HIV is a highly mutable virus, and over 30 years after its discovery, a vaccine or cure is still not available. The isolation
of broadly neutralizing antibodies (bnAbs) from HIV-infected patients has led to renewed hope for a prophylactic vaccine capable
of combating the scourge of HIV. A major challenge is the design of immunogens and vaccination protocols that can elicit bnAbs
that target regions of the virus’s spike proteins where the likelihood of mutational escape is low due to the high fitness
cost of mutations. Related challenges include the choice of combinations of bnAbs for therapy. An accurate representation
of viral fitness as a function of its protein sequences (a fitness landscape), with explicit accounting of the effects of
coupling between mutations, could help address these challenges. We describe a computational approach that has allowed us
to infer a fitness landscape for gp160, the HIV polyprotein that comprises the viral spike that is targeted by antibodies.
We validate the inferred landscape through comparisons with experimental fitness measurements, and various other metrics.
We show that an effective antibody that prevents immune escape must selectively bind to high escape cost residues that are
surrounded by those where mutations incur a low fitness cost, motivating future applications of our landscape for immunogen