Human immunodeficiency virus (HIV) evolves with extraordinary rapidity.
However, its evolution is constrained by interactions between mutations in its
fitness landscape. Here we show that an Ising model describing these
interactions, inferred from sequence data obtained prior to the use of
antiretroviral drugs, can be used to identify clinically significant sites of
resistance mutations. Successful predictions of the resistance sites indicate
progress in the development of successful models of real viral evolution at the
single residue level, and suggest that our approach may be applied to help
design new therapies that are less prone to failure even where resistance data
is not yet available.Comment: 5 pages, 3 figure
