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

William F Flynn,Bruce E Torbett,Ronald M Levy,Allan Haldane

doi:10.1093/molbev/msx095

Abstract

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.

Highlights

The ability of HIV-1 to rapidly mutate leads to antiretroviral therapy (ART) failure among infected patients
In subtype B protease sequences from the Stanford University HIV Drug Resistance Database (HIVDB) (Shafer 2006), we find that mutations above 1% frequency are observed at 55% (55/99) of protease positions among 5,610 drug-experienced sequences and at only 32% (32/99) of the positions among 15,300 drug-naive sequences
Correlations between some drug-associated sites have been identified through analysis of drug-naive sequences, or structural and/or evolutionary constraints (Hoffman et al 2003; Butler et al 2016), a more complete and accurate model of the epistatic landscape of drugresistance mutations can be constructed using the correlations found in a varied set of drug-experienced sequences

Summary

Introduction

The ability of HIV-1 to rapidly mutate leads to antiretroviral therapy (ART) failure among infected patients. The protease enzyme is responsible for the cleavage of the Gag and Gag-Pol polyproteins into functional constituent proteins and it has been estimated that resistance develops in as many as 50% of patients undergoing monotherapy (Richman et al 2004) and as many as 30% of patients undergoing modern combination antiretroviral therapy (c-ART) (Gupta et al 2008). The set of possible protein sequences grows as 20L as the protein length L increases This makes estimating the probability of observing a particular state, or a specific protein sequence, from a finite sample impractical. Given a collection or MSA of protein sequences, the single-site and pair-site amino acid frequencies are average quantities that can be estimated from the data (above some threshold that depends on sample size). The Potts model is a probabilistic model which aims to describe the probabilities of observing specific states of a system that is constructed to be as unbiased as possible except to agree with the average first- and second-order observables (marginals) from the data

Methods

Results

Conclusion