Improving Virtual Screening Performance against Conformational Variations of Receptors by Shape Matching with Ligand Binding Pocket

Abstract

In this report, we present a novel virtual high-throughput screening methodology to assist in computer-aided drug discovery. Our method, designated as SLIM, involves ligand-free shape and chemical feature matching. The procedure takes advantage of a negative image of a binding pocket in a target receptor. The negative image is a set of virtual atoms representing the inner shape and chemical features of the binding pocket. Using this image, SLIM implements a shape-based similarity search based on molecular volume superposition for the ensemble of conformers of each molecule. The superposed structures, prioritized by shape similarity, are subjected to comparison of chemical feature similarities. To validate the merits of the SLIM method, we compared its performance with those of three distinct widely used tools ROCS, GLIDE, and GOLD. ROCS was selected as a representative of the ligand-centric methods, and docking programs GLIDE and GOLD as representatives of the receptor-centric methods. Our data suggest that SLIM has overall hit ranking ability that is comparable to that of the docking method, retaining the high computational speed of the ligand-centric method. It is notable that the SLIM method offers consistently reliable screening quality against conformational variations of receptors, whereas the docking methods have limited screening performance.

Identification, Design and Bio-Evaluation of Novel Hsp90 Inhibitors by Ligand-Based Virtual Screening

“…Virtual screening of commercial available databases forms one aspect of an efficiently approach to find novel and potential leads for further development [31]. In this study, the best-ranked four-featured pharmacophore model, Hypo1, was used to screen SPECS database using 3D Database Search protocol in DS.…”

Biophysical and Structural Characterization of a Sequence-diverse Set of Solute-binding Proteins for Aromatic Compounds

“…The selected models were then employed for binding site prediction and ligand docking using BSP-SLIM. The method identifies ligand-binding sites by matching the predicted models to holo-structures in the Protein Data Bank (30). Ligand docking is then performed by pairing local shape and chemical features between ligand and the binding pockets.…”

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein–Protein Interactions

“…In the absence of small-molecule inhibitors (or natural substrates), others have also used features of the receptor to build pharmacophores 9, 10, 13, 15, 83-86 ; like these, the “exemplars” we describe here are essentially receptor-based pharmacophores. In general there are two types of approaches for building receptor-based pharmacophores: either by using the geometry of the receptor to generate the pharmacophore directly 10, 13 (as we have done in this study), or else by docking a series of “probe” compounds against the receptor to identify potential interaction sites at which these probes accumulate 9, 15, 83-86 .…”

“…Individual interactions presented by different probe molecules are then combined into a consensus pharmacophore, and used as a template to identify larger compounds that simultaneously recapitulate the interactions from multiple probes. As an alternative, other approaches instead define desirable three-dimensional properties of candidate ligands using the “negative image” of the binding pocket 10, 13 .…”