Fitness verification

We used in-vitro experimental fitness measurements compiled from the literature (Esteban-Riesco et al., 2013; Urbanowicz et al., 2015; Naik et al., 2017; Pantua et al., 2013) to validate that our inferred prevalence landscape is a good proxy of the E2 1b fitness landscape. The detailed selection procedure of the specific fitness measurements (listed in Data S1) from each study is presented below.

In (Esteban-Riesco et al., 2013), the authors compared in Figure 3A infectivity (in RLU) of subtype 1b HCVpps, subtype 1a reference strain, positive and negative controls. We selected all four measurements for subtype 1b and divided them into two groups, with the same E1 background for each group. In (Urbanowicz et al., 2015), the authors compared in Figure 2 infectivity (in RLU) of subtype 1b HCVpps. We selected all eight measurements and divided them into two groups, with the same E1 background for each group. In (Naik et al., 2017), the authors compared in Figure 1B infectivity (in RLU) of HCVpps from multiple genotypes. We selected four subtype 1b measurements, the only ones that have the same E1 background. In (Pantua et al., 2013), the authors compared in Figure 1F infectivity (in RLU) of HCVpps from multiple genotypes. We selected all five subtype 1b measurements.

For each study, normalization of fitness measurements and predicted model energies was performed by subtracting the mean from the dataset and dividing by its standard deviation. To further normalize for potential experimental biases, we considered the weighted average of Spearman correlation coefficients from different experiments. This is given by

where Si is the number of measurements and ri the Spearman correlation coefficient for experiment i, and sexp is the total number of experiments.