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Comprehensive Bioinformatic Analysis of the Specificity of Human Immunodeficiency Virus Type 1 Protease
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0001-5163-2997
2005 (English)In: Journal of Virology, ISSN 0022-538X, E-ISSN 1098-5514, Vol. 79, no 19, p. 12477-12486Article in journal (Refereed) Published
Abstract [en]

Rapidly developing viral resistance to licensed human immunodeficiency virus type 1 (HIV-1) protease inhibitors is an increasing problem in the treatment of HIV-infected individuals and AIDS patients. A rational design of more effective protease inhibitors and discovery of potential biological substrates for the HIV-1 protease require accurate models for protease cleavage specificity. In this study, several popular bioinformatic machine learning methods, including support vector machines and artificial neural networks, were used to analyze the specificity of the HIV-1 protease. A new, extensive data set (746 peptides that have been experimentally tested for cleavage by the HIV-1 protease) was compiled, and the data were used to construct different classifiers that predicted whether the protease would cleave a given peptide substrate or not. The best predictor was a nonlinear predictor using two physicochemical parameters (hydrophobicity, or alternatively polarity, and size) for the amino acids, indicating that these properties are the key features recognized by the HIV-1 protease. The present in silico study provides new and important insights into the workings of the HIV-1 protease at the molecular level, supporting the recent hypothesis that the protease primarily recognizes a conformation rather than a specific amino acid sequence. Furthermore, we demonstrate that the presence of 1 to 2 lysine residues near the cleavage site of octameric peptide substrates seems to prevent cleavage efficiently, suggesting that this positively charged amino acid plays an important role in hindering the activity of the HIV-1 protease.

Place, publisher, year, edition, pages
Washington, DC: The American Society for Microbiology , 2005. Vol. 79, no 19, p. 12477-12486
Keywords [en]
Bioinformatic Analysis, Human Immunodeficiency, Virus Type 1 Protease
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:hh:diva-268DOI: 10.1128/JVI.79.19.12477-12486.2005ISI: 000231992500036PubMedID: 16160175Scopus ID: 2-s2.0-25144487698Local ID: 2082/563OAI: oai:DiVA.org:hh-268DiVA, id: diva2:237447
Available from: 2006-11-27 Created: 2006-11-27 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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