Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Abstract

Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound's crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol's pose prediction accuracy (80-82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

“…Among these strategies, the combination of different scoring functions (consenus scoring) to improve VS hit rates is a well known practice introduced by Charifson et al in 1999 31 that is still commonly applied [33][34][35][36][37][38] . A very recent approach concerns the introduction of a ligand-based component within the docking protocol that consists in exploiting the ligand information relative to crystallographic ligand-protein complexes for guiding pose selection and ranking, in a sort of combined ligand/receptor-based approach 12,[39][40][41] . Surprisingly, only very few studies evaluated the possibility of combining the results of different docking methods (consensus docking) to obtain reliable ligand binding poses and to improve the success rate in VS studies, as recently reported by Houston and Walkinshaw 42 .…”

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

“…The first strategies for using crystallographic structures of ligand-protein complexes in docking methods are almost 20 years old [25,26]. Different ways of incorporating structural information have been proposed, for example by introducing spatial constraints targeting parts of the ligand that are common with a reference ligand, by guiding the placement of the ligand on reference atom-centered functions or electron density, or by scoring pose by 3D pharmacophore matching similarity [27,28,29]. Okuno et al suggested to combine multiple ligand-protein crystallographic structure into a reference grid [30,31].…”

Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2

“…First, the obtained data confirm the well-known bias of fast energy-based scoring functions to rank improper poses among the top-ranked solutions. Any knowledge-based rescoring scheme [32][33][34][35] similar in spirit to GRIM is therefore preferable to relying only on docking scores. In our current rescoring protocol, poses with very low predicted binding energies (predicted pkd < 2) were filtered out and not subjected to GRIM rescoring.…”