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Understanding Prediction Systems for HLA-Binding Peptides and T-Cell Epitope Identification
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
School of Land, Crop, and Food Sciences, University of Queensland, Brisbane, QLD, Australia.
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia.
School of Land, Crop, and Food Sciences, University of Queensland, Brisbane, QLD, Australia.
2007 (English)In: Pattern Recognition in Bioinformatics: Proceedings / [ed] Rajapakse, J C, Schmidt, B, Volkert, G, Berlin: Springer Berlin/Heidelberg, 2007, p. 337-348Conference paper, Published paper (Refereed)
Abstract [en]

Peptide binding to HLA molecules is a critical step in induction and regulation of T-cell mediated immune responses. Because of combinatorial complexity of immune responses, systematic studies require combination of computational methods and experimentation. Most of available computational predictions are based on discriminating binders from non-binders based on use of suitable prediction thresholds. We compared four state-of-the-art binding affinity prediction models and found that nonlinear models show better performance than linear models. A comprehensive analysis of HLA binders (A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801 and B*1501) showed that non-linear predictors predict peptide binding affinity with high accuracy. The analysis of known T-cell epitopes of survivin and known HIV T-cell epitopes showed lack of correlation between binding affinity and immunogenicity of HLA-presented peptides. T-cell epitopes, therefore, can not be directly determined from binding affinities by simple selection of the highest affinity binders.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2007. p. 337-348
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; Volume 4774/2007
Keywords [en]
HLA-Binding Peptides, T-Cell Epitope Identification
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-2038DOI: 10.1007/978-3-540-75286-8_32ISI: 000251314800032Scopus ID: 2-s2.0-38349072025Local ID: 2082/2433ISBN: 978-3-540-75285-1 OAI: oai:DiVA.org:hh-2038DiVA, id: diva2:239256
Conference
2nd International Workshop on Pattern Recognition in Bioinformatics, Singapore, Oct 01-02, 2007
Available from: 2008-10-14 Created: 2008-10-14 Last updated: 2018-03-23Bibliographically approved
In thesis
1. Computational prediction models for proteolytic cleavage and epitope identification
Open this publication in new window or tab >>Computational prediction models for proteolytic cleavage and epitope identification
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The biological functions of proteins depend on their physical interactions with other molecules, such as proteins and peptides. Therefore, modeling the protein-ligand interactions is important for understanding protein functions in different biological processes. We have focused on the cleavage specificities of HIV-1 protease, HCV NS3 protease and caspases on short oligopeptides or in native proteins; the binding affinity of MHC molecules with short oligopeptides and identification of T cell epitopes. we expect that our findings on HIV-1 protease, HCV NS3 protease and caspases generalize to other proteases. In this thesis, we have performed analysis on these interactions from different perspectives - we have extended and collected new substrate data sets; used and compared different prediction methods (e.g. linear support vector machines, neural networks, OSRE method, rough set theory and Gaussian processes) to understand the underlying interaction problems; suggested new methods (i.e. a hierarchical method and Gaussian processes with test reject method) to improve predictions; and extracted cleavage rules for protease cleavage specificities. From our studies, we have extended oligopeptide substrate data sets and collected native protein substrates for HIV-1 protease, and a new oligopeptide substrate data set for HCV protease. We have shown that all current HIV-1 protease oligopeptide substratde data sets and our HCV data set are linearly separable; for HIV-1 protease, size and hydrophobicity are two important physicochemical properties in the recognition of short oligopeptide substrates to the protease; and linear support vector mahine is the state-of-the-art for this protease cleavage prediction problem. Our hierarchical method combining protein secondary structure information and experimental short oligopeptide cleavage information an improve the prediction of HIV-1 protease cleavage sites in native proteins. Our rule extraction method provides simple an accurate cleavage rules with high fidelity for HIV-1 and HCV proteases. For MHC molecules, we showed that high binding affinities are not necessarily correlated to immunogenicity on HLA-restricted peptides. Our test reject method combined with Gaussian processes can simplify experimental design by reducing false positives for detecting potential epitopes in large pathogen genomes.

Place, publisher, year, edition, pages
Lund: Department of Theoretical Physics, Lund University, 2007. p. 84
Keywords
Binding affinity, Caspase, Cleavage predition, Cleavage specifictiy, Epitope, False positive, Gaussian process, HCV, Hierarchial method, HIV, Immunology, MHC, OSRE, Protease-peptide interaction, Rule extraction, Sequence analysis, SVM
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:hh:diva-1981 (URN)2082/2376 (Local ID)978-91-628-7218-2 (ISBN)2082/2376 (Archive number)2082/2376 (OAI)
Available from: 2008-09-29 Created: 2008-09-29 Last updated: 2018-03-23Bibliographically approved

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