Accurate ensemble molecular dynamics binding free energy ranking of multidrug-resistant HIV-1 proteases

Accurate calculation of important thermodynamic properties, such as macromolecular binding free energies, is one of the principal goals of molecular dynamics simulations. However, single long simulation frequently produces incorrectly converged quantitative results due to inadequate sampling of conformational space in a feasible wall-clock time. Multiple short (ensemble) simulations have been shown to explore conformational space more effectively than single long simulations, but the two methods have not yet been thermodynamically compared. Here we show that, for end-state binding free energy determination methods, ensemble simulations exhibit significantly enhanced thermodynamic sampling over single long simulations and result in accurate and converged relative binding free energies that are reproducible to within 0.5 kcal/mol. Completely correct ranking is obtained for six HIV-1 protease variants bound to lopinavir with a correlation coefficient of 0.89 and a mean relative deviation from experiment of 0.9 kcal/mol. Multidrug resistance to lopinavir is enthalpically driven and increases through a decrease in the protein-ligand van der Waals interaction, principally due to the V82A/I84V mutation, and an increase in net electrostatic repulsion due to water-mediated disruption of protein-ligand interactions in the catalytic region. Furthermore, we correctly rank, to within 1 kcal/mol of experiment, the substantially increased chemical potency of lopinavir binding to the wild-type protease compared to saquinavir and show that lopinavir takes advantage of a decreased net electrostatic repulsion to confer enhanced binding. Our approach is dependent on the combined use of petascale computing resources and on an automated simulation workflow to attain the required level of sampling and turn around time to obtain the results, which can be as little as three days. This level of performance promotes integration of such methodology with clinical decision support systems for the optimization of patient-specific therapy.