Greenidge, Paulette, Lewis, Richard and Ertl, Peter (2016) Boosting Pose Ranking Performance via Rescoring with MM-GBSA. Chemical Biology and Drug Design. ISSN 17470277

Abstract

In a previous self-docking study, we have shown that structure reproduction performance can be improved by rescoring GOLD ChemPLP docking poses with the MM-GBSA scoring function. In this work, we attempt to better understand this improvement. We increase the size and diversity of the examined dataset, and perform self-docking using a curated set of over 700 complexes. The “scoring problem” (the inability to unambiguously identify the biologically most relevant pose) is assessed with respect to both MM-GBSA and ChemPLP scoring functions. Heavy atom root mean squared deviation (RMSD) values are used to compare the docked poses with the crystallographic ones. In addition to this standard metric, “partial matching” is introduced. This algorithm captures the visual observation that the majority of a ligand can be well docked but yet report a RMSD 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 MM-GBSA scoring function improves pose ranking by better discriminating against non-cognate like poses. However, the key finding of this study, is that absolute rank is less important than the score between the docking poses. Thus, poses should not be retained solely on their ranks, but on the score difference relative to the best ranked pose.

Item Type:	Article
Keywords:	docking, scoring, pose ranking, MM-GBSA, ChemPLP, GOLD
Date Deposited:	26 Apr 2016 23:45
Last Modified:	26 Apr 2016 23:45
URI:	https://oak.novartis.com/id/eprint/24911