ArticleJournal of computer-aided molecular design2019
Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Docking-based virtual screening: Past, present, and future.Biophysical journal · 2026Review
- Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design.Communications in information and systems · 2024Article
- Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies.Proteins · 2023Article
- CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking.Computational and structural biotechnology journal · 2023Article
- An allosteric modulator activates BK channels by perturbing coupling between CaNature communications · 2022Article
- Structural Prediction of Peptide-MHC Binding Modes.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies.International journal of molecular sciences · 2021Article
- Modulating the voltage sensor of a cardiac potassium channel shows antiarrhythmic effects.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A PIPCommunications biology · 2020Article
- Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4.Journal of computer-aided molecular design · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Drug Design Data Resource (D3R) continues to release valuable benchmarking datasets to promote improvement and development of computational methods for new drug discovery. We have developed several methods for protein-ligand binding mode prediction during the participation in the D3R challenges. In the present study, these methods were integrated, automated, and systematically tested using the large-scale data from Continuous Evaluation of Ligand Pose Prediction (CELPP) and a subset of Grand challenge 3 (GC3). The results show that current molecular docking methods benefit from the increasing number of protein-ligand complex structures deposited in Protein Data Bank. Using an appropriate protein structure for docking significantly improves the success rate of the binding mode prediction. The results of our template-based method and docking method are compared and discussed. Our future direction include the combination of these two methods for binding mode prediction.
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Registered trials
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