Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model
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In order to improve the pose prediction performance of docking methods, we have previously developed the pose prediction using shape similarity (PoPSS) method. It identifies a ligand conformation of the highest shape similarity with target protein crystal ligands. The identified ligand conformation is then placed into the target protein binding pocket and refined using side-chain repacking and Monte Carlo energy minimization. Subsequently, we have reported a modification to PoPSS, named as PoPSS-Lite, using a simple grid-based energy minimization for side-chain repacking and Tversky correlation coefficient as the similarity metric. This modification has improved the pose prediction performance and PoPSS-Lite was one of the top performers in D3R GC3. Here we report a further modification to PoPSS that utilizes a continuum solvent model to account for water mediated protein ligand interactions. In this approach, named as PoPSS-PB, the ligand conformation of the highest shape similarity with crystal ligands is refined along with the target protein binding site by incorporating the Poisson–Boltzmann electrostatics. The performance of PoPSS-PB along with PoPSS and PoPSS-Lite was prospectively evaluated in D3R GC4. PoPSS-PB not only demonstrated excellent performance with mean and median RMSDs of 1.20 and 1.13 Å but also achieved improved performance over PoPSS and PoPSS-Lite. Furthermore, the comparison with other D3R GC4 pose prediction submissions revealed admirable performance. Our results showed that the binding poses of ligands with unknown binding modes can be successfully predicted by utilizing ligand 3D shape similarity with known crystallographic ligands and that taking the solvation into consideration improves pose prediction.
KeywordsMolecular docking Pose prediction Ligand 3D shape similarity Drug design data resource D3R D3R Grand Challenge 4
We acknowledge RIKEN ACCC for the supercomputing resources at the Hokusai BigWaterfall supercomputer used in this study. This research was supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP18am0101082. We thank members of our lab for help and discussions.
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