Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.
Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening (VS), which is most frequently manifested in the scoring functions' inability to discriminate between true ligands vs known nonbinders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from VS. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of VS in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE::HMSCORE, ChemScore, PLP, and Chemgauss3, in 6 out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP::LBX). We also compare our method with FLAP::RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP::RBLB, hinting effective directions for best VS applications. We suggest that this integrative VS approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.
Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Jui-Hua
JH
Hsieh
Shuangye
S
Yin
Xiang S
XS
Wang
Shubin
S
Liu
Nikolay V
NV
Dokholyan
Alexander
A
Tropsha
D000465:Algorithms; D001665:Binding Sites; D001696:Biomechanical Phenomena; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D048088:Informatics; D008024:Ligands; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation; D010447:Peptide Hydrolases / Q000737:chemistry*; D011480:Protease Inhibitors / Q000737:chemistry*; D011485:Protein Binding; D012107:Research Design; D013816:Thermodynamics; D014584:User-Computer Interface
D008024:Ligands; D011480:Protease Inhibitors; D010447:Peptide Hydrolases