Computational Prediction of HIV-1 Resistance to Protease Inhibitors

The development of mutations in HIV-1 protease (PR) hinders the activity of antiretroviral drugs, forcing changes in drug prescription. Most resistance assessments used to date rely on expert-based rules on predefined sets of stereotypical mutations; such an information-driven approach cannot capture new polymorphisms or be applied for new drugs. Computational modeling could provide a more general assessment of drug resistance and could be made available to clinicians through the Internet. We have created a protocol involving sequence comparison and all-atom protein ligand induced fit simulations to predict resistance at the molecular level. We first compared our predictions with the experimentally determined IC50 values of darunavir, amprenavir, ritonavir, and indinavir from reference PR mutants displaying different resistance levels. We then performed analyses on a large set of variants harboring more than 10 mutations. Finally, several sequences from real patients were analyzed for amprenavir and darunavir. Our computational approach detected all of the genotype changes triggering high-level resistance, even those involving a large number of mutations.