Abstract
A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.
Highlights
Drug-resistant viruses have a significant impact on the prognosis of HIV infections [1, 2]
Resistance to HIV-1 protease inhibitors was represented by the fold change (FC) increase of the IC50 compared to wild type HIV
The sample size varied among the drugs since they were not tested for all viral variants
Summary
Drug-resistant viruses have a significant impact on the prognosis of HIV infections [1, 2]. Predicting drug resistance from their genotypes would allow the selection of appropriate drugs for efficient treatment The development of such prediction models has been actively promoted [3,4,5,6,7], along with growing databases, such as the Stanford HIV Drug Resistance Database, which collects protein information and evaluates the resistance of drug-resistant viruses [4, 8]. These prediction models include classification or regression models by using various machine learning methods (support vector machine, deep learning, etc.). Geno2pheno, developed by Niko et al [4], addressed the regression problem based on a support vector regression (SVR) method and provided a determination coefficient
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