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

Abstract

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

“…The former translates the observed protein-ligand interactions into pharmacophoric features and quantitative structure-activity relationship (QSAR) models [ 39 , 69 , 70 ], which have been used for several applications, such as VS, the profiling of ligands, the analysis of pseudo-receptors, and de novo designs [ 71 , 72 , 73 , 74 ]. On the other hand, the combination of molecular similarity and docking techniques has been examined in the last years as an alternative procedure to assess the reliability of predicted poses of ligands by measuring the overlay against suitable templates [ 75 , 76 , 77 , 78 , 79 , 80 ].…”

“…( c ) The ROCS score is based on the Tversky coefficient. Reprinted with permission from Springer Nature [ 79 ].…”

Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking

“…Our proposed protocol is to first screen with ligand-based models, because they are so fast. However, at the end of a structure-based virtual screening pipeline, ligands should be available as protein–ligand complexes, so that other types of structure-based filtering is also possible (rescoring, − pharmacophore filter, molecular dynamics simulation after docking, etc.). Also, experiments show that docking usually has a better very early enrichment (EF 1% ) than the corresponding regressor (Table ).…”

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

“…Although these ligands represent a vast amount of information, they are generally not used explicitly in most docking algorithms, which rely primarily on protein structures. Some docking algorithms , are able to consider the similarity to a single reference ligand to improve the prediction of the pose of the docked ligand, and there are methods , that allow rescoring of the docked poses of fragments and ligands based on their similarity to a single or multiple known reference ligands.…”