Validating the vitality strategy for fighting drug resistance

Area of Science:

• Computational chemistry
• Drug discovery
• Virology

Background:

• Designing effective HIV-1 drugs faces challenges due to mutations causing drug resistance.
• Previous work introduced a computational strategy to predict viral evolution and drug resistance.
• This strategy relies on calculating "vitality" ratios to guide predictions.

Purpose of the Study:

• To extensively validate the computational strategy for predicting HIV-1 drug resistance.
• To assess the correlation between computationally determined vitality and experimental data.
• To demonstrate the utility of the approach in screening for resistance mutations.

Main Methods:

• Calculated binding affinity (K(i)) using the protein dipole Langevin dipole (PDLD/S) in its linear response approximation (LRA) β version (PDLD/S-LRA/β).
• Evaluated proteolytic efficiency (k(cat)/K(M)) by calculating transition state (TS) binding free energies.
• Applied the strategy to six existing clinical and experimental drug candidates.

Main Results:

• Computationally determined vitalities showed reasonable correlation with experimental data.
• The results indicate that calculated vitality can identify mutations crucial for viral survival.
• The approach successfully predicted resistance patterns for tested drug candidates.

Conclusions:

• The validated computational strategy can predict mutations conferring HIV-1 drug resistance.
• This method aids in screening for effective resistance mutations against antiviral drugs.
• The approach is valuable for designing drug molecules that minimize resistance development.