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

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

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish (US)
Article number062705
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume88
Issue number6
DOIs
StatePublished - Dec 4 2013
Externally publishedYes

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • General Medicine

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