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

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Title: 3DLigandSite: Structure-based prediction of protein-ligand binding sites
Authors: McGreig, J
Uri, H
Antczak, M
Michaelis, M
Sternberg, M
Wass, M
Item Type: Journal Article
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 visualization of the protein and the predicted binding sites.
Issue Date: 12-Apr-2022
Date of Acceptance: 29-Mar-2022
URI: http://hdl.handle.net/10044/1/96374
DOI: 10.1093/nar/gkac250
ISSN: 0305-1048
Publisher: Oxford University Press
Journal / Book Title: Nucleic Acids Research
Copyright Statement: © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Wellcome Trust
Wellcome Trust
Biotechnology and Biological Sciences Research Council (BBSRC)
Funder's Grant Number: WT/104955/Z/14/Z
Keywords: 05 Environmental Sciences
06 Biological Sciences
08 Information and Computing Sciences
Developmental Biology
Publication Status: Published online
Online Publication Date: 2022-04-03
Appears in Collections:Faculty of Natural Sciences

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