Structure‐based phenotyping predicts HIV‐1 protease inhibitor resistance

Abstract

Mutations in HIV-1 drug targets lead to resistance and consequent therapeutic failure of antiretroviral drugs. Phenotypic resistance assays are time-consuming and costly, and genotypic rules-based interpretations may fail to predict the effects of multiple mutations. We have developed a computational procedure that rapidly evaluates changes in the binding energy of inhibitors to mutant HIV-1 PR variants. Models of WT complexes were produced from crystal structures. Mutant complexes were built by amino acid substitutions in the WT complexes with subsequent energy minimization of the ligand and PR binding site residues. Accuracy of the models was confirmed by comparison with available crystal structures and by prediction of known resistance-related mutations. PR variants from clinical isolates were modeled in complex with six FDA-approved PIs, and changes in the binding energy (⌬E bind ) of mutant versus WT complexes were correlated with the ratios of phenotypic 50% inhibitory concentration (IC 50 ) values. The calculated ⌬E bind of five PIs showed significant correlations (R 2 ‫ס‬ 0.7-0.8) with IC 50 ratios from the Virco Antivirogram assay, and the ⌬E bind of six PIs showed good correlation (R 2 ‫ס‬ 0.76-0.85) with IC 50 ratios from the Virologic PhenoSense assay. ⌬E bind cutoffs corresponding to a four-fold increase in IC 50 were used to define the structure-based phenotype as susceptible, resistant, or equivocal. Blind predictions for 78 PR variants gave overall agreement of 92% (kappa ‫ס‬ 0.756) and 86% (kappa ‫ס‬ 0.666) with PhenoSense and Antivirogram phenotypes, respectively. The structural phenotyping predicted drug resistance of clinical HIV-1 PR variants with an accuracy approaching that of frequently used cell-based phenotypic assays.Keywords: HIV protease inhibitors; drug resistance; molecular modeling; binding energy; structure-based phenotyping Antiretroviral drugs targeting the RT and PR enzymes of HIV-1 may result in dramatic suppression of viral replication in infected individuals (Palella Jr. et al. 1998;Carpenter et al. 2000). However, when viral replication is incompletely suppressed, drug-resistant variants emerge through the accumulation of mutations in the HIV-1 RT or PR genes, leading to therapeutic failure . Genotypic testing for resistance is a relatively rapid and inexpensive method to identify PR and RT amino acid substitutions leading to drug resistance (Baxter et al. 2000;Schinazi et al. 2000;Shafer 2002). Genotyping is recommended for use in clinical practice . However, rules-based interpretation systems are retrospective in nature and must be frequently updated to accommodate new mutational patterns and new antiretrovirals. As a result, genotypic predictions for complex mutational patterns and for new antiretrovirals may be inaccurate (Baxter et al. 2000). Cell-based viral phenotyping assays (Hertogs et Reprint requests to: Mark D. Shenderovich, Cengent Therapeutics Inc., 10929 Technology Place, San Diego, CA 92127, USA; e-mail: marksh@cengent.com; fax: (858) 451-3828.Abbre...

X-Ray Crystal Structures of Human Immunodeficiency Virus Type 1 Protease Mutants Complexed with Atazanavir

“…Compared to the 3HVP apo biological dimer, the monomers in the PI Alternative atazanavir binding mode in response to V82F substitution. Several reports have previously analyzed and/or predicted the effects of various point mutations on the catalytic activity, inhibition, and structural stability of PRT (10,14,22,52). This analysis will focus on the V82F mutation, for which the CRM and IRM structures provide clear insight.…”

Computational Prediction of HIV-1 Resistance to Protease Inhibitors

“…The second set was obtained from the study by Shenderovich et al 15 For this case, we selected the more difficult set of sequences, with more than 10 mutations (and a maximum number of 17 mutations per monomer, giving a total of 15 sequences) against IDV inhibitor. The third set included 21 and 23 PR FASTA sequences with different resistance profiles to APV and DRV, respectively, obtained from routine genotypic resistance testing (TRUGENE® HIV-1 Genotyping Assay, Siemens Healthcare, Barcelona, Spain) in the HIV Unit and irsiCaixa AIDS Research institute, Hospital Universitari Germans Trias i Pujol, Badalona, Spain.…”

“…This is more clearly seen in the right panel where the natural logarithm of the IC50 values is used to estimate changes in binding affinities. For a second test, we used a subset of sequences derived from the work of Shenderovich et al 15 Using known and in-house prepared mutations, these authors developed possibly the most comprehensive computational predictor to date. However, as noticed by the same authors, the quality of predictions correlates negatively with the increase of number of mutations.…”

“…[10][11][12] Antiretroviral drug resistance testing is key for clinical management 13 and epidemiologic surveillance, 1,[10][11][12]14 but it is not trivial to assess. To this aim, we find multiple predictions techniques using molecular modeling 15,16 and bioinformatics tools. [17][18][19][20][21] These, however, often fail when addressing real patients sequences containing a large number of mutations and/or new ones.…”

Structure-based design and screening of inhibitors for an essential bacterial GTPase, Der

“…We used a procedure of Monte Carlo simulations with flexible ligand docking protocols developed at Sapient Discovery, LLC. 21 Monte Carlo search with minimization was used to optimize the positions and conformations of the ligand in the binding site, positions of Mg 2+ ion and water molecules involved in the ligand binding and conformations of Der side chains located in a 5.0-7.0 Å shell around the ligand. The GD1 switch I loop residues 24-37 that are absent in the X-ray structure were built into preliminary models of complexes.…”