Abstract
Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model we translate HIV protease sequence covariation data into quantitative predictions for the stability and fitness of individual proteins containing therapy-associated mutations which we compare to previously performed in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background and, although often destabilizing in a wildtype background, primary mutations are frequently stabilizing in the context of mutation patterns which arise in response to drug therapy. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.