Proteochemometric modeling of HIV protease susceptibility.

BMC Bioinformatics
Maris LapinsJarl E S Wikberg

Abstract

A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations. The model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inh...Continue Reading

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Citations

Sep 21, 2013·Journal of Chemical Information and Modeling·Dong-Sheng CaoShao Liu
Oct 15, 2013·Journal of Chemical Information and Modeling·Vigneshwari SubramanianGerd Wohlfahrt
Feb 1, 2012·Journal of Medicinal Chemistry·Peter C AndersonJoseph S Schoeniger
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Aug 15, 2014·Journal of Computational Chemistry·Chanin NantasenamatJarl E S Wikberg
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Aug 30, 2021·Environment International·Raquel Chamorro-GarcíaDaniel Zalko

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Methods Mentioned

BETA
PCAs

Software Mentioned

Molecular Networks
Corina
Multivariate Infometric Analysis
GRINDs
Almond
Proteochemometrics

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