Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery

Abstract

The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.

In Silico Selection and In Vitro Evaluation of New Molecules That Inhibit the Adhesion of Streptococcus mutans through Antigen I/II

“…A fundamental step for the search of molecules based on virtual structures, is the pocket selection, these sites must have typical characteristics such as concave, have a variety of hydrogen bridge donors and acceptors and hydrophobic characteristics [ 47 ]; otherwise, false negatives may occur when selecting molecules for in vitro assays, since errors may occur when there are no binding sites in the protein or when homology models are used, causing for example, small-volume pockets to be selected that will generate incorrect unions or conformations [ 48 ]. For that reason, in this study, two multiservers or specific programs were used for protein ligand binding site prediction, which have different selection algorithms; the MetaPocket uses a consensus method based on the predicted sites of four free access programs LIGSITEcs, PASS, Q-SiteFinder and SURFNET, which are combined to improve the success rate of the prediction and which is based on the geometry and surface of the proteins [ 49 ], unlike the COACH that uses the consensus of two methods, one based on the comparison of specific binding substructures (TM-SITE) and the other on the alignment of the sequence profile (S-SITE), for predictions of binding sites based on known proteins [ 50 ] ( Figure S2 ).…”

LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

“…We downloaded Hs PARP1, Hs Pin1p, and Sc Hxk2p crystal structures (6BHV [ 12 ], 3TDB [ 13 ], and 1IG8 [ 14 ], respectively) from the RCSB Protein Data Bank [ 15 , 16 ]. We selected the 6BHV Hs PARP1 structure because its co-crystallized ligand, benzamide adenine nucleotide, was the largest by mass of 46 co-crystallized ligands considered (Additional file 1 : Table S1), and some studies suggest that larger binding-pocket conformations are more amenable to VS [ 17 , 18 ]. We selected the 3TDB Hs Pin1p structure because its co-crystallized ligand was the largest by mass of 27 considered (Additional file 1 : Table S2).…”

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

“…In comparison with LBVS, SBVS possesses high accuracy and precision. However, SBVS is associated with the problem of an increasing number of disease-causing proteins and their complicated conformations [ 164 ]. To use ML for VS, there should be a filtered training set comprising of known active and inactive compounds.…”