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Bioinformatic approaches for modeling the substrate specificity of HIV-1 protease: an overview
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
Karolinska Institutet, Department of Molecular Medicine and Surgery, Karolinska University Hospital, SE-17176, Stockholm, Sweden.
2007 (English)In: Expert Review of Molecular Diagnostics, ISSN 1473-7159, E-ISSN 1744-8352, E-ISSN 1744-8352, Vol. 7, no 4, p. 435-451Article in journal (Refereed) Published
Abstract [en]

HIV-1 protease has a broad and complex substrate specificity, which hitherto has escaped a simple comprehensive definition. This, and the relatively high mutation rate of the retroviral protease, makes it challenging to design effective protease inhibitors. Several attempts have been made during the last two decades to elucidate the enigmatic cleavage specificity of HIV-1 protease and to predict cleavage of novel substrates using bioinformatic analysis methods. This review describes the methods that have been utilized to date to address this important problem and the results achieved. The data sets used are also reviewed and important aspects of these are highlighted.

Place, publisher, year, edition, pages
Expert Reviews Ltd , 2007. Vol. 7, no 4, p. 435-451
Keywords [en]
bioinformatics, cleavage rule, HIV, human immunodeficiency virus, physicochemical property, prediction, protease
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:hh:diva-2002DOI: 10.1586/14737159.7.4.435ISI: 000248620700010PubMedID: 17620050Scopus ID: 2-s2.0-34447517453Local ID: 2082/2397OAI: oai:DiVA.org:hh-2002DiVA, id: diva2:239220
Available from: 2008-10-06 Created: 2008-10-06 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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Rögnvaldsson, ThorsteinnYou, Liwen

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