Prediction of HIV-1 Protease Inhibitor Resistance using a Protein–Inhibitor Flexible Docking Approach

Abstract

Emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. Here we focus on resistance to HIV-1 protease inhibitors (PIs) at a molecular level, which can be analysed genotypically or phenotypically. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are problematic because of the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the amount of free drug bound to HIV-1 protease molecules, but this procedure is expensive and time-consuming. To overcome these problems, we have developed a docking protocol that takes protein–inhibitor flexibility into account to predict phenotypic drug resistance. For six FDA-approved PIs and a total of 1792 HIV-1 protease sequence mutants, we used a combination of inhibitor flexible docking and molecular dynamics (MD) simulations to calculate protein–inhibitor binding energies. Prediction results were expressed as fold changes of the calculated inhibitory constant (K_i), and the samples predicted to have fold-increase in calculated K_i above the fixed cut-off were defined as drug resistant. Our combined docking and MD protocol achieved accuracies ranging from 72–83% in predicting resistance/susceptibility for five of the six drugs evaluated. Evaluating the method only on samples where our predictions concurred with established knowledge-based methods resulted in increased accuracies of 83–94% for the six drugs. The results suggest that a physics-based approach, which is readily applicable to any novel PI and/or mutant, can be used judiciously with knowledge-based approaches that require experimental training data to devise accurate models of HIV-1 PI resistance prediction.