Decoding HIV resistance: from genotype to therapy

Abstract

Genetic variation in HIV poses a major challenge for prevention and treatment of the AIDS pandemic. Resistance occurs by mutations in the target proteins that lower affinity for the drug or alter the protein dynamics, thereby enabling viral replication in the presence of the drug. Due to the prevalence of drug-resistant strains, monitoring the genotype of the infecting virus is recommended. Computational approaches for predicting resistance from genotype data and guiding therapy are discussed. Many prediction methods rely on rules derived from known resistance-associated mutations, however, statistical or machine learning can improve the classification accuracy and assess unknown mutations. Adding classifiers such as information on the atomic structure of the protein can further enhance the predictions.