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3DLigandSite: Structure-based prediction of protein-ligand binding sites

McGreig, Jake E., Uri, Hannah, Antczak, Magdalena, Sternberg, Michael J.E., Michaelis, Martin, Wass, Mark N. (2022) 3DLigandSite: Structure-based prediction of protein-ligand binding sites. Nucleic Acids Research, . ISSN 0305-1048. E-ISSN 1362-4962. (doi:10.1093/nar/gkac250) (KAR id:93989)

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https://doi.org/10.1093/nar/gkac250

Abstract

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualisation of the protein and the predicted binding sites.

Item Type: Article
DOI/Identification number: 10.1093/nar/gkac250
Uncontrolled keywords: ligand-binding site prediction, structural bioinformatics, machine learning
Subjects: Q Science
Divisions: Divisions > Division of Natural Sciences > Biosciences
Depositing User: Mark Wass
Date Deposited: 11 Apr 2022 13:12 UTC
Last Modified: 04 Jul 2023 12:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93989 (The current URI for this page, for reference purposes)

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