Send to

Choose Destination
Mol Biol Evol. 2017 Jun 1;34(6):1291-1306. doi: 10.1093/molbev/msx095.

Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease.

Author information

Department of Physics and Astronomy, Rutgers University, New Brunswick, NJ.
Center for Biophysics and Computational Biology, Temple University, Philadelphia, PA.
Department of Chemistry, Temple University, Philadelphia, PA.
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA.


Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model, we translate HIV-1 protease sequence covariation data into quantitative predictions for the probability of observing specific mutation patterns which are in agreement with the observed sequence statistics. We find that the statistical energies of the Potts model are correlated with the fitness of individual proteins containing therapy-associated mutations as estimated by in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background. Primary mutations which lead to drug resistance can become highly advantageous (or entrenched) by the complex mutation patterns which arise in response to drug therapy despite being destabilizing in the wildtype background. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.


epistasis, mutational landscape, statistical inference, coevolution, HIV, drug resistance

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center