Structure-based methods to predict mutational resistance to diarylpyrimidine non-nucleoside reverse transcriptase...

Area of Science:

• Virology
• Computational Chemistry
• Drug Discovery

Background:

• Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are crucial in HIV treatment but resistance often leads to therapy failure.
• Diarylpyrimidine (DAPY) NNRTIs, like rilpivirine, are effective but can be impacted by mutations in the reverse transcriptase (RT) enzyme.
• Developing new NNRTIs requires efficient methods to predict potential resistance mutations.

Purpose of the Study:

• To evaluate a structure-based computational approach for predicting resistance mutations to DAPY NNRTIs.
• To assess the correlation between predicted changes in binding affinity/stability and experimental resistance data.
• To validate the computational predictions using structural analysis and enzymatic assays.

Main Methods:

• Utilized a residue scan and molecular dynamics strategy on RT crystal structures.
• Calculated changes in binding affinity and stability for mutant complexes.
• Validated predictions through structural analysis, molecular dynamics simulations, and enzymatic reverse transcription assays.

Main Results:

• Computational predictions of resistance mutations for DAPYs (rilpivirine, etravirine, dapivirine) showed correlation with existing resistance data.
• The K101P mutation was accurately predicted to confer high-level resistance to DAPYs.
• Predicted changes in affinity and stability for mutant complexes aligned with experimental and clinical resistance data.

Conclusions:

• Structure-based computational methods, specifically analyzing changes in affinity and stability, can effectively predict NNRTI resistance mutations.
• This approach offers a cost-effective and efficient strategy for pre-evaluating NNRTI compounds during drug development.
• The validated computational strategy holds promise for accelerating the design of new HIV therapeutics with reduced resistance potential.