COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking

By Qi Wu, Zhenling Peng, Yang Zhang, Jianyi Yang. 2018

259 citations recorded by the source corpus.

Abstract The identification of protein–ligand binding sites is critical to protein function annotation and drug discovery. The consensus algorithm COACH developed by us represents one of the most efficient approaches to protein–ligand binding sites prediction. One of the most commonly seen issues with the COACH prediction are the low quality of the predicted ligand-binding poses, which usually have severe steric clashes to the protein structure. Here, we present COACH-D, an enhanced version of COACH by utilizing molecular docking to refine the ligand-binding poses. The input to the COACH-D server is the amino acid sequence or the three-dimensional structure of a query protein. In addition, the users can also submit their own ligand of interest. For each job submission, the COACH algorithm is first used to predict the protein–ligand binding sites. The ligands from the users or the templates are then docked into the predicted binding pockets to build their complex structures. Blind tests show that the algorithm significantly outperforms other ligand-binding sites prediction methods. Benchmark tests show that the steric clashes between the ligand and the protein structures in the COACH models are reduced by 85% after molecular docking in COACH-D. The COACH-D server is freely available to all users at http://yanglab.nankai.edu.cn/COACH-D/.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, methodology): “COACH-D [74] represents an enhanced version of COACH [57] employing molecular docking to refine protein–ligand binding poses.”
UniProt: the Universal Protein Knowledgebase in 2023 (2022, background): “…ligand data taking advantage of the ChEBI chemical ontology hierarchy and chemical structure data, improves interoperability with other resources of cognate ligands (11–14), and provides better support for the development of computational approaches to predict protein-ligand interactions (15–17).”
CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting (2022, methodology): “Compared to the state-of-the-art blind docking servers, such as MTiAutoDock (12), SwissDock (9) and COACH-D (10), CB-Dock2 exhibited at least 16% higher success rate (Figure 2B), which suggests the significance of the update.”
AlphaFold2-aware protein-DNA binding site prediction using graph transformer (2022, methodology): “We compared GraphSite with three sequence-based (SVMnuc, NCBRPred and DNAPred) and four structure-based (COACH-D, NucBind, DNABind and GraphBind) predictors on Test_129, where GraphSite outperforms all other methods significantly (shown in Table 3 ).”