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Drug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

[This corrects the article DOI: 10.1371/journal.pgen.1005310.].

Viruses often encode proteins with multiple functions due to their compact genomes. Existing approaches to identify functional residues largely rely on sequence conservation analysis. Inferring functional residues from sequence conservation can produce false positives, in which the conserved residues are functionally silent, or false negatives, where functional residues are not identified since they are species-specific and therefore non-conserved. Furthermore, the tedious process of constructing and analyzing individual mutations limits the number of residues that can be examined in a single study. Here, we developed a systematic approach to identify the functional residues of a viral protein by coupling experimental fitness profiling with protein stability prediction using the influenza virus polymerase PA subunit as the target protein. We identified a significant number of functional residues that were influenza type-specific and were evolutionarily non-conserved among different influenza types. Our results indicate that type-specific functional residues are prevalent and may not otherwise be identified by sequence conservation analysis alone. More importantly, this technique can be adapted to any viral (and potentially non-viral) protein where structural information is available.