title: Tackling the problem of HIV drug resistance.
abstract: The virally-encoded HIV-1 protease is an effective target for antiviral drugs, however, treatment for HIV infections is limited by the prevalence of drug resistant viral mutants. In this review, we describe our three-pronged approach to analyze and combat drug resistance. Understanding the molecular basis for resistance due to protease inhibitors is a key initial step in this approach. This knowledge is being employed for the design of new, improved inhibitors with high affinity for resistant mutants as well as wild type enzyme. In parallel with experimental studies of diverse mutants and inhibitory compounds, we are developing efficient algorithms to predict drug resistance phenotype from genotype data. This approach has important practical applications in the clinic where genotyping is recommended for individuals with new infections.
authors.affiliation: Department of Biology, Georgia State University, Atlanta, GA, USA.
authors.forename: Irene T
authors.initials: IT
authors.lastname: Weber
authors.affiliation: Department of Computer Science, Georgia State University, Atlanta, GA, USA.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Harrison
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology* / Q000627:therapeutic use; D019295:Computational Biology / Q000379:methods*; D018360:Crystallography, X-Ray; D055808:Drug Discovery / Q000379:methods*; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy*; D016333:HIV Protease / Q000187:drug effects*; D015497:HIV-1 / Q000201:enzymology / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D013329:Structure-Activity Relationship
chemical_list: D019380:Anti-HIV Agents; D016333:HIV Protease; C514803:p16 protease, Human immunodeficiency virus 1
keywords: HIV/AIDS; X-ray crystallography; bioinformatics machine learning; enzymes