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

• Main Authors: Shekhar, Karthik (Contributor), Ruberman, Claire (Author), Ferguson, Andrew L. (Author), Kardar, Mehran (Contributor), Barton, John P. (Contributor), Chakraborty, Arup K (Author)
• Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor), Ragon Institute of MGH, MIT and Harvard (Contributor), Chakraborty, Arup K. (Contributor)
• Format: Article
• Language: English
• Published: American Physical Society, 2014-03-03T13:50:21Z.
• Subjects: Article
• Online Access: Get fulltext

 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.