Spin models inferred from patient data faithfully describe HIV fitness landscapes and enable rational vaccine design

TL;DR

This study demonstrates that spin models inferred from patient HIV sequences accurately reflect the virus's fitness landscape, aiding rational vaccine design by identifying mutational vulnerabilities.

Contribution

The paper shows that spin models derived from patient data can reliably infer HIV fitness landscapes despite evolutionary complexities.

Findings

Spin models reflect the correct fitness rank order of mutants.

Inferred models are applicable to diverse viruses.

Computer simulations support the model's validity.

Abstract

Mutational escape from vaccine induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of non-equilibrium viral evolution driven by patient-specific immune responses, and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory \'{a} la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses.