Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes

By Karthik Shekhar, Claire Ruberman, Andrew L. Ferguson, John P. Barton, Mehran Kardar, Arup K. Chakraborty. 2013

79 citations recorded by the source corpus.

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 nonequilibrium 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 á 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.

Subfields: Genetics, Molecular Biology, Virology

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “that patterns of polymorphism and covariation in patient derived (and thus highly similar) HIV sequences are informative about their replicative capacities [69, 70], thanks to high mutation rates in the HIV virus.”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “In a series of papers, Chakraborty and co-workers proposed a ﬁtness model for proteins in the Gag family of the HIV virus [55, 70, 122, 187].”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “…the collective behavior of neurons (41,42), infer gene-interaction networks from experimentally observed transcription proﬁles (43), predict residue-residue contacts from multiple sequence alignments (37,44,45), study protein ﬁtness landscapes (46,47), and infer epistatic effects from ﬁtness (48).”