Boosting Pose Ranking Performance via Rescoring with MM-GBSA.

In this self-docking study, we address the so-called scoring problem. The 'scoring problem' is the inability to unambiguously identify biologically the most relevant pose, when the docking score is the main selection criterion. We use the Molecular Mechanics/Generalized Born Surface Area and ChemPLP scoring functions to assess the structure reproduction performance. Heavy-atom root-mean-squared deviation values are used to compare the docked poses with the crystallographic ones. 'Partial matching' is introduced. This algorithm captures the visual observation that the majority of a ligand can be well docked, but yet report a root-mean-squared deviation value of >2.0 Å. Often this is attributable to arbitrary placements of flexible side chains in undefined solvent regions. The metrics introduced by this algorithm are applicable for assessing the contribution of ligand sampling to the scoring problem. It is shown that rescoring ChemPLP poses with the Molecular Mechanics/Generalized Born Surface Area scoring function improves pose ranking by better discriminating against non-cognate-like poses. We conclude that poses should not be retained solely on their ranks, but on the score difference relative to the best-ranked pose.