Quantifying the evolutionary potential and constraints of a drug-targeted viral protein
RNA viruses are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes. Here we systematically quantified the distribution of fitness effects (DFE) of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of Hepatitis C Virus (HCV). We found that the majority of non-synonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized in natural conditions. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug Daclatasvir. Both the selection coefficient and the number of beneficial mutations are found to increase with the level of environmental stress, which is modulated by the concentration of Daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. We test theoretical predictions regarding the distribution of beneficial fitness effects of mutations. We also interpret the data in the context of Fishers Geometric Model and find an increased distance to optimum as a function of environmental stress. Finally, we show that replication fitness of viruses is correlated with the pattern of sequence conservation in nature and viral evolution is constrained by the need to maintain protein stability.