Abstract
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 obtain more accurate dockings in medium-size virtual screening experiments of TβR1, 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 TβR1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.
Highlights
Due to the vast improvement of computing power and the rapid development of computational chemistry and biology, computer-aided drug design (CADD) technology has been successfully applied in drug discovery, accelerating the research process and reducing the associated costs and risks [1]
The performance of the docking programs for predicting the binding mode between ligand and Transforming growth factor-β type 1 receptor (TβR1) was evaluated with a success rate. 22 known active conformations of TβR1 inhibitors were re-docked into the corresponding binding pockets
An increasingly large number of studies reported that the docking method and the scoring functions are sensitive to receptors
Summary
Due to the vast improvement of computing power and the rapid development of computational chemistry and biology, computer-aided drug design (CADD) technology has been successfully applied in drug discovery, accelerating the research process and reducing the associated costs and risks [1]. Virtual screening is a common CADD technology used for the identification of hit molecules from large-scale compound libraries, providing a highly efficient approach to discover new compounds in due to its low accuracy, DBVS suffers from constant criticism. This method involves two basic processes: positioning ligands into a protein active site, scoring and ranking docked ligands. Several studies on docking and scoring methods demonstrated that most of the docking methods and scoring functions are sensitive to the receptor, Wang et al BMC Chemistry (2020) 14:52 and conclusions on different receptors may be inconsistent with each other [7,8,9,10]. New forms of examination are necessary to maximize the success rate of docking and scoring methods, to determine the best parameters for individual protein targets
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