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Comprehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences. Klinisk kemi och farmakologi. (Inflammation)
2005 (English)In: J Virol, ISSN 0022-538X, Vol. 79, no 19, 12477-86 p.Article 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
2005. Vol. 79, no 19, 12477-86 p.
Keyword [en]
Algorithms, Artificial Intelligence, Computational Biology, Computer Simulation, HIV Protease/chemistry/*genetics/*metabolism, HIV-1/drug effects/*enzymology/genetics, Neural Networks (Computer), Substrate Specificity
National Category
Medical and Health Sciences Natural Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-17309DOI: 10.1128/JVI.79.19.12477-12486.2005PubMedID: 16160175OAI: oai:DiVA.org:uu-17309DiVA: diva2:45080
Note

Publikation från annat universitet

Available from: 2008-06-18 Created: 2008-06-18 Last updated: 2015-05-19

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Garwicz, Daniel

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