Scoring Ensembles of Docked Protein: Ligand Interactions for Virtual Lead Optimization

Ensembles of protein structures to simulate protein flexibility are widely used throughout several applications including virtual lead optimization where they have been shown to improve ligand ranking. Yet, there is no established convention for weighting individual scores generated from ensemble members. To investigate the best method for weighting ensemble scores for proper ligand ranking, a series of dihydrofolate reductase inhibitors was docked to ensembles of Candida albicans dihydrofolate reductase (CaDHFR) structures created from a molecular dynamics (MD) simulation. From a single MD simulation, two ensemble collections were generated, one of which was subjected to a minimization procedure to create a group of structures of equal probability. As expected, ligand ranking accuracy was significantly improved when Boltzmann weighting was applied to the energies of the ensemble without structural minimization (60%), relative to that achieved with averaging (36%). However, accuracy was further improved (72%) by averaging docking scores across a minimized ensemble. To examine whether this accuracy results from structural variation in the single trajectory versus the possibility that error is minimized by averaging, a third collection of receptor structures was created in which each member was taken from an independent molecular dynamics simulation after minimization. Comparison of the docking accuracy results from the single trajectory (72%) to this third collection (61%) showed decreased accuracy, suggesting that ligands are more accurately oriented and assessed when docked to the minimized ensemble from a single MD trajectory, an effect that is more than simply error minimization. Averaging docking scores over a minimized ensemble of another target, influenza A neuraminidase, yielded a ligand ranking accuracy of 83%, representing a 24% improvement over other methods tested.



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