Dataset Information

Docking-based virtual screening of T?R1 inhibitors: evaluation of pose prediction and scoring functions.

ABSTRACT: To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (T?R1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 T?R1 protein-ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of T?R1, with a successful docking rate of 95%. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of T?R1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.

SUBMITTER: Wang S

PROVIDER: S-EPMC7427878 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions.

Wang Shuai S Jiang Jun-Hao JH Li Ruo-Yu RY Deng Ping P

BMC chemistry 20200814 1

To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein-ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to o ...[more]

PMID: 32818203

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Project description: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.

Dataset Information

Docking-based virtual screening of T?R1 inhibitors: evaluation of pose prediction and scoring functions.

Publications

Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions.

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