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BMC BioinformaticsBioinformaticsPeer-reviewed journal article

Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides

Sandeep SinghiD, Hemant Kumar SrivastavaiD, Gaurav KishoriD, Piyush AgrawaliD, Gajendra P. S. RaghavaiD

PDFDOIPreprint

Abstract

In the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking. In this study, we evaluated the performance of seven widely used docking methods (AutoDock, AutoDock Vina, DOCK 6, PLANTS, rDock, GEMDOCK and GOLD) on a dataset of 57 peptide-ligand complexes. Though these methods have been developed for docking ligands to proteins but we evaluate their ability to dock ligands to peptides. First, we compared TOP docking pose of these methods with original complex and achieved average RMSD from 4.74[A] for AutoDock to 12.63[A] for GEMDOCK. Next we evaluated BEST docking pose of these methods and achieved average RMSD from 3.82[A] for AutoDock to 10.83[A] for rDock. It has been observed that ranking of docking poses by these methods is not suitable for peptide-ligand docking as performance of their TOP pose is much inferior to their BEST pose. AutoDock clearly shows better performance compared to the other six docking methods based on their TOP docking poses. On the other hand, difference in performance of different docking methods (AutoDock, AutoDock Vina, PLANTS and DOCK 6) was marginal when evaluation was based on their BEST docking pose. Similar trend has been observed when performance is measured in terms of success rate at different cut-off values. In order to facilitate scientific community a web server PLDbench has been developed (http://webs.iiitd.edu.in/raghava/pldbench/).

Citations
207
FWCI
7.45
Field percentile
top 2%

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