bonds, secondary structure, and the existence of a related 3D structure in the Protein Databank. As a result, BactPepDB provides insights about candidate peptides, and provides information about their conservation, together with some of their expected biological/structural features. The BactPepDB interface allows to search for candidate peptides in the database, or to search for peptides similar to a query, according to the multiple properties predicted or related to genomic localization. Database URL: http://www.yeastgenome.org/ PMID:25377257

  15. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc.

    PubMed

    Barriga, Gonzalo P; Villalón-Letelier, Fernando; Márquez, Chantal L; Bignon, Eduardo A; Acuña, Rodrigo; Ross, Breyan H; Monasterio, Octavio; Mardones, Gonzalo A; Vidal, Simon E; Tischler, Nicole D

    2016-07-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses.

  16. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc

    PubMed Central

    Barriga, Gonzalo P.; Villalón-Letelier, Fernando; Márquez, Chantal L.; Bignon, Eduardo A.; Acuña, Rodrigo; Ross, Breyan H.; Monasterio, Octavio; Mardones, Gonzalo A.; Vidal, Simon E.; Tischler, Nicole D.

    2016-01-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses

  17. Insights from the analysis of predicted Rv0679c protein peptide from Mycobacterium tuberculosis with Toll like Receptors in host

    PubMed Central

    Lavarti, Rupa; Ganugapati, Jayasree; Ratcha, Shirisa; Rao, Lakshmana SS; SivaSai, Krovvidi SR

    2016-01-01

    Peptides of Rv0679c a membrane protein of the cell envelope (16.6 KDa) of Mycobacterium tuberculosis (M. tb), inhibited entry of live bacilli into epithelial (A549) and macrophage (U937) cell lines in vitro, suggesting a possible role in invasion. Receptors associated with Rv0679c antigen entry into cell lines were not characterized. We are reporting that Rv0679c peptides could bind to Toll like receptors (TLRs), the principal class of pathogen recognition receptors on host cells (PRR) by docking studies. Peptide structures were predicted using PEP FOLD and docking of truncated peptides with TLR’s was performed using Cluspro 2.0. Docked complexes were analyzed using Swiss-PDB Viewer. Nine peptides of Rv0679c protein assessed were able to bind to TLR2-1 and TLR 4-MD2; however the binding energy was better with TLR 4-MD2. Peptide 30985 (-866.4 kcal/mol) has better binding energy with TLR2-1, in contrast peptide 30982 showed a better binding energy to TLR 4-MD2 dimer with a score of -1291.7 kcal/mol. Interactive residue analysis revealed that GLU 173 and SER 454 of TLR 1; ARG 447 and ARG 486 of TLR2; ARG 264 of TLR 4 and SER 120, LYS 122 and GLU 92 of MD2 region are predominant residues interacting with peptides of Rv0679c protein. Our study suggests that predominant residues and receptors of TLR2 and TLR4 are important for Rv0679c protein binding, which could further lead to invasion of M. tb into the host cell. PMID:28246463

  18. Seminal quality prediction using data mining methods.

    PubMed

    Sahoo, Anoop J; Kumar, Yugal

    2014-01-01

    Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of

  19. A method of predicting anomalous flashovers

    SciTech Connect

    Shindo, Takatoshi; Suzuki, Toshio

    1995-07-01

    When a long air gap or an insulator string is tested with a switching impulse voltage, flashovers sometimes occur at a gap which is longer than the test specimen. This phenomenon has been called anomalous flashover and the results of several experiments have been already reported. Although the mechanism of this anomalous flashover phenomena is important in coordinating insulation of high voltage transmission systems, especially for a UHV transmission system, almost no studies have been conducted on it. The authors analyze anomalous flashover phenomena statistically and propose a calculation method to predict the probability of anomalous flashovers occurs. This calculation method is then used to estimate the safety clearance needed for a UHV substation.

  20. Drug permeability prediction using PMF method.

    PubMed

    Meng, Fancui; Xu, Weiren

    2013-03-01

    Drug permeability determines the oral availability of drugs via cellular membranes. Poor permeability makes a drug unsuitable for further development. The permeability may be estimated as the free energy change that the drug should overcome through crossing membrane. In this paper the drug permeability was simulated using molecular dynamics method and the potential energy profile was calculated with potential of mean force (PMF) method. The membrane was simulated using DPPC bilayer and three drugs with different permeability were tested. PMF studies on these three drugs show that doxorubicin (low permeability) should pass higher free energy barrier from water to DPPC bilayer center while ibuprofen (high permeability) has a lower energy barrier. Our calculation indicates that the simulation model we built is suitable to predict drug permeability.

  1. A parallel method for enumerating amino acid compositions and masses of all theoretical peptides.

    PubMed

    Nefedov, Alexey V; Sadygov, Rovshan G

    2011-11-07

    Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. Because of the high computational complexity of this task, reported methods for peptide enumeration were restricted to cover limited mass ranges (below 2 kDa). In addition, implementation details of these methods as well as their computational performance have not been provided. The increasing availability of parallel (multi-core) computers in all fields of research makes the development of parallel methods for peptide enumeration a timely topic. We describe a parallel method for enumerating all amino acid compositions up to a given length. We present recursive procedures which are at the core of the method, and show that a single task of enumeration of all peptide compositions can be divided into smaller subtasks that can be executed in parallel. The computational complexity of the subtasks is compared with the computational complexity of the whole task. Pseudocodes of processes (a master and workers) that are used to execute the enumerating procedure in parallel are given. We present computational times for our method executed on a computer cluster with 12 Intel Xeon X5650 CPUs (72 cores) running Windows HPC Server. Our method has been implemented as a 32- and 64-bit Windows application using Microsoft Visual C++ and the Message Passing Interface. It is available for download at https://ispace.utmb.edu/users/rgsadygo/Proteomics/ParallelMethod. We describe implementation of a parallel method for generating mass distributions of all theoretically possible amino acid compositions.

  2. High-yield peptide-extraction method for the discovery of subnanomolar biomarkers from small serum samples.

    PubMed

    Kawashima, Yusuke; Fukutomi, Toshiyuki; Tomonaga, Takeshi; Takahashi, Hiroki; Nomura, Fumio; Maeda, Tadakazu; Kodera, Yoshio

    2010-04-05

    Serum proteins/peptides reflect physiological or pathological states in humans and are an attractive target for the discovery of disease biomarkers. However, the existence of high-abundance proteins and the large dynamic range of serum proteins/peptides make any quantitative analysis of low-abundance proteins/peptides challenging. Furthermore, analyses of peptides, including the cleaved fragments of proteins, are difficult because of carrier protein binding. Here, we developed a differential solubilization (DS) method to extract low-molecular-weight proteins/peptides in serum with good reproducibility and yield as compared to typical peptide-extraction methods such as organic solvent precipitation and ultrafiltration. Using the DS method combined with reverse-phase HPLC fractionation followed by MALDI-TOF-MS, we performed high-quality comparative analyses of more than 1500 peptides from 1 microL of serum samples, including low-abundance peptides in the subnanomolar range and containing many peptides bound to carrier proteins such as albumin. We applied this method and successfully discovered four new biomarker candidates of colon cancer, none of which have previously been observed in serum and one of which is a fragment of the protein zyxin that possibly originated from tumor cells. Our results indicate that serum peptide analyses based on the DS method should greatly contribute to the discovery of novel low-abundance biomarkers.

  3. Pro-A-type natriuretic peptide and pro-adrenomedullin predict progression of chronic kidney disease: the MMKD Study.

    PubMed

    Dieplinger, Benjamin; Mueller, Thomas; Kollerits, Barbara; Struck, Joachim; Ritz, Eberhard; von Eckardstein, Arnold; Haltmayer, Meinhard; Kronenberg, Florian

    2009-02-01

    A-type natriuretic peptide (ANP) and adrenomedullin (ADM) are potent hypotensive, diuretic, and natriuretic peptides involved in maintaining cardiovascular and renal homeostasis. We conducted a prospective 7-year study of 177 nondiabetic patients with primary chronic kidney disease to see if ANP and ADM plasma concentrations predict the progression of their disease, using novel sandwich immunoassays covering the midregional epitopes of the stable prohormones (MRproANP and MR-proADM). Progression of chronic kidney disease was defined as doubling of baseline serum creatinine and/or terminal renal failure, which occurred in 65 patients. Analysis of the receiver operating characteristic curve for the prediction of renal endpoints showed similar areas under the curve for the glomerular filtration rate (GFR) (0.838), MR-proANP (0.810), and MRproADM (0.876), respectively, as did the Kaplan-Meier curve analyses of the patients stratified according to the median of the respective markers. In separate multiple Cox-proportional hazard regression analyses, increased plasma concentrations of both peptides were each strongly predictive of the progression of chronic kidney disease after adjustments for age, gender, GFR, proteinuria and amino-terminal pro-B-type natriuretic peptide. Our study suggests that MR-proANP and MR-proADM are useful new markers of progression of primary nondiabetic chronic kidney disease.

  4. Systematic study of substance P analogs. I. Evaluation of peptides synthesized by the multipin method for quantitative receptor binding assay.

    PubMed

    Wang, J X; Bray, A M; Dipasquale, A J; Maeji, N J; Geysen, H M

    1993-10-01

    The multipin peptide synthesis technique, a method for simultaneous multiple peptide synthesis, was developed for large-scale screening of oligopeptides [Geysen et al. (1984) Proc. Natl. Acad. Sci. USA, 81, 3998-4002]. A modification of the technique allows the peptides assembled on polyethylene pins to be cleaved in their native amide form and reconstituted into physiologically compatible solutions. In this study, the suitability of these peptides for quantitative receptor binding assay was evaluated. Substance P and 18 analogs, including a set of N-terminal truncated substance P and a set of naturally occurring substance P analogs, were synthesized by the multipin methods. An average yield of 20 +/- 3 nmol of peptide per pin was obtained. The purity of the peptides was estimated to be ca. 90%. The binding activities of these peptides were determined in a competition assay against 125I-BHSP binding to a rat brain synaptosome preparation. The rank order of the affinities of these peptides depicted a typical pharmacological profile of central NK1 receptor. The IC50 values obtained were also in good agreement with data reported by other groups using similar experimental conditions, except that bulk synthesized peptides were used. This study demonstrates that the peptides synthesized with the multipin technique are suitable for quantitative receptor studies, particularly for a high-volume screening of bioactive peptides.

  5. Phase analysis method for burst onset prediction

    NASA Astrophysics Data System (ADS)

    Stellino, Flavio; Mazzoni, Alberto; Storace, Marco

    2017-02-01

    The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting.

  6. Chemoselective one-step purification method for peptides synthesized by the solid-phase technique.

    PubMed Central

    Funakoshi, S; Fukuda, H; Fujii, N

    1991-01-01

    The specific reaction between SH and iodoacetamide groups has been explored as the basis of an affinity-type purification procedure for peptides synthesized by the solid-phase technique. For this affinity-type purification procedure, we synthesized an SH precursor reagent bearing an acid-labile S-protecting group, pMB-SCH2CONHCH2CH2-SO2CH2CH2OCO2pNP (compound I), in which pMB is p-methoxybenzyl and pNP is p-nitrophenyl. Using this reagent, the procedure involves the following sequence of four reactions: (i) attachment of the SH function of compound I to the alpha-amino group of a peptide-resin through a base-labile sulfonylethoxycarbonyl linkage in the final step of solid-phase peptide synthesis, (ii) acid treatment to remove the S-pMB and side-chain-protecting groups employed and cleave the modified peptide from the resin, (iii) immobilization of the derived SH-peptide on an iodoacetamide-resin column, and (iv) base (5% NH4OH) treatment to release the desired peptide from the resin in nearly pure form. To facilitate this purification procedure, unreacted amino groups were acetylated in each step during solid-phase synthesis. The usefulness of this method was demonstrated by the purification of several peptides (18 to approximately 44 amino acids in length) synthesized by the 9-fluorenylmethoxycarbonyl (Fmoc)-based solid-phase technique. The principle of this affinity-type purification procedure may also be applied to the tert-butoxycarbonyl (Boc)-based solid-phase technique. PMID:1871113

  7. A Novel Method for Anti-HLA Antibody Detection Using Personalized Peptide Arrays

    PubMed Central

    Liu, Pan; Souma, Tomokazu; Wei, Andrew Zu-Sern; Xie, Xueying; Luo, Xunrong; Jin, Jing

    2016-01-01

    Background HLA mismatches are the primary cause of alloantibody-mediated rejection (AMR) in organ transplantation. To delineate antigenic and immunogenic potentials among individual HLA mismatches, information regarding antibody specificity at the epitope level, instead of the allelic level, is needed. Methods This study explores a direct screening method for HLA linear epitopes in kidney transplant patients. We custom synthesized a large panel of 15-residue HLA peptides in an array format and measured alloantibody reactivity to these peptides from the sera of post and/or pretransplant patients. Two design concepts for the arrays were followed: a standard array of a fixed panel of peptides or personalized arrays. The standard array contains 420 peptides derived from a predetermined set of HLA-DQ allelic antigens based on templates also used in the single-antigen beads assay. Results The array detected distinct antiserum patterns among transplant subjects and revealed epitope levels of specificity largely in accordance with the single-antigen results. Two personalized arrays that each included donor-derived peptides of HLA-A, -B, -C, -DQ, and -DR sequences were separately designed for 2 transplant subjects. The personalized arrays detected de novo antibodies following transplantation. The new method also showed superior sensitivity to a single-antigen assay in one of the cases whose pathological diagnosis of AMR occurred before single-antigen assay could detect antibodies. Conclusions This pilot study proved the feasibility of using personalized peptide arrays to achieve detection of alloantibodies for linear HLA epitopes associated with distinct donor-recipient mismatches. Single or multiple reactive epitopes may occur on an individual HLA molecule, and donor-specific HLA-DQ-reactivity among 5 kidney transplant subjects revealed patterns of shared epitopes. PMID:27826602

  8. Quantum chemical calculations predict biological function: The case of T cell receptor interaction with a peptide/MHC class I

    NASA Astrophysics Data System (ADS)

    Antipas, Georgios S. E.; Germenis, Anastasios

    2015-02-01

    A combination of atomic correlation statistics and quantum chemical calculations are shown to predict biological function. In the present study, various antigenic peptide-Major Histocompatibility Complex (pMHC) ligands with near-identical stereochemistries, in complexation with the same T cell receptor (TCR), were found to consistently induce distinctly different quantum chemical behavior, directly dependent on the peptide’s electron spin density and intrinsically expressed by the protonation state of the peptide’s N-terminus. Furthermore, the cumulative coordination difference of any variant in respect to the native peptide was found to accurately reflect peptide biological function and immerges as the physical observable which is directly related to the immunological end-effect of pMHC-TCR interaction.

  9. Assay of protein and peptide adducts of cholesterol ozonolysis products by hydrophobic and click enrichment methods.

    PubMed

    Windsor, Katherine; Genaro-Mattos, Thiago C; Miyamoto, Sayuri; Stec, Donald F; Kim, Hye-Young H; Tallman, Keri A; Porter, Ned A

    2014-10-20

    Cholesterol undergoes ozonolysis to afford a variety of oxysterol products, including cholesterol-5,6-epoxide (CholEp) and the isomeric aldehydes secosterol A (seco A) and secosterol B (seco B). These oxysterols display numerous important biological activities, including protein adduction; however, much remains to be learned about the identity of the reactive species and the range of proteins modified by these oxysterols. Here, we synthesized alkynyl derivatives of cholesterol-derived oxysterols and employed a straightforward detection method to establish secosterols A and B as the most protein-reactive of the oxysterols tested. Model adduction studies with an amino acid, peptides, and proteins provide evidence for the potential role of secosterol dehydration products in protein adduction. Hydrophobic separation methods-Folch extraction and solid phase extraction (SPE)-were successfully applied to enrich oxysterol-adducted peptide species, and LC-MS/MS analysis of a model peptide-seco adduct revealed a unique fragmentation pattern (neutral loss of 390 Da) for that species. Coupling a hydrophobic enrichment method with proteomic analysis utilizing characteristic fragmentation patterns facilitates the identification of secosterol-modified peptides and proteins in an adducted protein. More broadly, these improved enrichment methods may give insight into the role of oxysterols and ozone exposure in the pathogenesis of a variety of diseases, including atherosclerosis, Alzheimer's disease, Parkinson's disease, and asthma.

  10. High-performance liquid chromatographic method for the determination of prolyl peptides in urine.

    PubMed

    Codini, M; Palmerini, C A; Fini, C; Lucarelli, C; Floridi, A

    1991-01-04

    A rapid and accurate method is described for the determination of prolyl peptides in urine, with specific reference to the dipeptide prolylhydroxyproline, and free hydroxyproline and proline. Free amino acids and peptides were isolated from urine on cation-exchange minicolumns, and free imino acids and prolyl-N-terminal peptides were selectively derivatized with 4-chloro-7-nitrobenzofurazan, after reaction of amino acids and N-terminal aminoacyl peptides with o-phthalaldehyde. The highly fluorescent adducts of imino acids and prolyl peptides were separated on a Spherisorb ODS 2 column by isocratic elution for 12 min using as mobile phase 17.5 mM aqueous trifluoracetic acid solution containing 12.5% acetonitrile (eluent A), followed by gradient elution from eluent A to 40% of 17.5 mM aqueous trifluoroacetic acid solution containing 80% acetonitrile in 20 min. Analytes of interest, in particular the dipeptide prolylhydroxyproline, can be easily quantified by fluorimetric detection (epsilon ex = 470 nm, epsilon em = 530 nm) without interference from primary amino-containing compounds.

  11. An Efficient Method for the In Vitro Production of Azol(in)e-Based Cyclic Peptides**

    PubMed Central

    Houssen, Wael E; Bent, Andrew F; McEwan, Andrew R; Pieiller, Nathalie; Tabudravu, Jioji; Koehnke, Jesko; Mann, Greg; Adaba, Rosemary I; Thomas, Louise; Hawas, Usama W; Liu, Huanting; Schwarz-Linek, Ulrich; Smith, Margaret C M; Naismith, James H; Jaspars, Marcel

    2014-01-01

    Heterocycle-containing cyclic peptides are promising scaffolds for the pharmaceutical industry but their chemical synthesis is very challenging. A new universal method has been devised to prepare these compounds by using a set of engineered marine-derived enzymes and substrates obtained from a family of ribosomally produced and post-translationally modified peptides called the cyanobactins. The substrate precursor peptide is engineered to have a non-native protease cleavage site that can be rapidly cleaved. The other enzymes used are heterocyclases that convert Cys or Cys/Ser/Thr into their corresponding azolines. A macrocycle is formed using a macrocyclase enzyme, followed by oxidation of the azolines to azoles with a specific oxidase. The work is exemplified by the production of 17 macrocycles containing 6–9 residues representing 11 out of the 20 canonical amino acids. PMID:25331823

  12. 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.

  13. A nozzle internal performance prediction method

    NASA Technical Reports Server (NTRS)

    Carlson, John R.

    1992-01-01

    A prediction method was written and incorporated into a three-dimensional Navier-Stokes code (PAB3D) for the calculation of nozzle internal performance. The following quantities are calculated: (1) discharge coefficient; (2) normal, side, and axial thrust ratios; (3) rolling, pitching, and yawing moments; and (4) effective pitch and yaw vector angles. Four different case studies are presented to confirm the applicability of the methodology. Internal and, in most situations, external flow-field regions are required to be modeled. The computed nozzle discharge coefficient matches both the level and the trend of the experimental data within quoted experimental data accuracy (0.5 percent). Moment and force ratios are generally within 1 to 2 percent of the absolute level of experimental data, with the trends of data matched accurately.

  14. Systems and methods for predicting materials properties

    DOEpatents

    Ceder, Gerbrand; Fischer, Chris; Tibbetts, Kevin; Morgan, Dane; Curtarolo, Stefano

    2007-11-06

    Systems and methods for predicting features of materials of interest. Reference data are analyzed to deduce relationships between the input data sets and output data sets. Reference data includes measured values and/or computed values. The deduced relationships can be specified as equations, correspondences, and/or algorithmic processes that produce appropriate output data when suitable input data is used. In some instances, the output data set is a subset of the input data set, and computational results may be refined by optionally iterating the computational procedure. To deduce features of a new material of interest, a computed or measured input property of the material is provided to an equation, correspondence, or algorithmic procedure previously deduced, and an output is obtained. In some instances, the output is iteratively refined. In some instances, new features deduced for the material of interest are added to a database of input and output data for known materials.

  15. Serum anti-cyclic citrullinated peptide antibodies may predict disease activity in rheumatoid arthritis.

    PubMed

    Esalatmanesh, Kamal; Jamali, Raika; Jamali, Arsia; Jamali, Bardia; Nikbakht, Mohammadreza

    2012-12-01

    To define the relationship between serum anti-cyclic citrullinated peptide antibodies (anti-CCP) and disease activity, and to construct a new disease activity index by using anti-CCP in rheumatoid arthritis (RA). One hundred and five RA patients were included. Disease activity based on DAS28-ESR and serum anti-CCP was measured. There was correlation between serum anti-CCP and DAS28-ESR. (R (2) = 0.71, P value < 0.01). New disease activity index was developed by replacing anti-CCP with ESR in DAS28-ESR. There was correlation between new model and DAS28-ESR. (R (2) = 0.91, P value < 0.01) The new composite index best cut-off values corresponding to DAS28-ESR values of 2.6, 3.2, and 5.1 were 3.21, 3.38, and 4.74, respectively. There was agreement between new model and DAS28-ESR for determination of patients in different disease activity categories. (Kappa = 0.71, P value < 0.01). The new disease activity index that applies serum anti-CCP may predict disease activity in RA.

  16. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex

    PubMed Central

    Lamiable, Alexis; Thévenet, Pierre; Rey, Julien; Vavrusa, Marek; Derreumaux, Philippe; Tufféry, Pierre

    2016-01-01

    Structure determination of linear peptides of 5–50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide–protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3. PMID:27131374

  17. A method to predict circulation control noise

    NASA Astrophysics Data System (ADS)

    Reger, Robert W.

    Underwater vehicles suffer from reduced maneuverability with conventional lifting append-\\ ages due to the low velocity of operation. Circulation control offers a method to increase maneuverability independent of vehicle speed. However, with circulation control comes additional noise sources, which are not well understood. To better understand these noise sources, a modal-based prediction method is developed, potentially offering a quantitative connection between flow structures and far-field noise. This method involves estimation of the velocity field, surface pressure field, and far-field noise, using only non-time-resolved velocity fields and time-resolved probe measurements. Proper orthogonal decomposition, linear stochastic estimation and Kalman smoothing are employed to estimate time-resolved velocity fields. Poisson's equation is used to calculate time-resolved pressure fields from velocity. Curle's analogy is then used to propagate the surface pressure forces to the far field. This method is developed on a direct numerical simulation of a two-dimensional cylinder at a low Reynolds number (150). Since each of the fields to be estimated are also known from the simulation, a means of obtaining the error from using the methodology is provided. The velocity estimation and the simulated velocity match well when the simulated additive measurement noise is low. The pressure field suffers due to a small domain size; however, the surface pressures estimates fare much better. The far-field estimation contains similar frequency content with reduced magnitudes, attributed to the exclusion of the viscous forces in Curle's analogy. In the absence of added noise, the estimation procedure performs quite nicely for this model problem. The method is tested experimentally on a 650,000 chord-Reynolds-number flow over a 2-D, 20% thick, elliptic circulation control airfoil. Slot jet momentum coefficients of 0 and 0.10 are investigated. Particle image velocimetry, unsteady

  18. Prediction and analysis of quorum sensing peptides based on sequence features.

    PubMed

    Rajput, Akanksha; Gupta, Amit Kumar; Kumar, Manoj

    2015-01-01

    Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew's correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like "QSMotifScan", "ProtFrag", "MutGen" and "PhysicoProp". Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm "QSPpred" (freely available at: http://crdd.osdd.net/servers/qsppred).

  19. Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

    PubMed Central

    Rajput, Akanksha; Gupta, Amit Kumar; Kumar, Manoj

    2015-01-01

    Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred). PMID:25781990

  20. Method For Determining And Modifying Protein/Peptide Solubilty

    DOEpatents

    Waldo, Geoffrey S.

    2005-03-15

    A solubility reporter for measuring a protein's solubility in vivo or in vitro is described. The reporter, which can be used in a single living cell, gives a specific signal suitable for determining whether the cell bears a soluble version of the protein of interest. A pool of random mutants of an arbitrary protein, generated using error-prone in vitro recombination, may also be screened for more soluble versions using the reporter, and these versions may be recombined to yield variants having further-enhanced solubility. The method of the present invention includes "irrational" (random mutagenesis) methods, which do not require a priori knowledge of the three-dimensional structure of the protein of interest. Multiple sequences of mutation/genetic recombination and selection for improved solubility are demonstrated to yield versions of the protein which display enhanced solubility.

  1. Cardiac natriuretic peptides are related to left ventricular mass and function and predict mortality in dialysis patients.

    PubMed

    Zoccali, C; Mallamaci, F; Benedetto, F A; Tripepi, G; Parlongo, S; Cataliotti, A; Cutrupi, S; Giacone, G; Bellanuova, I; Cottini, E; Malatino, L S

    2001-07-01

    This study was designed to investigate the relationship among brain natriuretic peptide (BNP) and atrial natriuretic peptide (ANP) and left ventricular mass (LVM), ejection fraction, and LV geometry in a large cohort of dialysis patients without heart failure (n = 246) and to test the prediction power of these peptides for total and cardiovascular mortality. In separate multivariate models of LVM, BNP and ANP were the strongest independent correlates of the LVM index. In these models, the predictive power of BNP was slightly stronger than that of ANP. Both natriuretic peptides also were the strongest independent predictors of ejection fraction, and again BNP was a slightly better predictor of ejection fraction than ANP. In separate multivariate Cox models, the relative risk of death was significantly higher in patients of the third tertile of the distribution of BNP and ANP than in those of the first tertile (BNP, 7.14 [95% confidence interval (CI), 2.83 to 18.01, P = 0.00001]; ANP, 4.22 [95% CI, 1.79 to 9.92, P = 0.001]), and a similar difference was found for cardiovascular death (BNP, 6.72 [95% CI, 2.44 to 18.54, P = 0.0002]; ANP, 3.80 [95% CI, 1.44 to 10.03, P = 0.007]). BNP but not ANP remained as an independent predictor of death in a Cox's model including LVM and ejection fraction. Cardiac natriuretic peptides are linked independently to LVM and function in dialysis patients and predict overall and cardiovascular mortality. The measurement of the plasma concentration of BNP and ANP may be useful for risk stratification in these patients.

  2. Predicting three-dimensional conformations of peptides constructed of only glycine, alanine, aspartic acid, and valine.

    PubMed

    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.

  3. 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.

  4. 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 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.

  5. Ratio of preoperative atrial natriuretic peptide to brain natriuretic peptide predicts the outcome of the maze procedure in mitral valve disease.

    PubMed

    Sato, Masafumi; Mikamo, Akihito; Kurazumi, Hiroshi; Suzuki, Ryo; Murakami, Masanori; Kobayashi, Toshiro; Yoshimura, Koich; Hamano, Kimikazu

    2013-02-28

    Although the maze procedure is an established surgical treatment for eliminating atrial fibrillation (AF), its efficacy in patients with mitral valve disease has remained unsatisfactory. A useful predictive marker for the outcome of the maze procedure is needed. The aim of this study was to investigate whether the preoperative ratio of atrial natriuretic peptide (ANP) to brain natriuretic peptide (BNP) reflects atrial fibrosis and can be used to predict the maze procedure outcome in patients with mitral valve disease. A total of 23 consecutive patients who underwent the radial approach to the maze procedure combined with mitral valve surgery were included in this study and were divided into a sinus rhythm (SR) group (n=16) and an AF group (n=7) based on postoperative cardiac rhythm. Plasma samples were obtained at rest before the operation and were analysed for ANP and BNP levels. Atrial tissue samples taken during surgery were used to quantify interstitial fibrosis. The preoperative ANP-to-BNP ratio in the SR group was significantly higher than that in the AF group (0.74 +/- 0.29 vs. 0.42 +/- 0.28, respectively; p=0.025). Receiver operating characteristic (ROC) curve analysis was used to identify factors that predict outcomes after the maze procedure. The area under the ROC curve for the ANP-to-BNP ratio (0.81) was greater than for any other preoperative factors. Moreover, the preoperative ANP-to-BNP ratio demonstrated a negative correlation with left atrial fibrosis (r=-0.69; p=0.003). The preoperative ANP-to-BNP ratio can predict maze procedure outcome in patients with mitral valve disease, and it represents a potential biomarker for left atrial fibrosis.

  6. Methods of Predicting Solid Waste Characteristics.

    ERIC Educational Resources Information Center

    Boyd, Gail B.; Hawkins, Myron B.

    The project summarized by this report involved a preliminary design of a model for estimating and predicting the quantity and composition of solid waste and a determination of its feasibility. The novelty of the prediction model is that it estimates and predicts on the basis of knowledge of materials and quantities before they become a part of the…

  7. A method for high-sensitivity peptide sequencing using postsource decay matrix-assisted laser desorption ionization mass spectrometry

    PubMed Central

    Keough, T.; Youngquist, R. S.; Lacey, M. P.

    1999-01-01

    A method has been developed for de novo peptide sequencing using matrix-assisted laser desorption ionization mass spectrometry. This method will facilitate biological studies that require rapid determination of peptide or protein sequences, e.g., determination of posttranslational modifications, identification of active compounds isolated from combinatorial peptide libraries, and the selective identification of proteins as part of proteome studies. The method involves fast, one-step addition of a sulfonic acid group to the N terminus of tryptic peptides followed by acquisition of postsource decay (PSD) fragment ion spectra. The derivatives are designed to promote efficient charge site-initiated fragmentation of the backbone amide bonds and to selectively enhance the detection of a single fragment ion series that contains the C terminus of the molecule (y-ions). The overall method has been applied to pmol quantities of peptides. The resulting PSD fragment ion spectra often exhibit uninterrupted sequences of 20 or more amino acid residues. However, fragmentation efficiency decreases considerably at amide bonds on the C-terminal side of Pro. The spectra are simple enough that de novo sequence tagging is routine. The technique has been successfully applied to peptide mixtures, to high-mass peptides (up to 3,600 Da) and to the unambiguous identification of proteins isolated from two-dimensional gel electrophoresis. The PSD spectra of these derivatized peptides often allow far more selective protein sequence database searches than those obtained from the spectra of native peptides. PMID:10377380

  8. An improved sample preparation method for the sensitive detection of peptides by MALDI-MS.

    PubMed

    Hioki, Yusaku; Kuyama, Hiroki; Hamana, Chikako; Takeyama, Kohei; Tanaka, Koichi

    2013-11-01

    We describe here an optimization study of the sample preparation conditions for sensitive detection of peptides by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Among many factors in the conditions, we varied the percent acetonitrile in the peptide solution, the percent acetonitrile in the matrix solution and the α-cyano-4-hydroxycinnamic acid (CHCA) concentration in the matrix solution. CHCA was chosen because it is the most frequently used matrix for analyzing peptides. The well-established dried-droplet method was employed for sample deposition. The examined range of the concentration of CHCA was from 0.01 to 10 mg/ml, and the MeCN content of the solvent for matrix/analyte was 10% to 50%. The indicator for the detection sensitivity was the S/N ratio of the peaks of peptides used. Highly increased sensitivity (100- to 1000-fold) was observed for the optimal CHCA concentration of 0.1 mg/ml in 20% MeCN/0.1% aq. trifluoroacetic acid (TFA), as compared with the conventional concentration (10 mg/ml) in 50% MeCN/0.1% aq. TFA. For example, the limit of detection of human ACTH 18-39 was 10 amol/well for the optimal condition but 10 fmol/well for the conventional condition. The optimal condition (0.1 mg/ml CHCA in 20% MeCN/0.1% aq. TFA) was verified with five model peptides and provided significant improvement in sensitivity (by two to three orders of magnitude) compared with the conventional conditions. Optimizing the CHCA concentration and solvent composition significantly improved the detection sensitivity in the analysis of peptides by MALDI-MS. Copyright © 2013 John Wiley & Sons, Ltd.

  9. Simple and scalable method for peptide inhalable powder production.

    PubMed

    Schoubben, Aurélie; Blasi, Paolo; Giovagnoli, Stefano; Ricci, Maurizio; Rossi, Carlo

    2010-01-31

    The aim of this work was to produce capreomycin dry powder and capreomycin loaded PLGA microparticles intended for tuberculosis inhalation therapy, using simple and scalable methods. Capreomycin physico-chemical characteristics have been modified by hydrophobic ion pairing with oleate. The powder suspension was processed by high pressure homogenization and spray-dried. Spray-drying was also used to prepare capreomycin oleate (CO) loaded PLGA microparticles. CO powder was suspended in the organic phase containing PLGA and the suspension was spray-dried. Particle dimensions were determined using photon correlation spectroscopy and Accusizer C770. Morphology was investigated by scanning electron microscopy (SEM) and capreomycin content by spectrophotometry. Capreomycin properties were modified to increase polymeric microparticle content and obtain respirable CO powder. High pressure homogenization allowed to reduce CO particle dimensions obtaining a population in the micrometric (6.18 microm) and one in the nanometric (approximately 317 nm) range. SEM pictures showed not perfectly spherical particles with a wrinkled surface, generally suitable for inhalation. PLGA particles were characterized by a high encapsulation efficiency (about 90%) and dimensions (approximately 6.69 microm) suitable for inhalation. Concluding, two different formulations were successfully developed for capreomycin pulmonary delivery. The hydrophobic ion pair strategy led to a noticeable drug content increase.

  10. Functional genomics of intracellular peptide recognition domains with combinatorial biology methods.

    PubMed

    Sidhu, Sachdev S; Bader, Gary D; Boone, Charles

    2003-02-01

    Phage-displayed peptide libraries have been used to identify specific ligands for peptide-binding domains that mediate intracellular protein-protein interactions. These studies have provided significant insights into the specificities of particular domains. For PDZ domains that recognize C-terminal sequences, the information has proven useful in identifying natural binding partners from genomic databases. For SH3 domains that recognize internal proline-rich motifs, the results of database searches with phage-derived ligands have been compared with the results of yeast-two-hybrid experiments to produce overlap networks that reliably predict natural protein-protein interactions. In addition, libraries of phage-displayed PDZ and SH3 domains have been used to identify the residues responsible for ligand recognition, and also to engineer domains with altered specificities.

  11. Structure of Trichamide, a Cyclic Peptide from the Bloom-Forming Cyanobacterium Trichodesmium erythraeum, Predicted from the Genome Sequence†

    PubMed Central

    Sudek, Sebastian; Haygood, Margo G.; Youssef, Diaa T. A.; Schmidt, Eric W.

    2006-01-01

    A gene cluster for the biosynthesis of a new small cyclic peptide, dubbed trichamide, was discovered in the genome of the global, bloom-forming marine cyanobacterium Trichodesmium erythraeum ISM101 because of striking similarities to the previously characterized patellamide biosynthesis cluster. The tri cluster consists of a precursor peptide gene containing the amino acid sequence for mature trichamide, a putative heterocyclization gene, an oxidase, two proteases, and hypothetical genes. Based upon detailed sequence analysis, a structure was predicted for trichamide and confirmed by Fourier transform mass spectrometry. Trichamide consists of 11 amino acids, including two cysteine-derived thiazole groups, and is cyclized by an N—C terminal amide bond. As the first natural product reported from T. erythraeum, trichamide shows the power of genome mining in the prediction and discovery of new natural products. PMID:16751554

  12. Size-exclusion HPLC as a sensitive and calibrationless method for complex peptide mixtures quantification.

    PubMed

    Bodin, Alice; Framboisier, Xavier; Alonso, Dominique; Marc, Ivan; Kapel, Romain

    2015-12-01

    This work describes an original methodology to quantify complex peptide mixtures by size-exclusion high-performance liquid chromatography (SE-HPLC). The methodology was first tested on simulated elutions of peptide mixtures. For this set of experiments, a good estimation of the total peptide concentration was observed (error less than 10 %). Then 30 fractions obtained by ultrafiltration of hydrolysates from two different sources were titrated by Kjeldahl or BCA analysis and analysed by SE-HPLC for an experimental validation of the methodology. Very good matchs between methods were obtained. The linear working range depends on the hydrolysate but is generally between 0.2 and 4gL(-1) (i.e. between 10 and 200μg). Moreover, the presence of organic solvents or salts in samples does not impact the accuracy of the methodology contrary to common quantification methods. Hence, the findings of this study show that total concentration of complex peptide mixture can be efficiently determinate by the proposed methodology using simple SE-HPLC analysis.

  13. A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

    SciTech Connect

    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.

    2011-07-15

    High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.

  14. 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.

  15. Prediction Methods in Solar Sunspots Cycles

    PubMed Central

    Ng, Kim Kwee

    2016-01-01

    An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

  16. Prediction Methods in Solar Sunspots Cycles.

    PubMed

    Ng, Kim Kwee

    2016-02-12

    An understanding of the Ohl's Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun's electromagnetic dipole is moving toward the Sun's Equator during a solar cycle.

  17. Prediction Methods in Solar Sunspots Cycles

    NASA Astrophysics Data System (ADS)

    Ng, Kim Kwee

    2016-02-01

    An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle.

  18. Anti-citrullinated peptide antibodies and their value for predicting responses to biologic agents: a review.

    PubMed

    Martin-Mola, Emilio; Balsa, Alejandro; García-Vicuna, Rosario; Gómez-Reino, Juan; González-Gay, Miguel Angel; Sanmartí, Raimon; Loza, Estíbaliz

    2016-08-01

    Anti-citrullinated peptide antibodies (ACPAs) play an important pathogenic role both at the onset and during the disease course. These antibodies precede the clinical appearance of rheumatoid arthritis (RA) and are associated with a less favorable prognosis, both clinically and radiologically. The objective of this work was to conduct a comprehensive review of studies published through September 2015 of ACPAs' role as a predictor of the therapeutic response to the biological agents in RA patients. The review also includes summary of the biology and detection of ACPAs as well as ACPAs in relation to joint disease and CV disease and the possible role of seroconversion. The reviews of studies examining TNF inhibitors and tocilizumab yielded negative results. In the case of rituximab, the data indicated a greater probability of clinical benefit in ACPA(+) patients versus ACPA(-) patients, as has been previously described for rheumatoid factor. Nonetheless, the effect is discreet and heterogeneous. Another drug that may have greater effectiveness in ACPA(+) patients is abatacept. Some studies have suggested that the drug is more efficient in ACPA(+) patients and that those patients show greater drug retention. In a subanalysis of the AMPLE trial, patients with very high ACPA titers who were treated with abatacept had a statistically significant response compared to patients with lower titers. In summary, the available studies suggest that the presence of or high titers of ACPA may predict a better response to rituximab and/or abatacept. Evidence regarding TNFi and tocilizumab is lacking. However, there is a lack of studies with appropriate designs to demonstrate that some drugs are superior to others for ACPA(+) patients.

  19. 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/.

  20. High throughput peptide mapping method for analysis of site specific monoclonal antibody oxidation.

    PubMed

    Li, Xiaojuan; Xu, Wei; Wang, Yi; Zhao, Jia; Liu, Yan-Hui; Richardson, Daisy; Li, Huijuan; Shameem, Mohammed; Yang, Xiaoyu

    2016-08-19

    Oxidation of therapeutic monoclonal antibodies (mAbs) often occurs on surface exposed methionine and tryptophan residues during their production in cell culture, purification, and storage, and can potentially impact the binding to their targets. Characterization of site specific oxidation is critical for antibody quality control. Antibody oxidation is commonly determined by peptide mapping/LC-MS methods, which normally require a long (up to 24h) digestion step. The prolonged sample preparation procedure could result in oxidation artifacts of susceptible methionine and tryptophan residues. In this paper, we developed a rapid and simple UV based peptide mapping method that incorporates an 8-min trypsin in-solution digestion protocol for analysis of oxidation. This method is able to determine oxidation levels at specific residues of a mAb based on the peptide UV traces within <1h, from either TBHP treated or UV light stressed samples. This is the simplest and fastest method reported thus far for site specific oxidation analysis, and can be applied for routine or high throughput analysis of mAb oxidation during various stability and degradation studies. By using the UV trace, the method allows more accurate measurement than mass spectrometry and can be potentially implemented as a release assay. It has been successfully used to monitor antibody oxidation in real time stability studies.

  1. Comprehensive conformational studies of five tripeptides and a deduced method for efficient determinations of peptide structures.

    PubMed

    Yu, Wenbo; Wu, Zhiqing; Chen, Huibin; Liu, Xu; MacKerell, Alexander D; Lin, Zijing

    2012-02-23

    Thorough searches on the potential energy surfaces of five tripeptides, GGG, GYG, GWG, TGG, and MGG, were performed by considering all possible combinations of the bond rotational degrees of freedom with a semiempirical and ab initio combined computational approach. Structural characteristics of the obtained stable tripeptide conformers were carefully analyzed. Conformers of the five tripeptides were found to be closely connected with conformers of their constituting dipeptides and amino acids. A method for finding all important tripeptide conformers by optimizing a small number of trial structures generated by suitable superposition of the parent amino acid and dipeptide conformers is thus proposed. Applying the method to another five tripeptides, YGG, FGG, WGG, GFA, and GGF, studied before shows that the new approach is both efficient and reliable by providing the most complete ensembles of tripeptide conformers. The method is further generalized for application to larger peptides by introducing the breeding and mutation concepts in a genetic algorithm way. The generalized method is verified to be capable of finding tetrapeptide conformers with secondary structures of strands, helices, and turns, which are highly populated in larger peptides. This show some promise for the proposed method to be applied for the structural determination of larger peptides. © 2012 American Chemical Society

  2. Comprehensive Conformational Studies of Five Tripeptides and a Deduced Method for Efficient Determinations of Peptide Structures

    PubMed Central

    Yu, Wenbo; Wu, Zhiqing; Chen, Huibin; Liu, Xu; MacKerell, Alexander D.

    2012-01-01

    Thorough searches on the potential energy surfaces of five tripeptides, GGG, GYG, GWG, TGG and MGG, were performed by considering all possible combinations of the bond rotational degrees of freedom with a semi-empirical and ab initio combined computational approach. Structural characteristics of the obtained stable tripeptide conformers were carefully analyzed. Conformers of the five tripeptides were found to be closely connected with conformers of their constituting dipeptides and amino acids. A method for finding all important tripeptide conformers by optimizing a small number of trial structures generated by suitable superposition of the parent amino acid and dipeptide conformers is thus proposed. Applying the method to another five tripeptides, YGG, FGG, WGG, GFA and GGF, studied before shows that the new approach is both efficient and reliable by providing the most complete ensembles of tripeptide conformers. The method is further generalized for application to larger peptides by introducing the breeding and mutation concepts in a genetic algorithm way. The generalized method is verified to be capable of finding tetrapeptide conformers with secondary structures of strands, helices and turns which are highly populated in larger peptides. This show some promise for the proposed method to be applied for the structural determination of larger peptides. PMID:22260814

  3. The Predikin webserver: improved prediction of protein kinase peptide specificity using structural information

    PubMed Central

    Saunders, Neil F. W.

    2008-01-01

    The Predikin webserver allows users to predict substrates of protein kinases. The Predikin system is built from three components: a database of protein kinase substrates that links phosphorylation sites with specific protein kinase sequences; a perl module to analyse query protein kinases and a web interface through which users can submit protein kinases for analysis. The Predikin perl module provides methods to (i) locate protein kinase catalytic domains in a sequence, (ii) classify them by type or family, (iii) identify substrate-determining residues, (iv) generate weighted scoring matrices using three different methods, (v) extract putative phosphorylation sites in query substrate sequences and (vi) score phosphorylation sites for a given kinase, using optional filters. The web interface provides user-friendly access to each of these functions and allows users to obtain rapidly a set of predictions that they can export for further analysis. The server is available at http://predikin.biosci.uq.edu.au. PMID:18477637

  4. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  5. Pro-atrial natriuretic peptide and prediction of atrial fibrillation and stroke: The Malmö Preventive Project.

    PubMed

    Berntsson, John; Smith, J Gustav; Nilsson, Peter M; Hedblad, Bo; Melander, Olle; Engström, Gunnar

    2017-01-01

    Background The increasing prevalence of atrial fibrillation and novel therapeutic tools to prevent cardioembolic stroke has increased the need for risk markers. Objectives This study explored the relationship between the midregional sequence of pro-atrial natriuretic peptide (MR-proANP) levels with the risk of atrial fibrillation and stroke, and whether measurement of MR-proANP improves the prediction of these outcomes. Methods MR-proANP was measured in fasting blood samples of 5130 subjects (69% men, mean age 69.2 ± 6.2 years) without a history of atrial fibrillation or stroke from the general population. The incidence of atrial fibrillation and stroke was monitored over a median follow-up of 5.6 years. C-statistics and net reclassification improvement was used to assess the predictive ability of MR-proANP in addition to conventional risk factors. Results Log-normalized MR-proANP was significantly associated with the incidence of atrial fibrillation ( n = 362; hazard ratio (HR); 95% confidence interval (CI) per 1 standard deviation (SD) 2.05, 1.86-2.27) and stroke from all causes ( n = 195; HR 1.30; 95% CI 1.12-1.50). The HR for stroke events related to atrial fibrillation was 1.79 (95% CI 1.25-2.58) per 1 SD. MR-proANP significantly improved the prediction of atrial fibrillation when added to a risk score of conventional risk factors (C statistic 0.69 vs. 0.75), mainly by down-classifying subjects who did not develop atrial fibrillation. A smaller improvement in predictive ability was observed for stroke (C statistic 0.66 vs. 0.68). Conclusion High plasma levels of MR-proANP are associated with the incidence of atrial fibrillation and stroke in the middle-aged and elderly population. MR-proANP may be useful to identify individuals with an increased risk of atrial fibrillation.

  6. 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.

  7. A simple method for primary screening of antibacterial peptides in plant seeds

    PubMed Central

    Aliahmadi, A; Roghanian, R; Emtiazi, G; Ghassempour, A

    2011-01-01

    Background and Objectives Regarding the importance of finding new antibacterial drugs, screening of plants as a promising resource are now conducted worldwide. In this study, we report the application of a simple previously described method for screening of different plant seeds in order to find the best resources of plant antimicrobial peptides. Materials and Methods Total water soluble protein of 10 different plant seeds were extracted and subjected to SDS-PAGE and subsequent agar-overlay bioassays. Standard strains of Staphylococcus aureus, Enterococcus faecium and Escherichia coli were included in the bioassays. This method also was used for total proteins precipitated by Ammonium sulphate which ensure the protein nature of the test substances. Molecular size and the amounts of effective peptides were estimated using Tricin-SDS-PAGE and densitometry. Results Two different plant seeds showed noticeable antibacterial activities against tested Gram positive bacteria and a moderate inhibitory effect on Gram negative ones. Based on the results of Tricin-SDS-PAGE analysis which were carried out in parallel to bioassays, it was concluded that effective antibacterial substances are peptides with molecular weight of slightly larger than 5 kDa. Conclusion On the basis of results of agar-overlay experiments and by screening of 10 different herbal seeds, we could introduce seeds of M. sativa L. and Onobrychis sativa Lam., as great sources of putative plant antibacterial peptides. The proposed screening method can be used for screening of large number of different plant seeds and even other parts of the plant body, regarding some necessary modification in total water soluble protein extraction steps. PMID:22347591

  8. Liposomal angiogenic peptides for ischemic limb perfusion: comparative study between different administration methods.

    PubMed

    Hwang, Hyosook; Kim, Hyeon-Soo; Jeong, Hwan-Seok; Rajasaheb, Bagalkot Tarique; Kim, Minjoo; Oh, Phil-Sun; Lim, Seok Tae; Sohn, Myung-Hee; Jeong, Hwan-Jeong

    2016-11-01

    We investigated the therapeutic effectiveness of PEGylated liposomes loaded with angiogenic peptides for treating hindlimb ischemia. Rats received a femoral artery occlusion. Red blood cells collected from the animals were labeled with technetium-99m. Limb perfusion gamma imaging was performed. PEGylated liposomes loaded with angiogenic peptides were administered intra-arterially. Technetium-99m red blood cell imaging was repeated 1 week later. The animals were sacrificed the next day. The expression of angiogenic proteins was studied. Later, changes in limb perfusion after intra-arterial infusion versus intra-muscular injection were also compared to determine the therapeutic effectiveness of different administration methods. Femoral artery occlusion dramatically reduced ischemic limb perfusion (by an average of 69%, compared to contralateral limb). This was not different among groups (p > 0.05). Liposomes loaded with angiogenic peptides significantly improved ischemic limb perfusion, compared to controls (210% of baseline, versus 100% of baseline in control; p < 0.05 versus controls). The enhanced ischemic limb perfusion was accompanied by an increased expression of CD 31 (an average of 1.6-fold increase of controls; p < 0.05). The liposomes or peptides treatment alone did not affect ischemic perfusion (liposomes alone: 100% of baseline; peptides alone: 120% of baseline; p > 0.05 versus controls, respectively) or the angiogenic response (1.1-fold of controls in liposomes alone; 1.0-fold of controls in peptides alone; p > 0.05 versus controls, respectively). Intra-muscular injection induced similar liposomal treatment effects on ischemic limb perfusion (230% of baseline) as those by intra-arterial infusion (210% of baseline; p < 0.05 versus intra-muscular). PEGylated liposomes loaded with angiogenic peptides improved ischemic limb perfusion and promoted angiogenic responses. Liposomal angiogenic treatment via intra-arterial infusion resulted

  9. Nonlinear Methods Applied to Atmospheric Prediction

    DTIC Science & Technology

    2007-11-02

    states being a minimum and the spatial correlation being a maximum to determine the best analogs. They also started exploring the value of finding...the best analog of each of the trajectories in the ensemble of numerical predictions from the start of the prediction to the verification time. These...Benard convection, with the fluid layer heated below and cooled above, cellular convection occurs with cells of width very nearly equal to their

  10. 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

  11. Computational Prediction and Experimental Validation of Signal Peptide Cleavage in the Extracellular Proteome of a Natural Microbial Community

    SciTech Connect

    Erickson, Brian K; Mueller, Ryan; Verberkmoes, Nathan C; Shah, Manesh B; Singer, Steven; Thelen, Michael P.; Banfield, Jillian F.; Hettich, Robert {Bob} L

    2010-01-01

    An integrated computational/experimental approach was used to predict and identify signal peptide cleavages among microbial proteins of environmental biofilm communities growing in acid mine drainage (AMD). SignalP-3.0 was employed to computationally query the AMD protein database of >16,000 proteins, which resulted in 1,480 predicted signal peptide cleaved proteins. LC-MS/MS analyses of extracellular (secretome) microbial preparations from different locations and developmental states empirically confirmed 531 of these signal peptide cleaved proteins. The majority of signal-cleavage proteins (58.4%) are annotated to have unknown functions; however, Pfam domain analysis revealed that many may be involved in extracellular functions expected within the AMD system. Examination of the abundances of signal-cleaved proteins across 28 proteomes from biofilms collected over a 4-year period demonstrated a strong correlation with the developmental state of the biofilm. For example, class I cytochromes are abundant in early growth states, whereas cytochrome oxidases from the same organism increase in abundance later in development. These results likely reflect shifts in metabolism that occur as biofilms thicken and communities diversify. In total, these results provide experimental confirmation of proteins that are designed to function in the extreme acidic extracellular environment and will serve as targets for future biochemical analysis.

  12. Assay of Protein and Peptide Adducts of Cholesterol Ozonolysis Products by Hydrophobic and Click Enrichment Methods

    PubMed Central

    2015-01-01

    Cholesterol undergoes ozonolysis to afford a variety of oxysterol products, including cholesterol-5,6-epoxide (CholEp) and the isomeric aldehydes secosterol A (seco A) and secosterol B (seco B). These oxysterols display numerous important biological activities, including protein adduction; however, much remains to be learned about the identity of the reactive species and the range of proteins modified by these oxysterols. Here, we synthesized alkynyl derivatives of cholesterol-derived oxysterols and employed a straightforward detection method to establish secosterols A and B as the most protein-reactive of the oxysterols tested. Model adduction studies with an amino acid, peptides, and proteins provide evidence for the potential role of secosterol dehydration products in protein adduction. Hydrophobic separation methods—Folch extraction and solid phase extraction (SPE)—were successfully applied to enrich oxysterol-adducted peptide species, and LC-MS/MS analysis of a model peptide–seco adduct revealed a unique fragmentation pattern (neutral loss of 390 Da) for that species. Coupling a hydrophobic enrichment method with proteomic analysis utilizing characteristic fragmentation patterns facilitates the identification of secosterol-modified peptides and proteins in an adducted protein. More broadly, these improved enrichment methods may give insight into the role of oxysterols and ozone exposure in the pathogenesis of a variety of diseases, including atherosclerosis, Alzheimer’s disease, Parkinson’s disease, and asthma. PMID:25185119

  13. Earthquake prediction: Simple methods for complex phenomena

    NASA Astrophysics Data System (ADS)

    Luen, Bradley

    2010-09-01

    Earthquake predictions are often either based on stochastic models, or tested using stochastic models. Tests of predictions often tacitly assume predictions do not depend on past seismicity, which is false. We construct a naive predictor that, following each large earthquake, predicts another large earthquake will occur nearby soon. Because this "automatic alarm" strategy exploits clustering, it succeeds beyond "chance" according to a test that holds the predictions _xed. Some researchers try to remove clustering from earthquake catalogs and model the remaining events. There have been claims that the declustered catalogs are Poisson on the basis of statistical tests we show to be weak. Better tests show that declustered catalogs are not Poisson. In fact, there is evidence that events in declustered catalogs do not have exchangeable times given the locations, a necessary condition for the Poisson. If seismicity followed a stochastic process, an optimal predictor would turn on an alarm when the conditional intensity is high. The Epidemic-Type Aftershock (ETAS) model is a popular point process model that includes clustering. It has many parameters, but is still a simpli_cation of seismicity. Estimating the model is di_cult, and estimated parameters often give a non-stationary model. Even if the model is ETAS, temporal predictions based on the ETAS conditional intensity are not much better than those of magnitude-dependent automatic (MDA) alarms, a much simpler strategy with only one parameter instead of _ve. For a catalog of Southern Californian seismicity, ETAS predictions again o_er only slight improvement over MDA alarms

  14. 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

  15. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy

    2001-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communication. The early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occurs allows us to have earlier warnings as to when they will affect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME’s could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  16. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy; Roberts, J. A.; Scott, J. B.; University Of North Texas Team

    2000-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communications. The importance of an early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occur allows us to have earlier warnings as to when they will effect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME's could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  17. A Method for Structure–Activity Analysis of Quorum-Sensing Signaling Peptides from Naturally Transformable Streptococci

    PubMed Central

    2009-01-01

    Many species of streptococci secrete and use a competence-stimulating peptide (CSP) to initiate quorum sensing for induction of genetic competence, bacteriocin production, and other activities. These signaling molecules are small, unmodified peptides that induce powerful strain-specific activity at nano-molar concentrations. This feature has provided an excellent opportunity to explore their structure–function relationships. However, CSP variants have also been identified in many species, and each specifically activates its cognate receptor. How such minor changes dramatically affect the specificity of these peptides remains unclear. Structure–activity analysis of these peptides may provide clues for understanding the specificity of signaling peptide–receptor interactions. Here, we use the Streptococcus mutans CSP as an example to describe methods of analyzing its structure–activity relationship. The methods described here may provide a platform for studying quorum-sensing signaling peptides of other naturally transformable streptococci. PMID:19517207

  18. A Variant of Peptide Transporter 2 Predicts the Severity of Porphyria-Associated Kidney Disease.

    PubMed

    Tchernitchko, Dimitri; Tavernier, Quentin; Lamoril, Jérôme; Schmitt, Caroline; Talbi, Neila; Lyoumi, Said; Robreau, Anne-Marie; Karim, Zoubida; Gouya, Laurent; Thervet, Eric; Karras, Alexandre; Puy, Hervé; Pallet, Nicolas

    2017-06-01

    CKD occurs in most patients with acute intermittent porphyria (AIP). During AIP, δ-aminolevulinic acid (ALA) accumulates and promotes tubular cell death and tubulointerstitial damage. The human peptide transporter 2 (PEPT2) expressed by proximal tubular cells mediates the reabsorption of ALA, and variants of PEPT2 have different affinities for ALA. We tested the hypothesis that PEPT2 genotypes affect the severity and prognosis of porphyria-associated kidney disease. We analyzed data from 122 individuals with AIP who were followed from 2003 to 2013 and genotyped for PEPT2 At last follow-up, carriers of the PEPT2*1*1 genotype (higher affinity variant) exhibited worse renal function than carriers of the lower affinity variants PEPT2*1/*2 and PEPT2*2/*2 (mean±SD eGFR: 54.4±19.1, 66.6±23.8, and 78.1±19.9 ml/min per 1.73 m(2), respectively). Change in eGFR (mean±SD) over the 10-year period was -11.0±3.3, -2.4±1.9, and 3.4±2.6 ml/min per 1.73 m(2) for PEPT2*1/*1, PEPT2*1*2, and PEPT*2*2*2 carriers, respectively. At the end of follow-up, 68% of PEPT2*1*1 carriers had an eGFR<60 ml/min per 1.73 m(2), compared with 37% of PEPT2*1*2 carriers and 15% of PEPT2*2*2 carriers. Multiple regression models including all confounders indicated that the PEPT2*1*1 genotype independently associated with an eGFR<60 ml/min per 1.73 m(2) (odds ratio, 6.85; 95% confidence interval, 1.34 to 46.20) and an annual decrease in eGFR of >1 ml/min per 1.73 m(2) (odds ratio, 3.64; 95% confidence interval, 1.37 to 9.91). Thus, a gene variant is predictive of the severity of a chronic complication of AIP. The therapeutic value of PEPT2 inhibitors in preventing porphyria-associated kidney disease warrants investigation. Copyright © 2017 by the American Society of Nephrology.

  19. Molecular modeling of peptides.

    PubMed

    Kuczera, Krzysztof

    2015-01-01

    This article presents a review of the field of molecular modeling of peptides. The main focus is on atomistic modeling with molecular mechanics potentials. The description of peptide conformations and solvation through potentials is discussed. Several important computer simulation methods are briefly introduced, including molecular dynamics, accelerated sampling approaches such as replica-exchange and metadynamics, free energy simulations and kinetic network models like Milestoning. Examples of recent applications for predictions of structure, kinetics, and interactions of peptides with complex environments are described. The reliability of current simulation methods is analyzed by comparison of computational predictions obtained using different models with each other and with experimental data. A brief discussion of coarse-grained modeling and future directions is also presented.

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

    PubMed

    Pandya, Mital; Rasmussen, Michael; Hansen, Andreas; Nielsen, Morten; Buus, Soren; Golde, William; Barlow, John

    2015-11-01

    Major histocompatibility complex (MHC) class Imolecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network-based predictions (NetMHCpan). The updated NetMHCpan server was used to predict BoLA-I binding peptides within the P1 structural polyprotein sequence of FMDV (strain A24 Cruzeiro) for Bo-LA-1*01901, BoLA-2*00801, BoLA-2*01201, and BoLA-4*02401. Peptide binding affinity and stability were determined for these BoLA-I molecules using the luminescent oxygen channeling immunoassay (LOCI) and scintillation proximity assay (SPA). The functional diversity of known BoLA alleles was predicted using theMHCcluster tool, and functional predictions for peptide motifs were compared to observed data from this and prior studies. The results of these analyses showed that BoLA alleles cluster into three distinct groups with the potential to define BBoLA supertypes.^ This streamlined approach identifies potential T cell epitopes from pathogens, such as FMDV, and provides insight into T cell immunity following infection or vaccination.

  1. Integrated method for combustion stability prediction

    NASA Astrophysics Data System (ADS)

    Yu, Y. C.; O'Hara, L.; Smith, R. J.; Anderson, W. E.; Merkle, C. L.

    2011-10-01

    Major obstacles in overcoming combustion instability include the absence of a mechanistic and a priori prediction capability, and the difficulty in studying instability in the laboratory due to the perceived need for testing at the full-scale pressure and geometry to ensure that important processes are maintained. A hierarchal approach toward combustion instability is described that comprises experiment, analysis, and highfidelity computation to develop combustion response submodels that can be used in engineering-level design analysis. The paper provides an illustrative example of how these elements are used to develop a prediction for growth rates in model rocket combustors that generate spontaneous longitudinal combustion instabilities.

  2. Nonprotein Based Enrichment Method to Analyze Peptide Cross-Linking in Protein Complexes

    PubMed Central

    Yan, Funing; Che, Fa-Yun; Rykunov, Dmitry; Nieves, Edward; Fiser, Andras; Weiss, Louis M.; Angeletti, Ruth Hogue

    2009-01-01

    Cross-linking analysis of protein complexes and structures by tandem mass spectrometry (MS/MS) has advantages in speed, sensitivity, specificity, and the capability of handling complicated protein assemblies. However, detection and accurate assignment of the cross-linked peptides are often challenging due to their low abundance and complicated fragmentation behavior in collision-induced dissociation (CID). To simplify the MS analysis and improve the signal-to-noise ratio of the cross-linked peptides, we developed a novel peptide enrichment strategy that utilizes a cross-linker with a cryptic thiol group and using beads modified with a photocleavable cross-linker. The functional cross-linkers were designed to react with the primary amino groups in proteins. Human serum albumin was used as a model protein to detect intra- and intermolecular cross-linkages. Use of this protein-free selective retrieval method eliminates the contamination that can result from avidin–biotin based retrieval systems and simplifies data analysis. These features may make the method suitable to investigate protein–protein interactions in biological samples. PMID:19642656

  3. Methods for the creation of cyclic Peptide libraries for use in lead discovery.

    PubMed

    Foster, Andrew D; Ingram, James D; Leitch, Eilidh K; Lennard, Katherine R; Osher, Eliot L; Tavassoli, Ali

    2015-06-01

    The identification of initial hits is a crucial stage in the drug discovery process. Although many projects adopt high-throughput screening of small-molecule libraries at this stage, there is significant potential for screening libraries of macromolecules created using chemical biology approaches. Not only can the production of the library be directly interfaced with a cell-based assay, but these libraries also require significantly fewer resources to generate and maintain. In this context, cyclic peptides are increasingly viewed as ideal scaffolds and have proven capability against challenging targets such as protein-protein interactions. Here we discuss a range of methods used for the creation of cyclic peptide libraries and detail examples of their successful implementation.

  4. MARQUIS: A Multiplex Method for Absolute Quantification of Peptides and Post-Translational Modifications

    PubMed Central

    Curran, Timothy G; Zhang, Yi; Ma, Daniel J.; Sarkaria, Jann N.; White, Forest M

    2014-01-01

    Absolute quantification of protein expression and post-translational modifications by mass spectrometry has been challenging due to a variety of factors, including the potentially large dynamic range of phosphorylation response. To address these issues, we have developed MARQUIS — Multiplex Absolute Regressed Quantification with Internal Standards — a novel mass spectrometry-based approach using a combination of isobaric tags and heavy-labeled standard peptides to construct internal standard curves for peptides derived from key nodes in signal transduction networks. We applied MARQUIS to quantify phosphorylation dynamics within the EGFR network at multiple time points following stimulation with several ligands, enabling a quantitative comparison of EGFR phosphorylation sites and demonstrating that receptor phosphorylation is qualitatively similar but quantitatively distinct for each EGFR ligand tested. MARQUIS was also applied to quantify the effect of EGFR kinase inhibition on glioblastoma patient derived xenografts. MARQUIS is a versatile method, broadly applicable and extendable to multiple mass spectrometric platforms. PMID:25581283

  5. A Novel Method for Satellite Maneuver Prediction

    NASA Astrophysics Data System (ADS)

    Shabarekh, C.; Kent-Bryant, J.; Keselman, G.; Mitidis, A.

    2016-09-01

    A space operations tradecraft consisting of detect-track-characterize-catalog is insufficient for maintaining Space Situational Awareness (SSA) as space becomes increasingly congested and contested. In this paper, we apply analytical methodology from the Geospatial-Intelligence (GEOINT) community to a key challenge in SSA: predicting where and when a satellite may maneuver in the future. We developed a machine learning approach to probabilistically characterize Patterns of Life (PoL) for geosynchronous (GEO) satellites. PoL are repeatable, predictable behaviors that an object exhibits within a context and is driven by spatio-temporal, relational, environmental and physical constraints. An example of PoL are station-keeping maneuvers in GEO which become generally predictable as the satellite re-positions itself to account for orbital perturbations. In an earlier publication, we demonstrated the ability to probabilistically predict maneuvers of the Galaxy 15 (NORAD ID: 28884) satellite with high confidence eight days in advance of the actual maneuver. Additionally, we were able to detect deviations from expected PoL within hours of the predicted maneuver [6]. This was done with a custom unsupervised machine learning algorithm, the Interval Similarity Model (ISM), which learns repeating intervals of maneuver patterns from unlabeled historical observations and then predicts future maneuvers. In this paper, we introduce a supervised machine learning algorithm that works in conjunction with the ISM to produce a probabilistic distribution of when future maneuvers will occur. The supervised approach uses a Support Vector Machine (SVM) to process the orbit state whereas the ISM processes the temporal intervals between maneuvers and the physics-based characteristics of the maneuvers. This multiple model approach capitalizes on the mathematical strengths of each respective algorithm while incorporating multiple features and inputs. Initial findings indicate that the combined

  6. A survey of aftbody flow prediction methods

    NASA Technical Reports Server (NTRS)

    Putnam, L. E.; Mace, J.

    1981-01-01

    A survey of computational methods used in the calculation of nozzle aftbody flows is presented. One class of methods reviewed are those which patch together solutions for the inviscid, boundary layer, and plume flow regions. The second class of methods reviewed are those which computationally solve the Navier Stokes equations over nozzle aftbodies with jet exhaust flow. Computed results from the methods are compared with experiment. Advantages and disadvantages of the various methods are discussed along with opportunities for further development of these methods.

  7. From Protein-RNA Predictions toward a Peptide-RNA Code.

    PubMed

    Brannan, Kristopher W; Yeo, Gene W

    2016-11-03

    The RNA field is undergoing a renaissance, with a deluge of proteins being identified to bind RNA. Two reports now introduce proteome-wide approaches that identify the peptides that are crosslinked to RNA (Castello et al., 2016; He et al., 2016). Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Gene structure prediction by linguistic methods

    SciTech Connect

    Dong, S.; Searls, D.B.

    1994-10-01

    The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters of the grammar rules are optimized for several different species, and mixing experiments are performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. 24 refs., 5 figs., 3 tabs.

  9. Development of a method for environmentally friendly chemical peptide synthesis in water using water-dispersible amino acid nanoparticles

    PubMed Central

    2011-01-01

    Due to the vast importance of peptides in biological processes, there is an escalating need for synthetic peptides to be used in a wide variety of applications. However, the consumption of organic solvent is extremely large in chemical peptide syntheses because of the multiple condensation steps in organic solvents. That is, the current synthesis method is not environmentally friendly. From the viewpoint of green sustainable chemistry, we focused on developing an organic solvent-free synthetic method using water, an environmentally friendly solvent. Here we described in-water synthesis technology using water-dispersible protected amino acids. PMID:21867548

  10. Development of an at-line method for the identification of angiotensin-I inhibiting peptides in protein hydrolysates.

    PubMed

    van Platerink, Chris J; Janssen, Hans-Gerd M; Haverkamp, Johan

    2007-02-01

    A fast at-line method was developed for the identification of ACE inhibiting (ACEI) peptides in protein hydrolysates. The method consists of activity measurements of fractions collected from a two-dimensional HPLC fractionation of the peptide mixture followed by MS identification of the peptides in the inhibiting fractions. The inhibition assay is based on the inhibiting effect of ACEI peptides on the hydrolytic scission of the substrate Hippuric acid-His-Leu (HHL) during the ACE-catalysed hydrolysis reaction. A fast LC method was developed for the quantification of Hippuric acid (H) and Hippuric acid-Histidine-Leucine (HHL), allowing a large number of fractions to be analysed within a reasonable time period. The method is sensitive and uses only standard laboratory equipment. The limit of detection is 0.34 microM for the known ACEI peptide IPP. This is sufficiently sensitive for the identification of only moderately active peptides and/or ACEI peptides present at low concentrations. The relative standard deviation of the inhibition assay was 12% measured over a time period of 2 months. The IC50 value of IPP measured with the assay was 5.6 microM, which is comparable to the values of 5 microM and 5.15 microM reported in literature for the standard Matsui method. The assay was successfully applied in the identification of ACEI peptides in enzymatically hydrolysed caseinate samples. Two new, not earlier published ACEI peptides were identified; MAP (beta-casein f102-104) and ITP (alpha-s2-casein f119-121) with IC50 values of 3.8 microM and 50 microM, respectively.

  11. How to address CPP and AMP translocation? Methods to detect and quantify peptide internalization in vitro and in vivo (Review).

    PubMed

    Henriques, Sónia Troeira; Melo, Manuel Nuno; Castanho, Miguel A R B

    2007-01-01

    Membrane translocation is a crucial issue when addressing the activity of both cell-penetrating and antimicrobial peptides. Translocation is responsible for the therapeutic potential of cell-penetrating peptides as drug carriers and can dictate the killing mechanisms, selectivity and efficiency of antimicrobial peptides. It is essential to evaluate if the internalization of cell-penetrating peptides is mediated by endocytosis and if it is able to internalize attached cargoes. The mode of action of an antimicrobial peptide cannot be fully understood if it is not known whether the peptide acts exclusively at the membrane level or also at the cytoplasm. Therefore, experimental methods to evaluate and quantify translocation processes are of first importance. In this work, over 20 methods described in the literature for the assessment of peptide translocation in vivo and in vitro, with and without attached macromolecular cargoes, are discussed and their applicability, advantages and disadvantages reviewed. In addition, a classification of these methods is proposed, based on common approaches to detect translocation.

  12. Gaussian mixture models as flux prediction method for central receivers

    NASA Astrophysics Data System (ADS)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  13. Sann: solvent accessibility prediction of proteins by nearest neighbor method.

    PubMed

    Joo, Keehyoung; Lee, Sung Jong; Lee, Jooyoung

    2012-07-01

    We present a method to predict the solvent accessibility of proteins which is based on a nearest neighbor method applied to the sequence profiles. Using the method, continuous real-value prediction as well as two-state and three-state discrete predictions can be obtained. The method utilizes the z-score value of the distance measure in the feature vector space to estimate the relative contribution among the k-nearest neighbors for prediction of the discrete and continuous solvent accessibility. The Solvent accessibility database is constructed from 5717 proteins extracted from PISCES culling server with the cutoff of 25% sequence identities. Using optimal parameters, the prediction accuracies (for discrete predictions) of 78.38% (two-state prediction with the threshold of 25%), 65.1% (three-state prediction with the thresholds of 9 and 36%), and the Pearson correlation coefficient (between the predicted and true RSA's for continuous prediction) of 0.676 are achieved An independent benchmark test was performed with the CASP8 targets where we find that the proposed method outperforms existing methods. The prediction accuracies are 80.89% (for two state prediction with the threshold of 25%), 67.58% (three-state prediction), and the Pearson correlation coefficient of 0.727 (for continuous prediction) with mean absolute error of 0.148. We have also investigated the effect of increasing database sizes on the prediction accuracy, where additional improvement in the accuracy is observed as the database size increases. The SANN web server is available at http://lee.kias.re.kr/~newton/sann/. Copyright © 2012 Wiley Periodicals, Inc.

  14. 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.

  15. Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Walshe, Valerie A; Borrow, Persephone; Flower, Darren R

    2005-11-17

    Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) analysis of proteins and peptides. In the present study, descriptors were used to characterize peptides binding to the human MHC allele HLA-A0201. Two sets of amino acid descriptors were chosen: 93 descriptors taken from the amino acid descriptor database AAindex and the z descriptors defined by Wold and Sandberg. Variable selection techniques (SIMCA, genetic algorithm, and GOLPE) were applied to remove redundant descriptors. Our results indicate that QSAR models generated using five z descriptors had the highest predictivity and explained variance (q2 between 0.6 and 0.7 and r2 between 0.6 and 0.9). Further to the QSAR analysis, 15 peptides were synthesized and tested using a T2 stabilization assay. All peptides bound to HLA-A0201 well, and four peptides were identified as high-affinity binders.

  16. A survey of the broadband shock associated noise prediction methods

    NASA Technical Reports Server (NTRS)

    Kim, Chan M.; Krejsa, Eugene A.; Khavaran, Abbas

    1992-01-01

    Several different prediction methods to estimate the broadband shock associated noise of a supersonic jet are introduced and compared with experimental data at various test conditions. The nozzle geometries considered for comparison include a convergent and a convergent-divergent nozzle, both axisymmetric. Capabilities and limitations of prediction methods in incorporating the two nozzle geometries, flight effect, and temperature effect are discussed. Predicted noise field shows the best agreement for a convergent nozzle geometry under static conditions. Predicted results for nozzles in flight show larger discrepancies from data and more dependable flight data are required for further comparison. Qualitative effects of jet temperature, as observed in experiment, are reproduced in predicted results.

  17. Adaptive method for electron bunch profile prediction

    NASA Astrophysics Data System (ADS)

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.

  18. Adaptive method for electron bunch profile prediction

    SciTech Connect

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.

  19. Adaptive method for electron bunch profile prediction

    DOE PAGES

    Scheinker, Alexander; Gessner, Spencer

    2015-10-15

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. Thus, the simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrialmore » control system. Finally, the main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.« less

  20. A simple and convenient method for the preparation of antioxidant peptides from walnut (Juglans regia L.) protein hydrolysates.

    PubMed

    Liu, Ming-Chuan; Yang, Sheng-Jie; Hong, Da; Yang, Jin-Ping; Liu, Min; Lin, Yun; Huang, Chia-Hui; Wang, Chao-Jih

    2016-01-01

    Walnut (Juglans regia L.), that belongs to the Juglandaceae family, is one of the nuts commonly found in Chinese diets. Researchers had obtained peptides from walnut protein hydrolysates, and these peptides exhibited the high antioxidant activities. The objective of this study was to develop a simple and convenient method for a facile and reproducible preparation of antioxidant peptides from walnut protein hydrolysates. Walnut proteins were extracted from walnut kernels using continuous countercurrent extraction process, and were separately hydrolyzed with six types of proteases (neutrase, papain, bromelain, alcalase, pepsin, and pancreatin). Then, hydrolysates were purified by ultrafiltration. The yields and purity of the peptides prepared using neutrase and papain were 16 and 81 % at least, respectively, higher than others, and had low molecular weight, 99 % of which were less than 1500 Da. Furthermore, the bioassay indicated that the two peptides exhibited the high antioxidant activities in the DPPH (IC50 values: 59.40 and 31.02 µg/mL, respectively), ABTS (IC50 values: 80.36 and 62.22 µg/mL, respectively), and superoxide radical scavenging assay (IC50 values: 107.47 and 80.00 µg/mL, respectively). The method combines the advantages of generality, rapidity, simplicity, and is useful for the mass production of walnut peptides.Graphical AbstractPreparation of antioxidant peptides from walnut (Juglans regia L.) protein hydrolysates.

  1. A novel prefractionation method combining protein and peptide isoelectric focusing in immobilized pH gradient strips.

    PubMed

    Pernemalm, Maria; Lehtiö, Janne

    2013-02-01

    To increase sensitivity and analytical depth in shotgun proteomics, prefractionation of complex samples is often used. Here we describe a novel prefractionation method, Sandwich high resolution isoelectric focusing, which combines both protein and peptide isoelectric focusing. In the first step, intact proteins are separated on the basis of isoelectric point (pI) using traditional immobilized pH gradient (IPG) strips. Segments in the IPG-strip containing proteins of interest are subsequently cut out and applied to in-strip digestion, without subsequent peptide elution. In the second peptide isoelectric focusing step, the strip segments are used as loading bridges. The peptides are thereby directly applied to the peptide isoelectric focusing, without an intermediate elution step, and separated on narrow range IPG strips to reduce the complexity on the peptide level. In the final step, the peptides are eluted into 96-well plates and analyzed with mass spectrometry. In a proof of principle experiment, using this method to zoom in on pI regions of interest in human plasma, we identify over 800 proteins, with concentrations spanning over 6 orders of magnitude.

  2. 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.

  3. Impact of Sample Matrix on Accuracy of Peptide Quantification: Assessment of Calibrator and Internal Standard Selection and Method Validation.

    PubMed

    Arnold, Samuel L; Stevison, Faith; Isoherranen, Nina

    2016-01-05

    Protein quantification based on peptides using LC-MS/MS has emerged as a promising method to measure biomarkers, protein drugs, and endogenous proteins. However, the best practices for selection, optimization, and validation of the quantification peptides are not well established, and the influence of different matrices on protein digestion, peptide stability, and MS detection has not been systematically addressed. The aim of this study was to determine how biological matrices affect digestion, detection, and stability of peptides. The microsomal retinol dehydrogenase (RDH11) and cytosolic soluble aldehyde dehydrogenases (ALDH1As) involved in the synthesis of retinoic acid (RA) were chosen as model proteins. Considerable differences in the digestion efficiency, sensitivity, and matrix effects between peptides were observed regardless of the target protein's subcellular localization. The precision and accuracy of the quantification of RDH11 and ALDH1A were affected by the choice of calibration and internal standards. The final method using recombinant protein calibrators and stable isotope labeled (SIL) peptide internal standards was validated for human liver. The results demonstrate that different sample matrices have peptide, time, and matrix specific effects on protein digestion and absolute quantification.

  4. Predicted disease susceptibility in a Panamanian amphibian assemblage based on skin peptide defenses.

    PubMed

    Woodhams, Douglas C; Voyles, Jamie; Lips, Karen R; Carey, Cynthia; Rollins-Smith, Louise A

    2006-04-01

    Chytridiomycosis is an emerging infectious disease of amphibians caused by a chytrid fungus, Batrachochytrium dendrobatidis. This panzootic does not equally affect all amphibian species within an assemblage; some populations decline, others persist. Little is known about the factors that affect disease resistance. Differences in behavior, life history, biogeography, or immune function may impact survival. We found that an innate immune defense, antimicrobial skin peptides, varied significantly among species within a rainforest stream amphibian assemblage that has not been exposed to B. dendrobatidis. If exposed, all amphibian species at this central Panamanian site are at risk of population declines. In vitro pathogen growth inhibition by peptides from Panamanian species compared with species with known resistance (Rana pipiens and Xenopus laevis) or susceptibility (Bufo boreas) suggests that of the nine species examined, two species (Centrolene prosoblepon and Phyllomedusa lemur) may demonstrate strong resistance, and the other species will have a higher risk of disease-associated population declines. We found little variation among geographically distinct B. dendrobatidis isolates in sensitivity to an amphibian skin peptide mixture. This supports the hypothesis that B. dendrobatidis is a generalist pathogen and that species possessing an innate immunologic defense at the time of disease emergence are more likely to survive.

  5. Immunogenicity and immune modulatory effects of in silico predicted L. donovani candidate peptide vaccines.

    PubMed

    Elfaki, Mona E E; Khalil, Eltahir A G; De Groot, Anne S; Musa, Ahmed M; Gutierrez, Andres; Younis, Brima M; Salih, Kawthar A M; El-Hassan, Ahmed M

    2012-12-01

    Visceral leishmaniasis (VL) is a serious parasitic disease for which control measures are limited and drug resistance is increasing. First and second generation vaccine candidates have not been successful. The goal of the present study was to select possibly immunogenic L. donovani donovani GP63 peptides using immunoinformatics tools and to test their immunogenicity in vitro. The amino acid sequence of L. donovani donovani GP63 [GenBank accession: ACT31401] was screened using the EpiMatrix algorithm for putative T cell epitopes that would bind to the most common HLA class II alleles (DRB1*1101 and DRB1*0804) among at-risk populations. Four T cell epitopes were selected from nine potential candidates. Stimulation of whole blood from healthy volunteers using the peptides separately produced mean IFN-γ and IL-4 levels that were not significantly different from negative controls, while the pooled peptides produced a moderate IFN-γ increase in some volunteers. However, mean IL-10 levels were significantly reduced for all individuals compared with controls. The immunogenicity of these epitopes may be harnessed most effectively in a vaccine delivered in combination with immune-modulating adjuvants.

  6. A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin.

    PubMed

    Patro, Rob; Norel, Raquel; Prill, Robert J; Saez-Rodriguez, Julio; Lorenz, Peter; Steinbeck, Felix; Ziems, Bjoern; Luštrek, Mitja; Barbarini, Nicola; Tiengo, Alessandra; Bellazzi, Riccardo; Thiesen, Hans-Jürgen; Stolovitzky, Gustavo; Kingsford, Carl

    2016-04-08

    Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

  7. Screening and selection of synthetic peptides for a novel and optimized endotoxin detection method.

    PubMed

    Mujika, M; Zuzuarregui, A; Sánchez-Gómez, S; Martínez de Tejada, G; Arana, S; Pérez-Lorenzo, E

    2014-09-30

    The current validated endotoxin detection methods, in spite of being highly sensitive, present several drawbacks in terms of reproducibility, handling and cost. Therefore novel approaches are being carried out in the scientific community to overcome these difficulties. Remarkable efforts are focused on the development of endotoxin-specific biosensors. The key feature of these solutions relies on the proper definition of the capture protocol, especially of the bio-receptor or ligand. The aim of the presented work is the screening and selection of a synthetic peptide specifically designed for LPS detection, as well as the optimization of a procedure for its immobilization onto gold substrates for further application to biosensors.

  8. Protein beta-turn prediction using nearest-neighbor method.

    PubMed

    Kim, Saejoon

    2004-01-01

    With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein beta-turn prediction. In this study, we perform protein beta-turn prediction using a k-nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure information. Traditional beta-turn prediction from k-nearest neighbor method is modified to account for the unbalanced ratio of the natural occurrence of beta-turns and non-beta-turns. Our prediction scheme is tested on a set of 426 non-homologous protein sequences. The prediction scheme consists of two stages: k-nearest neighbor method stage and filtering stage. Variations of the k-nearest neighbor method were used to take property of beta-turns into consideration. Our filtering method uses beta-turn/non-beta-turn estimates from the k-nearest neighbor method stage and predicted protein secondary structure information from PSI-PRED in order to get new beta-turn/non-beta-turn estimate. Our result is compared with the previously best known beta-turn prediction method on the dataset of 426 non-homologous protein sequences and is shown to give slightly superior performance at significantly lower computational complexity. Contact the author for information on the source code of the programs used.

  9. Method for Predicting Which Customers' Time Deposit Balances Will Increase

    NASA Astrophysics Data System (ADS)

    Ono, Toshiyuki; Yoshikawa, Hiroshi; Morita, Masahiro; Komoda, Norihisa

    This paper proposes a method of predicting which customers' account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. The selection of which features are used for prediction is a critical issue. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with other conventional methods.

  10. Improved method for predicting protein fold patterns with ensemble classifiers.

    PubMed

    Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

    2012-01-27

    Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

  11. Methods for Elucidating the Mechanism of Action of Proline-Rich and Other Non-lytic Antimicrobial Peptides.

    PubMed

    Benincasa, Monica; Runti, Giulia; Mardirossian, Mario; Gennaro, Renato; Scocchi, Marco

    2017-01-01

    A distinct group of antimicrobial peptides kills bacteria by interfering with internal cellular functions and without concurrent lytic effects on cell membranes. Here we describe some methods to investigate the mechanisms of action of these antimicrobial peptides. They include assays to detect the possible temporal separation between membrane permeabilization and bacterial killing events, to assess the capacity of antimicrobial peptides to cross the bacterial membranes and reside in the cytoplasm, and later to inhibit vital cell functions such as DNA transcription and protein translation.

  12. Efficient Methods to Compute Genomic Predictions

    USDA-ARS?s Scientific Manuscript database

    Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and simultaneously estimate thousands of marker effects. Algorithms were derived and computer programs tested on simulated data for 50,000 markers and 2,967 bulls. Accurate estimates of ...

  13. Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants.

    PubMed

    Chuah, Jo-Ann; Horii, Yoko; Numata, Keiji

    2016-12-16

    The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as well as new applications in the field of plant biotechnology. Viable methods that currently exist for protein or gene transfer into plant cells, namely Agrobacterium and microprojectile bombardment, have disadvantages of low transformation frequency, limited host range, or a high cost of equipment and microcarriers. The following protocol outlines a simple and versatile method, which employs rationally-designed peptides as delivery agents for a variety of nucleic acid- and protein-based cargoes into plants. Peptides are selected as tools for development of the system due to their biodegradability, reduced size, diverse and tunable properties as well as the ability to gain intracellular/organellar access. The preparation, characterization and application of optimized formulations for each type of the wide range of delivered cargoes (plasmid DNA, double-stranded DNA or RNA, and protein) are described. Critical steps within the protocol, possible modifications and existing limitations of the method are also discussed.

  14. Fluorescence and Absorbance Spectroscopy Methods to Study Membrane Perturbations by Antimicrobial Host Defense Peptides.

    PubMed

    Arias, Mauricio; Vogel, Hans J

    2017-01-01

    Antimicrobial peptides (AMPs) are currently intensely studied because of their potential as new bactericidal and bacteriostatic agents. The mechanism of action of numerous AMPs involves the permeabilization of bacterial membranes. Several methods have been developed to study peptide-membrane interactions; in particular optical spectroscopy methods are widely used. The intrinsic fluorescence properties of the Trp indole ring in Trp-containing AMPs can be exploited by measuring the fluorescence blue shift and acrylamide-induced fluorescence quenching. One important aspect of such studies is the use of distinct models of the bacterial membrane, in most cases large unilamellar vesicles (LUVs) with different, yet well-defined, phospholipid compositions. Deploying LUVs that are preloaded with fluorescent dyes, such as calcein, also allows for the study of vesicle permeabilization by AMPs. In addition, experiments using genetically engineered live Escherichia coli cells can be used to distinguish between the effects of AMPs on the outer and inner membranes of gram-negative bacteria. In combination, these methods can provide a detailed insight into the mode of action of AMPs.

  15. Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants

    PubMed Central

    Chuah, Jo-Ann; Horii, Yoko; Numata, Keiji

    2016-01-01

    The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as well as new applications in the field of plant biotechnology. Viable methods that currently exist for protein or gene transfer into plant cells, namely Agrobacterium and microprojectile bombardment, have disadvantages of low transformation frequency, limited host range, or a high cost of equipment and microcarriers. The following protocol outlines a simple and versatile method, which employs rationally-designed peptides as delivery agents for a variety of nucleic acid- and protein-based cargoes into plants. Peptides are selected as tools for development of the system due to their biodegradability, reduced size, diverse and tunable properties as well as the ability to gain intracellular/organellar access. The preparation, characterization and application of optimized formulations for each type of the wide range of delivered cargoes (plasmid DNA, double-stranded DNA or RNA, and protein) are described. Critical steps within the protocol, possible modifications and existing limitations of the method are also discussed. PMID:28060264

  16. Drawability Prediction Method using Continuous Texture Evolution Model

    NASA Astrophysics Data System (ADS)

    Morimoto, Toshiharu; Yanagimoto, Jun

    2011-08-01

    Drawability is one of steel strip properties which control press forming. Many predicted method for the Lankford value have been proposed. First, we predict recystallization texture based on the idea that total amount of microscopic crystal slips is proportional to accumulated dislocation density in grain boundaries. Next, we can predict Lankford value of ultra low carbon strips and ferritic stainless steel strips using Sachs model. Our method is very practical to use in hot and cold steel rolling industry.

  17. Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models.

    PubMed

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-05-04

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

  18. Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

    PubMed Central

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-01-01

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein–protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue–residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain–peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein–protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain–peptide interactions. PMID:22468754

  19. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  20. Immune reactivity against a novel HLA-A3-restricted influenza virus peptide identified by predictive algorithms and interferon-gamma quantitative PCR.

    PubMed

    Trojan, Andreas; Urosevic, Mirjana; Hummerjohann, Jörg; Giger, Robin; Schanz, Urs; Stahel, Rolf A

    2003-01-01

    The use of appropriate antigenic peptides for the most common human major histocompatibility complex (MHC) alleles is required for the amplification of the autologous cytotoxic compartment and the development of cytotoxic T cell-mediated immunity. The human A2 allele of the MHC plays an important role for the identification of peptide-specific cytotoxic T cells (CTL) against tumor and viral epitopes. Computer-based prediction algorithms, which are available on the Internet, have already proved to be applicable for the identification of novel CTL epitopes. Using the bioinformatics approach, the authors have identified the novel influenza matrix protein-derived and HLA-A3-restricted 9-mer peptide RLEDVFAGK capable of inducing peptide specific CTL reactivity. Peripheral blood mononuclear cells (PBMC) from healthy individuals and patients with lung cancer were pulsed with this peptide and with the well-characterized HLA-A2-restricted influenza A virus matrix peptide GILGFVFTL. Using quantitative PCR (TaqMan; Applied Biosystems, Foster City, CA, U.S.A), reactivity for both peptides was determined by measuring the change in type 1 cytokine (IFN-gamma) expression upon in vitro stimulation. Peptide-specific reactivity matched well with the subsequently determined MHC-class I alleles of the tested individuals. Results from this study indicate that the use of bioinformatics and the PCR-based screening system for the monitoring of T cell reactivity may allow for the identification of novel CTL epitopes.

  1. A novel PCR-based method for high throughput prokaryotic expression of antimicrobial peptide genes

    PubMed Central

    2012-01-01

    Background To facilitate the screening of large quantities of new antimicrobial peptides (AMPs), we describe a cost-effective method for high throughput prokaryotic expression of AMPs. EDDIE, an autoproteolytic mutant of the N-terminal autoprotease, Npro, from classical swine fever virus, was selected as a fusion protein partner. The expression system was used for high-level expression of six antimicrobial peptides with different sizes: Bombinin-like peptide 7, Temporin G, hexapeptide, Combi-1, human Histatin 9, and human Histatin 6. These expressed AMPs were purified and evaluated for antimicrobial activity. Results Two or four primers were used to synthesize each AMP gene in a single step PCR. Each synthetic gene was then cloned into the pET30a/His-EDDIE-GFP vector via an in vivo recombination strategy. Each AMP was then expressed as an Npro fusion protein in Escherichia coli. The expressed fusion proteins existed as inclusion bodies in the cytoplasm and the expression levels of the six AMPs reached up to 40% of the total cell protein content. On in vitro refolding, the fusion AMPs was released from the C-terminal end of the autoprotease by self-cleavage, leaving AMPs with an authentic N terminus. The released fusion partner was easily purified by Ni-NTA chromatography. All recombinant AMPs displayed expected antimicrobial activity against E. coli, Micrococcus luteus and S. cerevisia. Conclusions The method described in this report allows the fast synthesis of genes that are optimized for over-expression in E. coli and for the production of sufficiently large amounts of peptides for functional and structural characterization. The Npro partner system, without the need for chemical or enzymatic removal of the fusion tag, is a low-cost, efficient way of producing AMPs for characterization. The cloning method, combined with bioinformatic analyses from genome and EST sequence data, will also be useful for screening new AMPs. Plasmid pET30a/His-EDDIE-GFP also provides

  2. A Versatile Nonlinear Method for Predictive Modeling

    NASA Technical Reports Server (NTRS)

    Liou, Meng-Sing; Yao, Weigang

    2015-01-01

    As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.

  3. An evidential link prediction method and link predictability based on Shannon entropy

    NASA Astrophysics Data System (ADS)

    Yin, Likang; Zheng, Haoyang; Bian, Tian; Deng, Yong

    2017-09-01

    Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster-Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.

  4. A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.

    PubMed

    Shao, Xiaojian; Tan, Chris S H; Voss, Courtney; Li, Shawn S C; Deng, Naiyang; Bader, Gary D

    2011-02-01

    Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders. We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.

  5. New Methods for Estimating Seasonal Potential Climate Predictability

    NASA Astrophysics Data System (ADS)

    Feng, Xia

    This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that

  6. Computer prediction of peptide maps: assignment of polypeptides to human and mouse mitochondrial DNA genes by analysis of two-dimensional-proteolytic digest gels.

    PubMed Central

    Wallace, D C; Yang, J H; Ye, J H; Lott, M T; Oliver, N A; McCarthy, J

    1986-01-01

    We have prepared a computer program that predicts complete and partial peptide maps from amino acid sequences. The program fragments amino acid sequences at designated cleavage sites and calculates the molecular weight and relative labeling of each peptide. These data are graphed as log molecular weight of the original protein (X-axis) vs. log molecular weight of the component peptides (Y-axis). The program is interactive, permitting adjustment of a number of graphic parameters and alteration of the position of proteins in the first dimension to accommodate aberrations in protein mobility. The program has been used to predict the V8 protease peptide maps of the 13 open reading frames (ORFs) identified in the human and the mouse mitochondrial DNA (mtDNA) sequences. The results were compared to the V8 protease peptide maps obtained for mouse and human mitochondrially synthesized proteins by two-dimensional proteolytic digest gels. A high correlation was observed between the predicted and observed peptide maps. These results suggest the assignment of several proteins to mtDNA genes. Images Fig. 1 Fig. 2 Fig. 3 Fig. 4 PMID:3518425

  7. Overview of Antioxidant Peptides Derived from Marine Resources: The Sources, Characteristic, Purification, and Evaluation Methods.

    PubMed

    Wu, RiBang; Wu, CuiLing; Liu, Dan; Yang, XingHao; Huang, JiaFeng; Zhang, Jiang; Liao, Binqiang; He, HaiLun; Li, Hao

    2015-08-01

    Marine organisms are rich sources of structurally diverse bioactive nitrogenous components. In recent years, numerous bioactive peptides have been identified in a range of marine protein resources, such as antioxidant peptides. Many studies have approved that marine antioxidant peptides have a positive effect on human health and the food industry. Antioxidant activity of peptides can be attributed to free radicals scavenging, inhibition of lipid peroxidation, and metal ion chelating. Moreover, it has also been verified that peptide structure and its amino acid sequence can mainly affect its antioxidant properties. The aim of this review is to summarize kinds of antioxidant peptides from various marine resources. Additionally, the relationship between structure and antioxidant activities of peptides is discussed in this paper. Finally, current technologies used in the preparation, purification, and evaluation of marine-derived antioxidant peptides are also reviewed.

  8. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1988-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to react chemically and reach states in thermal nonequilibrium. In this paper, a new procedure based on Gauss-Seidel line relaxation is shown to solve the equations of hypersonic flow fields containing finite reaction rate chemistry and thermal nonequilibrium. The method requires a few hundred time steps and small computer times for axisymmetric flows about simple body shapes. The extension to more complex two-dimensional body geometries appears straightforward.

  9. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1988-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to react chemically and reach states in thermal nonequilibrium. In this paper, a new procedure based on Gauss-Seidel line relaxation is shown to solve the equations of hypersonic flow fields containing finite reaction rate chemistry and thermal nonequilibrium. The method requires a few hundred time steps and small computer times for axisymmetric flows about simple body shapes. The extension to more complex two-dimensional body geometries appears straightforward.

  10. A survey of spectrum prediction methods in cognitive radio networks

    NASA Astrophysics Data System (ADS)

    Wu, Jianwei; Li, Yanling

    2017-04-01

    Spectrum prediction technology is an effective way to solve the problems of processing latency, spectrum access, spectrum collision and energy consumption in cognitive radio networks. Spectral prediction technology is divided into three categories according to its nature, namely, spectral prediction method based on regression analysis, spectrum prediction method based on Markov model and spectrum prediction method based on machine learning. By analyzing and comparing the three kinds of prediction models, the author hopes to provide some reference for the later researchers. In this paper, the development situation, practical application and existent problems of three kinds of forecasting models are analyzed and summarized. On this basis, this paper discusses the development trend of the next step.

  11. The predictive integration method for dynamics of infrequent events

    NASA Astrophysics Data System (ADS)

    Cubuk, Ekin; Waterland, Amos; Kaxiras, Efthimios

    2012-02-01

    With the increasing prominence and availability of multi-processor computers, recasting problems in a form amenable to parallel solution is becoming a critical step in effective scientific computation. We present a method for parallelizing molecular dynamics simulations in time scale, by using predictive integration. Our method is closely related to Voter's parallel replica method, but goes beyond that approach in that it involves speculatively initializing processors in more than one basin. Our predictive integration method requires predicting possible future configurations while it does not suffer from restrictions due to correlation time after transitions between basins.

  12. Scaling--which methods best predict performance?

    PubMed

    Comfort, Paul; Pearson, Stephen J

    2014-06-01

    Athletes with a higher body mass (BM) tend to be stronger, with ratio scaling possibly eliminating this effect. The aim of this study was to compare relationships between sprint performances with scaled measures of strength and power. Fifteen professional rugby league players (age, 26.27 6 3.87 years; height, 183.33 6 6.37 cm; BM, 96.86 6 11.49 kg) performed 1 repetition maximum back squats, power cleans, squat jumps, and sprints (5, 10, and 20 m). Heavier athletes (forward) generated significantly greater absolute levels of power during the squat jump (5,659.11 6 710.35 vs.4,740.16 6 558.61 W; p , 0.001); however, when power data were scaled no differences were observed. Squat performance indicated no differences in absolute ability between the subgroups (190.6 6 14.25 vs. 205.7 6 18.35 kg), although the lighter group was significantly (p # 0.05) stronger than the heavier group when using ratio and allometric methods (2.1 vs. 1.9 kg · kg(-1) and 10.42 vs. 9.87 kg · kg(0.28)), respectively. Significant relationships with 5-m sprints were only observed for ratio and allometrically scaled power cleans (r = 20.625, p , 0.02; r = 20.675, p , 0.02), with similar correlations between allometrically scaled 10-m sprint and both back squat and power clean performances. Scaled power clean performances were also inversely correlated with 20-m sprints (r = 20.620, r = 20.638, p , 0.02). Where differences in absolute strength are apparent between individuals of different BM, then the use of scaling is required. Because of the similarity between ratio and allometric methods, simple ratio scaling is recommended.

  13. 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

  14. Deep learning methods for protein torsion angle prediction.

    PubMed

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  15. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic

    NASA Astrophysics Data System (ADS)

    Maiwald, Thomas; Winterhalder, Matthias; Aschenbrenner-Scheibe, Richard; Voss, Henning U.; Schulze-Bonhage, Andreas; Timmer, Jens

    2004-07-01

    Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. The predictability of these seizures would render novel therapeutic approaches possible. Several prediction methods have claimed to be able to predict seizures based on EEG recordings minutes in advance. However, the term seizure prediction is not unequivocally defined, different criteria to assess prediction methods exist, and only little attention has been paid to issues of sensitivity and false prediction rate. We introduce an assessment criterion called the seizure prediction characteristic that incorporates the assessment of sensitivity and false prediction rate. Within this framework, three nonlinear seizure prediction methods were evaluated on a large EEG data pool of 21 patients. Altogether, 582 h intracranial EEG data and 88 seizures were examined. With a rate of 1-3.6 false predictions per day, the “dynamical similarity index” achieves a sensitivity between 21 and 42%, which was the best result of the three methods. Sensitivity was between 18 and 31% for the extended, prospective version of the “accumulated energy” and between 13 and 30% for the “effective correlation dimension”. These results still are not sufficient for clinical applications.

  16. Review of methods for determination of total protein and peptide concentration in biological samples.

    PubMed

    Sapan, Christine V; Lundblad, Roger L

    2015-04-01

    Clinical proteomics can be defined as the use of proteomic technologies to identify and measure biomarkers in fluids and tissues. The current work is intended to review various methods used for the determination of the total concentration of protein or peptide in fluids and tissues and the application of such methods to clinical proteomics. Specifically, this article considers the approaches to the measurement of total protein concentration, not the measurement of the concentration of a specific protein or group of proteins in a larger mixture of proteins. The necessity of understanding various concepts such as fit-for-use, quality-by-design, and other regulatory elements is discussed, as is the significance of using suitable standards for the protein quality of various samples.

  17. Experimental validation of boundary element methods for noise prediction

    NASA Technical Reports Server (NTRS)

    Seybert, A. F.; Oswald, Fred B.

    1992-01-01

    Experimental validation of methods to predict radiated noise is presented. A combined finite element and boundary element model was used to predict the vibration and noise of a rectangular box excited by a mechanical shaker. The predicted noise was compared to sound power measured by the acoustic intensity method. Inaccuracies in the finite element model shifted the resonance frequencies by about 5 percent. The predicted and measured sound power levels agree within about 2.5 dB. In a second experiment, measured vibration data was used with a boundary element model to predict noise radiation from the top of an operating gearbox. The predicted and measured sound power for the gearbox agree within about 3 dB.

  18. Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins.

    PubMed

    Li, Chen; Ching Han Chang, Catherine; Nagel, Jeremy; Porebski, Benjamin T; Hayashida, Morihiro; Akutsu, Tatsuya; Song, Jiangning; Buckle, Ashley M

    2016-03-01

    Coiled-coils refer to a bundle of helices coiled together like strands of a rope. It has been estimated that nearly 3% of protein-encoding regions of genes harbour coiled-coil domains (CCDs). Experimental studies have confirmed that CCDs play a fundamental role in subcellular infrastructure and controlling trafficking of eukaryotic cells. Given the importance of coiled-coils, multiple bioinformatics tools have been developed to facilitate the systematic and high-throughput prediction of CCDs in proteins. In this article, we review and compare 12 sequence-based bioinformatics approaches and tools for coiled-coil prediction. These approaches can be categorized into two classes: coiled-coil detection and coiled-coil oligomeric state prediction. We evaluated and compared these methods in terms of their input/output, algorithm, prediction performance, validation methods and software utility. All the independent testing data sets are available at http://lightning.med.monash.edu/coiledcoil/. In addition, we conducted a case study of nine human polyglutamine (PolyQ) disease-related proteins and predicted CCDs and oligomeric states using various predictors. Prediction results for CCDs were highly variable among different predictors. Only two peptides from two proteins were confirmed to be CCDs by majority voting. Both domains were predicted to form dimeric coiled-coils using oligomeric state prediction. We anticipate that this comprehensive analysis will be an insightful resource for structural biologists with limited prior experience in bioinformatics tools, and for bioinformaticians who are interested in designing novel approaches for coiled-coil and its oligomeric state prediction.

  19. Magnetic Microsphere-Based Methods to Study the Interaction of Teicoplanin with Peptides and Bacteria

    PubMed Central

    Piyasena, Menake E.; Real, Lilian J.; Diamond, Rochelle A.; Xu, H. Howard; Gomez, Frank A.

    2009-01-01

    Teicoplanin (teic) from Actinoplanes teichomyceticus is a glycopeptide antibiotic used to treat many gram-positive bacterial infections. Glycopeptide antibiotics inhibit the bacteria growth by binding to carboxy-terminal D-Ala-D-Ala intermediates in the peptidoglycan of cell wall of gram positive bacteria. In this paper we report the derivatization of magnetic microspheres with teic (teic-microspheres). Fluorescence based techniques have been developed to analyze the binding properties of the microspheres to two D-Ala-D-Ala terminus peptides. The dissociation constants for the binding of carboxyfluorescein labeled D-Ala-D-Ala-D-Ala to teic on microspheres is established via fluorometry and flow cytometry and was determined to be 0.5 × 10−6 and 3.0 × 10−6 M, respectively. Feasibility of utilizing microparticles with fluorescence methods to detect low levels (limit of bacterial detection was determined to be 200 colony forming units [cfu]) of gram-positive bacteria has been demonstrated. A simple microfluidic experiment is reported to demonstrate the possibility of developing microsphere based affinity assays to study peptide-antibiotic interaction. PMID:18712518

  20. A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

    PubMed

    Singh, Shailendra; Kaur, Sukhbir; Goel, Neelam

    2015-01-01

    In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.

  1. Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications.

    PubMed

    Brusic, Vladimir; Bajic, Vladimir B; Petrovsky, Nikolai

    2004-12-01

    Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

  2. A method to predict electromigration failure of metal lines

    NASA Astrophysics Data System (ADS)

    Sasagawa, Kazuhiko; Naito, Kazushi; Saka, Masumi; Abé, Hiroyuki

    1999-12-01

    A new calculation method of atomic flux divergence (AFDgen) due to electromigration has recently been proposed by considering all the factors on void formation, and AFDgen has been identified as a parameter governing void formation by observing agreement of the numerical prediction of the void with experiment. In this article, a method to predict the electromigration failure of metal lines was proposed by using AFDgen. Lifetime and failure site in a polycrystalline line were predicted by numerical simulation of the processes of void initiation, its growth to line failure, where the change in distributions of current density and temperature with void growth was taken into account. The usefulness of this prediction method was verified by the experiment where the angled aluminum line was treated. The failure location was determined by the line shape and the operating condition. The present simulation accurately predicted the lifetime as well as the failure location of the metal line.

  3. The POLARIS Gene of Arabidopsis Encodes a Predicted Peptide Required for Correct Root Growth and Leaf Vascular Patterning

    PubMed Central

    Casson, Stuart A.; Chilley, Paul M.; Topping, Jennifer F.; Evans, I. Marta; Souter, Martin A.; Lindsey, Keith

    2002-01-01

    The POLARIS (PLS) gene of Arabidopsis was identified as a promoter trap transgenic line, showing β-glucuronidase fusion gene expression predominantly in the embryonic and seedling root, with low expression in aerial parts. Cloning of the PLS locus revealed that the promoter trap T-DNA had inserted into a short open reading frame (ORF). Rapid amplification of cDNA ends PCR, RNA gel blot analysis, and RNase protection assays showed that the PLS ORF is located within a short (∼500 nucleotides) auxin-inducible transcript and encodes a predicted polypeptide of 36 amino acid residues. pls mutants exhibit a short-root phenotype and reduced vascularization of leaves. pls roots are hyperresponsive to exogenous cytokinins and show increased expression of the cytokinin-inducible gene ARR5/IBC6 compared with the wild type. pls seedlings also are less responsive to the growth-inhibitory effects of exogenous auxin and show reduced expression of the auxin-inducible gene IAA1 compared with the wild type. The PLS peptide-encoding region of the cDNA partially complements the pls mutation and requires the PLS ORF ATG for activity, demonstrating the functionality of the peptide-encoding ORF. Ectopic expression of the PLS ORF reduces root growth inhibition by exogenous cytokinins and increases leaf vascularization. We propose that PLS is required for correct auxin-cytokinin homeostasis to modulate root growth and leaf vascular patterning. PMID:12172017

  4. How accurately do current force fields predict experimental peptide conformations? An adiabatic free energy dynamics study.

    PubMed

    Tzanov, Alexandar T; Cuendet, Michel A; Tuckerman, Mark E

    2014-06-19

    The quality of classical biomolecular simulations is inevitably limited by two problems: the accuracy of the force field used and the comprehensiveness of configuration space sampling. In this work we tackle the sampling problem by carrying out driven adiabatic free energy dynamics to obtain converged free energy surfaces of dipeptides in the gas phase and in solution using selected dihedral angles as collective variables. To calculate populations of conformational macrostates observed in experiment, we introduce a fuzzy clustering algorithm in collective-variable space, which delineates macrostates without prior definition of arbitrary boundaries. With this approach, we calculate the conformational preferences of small peptides with six biomolecular force fields chosen from among the most recent and widely used. We assess the accuracy of each force field against recently published Raman or IR-UV spectroscopy measurements of conformer populations for the dipeptides in solution or in the gas phase.

  5. Predicting peptide vaccine candidates against H1N1 influenza virus through theoretical approaches.

    PubMed

    Bello, Martiniano; Campos-Rodriguez, Rafael; Rojas-Hernandez, Saul; Contis-Montes de Oca, Arturo; Correa-Basurto, José

    2015-05-01

    Identification of potential epitopes that might activate the immune system has been facilitated by the employment of algorithms that use experimental data as templates. However, in order to prove the affinity and the map of interactions between the receptor (major histocompatibility complex, MHC, or T-cell receptor) and the potential epitope, further computational studies are required. Docking and molecular dynamics (MDs) simulations have been an effective source of generating structural information at molecular level in immunology. Herein, in order to provide a detailed understanding of the origins of epitope recognition and to select the best peptide candidate to develop an epitope-based vaccine, docking and MDs simulations in combination with MMGBSA free energy calculations and per-residue free energy decomposition were performed, taking as starting complexes those formed between four designed epitopes (P1-P4) from hemagglutinin (HA) of the H1N1 influenza virus and MHC-II anchored in POPC membrane. Our results revealed that the energetic contributions of individual amino acids within the pMHC-II complexes are mainly dictated by van der Waals interactions and the nonpolar part of solvation energy, whereas the electrostatic interactions corresponding to hydrogen bonds and salt bridges determine the binding specificity, being the most favorable interactions formed between p4 and MHC-II. Then, P1-P4 epitopes were synthesized and tested experimentally to compare theoretical and experimental results. Experimental results show that P4 elicited the highest strong humoral immune response to HA of the H1N1 and may induce antibodies that are cross-reactive to other influenza subtypes, suggesting that it could be a good candidate for the development of a peptide-based vaccine.

  6. A K-nearest neighbors survival probability prediction method.

    PubMed

    Lowsky, D J; Ding, Y; Lee, D K K; McCulloch, C E; Ross, L F; Thistlethwaite, J R; Zenios, S A

    2013-05-30

    We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan-Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method.

  7. A graph kernel method for DNA-binding site prediction.

    PubMed

    Yan, Changhui; Wang, Yingfeng

    2014-01-01

    Protein-DNA interactions play important roles in many biological processes. Computational methods that can accurately predict DNA-binding sites on proteins will greatly expedite research on problems involving protein-DNA interactions. This paper presents a method for predicting DNA-binding sites on protein structures. The method represents protein surface patches using labeled graphs and uses a graph kernel method to calculate the similarities between graphs. A new surface patch is predicted to be interface or non-interface patch based on its similarities to known DNA-binding patches and non-DNA-binding patches. The proposed method achieved high accuracy when tested on a representative set of 146 protein-DNA complexes using leave-one-out cross-validation. Then, the method was applied to identify DNA-binding sites on 13 unbound structures of DNA-binding proteins. In each of the unbound structure, the top 1 patch predicted by the proposed method precisely indicated the location of the DNA-binding site. Comparisons with other methods showed that the proposed method was competitive in predicting DNA-binding sites on unbound proteins. The proposed method uses graphs to encode the feature's distribution in the 3-dimensional (3D) space. Thus, compared with other vector-based methods, it has the advantage of taking into account the spatial distribution of features on the proteins. Using an efficient kernel method to compare graphs the proposed method also avoids the demanding computations required for 3D objects comparison. It provides a competitive method for predicting DNA-binding sites without requiring structure alignment.

  8. SPA: Short peptide analyzer of intrinsic disorder status of short peptides

    PubMed Central

    Xue, Bin; Hsu, Wei-Lun; Lee, Jun-Ho; Lu, Hua; Dunker, A. Keith; Uversky, Vladimir N.

    2010-01-01

    Disorder prediction for short peptides is important and difficult. All modern predictors have to be optimized on a preselected dataset prior to prediction. In the succeeding prediction process, the predictor works on a query sequence or its short segment. For implementing the prediction smoothly and obtaining sound prediction results, a specific length of the sequence or segment is usually required. The need of the preselected dataset in the optimization process and the length limitation in the prediction process restrict predictors’ performance. To minimize the influence of these limitations, we developed a method for the prediction of intrinsic disorder in short peptides based on large dataset sampling and statistics. As evident from the data analysis, this method provides more reliable prediction of the intrinsic disorder status of short peptides. PMID:20497238

  9. Tetrahedron-tiling method for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Hong, Qi-Jun; Yasi, Joseph; van de Walle, Axel

    2017-07-01

    Reliable and robust methods of predicting the crystal structure of a compound, based only on its chemical composition, is crucial to the study of materials and their applications. Despite considerable ongoing research efforts, crystal structure prediction remains a challenging problem that demands large computational resources. Here we propose an efficient approach for first-principles crystal structure prediction. The new method explores and finds crystal structures by tiling together elementary tetrahedra that are energetically favorable and geometrically matching each other. This approach has three distinguishing features: a favorable building unit, an efficient calculation of local energy, and a stochastic Monte Carlo simulation of crystal growth. By applying the method to the crystal structure prediction of various materials, we demonstrate its validity and potential as a promising alternative to current methods.

  10. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    ERIC Educational Resources Information Center

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  11. Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method.

    PubMed

    Bagos, Pantelis G; Liakopoulos, Theodore D; Hamodrakas, Stavros J

    2005-01-12

    Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at http://bioinformatics.biol.uoa.gr/ConBBPRED.

  12. Novel Antimicrobial Peptides Derived from Flatfish Genes†

    PubMed Central

    Patrzykat, Aleksander; Gallant, Jeffrey W.; Seo, Jung-Kil; Pytyck, Jennifer; Douglas, Susan E.

    2003-01-01

    We report on the identification of active novel antimicrobials determined by screening both the genomic information and the mRNA transcripts from a number of different flatfish for sequences encoding antimicrobial peptides, predicting the sequences of active peptides from the genetic information, producing the predicted peptides chemically, and testing them for their activities. We amplified 35 sequences from various species of flatfish using primers whose sequences are based on conserved flanking regions of a known antimicrobial peptide from winter flounder, pleurocidin. We analyzed the sequences of the amplified products and predicted which sequences were likely to encode functional antimicrobial peptides on the basis of charge, hydrophobicity, relation to flanking sequences, and similarity to known active peptides. Twenty peptides were then produced synthetically and tested for their activities against gram-positive and gram-negative bacteria and the yeast Candida albicans. The most active peptide (with the carboxy-terminus amidated sequence GWRTLLKKAEVKTVGKLALKHYL, derived from American plaice) showed inhibitory activity over a concentration range of 1 to 8 μg/ml against a test panel of pathogens, including the intrinsically antibiotic-resistant organism Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and C. albicans. The methods described here will be useful for the identification of novel peptides with good antimicrobial activities. PMID:12878506

  13. Bioactive Peptides

    PubMed Central

    Daliri, Eric Banan-Mwine; Oh, Deog H.; Lee, Byong H.

    2017-01-01

    The increased consumer awareness of the health promoting effects of functional foods and nutraceuticals is the driving force of the functional food and nutraceutical market. Bioactive peptides are known for their high tissue affinity, specificity and efficiency in promoting health. For this reason, the search for food-derived bioactive peptides has increased exponentially. Over the years, many potential bioactive peptides from food have been documented; yet, obstacles such as the need to establish optimal conditions for industrial scale production and the absence of well-designed clinical trials to provide robust evidence for proving health claims continue to exist. Other important factors such as the possibility of allergenicity, cytotoxicity and the stability of the peptides during gastrointestinal digestion would need to be addressed. This review discusses our current knowledge on the health effects of food-derived bioactive peptides, their processing methods and challenges in their development. PMID:28445415

  14. Bioactive Peptides.

    PubMed

    Daliri, Eric Banan-Mwine; Oh, Deog H; Lee, Byong H

    2017-04-26

    The increased consumer awareness of the health promoting effects of functional foods and nutraceuticals is the driving force of the functional food and nutraceutical market. Bioactive peptides are known for their high tissue affinity, specificity and efficiency in promoting health. For this reason, the search for food-derived bioactive peptides has increased exponentially. Over the years, many potential bioactive peptides from food have been documented; yet, obstacles such as the need to establish optimal conditions for industrial scale production and the absence of well-designed clinical trials to provide robust evidence for proving health claims continue to exist. Other important factors such as the possibility of allergenicity, cytotoxicity and the stability of the peptides during gastrointestinal digestion would need to be addressed. This review discusses our current knowledge on the health effects of food-derived bioactive peptides, their processing methods and challenges in their development.

  15. Interim prediction method for low frequency core engine noise

    NASA Technical Reports Server (NTRS)

    Huff, R. G.; Clark, B. J.; Dorsch, R. G.

    1974-01-01

    A literature survey on low-frequency core engine noise is presented. Possible sources of low frequency internally generated noise in core engines are discussed with emphasis on combustion and component scrubbing noise. An interim method is recommended for predicting low frequency core engine noise that is dominant when jet velocities are low. Suggestions are made for future research on low frequency core engine noise that will aid in improving the prediction method and help define possible additional internal noise sources.

  16. Predictive value of B-type natriuretic peptide level on the postoperative course of infants with congenital heart disease.

    PubMed

    Nahum, Elhanan; Pollak, Uri; Dagan, Ovdi; Amir, Gabriel; Frenkel, George; Birk, Einat

    2013-05-01

    B-type natriuretic peptide (BNP) has been shown to have prognostic value for morbidity and mortality after cardiac surgery. Less is known about its prognostic value in infants. To investigate the predictive value of BNP levels regarding the severity of the postoperative course in infants undergoing surgical repair of congenital heart disease. We conducted a prospective comparative study. Plasma BNP levels in infants aged 1-12 months with congenital heart disease undergoing complete repair were measured preoperatively and 8, 24 and 48 hours postoperatively. Demographic and clinical data included postoperative inotropic support and lactate level, duration of mechanical ventilation, intensive care unit (ICU) and hospitalization stay. Cardiac surgery was performed in 19 infants aged 1-12 months. Preoperative BNP level above 170 pg/ml had a positive predictive value of 100% for inotropic score > or = 7.5 at 24 hours (specificity 100%, sensitivity 57%) and 48 hours (specificity 100%, sensitivity 100%), and was associated with longer ICU stay (P = 0.05) and a trend for longer mechanical ventilation (P = 0.12). Similar findings were found for 8 hours postoperative BNP above 1720 pg/ml. BNP level did not correlate with measured fractional shortening. In infants undergoing heart surgery, preoperative and 8 hour BNP levels were predictive of inotropic support and longer ICU stay. These findings may have implications for preplanning ICU loads in clinical practice. Further studies with larger samples are needed.

  17. Optimization of Preparation of Antioxidative Peptides from Pumpkin Seeds Using Response Surface Method

    PubMed Central

    Fan, Sanhong; Hu, Yanan; Li, Chen; Liu, Yanrong

    2014-01-01

    Protein isolates of pumpkin (Cucurbita pepo L) seeds were hydrolyzed by acid protease to prepare antioxidative peptides. The hydrolysis conditions were optimized through Box-Behnken experimental design combined with response surface method (RSM). The second-order model, developed for the DPPH radical scavenging activity of pumpkin seed hydrolysates, showed good fit with the experiment data with a high value of coefficient of determination (0.9918). The optimal hydrolysis conditions were determined as follows: hydrolyzing temperature 50°C, pH 2.5, enzyme amount 6000 U/g, substrate concentration 0.05 g/ml and hydrolyzing time 5 h. Under the above conditions, the scavenging activity of DPPH radical was as high as 92.82%. PMID:24637721

  18. Optimization of preparation of antioxidative peptides from pumpkin seeds using response surface method.

    PubMed

    Fan, Sanhong; Hu, Yanan; Li, Chen; Liu, Yanrong

    2014-01-01

    Protein isolates of pumpkin (Cucurbita pepo L) seeds were hydrolyzed by acid protease to prepare antioxidative peptides. The hydrolysis conditions were optimized through Box-Behnken experimental design combined with response surface method (RSM). The second-order model, developed for the DPPH radical scavenging activity of pumpkin seed hydrolysates, showed good fit with the experiment data with a high value of coefficient of determination (0.9918). The optimal hydrolysis conditions were determined as follows: hydrolyzing temperature 50°C, pH 2.5, enzyme amount 6000 U/g, substrate concentration 0.05 g/ml and hydrolyzing time 5 h. Under the above conditions, the scavenging activity of DPPH radical was as high as 92.82%.

  19. Assessment of a method for the prediction of mandibular rotation.

    PubMed

    Lee, R S; Daniel, F J; Swartz, M; Baumrind, S; Korn, E L

    1987-05-01

    A new method to predict mandibular rotation developed by Skieller and co-workers on a sample of 21 implant subjects with extreme growth patterns has been tested against an alternative sample of 25 implant patients with generally similar mean values, but with less extreme facial patterns. The method, which had been highly successful in retrospectively predicting changes in the sample of extreme subjects, was much less successful in predicting individual patterns of mandibular rotation in the new, less extreme sample. The observation of a large difference in the strength of the predictions for these two samples, even though their mean values were quite similar, should serve to increase our awareness of the complexity of the problem of predicting growth patterns in individual cases.

  20. Prediction methods for jet V/STOL propulsion aerodynamics

    NASA Technical Reports Server (NTRS)

    Platzer, M. F.; Margason, R. J.

    1976-01-01

    The current status of prediction methods for propulsive flows and propulsion-induced effects which occur on jet V/STOL aircraft is reviewed. Among the major topics studied are flows in propulsive ducts, propulsion-induced ground and thermal effects, aerodynamic loads induced during V/STOL and transition flight, flow vectoring devices, and thrust augmented ejector and lift-fan studies. The current predictive capability in jet V/STOL propulsion aerodynamics is assessed. Future research needs are identified, with particular reference to activities that can improve the usefulness of prediction methods for jet V/STOL aircraft.

  1. Comparison of prediction performance using statistical postprocessing methods

    NASA Astrophysics Data System (ADS)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2016-11-01

    As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.

  2. A method to objectively optimize coral bleaching prediction techniques

    NASA Astrophysics Data System (ADS)

    van Hooidonk, R. J.; Huber, M.

    2007-12-01

    Thermally induced coral bleaching is a global threat to coral reef health. Methodologies, e.g. the Degree Heating Week technique, have been developed to predict bleaching induced by thermal stress by utilizing remotely sensed sea surface temperature (SST) observations. These techniques can be used as a management tool for Marine Protected Areas (MPA). Predictions are valuable to decision makers and stakeholders on weekly to monthly time scales and can be employed to build public awareness and support for mitigation. The bleaching problem is only expected to worsen because global warming poses a major threat to coral reef health. Indeed, predictive bleaching methods combined with climate model output have been used to forecast the global demise of coral reef ecosystems within coming decades due to climate change. Accuracy of these predictive techniques has not been quantitatively characterized despite the critical role they play. Assessments have typically been limited, qualitative or anecdotal, or more frequently they are simply unpublished. Quantitative accuracy assessment, using well established methods and skill scores often used in meteorology and medical sciences, will enable objective optimization of existing predictive techniques. To accomplish this, we will use existing remotely sensed data sets of sea surface temperature (AVHRR and TMI), and predictive values from techniques such as the Degree Heating Week method. We will compare these predictive values with observations of coral reef health and calculate applicable skill scores (Peirce Skill Score, Hit Rate and False Alarm Rate). We will (a) quantitatively evaluate the accuracy of existing coral reef bleaching predictive methods against state-of- the-art reef health databases, and (b) present a technique that will objectively optimize the predictive method for any given location. We will illustrate this optimization technique for reefs located in Puerto Rico and the US Virgin Islands.

  3. Mimicking Tumors: Toward More Predictive In Vitro Models for Peptide- and Protein-Conjugated Drugs.

    PubMed

    van den Brand, Dirk; Massuger, Leon F; Brock, Roland; Verdurmen, Wouter P R

    2017-03-15

    Macromolecular drug candidates and nanoparticles are typically tested in 2D cancer cell culture models, which are often directly followed by in vivo animal studies. The majority of these drug candidates, however, fail in vivo. In contrast to classical small-molecule drugs, multiple barriers exist for these larger molecules that two-dimensional approaches do not recapitulate. In order to provide better mechanistic insights into the parameters controlling success and failure and due to changing ethical perspectives on animal studies, there is a growing need for in vitro models with higher physiological relevance. This need is reflected by an increased interest in 3D tumor models, which during the past decade have evolved from relatively simple tumor cell aggregates to more complex models that incorporate additional tumor characteristics as well as patient-derived material. This review will address tissue culture models that implement critical features of the physiological tumor context such as 3D structure, extracellular matrix, interstitial flow, vascular extravasation, and the use of patient material. We will focus on specific examples, relating to peptide-and protein-conjugated drugs and other nanoparticles, and discuss the added value and limitations of the respective approaches.

  4. Mimicking Tumors: Toward More Predictive In Vitro Models for Peptide- and Protein-Conjugated Drugs

    PubMed Central

    2017-01-01

    Macromolecular drug candidates and nanoparticles are typically tested in 2D cancer cell culture models, which are often directly followed by in vivo animal studies. The majority of these drug candidates, however, fail in vivo. In contrast to classical small-molecule drugs, multiple barriers exist for these larger molecules that two-dimensional approaches do not recapitulate. In order to provide better mechanistic insights into the parameters controlling success and failure and due to changing ethical perspectives on animal studies, there is a growing need for in vitro models with higher physiological relevance. This need is reflected by an increased interest in 3D tumor models, which during the past decade have evolved from relatively simple tumor cell aggregates to more complex models that incorporate additional tumor characteristics as well as patient-derived material. This review will address tissue culture models that implement critical features of the physiological tumor context such as 3D structure, extracellular matrix, interstitial flow, vascular extravasation, and the use of patient material. We will focus on specific examples, relating to peptide-and protein-conjugated drugs and other nanoparticles, and discuss the added value and limitations of the respective approaches. PMID:28122451

  5. Diagnostic performance and predictive value of rheumatoid factor, anti-cyclic-citrullinated peptide antibodies and HLA-DRB1 locus genes in rheumatoid arthritis

    PubMed Central

    Fathi, Nihal A; Ezz-Eldin, Azza M; Mosad, Eman; Bakry, Rania M; Hamed, Hosny B; Ahmed, Sahar; Mahmoud, Marwa; Rashed, Hebat-Allah G; Abdullah, Fatma

    2008-01-01

    Background We evaluated the significance of the genes, defined as DRB1*04 or DRB1*01, in rheumatoid arthritis (RA) patients. We focused on the role of genetic and serologic markers to predict disease activity and destructive process of joints. Methods Sixty patients with RA were examined. Radiographic changes were evaluated by (Larsen score) and disease activity was measured by disease activity score 28 (DAS28). The markers analyzed were: erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), rheumatoid factor (RF), anti-cyclic citrullinated peptides (anti-CCP2) and HLA-DRB1 alleles typed by PCR. Results In this study, anti-CCP antibodies, CRP, RF and AKA were detected in 83.3%, 56.7%, 71.7% and 52% of patients respectively. HLA-DRB1*01 was found in 45% of patients and 35% of them had one or two HLA-DRB1*04 alleles. According to DRB1*04 subtypes, (DRB1* 0405) was present in of 80% them. For prediction of grade of activity, the independent predictors were anti-CCP (OR 19.6), and DRB1*04 positive allele (OR 5.1). The combination of DRB1*04 + anti-CCP antibodies gave increase in the specificity and positive predictive value to 92% and 90 respectively. As regards to the prediction of radiological joint damage, the independent predictors were HLA-DRB1*04, HLA-DRB1*01, RF, and CRP > 18 (OR 5.5, 4.5, 2.5, 2.0 respectively). Conclusion Our findings suggest that anti-CCP2 is superior to RF for the detection of RA and provided predictive information on joint destruction and disease activity. The presence of RA associated antibodies (ACCP or RF) and/or the SE genes are indicative for a poorer radiological outcome and higher grade of activity. PMID:18945361

  6. Modified-Fibonacci-Dual-Lucas method for earthquake prediction

    NASA Astrophysics Data System (ADS)

    Boucouvalas, A. C.; Gkasios, M.; Tselikas, N. T.; Drakatos, G.

    2015-06-01

    The FDL method makes use of Fibonacci, Dual and Lucas numbers and has shown considerable success in predicting earthquake events locally as well as globally. Predicting the location of the epicenter of an earthquake is one difficult challenge the other being the timing and magnitude. One technique for predicting the onset of earthquakes is the use of cycles, and the discovery of periodicity. Part of this category is the reported FDL method. The basis of the reported FDL method is the creation of FDL future dates based on the onset date of significant earthquakes. The assumption being that each occurred earthquake discontinuity can be thought of as a generating source of FDL time series The connection between past earthquakes and future earthquakes based on FDL numbers has also been reported with sample earthquakes since 1900. Using clustering methods it has been shown that significant earthquakes (<6.5R) can be predicted with very good accuracy window (+-1 day). In this contribution we present an improvement modification to the FDL method, the MFDL method, which performs better than the FDL. We use the FDL numbers to develop possible earthquakes dates but with the important difference that the starting seed date is a trigger planetary aspect prior to the earthquake. Typical planetary aspects are Moon conjunct Sun, Moon opposite Sun, Moon conjunct or opposite North or South Modes. In order to test improvement of the method we used all +8R earthquakes recorded since 1900, (86 earthquakes from USGS data). We have developed the FDL numbers for each of those seeds, and examined the earthquake hit rates (for a window of 3, i.e. +-1 day of target date) and for <6.5R. The successes are counted for each one of the 86 earthquake seeds and we compare the MFDL method with the FDL method. In every case we find improvement when the starting seed date is on the planetary trigger date prior to the earthquake. We observe no improvement only when a planetary trigger coincided with

  7. Diagnosis of bacterial vaginosis by a new multiplex peptide nucleic acid fluorescence in situ hybridization method

    PubMed Central

    Machado, António; Castro, Joana; Cereija, Tatiana; Almeida, Carina

    2015-01-01

    Bacterial vaginosis (BV) is one of most common vaginal infections. However, its diagnosis by classical methods reveals low specificity. Our goal was to evaluate the accuracy diagnosis of 150 vaginal samples with research gold standard methods and our Peptide Nucleic Acid (PNA) probes by Fluorescence in situ Hybridization (FISH) methodology. Also, we described the first PNA-FISH methodology for BV diagnosis, which provides results in approximately 3 h. The results showed a sensitivity of 84.6% (95% confidence interval (CI), from 64.3 to 95.0%) and a specificity of 97.6% (95% CI [92.6–99.4%]), demonstrating the higher specificity of the PNA-FISH method and showing false positive results in BV diagnosis commonly obtained by the classical methods. This methodology combines the specificity of PNA probes for Lactobacillus species and G. vaginalis visualization and the calculation of the microscopic field by Nugent score, allowing a trustful evaluation of the bacteria present in vaginal microflora and avoiding the occurrence of misleading diagnostics. Therefore, the PNA-FISH methodology represents a valuable alternative for BV diagnosis. PMID:25737820

  8. Multiple and sequential data acquisition method: an improved method for fragmentation and detection of cross-linked peptides on a hybrid linear trap quadrupole Orbitrap Velos mass spectrometer.

    PubMed

    Rudashevskaya, Elena L; Breitwieser, Florian P; Huber, Marie L; Colinge, Jacques; Müller, André C; Bennett, Keiryn L

    2013-02-05

    The identification and validation of cross-linked peptides by mass spectrometry remains a daunting challenge for protein-protein cross-linking approaches when investigating protein interactions. This includes the fragmentation of cross-linked peptides in the mass spectrometer per se and following database searching, the matching of the molecular masses of the fragment ions to the correct cross-linked peptides. The hybrid linear trap quadrupole (LTQ) Orbitrap Velos combines the speed of the tandem mass spectrometry (MS/MS) duty circle with high mass accuracy, and these features were utilized in the current study to substantially improve the confidence in the identification of cross-linked peptides. An MS/MS method termed multiple and sequential data acquisition method (MSDAM) was developed. Preliminary optimization of the MS/MS settings was performed with a synthetic peptide (TP1) cross-linked with bis[sulfosuccinimidyl] suberate (BS(3)). On the basis of these results, MSDAM was created and assessed on the BS(3)-cross-linked bovine serum albumin (BSA) homodimer. MSDAM applies a series of multiple sequential fragmentation events with a range of different normalized collision energies (NCE) to the same precursor ion. The combination of a series of NCE enabled a considerable improvement in the quality of the fragmentation spectra for cross-linked peptides, and ultimately aided in the identification of the sequences of the cross-linked peptides. Concurrently, MSDAM provides confirmatory evidence from the formation of reporter ions fragments, which reduces the false positive rate of incorrectly assigned cross-linked peptides.

  9. A review of statistical updating methods for clinical prediction models.

    PubMed

    Su, Ting-Li; Jaki, Thomas; Hickey, Graeme L; Buchan, Iain; Sperrin, Matthew

    2016-07-26

    A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods. © The Author(s) 2016.

  10. Connecting clinical and actuarial prediction with rule-based methods.

    PubMed

    Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

    2015-06-01

    Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

  11. Prediction methods of spudcan penetration for jack-up units

    NASA Astrophysics Data System (ADS)

    Zhang, Ai-xia; Duan, Meng-lan; Li, Hai-ming; Zhao, Jun; Wang, Jian-jun

    2012-12-01

    Jack-up units are extensively playing a successful role in drilling engineering around the world, and their safety and efficiency take more and more attraction in both research and engineering practice. An accurate prediction of the spudcan penetration depth is quite instrumental in deciding on whether a jack-up unit is feasible to operate at the site. The prediction of a too large penetration depth may lead to the hesitation or even rejection of a site due to potential difficulties in the subsequent extraction process; the same is true of a too small depth prediction due to the problem of possible instability during operation. However, a deviation between predictive results and final field data usually exists, especially when a strong-over-soft soil is included in the strata. The ultimate decision sometimes to a great extent depends on the practical experience, not the predictive results given by the guideline. It is somewhat risky, but no choice. Therefore, a feasible predictive method for the spudcan penetration depth, especially in strata with strong-over-soft soil profile, is urgently needed by the jack-up industry. In view of this, a comprehensive investigation on methods of predicting spudcan penetration is executed. For types of different soil profiles, predictive methods for spudcan penetration depth are proposed, and the corresponding experiment is also conducted to validate these methods. In addition, to further verify the feasibility of the proposed methods, a practical engineering case encountered in the South China Sea is also presented, and the corresponding numerical and experimental results are also presented and discussed.

  12. Ability of B-type natriuretic peptide in predicting postoperative atrial fibrillation in patients undergoing coronary artery bypass grafting.

    PubMed

    Ata, Yusuf; Turk, Tamer; Ay, Derih; Eris, Cuneyt; Demir, Mihriban; Ari, Hasan; Ata, Filiz; Yavuz, Senol; Ozyazicioglu, Ahmet

    2009-08-01

    Atrial fibrillation (AF) is still the most frequent rhythm disturbance after coronary artery surgery. Our aim was to evaluate the predictive value of preoperative brain natriuretic peptide (BNP) levels for determining postoperative new-onset AF in patients undergoing isolated first-time coronary artery bypass grafting (CABG) using cardiopulmonary bypass (CPB). We recruited 144 consecutive patients (51 women and 93 men) who underwent isolated CABG. Preoperative and postoperative data were collected. Preoperative BNP levels were measured the day before surgery. The median preoperative BNP level was 68 pg/mL. Postoperative AF occurred in 36 (25%) of the patients. Univariate analyses showed that both advanced age and median preoperative BNP levels were associated with postoperative AF (63.9 +/- 8 years versus 57.3 +/- 9.8 years, P < .001; 226 pg/mL versus 65.2 pg/mL, P <.001). Both variables remained independent predictors of postoperative AF after multivariate logistic regression analyses. For advanced age, the odds ratio was 1.074 (95% confidence interval [CI], 1.019-1.131; P = .008); for preoperative BNP level, the odds ratio was 1.004 (95% CI, 1.001-1.006; P = .002). A receiver operating characteristic (ROC) curve demonstrated that preoperative BNP level was a predictor of postoperative AF, with an area under the ROC curve of 0.750. A cutoff value of 135 pg/mL for AF demonstrated a 72.2% sensitivity, a 71.2% specificity, a 45.6% positive predictive value, a 88.5% negative predictive value, and a 71.5% accuracy for predicting postoperative AF. Elevated preoperative BNP levels and advanced age together are significant predictors for the development of postoperative AF in patients undergoing isolated CABG with CPB.

  13. The trajectory prediction of spacecraft by grey method

    NASA Astrophysics Data System (ADS)

    Wang, Qiyue; Zhang, Zili; Wang, Zhongyu; Wang, Yanqing; Zhou, Weihu

    2016-08-01

    The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively.

  14. Predicted peptides from non-structural proteins of porcine reproductive and respiratory syndrome virus are able to induce IFN-γ and IL-10.

    PubMed

    Burgara-Estrella, Alexel; Díaz, Ivan; Rodríguez-Gómez, Irene M; Essler, Sabine E; Hernández, Jesús; Mateu, Enric

    2013-02-11

    This work describes peptides from non-structural proteins (nsp) of porcine reproductive and respiratory syndrome virus (PRRSV) predicted as potential T cell epitopes by bioinfornatics and tested for their ability to induce IFN-γ and IL-10 responses. Pigs immunized with either genotype 1 or genotype 2 PRRSV attenuated vaccines (n=5/group) and unvaccinated pigs (n = 4) were used to test the peptides. Swine leukocyte antigen haplotype of each pig was also determined. Pigs were initially screened for IFN-γ responses (ELISPOT) and three peptides were identified; two of them in non-conserved segments of nsp2 and nsp5 and the other in a conserved region of nsp5 peptide. Then, peptides were screened for IL-10 inducing properties. Six peptides were found to induce IL-10 release in PBMC and some of them were also able to inhibit IFN-γ responses on PHA-stimulated cells. Interestingly, the IFN-γ low responder pigs against PRRSV were mostly homozygous for their SLA haplotypes. In conclusion, these results indicate that nsp of PRRSV contain T-cell epitopes inducing IFN-γ responses as well as IL-10 inducing segments with inhibitory capabilities.

  15. HOPE: a homotopy optimization method for protein structure prediction.

    PubMed

    Dunlavy, Daniel M; O'Leary, Dianne P; Klimov, Dmitri; Thirumalai, D

    2005-12-01

    We use a homotopy optimization method, HOPE, to minimize the potential energy associated with a protein model. The method uses the minimum energy conformation of one protein as a template to predict the lowest energy structure of a query sequence. This objective is achieved by following a path of conformations determined by a homotopy between the potential energy functions for the two proteins. Ensembles of solutions are produced by perturbing conformations along the path, increasing the likelihood of predicting correct structures. Successful results are presented for pairs of homologous proteins, where HOPE is compared to a variant of Newton's method and to simulated annealing.

  16. Interim prediction method for fan and compressor source noise

    NASA Technical Reports Server (NTRS)

    Heidmann, M. F.

    1975-01-01

    A method is presented for interim use in assessing the noise generated by fans and compressors in turbojet and turbofan engines. One-third octave band sound pressure levels consisting of broadband, discrete tone, and combination-tone noise components are predicted. Spectral distributions and directivity variations are specified. The method is based on that developed by other investigators with modifications derived from an analysis of full-scale, single-stage fan data. Comparisons of predicted and measured noise performance are presented, and requirements for improving the method are discussed.

  17. Predicting the past: a simple reverse stand table projection method

    Treesearch

    Quang V. Cao; Shanna M. McCarty

    2006-01-01

    A stand table gives number of trees in each diameter class. Future stand tables can be predicted from current stand tables using a stand table projection method. In the simplest form of this method, a future stand table can be expressed as the product of a matrix of transitional proportions (based on diameter growth rates) and a vector of the current stand table. There...

  18. A comparative study of extraction methods reveals preferred solvents for cystine knot peptide isolation from Momordica cochinchinensis seeds.

    PubMed

    Mahatmanto, Tunjung; Poth, Aaron G; Mylne, Joshua S; Craik, David J

    2014-06-01

    MCoTI-I and MCoTI-II (short for Momordica cochinchinensis Trypsin Inhibitor-I and -II, respectively) are attractive candidates for developing novel intracellular-targeting drugs because both are exceptionally stable and can internalize into cells. These seed-derived cystine knot peptides are examples of how natural product discovery efforts can lead to biomedical applications. However, discovery efforts are sometimes hampered by the limited availability of seed materials, highlighting the need for efficient extraction methods. In this study, we assessed five extraction methods using M. cochinchinensis seeds, a source of well-characterized cystine knot peptides. The most efficient extraction of nine known cystine knot peptides was achieved by a method based on acetonitrile/water/formic acid (25:24:1), followed by methods based on sodium acetate (20 mM, pH 5.0), ammonium bicarbonate (5 mM, pH 8.0), and boiling water. On average, the yields obtained by these four methods were more than 250-fold higher than that obtained using dichloromethane/methanol (1:1) extraction, a previously applied standard method. Extraction using acetonitrile/water/formic acid (25:24:1) yielded the highest number of reconstructed masses within the majority of plant-derived cystine knot peptide mass range but only accounted for around 50% of the total number of masses, indicating that any single method may result in under-sampling. Applying acetonitrile/water/formic acid (25:24:1), boiling water, and ammonium bicarbonate (5 mM, pH 8.0) extractions either successively or discretely significantly increased the sampling number. Overall, acetonitrile/water/formic acid (25:24:1) can facilitate efficient extraction of cystine-knot peptides from M. cochinchinensis seeds but for discovery purposes the use of a combination of extraction methods is recommended where practical. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. A CD44-specific peptide, RP-1, exhibits capacities of assisting diagnosis and predicting prognosis of gastric cancer.

    PubMed

    Li, Weiming; Jia, Huan; Wang, Jichang; Guan, Hao; Li, Yan; Zhang, Dan; Tang, Yanan; Wang, Thomas D; Lu, Shaoying

    2017-05-02

    Early diagnosis and evaluation of prognosis are both crucial for preventing poor prognosis of patients with gastric cancer (GC), a leading cause of cancer-related deaths worldwide. Cluster of differentiation 44 (CD44), an indicator of cancer stem cells, can be specifically targeted by molecular probes and detected in tissues of GC in a large quantity. In current study we found that RP-1, a specific peptide binding to CD44 protein, exhibited the potentials of specific binding to CD44 high-expressing cancer cells both in vitro and in vivo, and the capacity of predicting prognosis of human GC in a microarray assay. Results showed that RP-1 was characterized by high affinity, sensitivity and specificity, and low toxicity, suggesting RP-1 could be an ideal bio-probe for accessory diagnosis of GC. Further immunohistochemical studies and statistical analysis of tissue microarray of human GC demonstrated similar sensitivity and specificity of RP-1 with the monoclonal anti-CD44 antibody in the diagnosis of GC, and even proved that positive RP-1 could be an independent risk factor. Therefore, this study suggests RP-1 has the potentials of binding to CD44 protein expressed on the membrane of GC cells, and demonstrates the feasibility and reliability of its further application in molecular diagnosis and prognostic prediction of GC.

  20. A CD44-specific peptide, RP-1, exhibits capacities of assisting diagnosis and predicting prognosis of gastric cancer

    PubMed Central

    Li, Weiming; Jia, Huan; Wang, Jichang; Guan, Hao; Li, Yan; Zhang, Dan; Tang, Yanan; Wang, Thomas D.; Lu, Shaoying

    2017-01-01

    Early diagnosis and evaluation of prognosis are both crucial for preventing poor prognosis of patients with gastric cancer (GC), a leading cause of cancer-related deaths worldwide. Cluster of differentiation 44 (CD44), an indicator of cancer stem cells, can be specifically targeted by molecular probes and detected in tissues of GC in a large quantity. In current study we found that RP-1, a specific peptide binding to CD44 protein, exhibited the potentials of specific binding to CD44 high-expressing cancer cells both in vitro and in vivo, and the capacity of predicting prognosis of human GC in a microarray assay. Results showed that RP-1 was characterized by high affinity, sensitivity and specificity, and low toxicity, suggesting RP-1 could be an ideal bio-probe for accessory diagnosis of GC. Further immunohistochemical studies and statistical analysis of tissue microarray of human GC demonstrated similar sensitivity and specificity of RP-1 with the monoclonal anti-CD44 antibody in the diagnosis of GC, and even proved that positive RP-1 could be an independent risk factor. Therefore, this study suggests RP-1 has the potentials of binding to CD44 protein expressed on the membrane of GC cells, and demonstrates the feasibility and reliability of its further application in molecular diagnosis and prognostic prediction of GC. PMID:28415792

  1. Supramolecular structures of peptide assemblies in membranes by neutron off-plane scattering: method of analysis

    PubMed Central

    Yang, L; Weiss, TM; Harroun, TA; Heller, WT; Huang, HW

    1999-01-01

    In a previous paper (Yang et al., Biophys. J. 75:641-645, 1998), we showed a simple, efficient method of recording the diffraction patterns of supramolecular peptide assemblies in membranes where the samples were prepared in the form of oriented multilayers. Here we develop a method of analysis based on the diffraction theory of two-dimensional liquids. Gramicidin was used as a prototype model because its pore structure in membrane in known. At full hydration, the diffraction patterns of alamethicin and magainin are similar to gramicidin except in the scale of q (the momentum transfer of scattering), clearly indicating that both alamethicin and magainin form pores in membranes but of different sizes. When the hydration of the multilayer samples was decreased while the bilayers were still fluid, the in-plane positions of the membrane pores became correlated from one bilayer to the next. We believe that this is a new manifestation of the hydration force. The effect is most prominent in magainin patterns, which are used to demonstrate the method of analysis. When magainin samples were further dehydrated or cooled, the liquid-like diffraction turned into crystal-like patterns. This discovery points to the possibility of investigating the supramolecular structures with high-order diffraction. PMID:10545365

  2. An improved method for high-level soluble expression and purification of recombinant amyloid-beta peptide for in vitro studies.

    PubMed

    Chhetri, Gaurav; Pandey, Tripti; Chinta, Ramesh; Kumar, Awanish; Tripathi, Timir

    2015-10-01

    Amyloid-beta (Aβ) peptide mediates several neurodegenerative diseases. The 42 amino acid (Aβ1-42) is the predominant form of peptide found in the neuritic plaques and has been demonstrated to be neurotoxic in vivo and in vitro. The availability of large quantities of Aβ peptide will help in several biochemical and biophysical studies that may help in exploring the aggregation mechanism and toxicity of Aβ peptide. We report a convenient and economical method to obtain such a peptide biologically. Synthetic oligonucleotides encoding Aβ1-42 were constructed and amplified through the polymerase cycling assembly (also known as assembly PCR), followed by the amplification PCR. Aβ1-42 gene was cloned into pET41a(+) vector for expression. Interestingly, the addition of 3% (v/v) ethanol to the culture medium resulted in the production of large amounts of soluble Aβ fusion protein. The Aβ fusion protein was subjected to a Ni-NTA affinity chromatography followed by enterokinase digestion, and the Aβ peptide was purified using glutathione Sepharose affinity chromatography. The peptide yield was ∼15mg/L culture, indicating the utility of this method for high-yield production of soluble Aβ peptide. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis and immunoblotting with anti-His antibody confirmed the identity of purified Aβ fusion protein and Aβ peptide. In addition, this method provides an advantage over the chemical synthesis and other conventional methods used for large-scale production of recombinant Aβ peptide.

  3. 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.

  4. Rapid Evaluation of Prediction Methods with DIPPR's Automated Property Prediction Package

    NASA Astrophysics Data System (ADS)

    Rowley, J. R.; Wilding, W. V.; Oscarson, J. L.; Rowley, R. L.

    2007-06-01

    An automated property prediction package has been developed that permits rapid evaluation of group-contribution, corresponding states, empirical, and theoretical property estimation methods. The property prediction package, which is part of the DIPPR® Information And Data Evaluation Manager (DIADEM) software, is used in conjunction with the DIPPR® 801 database to develop and test new prediction methods. The software is freely available to all DIPPR sponsor companies, but is also commercially available. The estimation engine is based on an automated SMILES (Simplified Molecular Input Line Entry Specification) formula parser to provide required molecular structural information, retrieval of required secondary properties from the DIPPR® database, and defined rules for the method. Automatic comparisons of predicted values to experimental data in the DIPPR® database can be made for properties at specified accuracy levels, by chemical family or type, or over the entire database. This allows evaluation of the relative effectiveness of methods for specific chemical families and tailoring of the selected method to specific chemical classes. New methods can readily be added by input using a simple input form. Nearly 200 thermophysical property prediction methods are currently available in DIADEM.

  5. Computational Methods for Failure Analysis and Life Prediction

    NASA Technical Reports Server (NTRS)

    Noor, Ahmed K. (Compiler); Harris, Charles E. (Compiler); Housner, Jerrold M. (Compiler); Hopkins, Dale A. (Compiler)

    1993-01-01

    This conference publication contains the presentations and discussions from the joint UVA/NASA Workshop on Computational Methods for Failure Analysis and Life Prediction held at NASA Langley Research Center 14-15 Oct. 1992. The presentations focused on damage failure and life predictions of polymer-matrix composite structures. They covered some of the research activities at NASA Langley, NASA Lewis, Southwest Research Institute, industry, and universities. Both airframes and propulsion systems were considered.

  6. Methods, apparatus and system for notification of predictable memory failure

    DOEpatents

    Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-01-03

    A method for providing notification of a predictable memory failure includes the steps of: obtaining information regarding at least one condition associated with a memory; calculating a memory failure probability as a function of the obtained information; calculating a failure probability threshold; and generating a signal when the memory failure probability exceeds the failure probability threshold, the signal being indicative of a predicted future memory failure.

  7. 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.

  8. Three-dimensional protein structure prediction: Methods and computational strategies.

    PubMed

    Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

    2014-10-12

    A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.

  9. A simple method of predicting S-wave velocity

    USGS Publications Warehouse

    Lee, M.W.

    2006-01-01

    Prediction of shear-wave velocity plays an important role in seismic modeling, amplitude analysis with offset, and other exploration applications. This paper presents a method for predicting S-wave velocity from the P-wave velocity on the basis of the moduli of dry rock. Elastic velocities of water-saturated sediments at low frequencies can be predicted from the moduli of dry rock by using Gassmann's equation; hence, if the moduli of dry rock can be estimated from P-wave velocities, then S-wave velocities easily can be predicted from the moduli. Dry rock bulk modulus can be related to the shear modulus through a compaction constant. The numerical results indicate that the predicted S-wave velocities for consolidated and unconsolidated sediments agree well with measured velocities if differential pressure is greater than approximately 5 MPa. An advantage of this method is that there are no adjustable parameters to be chosen, such as the pore-aspect ratios required in some other methods. The predicted S-wave velocity depends only on the measured P-wave velocity and porosity. ?? 2006 Society of Exploration Geophysicists.

  10. Supervised learning method for predicting chromatin boundary associated insulator elements.

    PubMed

    Bednarz, Paweł; Wilczyński, Bartek

    2014-12-01

    In eukaryotic cells, the DNA material is densely packed inside the nucleus in the form of a DNA-protein complex structure called chromatin. Since the actual conformation of the chromatin fiber defines the possible regulatory interactions between genes and their regulatory elements, it is very important to understand the mechanisms governing folding of chromatin. In this paper, we show that supervised methods for predicting chromatin boundary elements are much more effective than the currently popular unsupervised methods. Using boundary locations from published Hi-C experiments and modEncode tracks as features, we can tell the insulator elements from randomly selected background sequences with great accuracy. In addition to accurate predictions of the training boundary elements, our classifiers make new predictions. Many of them correspond to the locations of known insulator elements. The key features used for predicting boundary elements do not depend on the prediction method. Because of its miniscule size, chromatin state cannot be measured directly, we need to rely on indirect measurements, such as ChIP-Seq and fill in the gaps with computational models. Our results show that currently, at least in the model organisms, where we have many measurements including ChIP-Seq and Hi-C, we can make accurate predictions of insulator positions.

  11. Optimization of a Method to Prepare Liposomes Containing HER2/Neu- Derived Peptide as a Vaccine Delivery System for Breast Cancer.

    PubMed

    Shariat, Sheyda; Badiee, Ali; Jaafari, Mahmoud Reza; Mortazavi, Seyed Alireza

    2014-01-01

    The purpose of this study was to optimize a method for the encapsulation of P5 peptide, a new designed peptide containing MHC class I epitopes from rat HER2/neu protein, into liposomes as an approach for breast cancer vaccine formulation. The efficiency of liposomal encapsulation of peptides is generally low and development of an optimized method to increase encapsulation efficiency is a big challenge. In this study, P5 peptide was encapsulated into liposomes using the following three different methods based on film-hydration procedure. In method A, the lipid film containing P5 was hydrated using buffer and then extruded to 100 nm using polycarbonate filter. In method B all the steps were the same as method A, except that the lipid film was hydrated in buffer containing 10% (v/v) of DMSO and P5 peptide. In method C, P5 peptide was added to preformed liposomes (40 mM) in the presence of ethanol (30% v/v) and incubated at 25 ºC for 1h. The highest peptide encapsulation efficiency was achieved using method C (44%). The presence of P5 peptide in purified liposomes was also confirmed using SDS- PAGE analysis. Investigation on the effects of procedure parameters of method C on encapsulation efficiency demonstrated that method is an optimized procedure for encapsulating P5 peptide. Maximal recovery from liposomes for the accurate quantification of peptide was discovered using acidified isopropanol at 1:2 of sample to solvent ratio (v/v). In conclusion, the optimal methods of encapsulation and peptide content determination in liposomes can accelerate the development of liposomal vaccine formulations.

  12. Optimization of a Method to Prepare Liposomes Containing HER2/Neu- Derived Peptide as a Vaccine Delivery System for Breast Cancer

    PubMed Central

    Shariat, Sheyda; Badiee, Ali; Jaafari, Mahmoud Reza; Mortazavi, Seyed Alireza

    2014-01-01

    The purpose of this study was to optimize a method for the encapsulation of P5 peptide, a new designed peptide containing MHC class I epitopes from rat HER2/neu protein, into liposomes as an approach for breast cancer vaccine formulation. The efficiency of liposomal encapsulation of peptides is generally low and development of an optimized method to increase encapsulation efficiency is a big challenge. In this study, P5 peptide was encapsulated into liposomes using the following three different methods based on film-hydration procedure. In method A, the lipid film containing P5 was hydrated using buffer and then extruded to 100 nm using polycarbonate filter. In method B all the steps were the same as method A, except that the lipid film was hydrated in buffer containing 10% (v/v) of DMSO and P5 peptide. In method C, P5 peptide was added to preformed liposomes (40 mM) in the presence of ethanol (30% v/v) and incubated at 25 ºC for 1h. The highest peptide encapsulation efficiency was achieved using method C (44%). The presence of P5 peptide in purified liposomes was also confirmed using SDS- PAGE analysis. Investigation on the effects of procedure parameters of method C on encapsulation efficiency demonstrated that method is an optimized procedure for encapsulating P5 peptide. Maximal recovery from liposomes for the accurate quantification of peptide was discovered using acidified isopropanol at 1:2 of sample to solvent ratio (v/v). In conclusion, the optimal methods of encapsulation and peptide content determination in liposomes can accelerate the development of liposomal vaccine formulations. PMID:24711825

  13. Anti-cyclic citrullinated peptide antibody titer predicts time to rheumatoid arthritis onset in patients with undifferentiated arthritis: results from a 2-year prospective study

    PubMed Central

    2013-01-01

    Introduction The diagnostic, predictive and prognostic role of anti-cyclic citrullinated peptide (CCP) antibodies in rheumatoid arthritis (RA) patients is widely accepted. Moreover, detection of these antibodies in subjects presenting with undifferentiated arthritis (UA) is associated with a significant risk to develop the disease. On the other hand, clinical and prognostic significance of evaluating anti-CCP levels in subjects with inflammatory arthritis at disease onset has not been fully clarified. The goal of this prospective study is to analyze the value and prognostic significance of anti-CCP titer quantification in UA subjects. Methods Serial anti-CCP assays were measured in 192 consecutive patients presenting with UA lasting less than 12 weeks. Clinical and serological data and arthritis outcome were evaluated every 6 months until two years of follow-up. Results Anti-CCP positivity, at both low and high titer, and arthritis of hand joints significantly predicted RA at two years, risk increasing in subjects with high anti-CCP titers at baseline. Moreover, time to RA diagnosis was shorter in patients with high anti-CCP2 titers at enrollment with respect to those with low antibody concentration. Conclusions Presence of anti-CCP antibodies, at both low and high concentration, is significantly associated with RA development in subjects with recent onset UA. However, time interval from the onset of the first symptoms to the fulfilment of the classification criteria appears to be directly related to the initial anti-CCP level. PMID:23339296

  14. 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

  15. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    PubMed Central

    Chang, Ji-Wei; Zhou, Yan-Qing; Ul Qamar, Muhammad Tahir; Chen, Ling-Ling; Ding, Yu-Duan

    2016-01-01

    Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. PMID:27879651

  16. Recent advances in B-cell epitope prediction methods

    PubMed Central

    2010-01-01

    Identification of epitopes that invoke strong responses from B-cells is one of the key steps in designing effective vaccines against pathogens. Because experimental determination of epitopes is expensive in terms of cost, time, and effort involved, there is an urgent need for computational methods for reliable identification of B-cell epitopes. Although several computational tools for predicting B-cell epitopes have become available in recent years, the predictive performance of existing tools remains far from ideal. We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods. PMID:21067544

  17. A Micromechanics-Based Method for Multiscale Fatigue Prediction

    NASA Astrophysics Data System (ADS)

    Moore, John Allan

    An estimated 80% of all structural failures are due to mechanical fatigue, often resulting in catastrophic, dangerous and costly failure events. However, an accurate model to predict fatigue remains an elusive goal. One of the major challenges is that fatigue is intrinsically a multiscale process, which is dependent on a structure's geometric design as well as its material's microscale morphology. The following work begins with a microscale study of fatigue nucleation around non- metallic inclusions. Based on this analysis, a novel multiscale method for fatigue predictions is developed. This method simulates macroscale geometries explicitly while concurrently calculating the simplified response of microscale inclusions. Thus, providing adequate detail on multiple scales for accurate fatigue life predictions. The methods herein provide insight into the multiscale nature of fatigue, while also developing a tool to aid in geometric design and material optimization for fatigue critical devices such as biomedical stents and artificial heart valves.

  18. Evaluation of Methods to Predict Reactivity of Gold Nanoparticles

    SciTech Connect

    Allison, Thomas C.; Tong, Yu ye J.

    2011-06-20

    Several methods have appeared in the literature for predicting reactivity on metallic surfaces and on the surface of metallic nanoparticles. All of these methods have some relationship to the concept of frontier molecular orbital theory. The d-band theory of Hammer and Nørskov is perhaps the most widely used predictor of reactivity on metallic surfaces, and it has been successfully applied in many cases. Use of the Fukui function and the condensed Fukui function is well established in organic chemistry, but has not been so widely applied in predicting the reactivity of metallic nanoclusters. In this article, we will evaluate the usefulness of the condensed Fukui function in predicting the reactivity of a family of cubo-octahedral gold nanoparticles and make comparison with the d-band method.

  19. Assessing Computational Methods of Cis-Regulatory Module Prediction

    PubMed Central

    Su, Jing; Teichmann, Sarah A.; Down, Thomas A.

    2010-01-01

    Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods. PMID:21152003

  20. Predicting Spacecraft Trajectories by the WeavEncke Method

    NASA Technical Reports Server (NTRS)

    Weaver, Jonathan K.; Adamo, Daniel R.

    2011-01-01

    A combination of methods is proposed of predicting spacecraft trajectories that possibly include multiple maneuvers and/or perturbing accelerations, with greater speed, accuracy, and repeatability than were heretofore achievable. The combination is denoted the WeavEncke method because it is based on unpublished studies by Jonathan Weaver of the orbit-prediction formulation of the noted astronomer Johann Franz Encke. Weaver evaluated a number of alternatives that arise within that formulation, arriving at an orbit-predicting algorithm optimized for complex trajectory operations. In the WeavEncke method, Encke's method of prediction of perturbed orbits is enhanced by application of modern numerical methods. Among these methods are efficient Kepler s-equation time-of-flight solutions and self-starting numerical integration with time as the independent variable. Self-starting numerical integration satisfies the requirements for accuracy, reproducibility, and efficiency (and, hence, speed). Self-starting numerical integration also supports fully analytic regulation of integration step sizes, thereby further increasing speed while maintaining accuracy.

  1. Predictive toxicology: benchmarking molecular descriptors and statistical methods.

    PubMed

    Feng, Jun; Lurati, Laura; Ouyang, Haojun; Robinson, Tracy; Wang, Yuanyuan; Yuan, Shenglan; Young, S Stanley

    2003-01-01

    The development of drugs depends on finding compounds that have beneficial effects with a minimum of toxic effects. The measurement of toxic effects is typically time-consuming and expensive, so there is a need to be able to predict toxic effects from the compound structure. Predicting toxic effects is expected to be challenging because there are usually multiple toxic mechanisms involved. In this paper, combinations of different chemical descriptors and popular statistical methods were applied to the problem of predictive toxicology. Four data sets were collected and cleaned, and four different sets of chemical descriptors were calculated for the compounds in each of the four data sets. Three statistical methods (recursive partitioning, neural networks, and partial least squares) were used to attempt to link chemical descriptors to the response. Good predictions were achieved in the two smaller data sets; we found for large data sets that the results were less effective, indicating that new chemical descriptors or statistical methods are needed. All of the methods and descriptors worked to a degree, but our work hints that certain descriptors work better with specific statistical methods so there is a need for better understanding and for continued methods development.

  2. Thermochemical Fragment Energy Method for Biomolecules: Application to a Collagen Model Peptide.

    PubMed

    Suárez, Ernesto; Díaz, Natalia; Suárez, Dimas

    2009-06-09

    Herein, we first review different methodologies that have been proposed for computing the quantum mechanical (QM) energy and other molecular properties of large systems through a linear combination of subsystem (fragment) energies, which can be computed using conventional QM packages. Particularly, we emphasize the similarities among the different methods that can be considered as variants of the multibody expansion technique. Nevertheless, on the basis of thermochemical arguments, we propose yet another variant of the fragment energy methods, which could be useful for, and readily applicable to, biomolecules using either QM or hybrid quantum mechanical/molecular mechanics methods. The proposed computational scheme is applied to investigate the stability of a triple-helical collagen model peptide. To better address the actual applicability of the fragment QM method and to properly compare with experimental data, we compute average energies by carrying out single-point fragment QM calculations on structures generated by a classical molecular dynamics simulation. The QM calculations are done using a density functional level of theory combined with an implicit solvent model. Other free-energy terms such as attractive dispersion interactions or thermal contributions are included using molecular mechanics. The importance of correcting both the intermolecular and intramolecular basis set superposition error (BSSE) in the QM calculations is also discussed in detail. On the basis of the favorable comparison of our fragment-based energies with experimental data and former theoretical results, we conclude that the fragment QM energy strategy could be an interesting addition to the multimethod toolbox for biomolecular simulations in order to investigate those situations (e.g., interactions with metal clusters) that are beyond the range of applicability of common molecular mechanics methods.

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

    DOEpatents

    Stupp, Samuel I.; Beniash, Elia; Hartgerink, Jeffrey D.

    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.

  4. 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.

  5. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

    PubMed

    Heck, Gabriela S; Pintro, Val O; Pereira, Richard R; de Ávila, Mauricio B; Levin, Nayara M B; de Azevedo, Walter F

    2017-01-01

    Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. Reporting and Methods in Clinical Prediction Research: A Systematic Review

    PubMed Central

    Mallett, Susan; Geerlings, Mirjam I.; Vergouwe, Yvonne; Steyerberg, Ewout W.; Altman, Douglas G.; Moons, Karel G. M.

    2012-01-01

    Background We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. Methods and Findings We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively. Conclusions The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability. Please see later in the article for the Editors' Summary PMID:22629234

  7. An analytical method to predict efficiency of aircraft gearboxes

    NASA Technical Reports Server (NTRS)

    Anderson, N. E.; Loewenthal, S. H.; Black, J. D.

    1984-01-01

    A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.

  8. Preface to the Focus Issue: Chaos Detection Methods and Predictability

    SciTech Connect

    Gottwald, Georg A.; Skokos, Charalampos

    2014-06-01

    This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17–21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

  9. An analytical method to predict efficiency of aircraft gearboxes

    NASA Technical Reports Server (NTRS)

    Anderson, N. E.; Loewenthal, S. H.; Black, J. D.

    1984-01-01

    A spur gear efficiency prediction method previously developed by the authors was extended to include power loss of planetary gearsets. A friction coefficient model was developed for MIL-L-7808 oil based on disc machine data. This combined with the recent capability of predicting losses in spur gears of nonstandard proportions allows the calculation of power loss for complete aircraft gearboxes that utilize spur gears. The method was applied to the T56/501 turboprop gearbox and compared with measured test data. Bearing losses were calculated with large scale computer programs. Breakdowns of the gearbox losses point out areas for possible improvement.

  10. Preface to the Focus Issue: chaos detection methods and predictability.

    PubMed

    Gottwald, Georg A; Skokos, Charalampos

    2014-06-01

    This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17-21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

  11. Development of aerodynamic prediction methods for irregular planform wings

    NASA Technical Reports Server (NTRS)

    Benepe, D. B., Sr.

    1983-01-01

    A set of empirical methods was developed to predict low-speed lift, drag and pitching-moment variations with angle of attack for a class of low aspect ratio irregular planform wings suitable for application to advanced aerospace vehicles. The data base, an extensive series of wind-tunnel tests accomplished by the Langley Research Center of the National Aeronautics and Space Administration, is summarized. The approaches used to analyze the wind tunnel data, the evaluation of previously existing methods, data correlation efforts, and the development of the selected methods are presented and discussed. A summary of the methods is also presented to document the equations, computational charts and design guides which have been programmed for digital computer solution. Comparisons of predictions and test data are presented which show that the new methods provide a significant improvement in capability for evaluating the landing characteristics of advanced aerospace vehicles during the preliminary design phase of the configuration development cycle.

  12. Development of Improved Surface Integral Methods for Jet Aeroacoustic Predictions

    NASA Technical Reports Server (NTRS)

    Pilon, Anthony R.; Lyrintzis, Anastasios S.

    1997-01-01

    The accurate prediction of aerodynamically generated noise has become an important goal over the past decade. Aeroacoustics must now be an integral part of the aircraft design process. The direct calculation of aerodynamically generated noise with CFD-like algorithms is plausible. However, large computer time and memory requirements often make these predictions impractical. It is therefore necessary to separate the aeroacoustics problem into two parts, one in which aerodynamic sound sources are determined, and another in which the propagating sound is calculated. This idea is applied in acoustic analogy methods. However, in the acoustic analogy, the determination of far-field sound requires the solution of a volume integral. This volume integration again leads to impractical computer requirements. An alternative to the volume integrations can be found in the Kirchhoff method. In this method, Green's theorem for the linear wave equation is used to determine sound propagation based on quantities on a surface surrounding the source region. The change from volume to surface integrals represents a tremendous savings in the computer resources required for an accurate prediction. This work is concerned with the development of enhancements of the Kirchhoff method for use in a wide variety of aeroacoustics problems. This enhanced method, the modified Kirchhoff method, is shown to be a Green's function solution of Lighthill's equation. It is also shown rigorously to be identical to the methods of Ffowcs Williams and Hawkings. This allows for development of versatile computer codes which can easily alternate between the different Kirchhoff and Ffowcs Williams-Hawkings formulations, using the most appropriate method for the problem at hand. The modified Kirchhoff method is developed primarily for use in jet aeroacoustics predictions. Applications of the method are shown for two dimensional and three dimensional jet flows. Additionally, the enhancements are generalized so that

  13. Application of two direct runoff prediction methods in Puerto Rico

    USGS Publications Warehouse

    Sepulveda, N.

    1997-01-01

    Two methods for predicting direct runoff from rainfall data were applied to several basins and the resulting hydrographs compared to measured values. The first method uses a geomorphology-based unit hydrograph to predict direct runoff through its convolution with the excess rainfall hyetograph. The second method shows how the resulting hydraulic routing flow equation from a kinematic wave approximation is solved using a spectral method based on the matrix representation of the spatial derivative with Chebyshev collocation and a fourth-order Runge-Kutta time discretization scheme. The calibrated Green-Ampt (GA) infiltration parameters are obtained by minimizing the sum, over several rainfall events, of absolute differences between the total excess rainfall volume computed from the GA equations and the total direct runoff volume computed from a hydrograph separation technique. The improvement made in predicting direct runoff using a geomorphology-based unit hydrograph with the ephemeral and perennial stream network instead of the strictly perennial stream network is negligible. The hydraulic routing scheme presented here is highly accurate in predicting the magnitude and time of the hydrograph peak although the much faster unit hydrograph method also yields reasonable results.

  14. Increased Fidelity in Prediction Methods For Landing Gear Noise

    NASA Technical Reports Server (NTRS)

    Lopes, Leonard V.; Brentner, Kenneth S.; Morris, Philip J.; Lockard, David P.

    2006-01-01

    An aeroacoustic prediction scheme has been developed for landing gear noise. The method is designed to handle the complex landing gear geometry of current and future aircraft. The gear is represented by a collection of subassemblies and simple components that are modeled using acoustic elements. These acoustic elements are generic, but generate noise representative of the physical components on a landing gear. The method sums the noise radiation from each component of the undercarriage in isolation accounting for interference with adjacent components through an estimate of the local upstream and downstream flows and turbulence intensities. The acoustic calculations are made in the code LGMAP, which computes the sound pressure levels at various observer locations. The method can calculate the noise from the undercarriage in isolation or installed on an aircraft for both main and nose landing gear. Comparisons with wind tunnel and flight data are used to initially calibrate the method, then it may be used to predict the noise of any landing gear. In this paper, noise predictions are compared with wind tunnel data for model landing gears of various scales and levels of fidelity, as well as with flight data on fullscale undercarriages. The present agreement between the calculations and measurements suggests the method has promise for future application in the prediction of airframe noise.

  15. A new method to predict unsteady aeroelastic behavior

    NASA Technical Reports Server (NTRS)

    Strganac, Thomas W.; Mook, Dean T.

    1987-01-01

    A new method for predicting subsonic flutter and static deflections, including divergence, has been developed. The present method accounts for aspect ratio and, in the case of flutter, static deflections. The angle of attack is limited only by the occurrence of stall or vortex bursting near the wing. The innovation in the present method is to integrate simultaneously and interactively the equations of motion of the structure and the flowfield. The present approach employs an iterative scheme based on the predictor-corrector method. The general unsteady vortex-lattice method (UVLM) is used to predict the aerodynamic loads. Because the UVLM predicts the wakes as part of the solution, the history of the motion is taken into account; hysteresis is predicted. The deflection (for both bending and torsion) is expressed as an expansion in terms of the free-vibration modes. The time-dependent coefficients in these expansions serve as the generalized coordinates. Numeric