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 (DeltaE(bind)) of mutant versus WT complexes were correlated with the ratios of phenotypic 50% inhibitory concentration (IC(50)) values. The calculated DeltaE(bind) of five PIs showed significant correlations (R(2) = 0.7-0.8) with IC(50) ratios from the Virco Antivirogram assay, and the DeltaE(bind) of six PIs showed good correlation (R(2) = 0.76-0.85) with IC(50) ratios from the Virologic PhenoSense assay. DeltaE(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 = 0.756) and 86% (kappa = 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.

Figure 1.

Structures of the FDA-approved HIV-1 PIs used in this study.

Figure 2.

Superimposed stereo views of the crystal structure (yellow; Hong et al. 2000) and our model (white) of the SQV complex with the G48V/L90M mutant HIV PR. The crystal structure of WT PR–SQV complex (blue) is also shown for comparison. The SQV molecules are shown as tube models. Only PR residues closest to the ligand are displayed.

Figure 3.

Binding energy profiles for SQV (A) and APV (B). PR mutations described in the literature are ranked by the increasing ΔE_bind. Predicted resistant (ΔE_bind ≥ 2.0 kcal/mole) and highly resistant (ΔE_bind ≥ 3.0 kcal/mole) mutants are shown as the gray and the black bars, respectively.

Figure 4.

Superimposed stereo views of the model SQV complexes with the WT (yellow) and the quadruple mutant (M46I/G48V/I50V/I84L, white) HIV-1 PRs. The SQV molecules are shown as tube models. Hydrogen bonds of the ligand and flap water molecule in the mutant complex are displayed. Mutated residues and other important residues are labeled.

Figure 5.

Total correlation between the calculated changes in binding energies (ΔE_bind ) of the WT vs. mutant HIV-1 PR–inhibitor complexes and the estimates of the changes in binding free energies obtained from experimental IC₅₀ ratios (see equation 5 in Materials and Methods). The correlation plot includes 35 PR variants in complex with LPV and 65 PR variants in complex with five other PIs (total 360 data points). Experimental IC₅₀ ratios were obtained from PhenoSence resistance assay (ViroLogic Inc., http://www.ViroLogic.com).

Figure 6.

A semiquantitative model for a structure-based PI resistance assay. Distributions of calculated ΔE_bind for PR variants phenotypically sensitive and resistant to six PIs were used to define the binding energy cutoffs c₁ and c₂ for prediction of sensitivity (S) or resistance (R) to each PI, respectively. Cases c₁ ≤ ΔE_bind ≤ c₂ are considered equivocal (E).