The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing

Abstract

Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10−6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10−12). Performance of the Potts model (r = −0.73, p = 9.7×10−9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

“…As with the stability measurements, we find that the relative Potts energy correlates well with infectivity (r ¼ À0:64; P < 10 À5 ), shown in figure 3B. In the same comparison using the inde- The results presented here are reinforced by other recent studies of protein evolutionary landscapes Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017) where varying measures of experimental fitness are compared with statistical energies derived from correlated Potts models constructed from MSAs. The range of statistical energies and the correlation with fitness are qualitatively similar to those presented by Ferguson et al (2013) and Mann et al (2014) where statistical energies of engineered HIV-1 Gag variants generated using a similar inference technique are compared with replicative fitness assays.…”

“…The same can be said for correlations between Potts scores and relative folding free energies of beta lactamase TEM-1 presented by Figliuzzi et al (2015). This collection of studies demonstrate that Potts model statistical energies correlate with the fitness of protein sequences in different contexts, including protein families evolving under weak selective pressure (Figliuzzi et al 2015;Hopf et al 2017), viral proteins evolving under immune pressure Mann et al 2014), and as presented here, viral proteins evolving under drug pressure.…”

“…The correlations induce epistatic effects, a primary or accessory resistance mutation can be either stabilizing or destabilizing depending on the genetic background. Recently epistasis has become a focus for analysis in structural biology and genomics as researchers have begun to successfully link the coevolutionary information in collections of protein sequences with the structural and functional fitness of those proteins (Hinkley et al 2011;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017;Barton et al 2016b;Butler et al 2016). In the current study, we have used the correlated mutations encoded in a MSA of drug-experienced HIV-1 protease sequences to parametrize a Potts model of sequence statistical energies that can be used as an estimator of stability and relative replicative capacity of individual protease sequences containing drug resistance mutations.…”

“…Further, phylogenetic corrections based on pairwise sequence similarity cut-offs of 40% of sequence length or more which are common in studies utilizing direct coupling analysis (DCA) (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014 of protein families would drastically reduce the number of effective sequences in our MSA and would lead to mischaracterization of the true underlying mutational landscape. We note that Potts models of other HIV-1 protein sequences under immune pressure have been parameterized with no phylogenetic corrections Mann et al 2014;Barton et al 2016b). …”

“…Given a multiple sequence alignment (MSA) of related protein sequences, a probabilistic model of the network of interacting protein residues can be inferred from the pair correlations encoded in the MSA. Recently, probabilistic models, called Potts models, have been used to assign scores to individual protein sequences which correlate with experimental measures of fitness (Haq et al 2012;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017). These advances build upon previous and ongoing work in which Potts models have been used to extract information from sequence data regarding tertiary and quaternary structure of protein families (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014Marks et al 2012;Sulkowska et al 2012;Sutto et al 2015;Barton et al 2016a;Haldane et al 2016;Jacquin et al 2016) and sequencespecific quantitative predictions of viral protein stability and fitness (Haq et al 2012;Shekhar et al 2013;Barton et al 2016b;Butler et al 2016).…”