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
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/).
- assay: protein structure
- research focus: benchmarking
- research focus: protein structure prediction
- research focus: protein design
- research focus: drug response / drug discovery
- approach: statistical model
- approach: benchmark study
- Citations
- 207
- FWCI
- 7.45
- Field percentile
- top 2%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Isak Johansson-Åkhe, Claudio Mirabello, Björn Wallner·Frontiers in BioinformaticsPeer-reviewed·Oct 25, 2021
- Bento: Benchmarking Classical and AI Docking on Drug Design-Relevant Data
Marina A. Pak, Daria Frolova, … Dmitry N. Ivankov·bioRxiv·Dec 30, 2025
- Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning
Zicong Zhang, Jacob C. Verburgt, … Daisuke Kihara·ACS OmegaPeer-reviewed·Jun 16, 2025
- InterPep2: Global Peptide-Protein Docking with Structural Templates
Isak Johansson-Åkhe, Claudio Mirabello, Björn Wallner·bioRxiv·Oct 21, 2019
- ArtiDock: accurate Machine Learning approach to protein-ligand docking optimized for high-throughput virtual screening
Taras Voitsitskyi, Ihor Koleiev, … Алан Нафієв·Journal of Chemical Information and ModelingPeer-reviewed·Jan 30, 2026
- High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock
Nawsad Alam, Oriel Goldstein, … Ora Schueler‐Furman·PLoS Computational BiologyPeer-reviewed·Dec 27, 2017