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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.<\/jats:p>","DOI":"10.1093\/nar\/gkac250","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T14:03:16Z","timestamp":1649772196000},"page":"W13-W20","source":"Crossref","is-referenced-by-count":56,"title":["3DLigandSite: structure-based prediction of protein\u2013ligand binding sites"],"prefix":"10.1093","volume":"50","author":[{"given":"Jake E","family":"McGreig","sequence":"first","affiliation":[{"name":"School of Biosciences, Division of Natural Sciences, University of Kent , Canterbury , Kent \u00a0CT2 7NJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Uri","sequence":"additional","affiliation":[{"name":"School of Biosciences, Division of Natural Sciences, University of Kent , Canterbury , Kent \u00a0CT2 7NJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Antczak","sequence":"additional","affiliation":[{"name":"School of Biosciences, Division of Natural Sciences, University of Kent , Canterbury , Kent \u00a0CT2 7NJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J E","family":"Sternberg","sequence":"additional","affiliation":[{"name":"Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London , London \u00a0SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5710-5888","authenticated-orcid":false,"given":"Martin","family":"Michaelis","sequence":"additional","affiliation":[{"name":"School of Biosciences, Division of Natural Sciences, University of Kent , Canterbury , Kent \u00a0CT2 7NJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5428-6479","authenticated-orcid":false,"given":"Mark\u00a0N","family":"Wass","sequence":"additional","affiliation":[{"name":"School of Biosciences, Division of Natural Sciences, University of Kent , Canterbury , Kent \u00a0CT2 7NJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"key":"2022070423581404600_B1","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: the universal protein knowledgebase in 2021","volume":"49","author":"UniProt Consortium","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"2022070423581404600_B2","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1186\/s13059-019-1835-8","article-title":"The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens","volume":"20","author":"Zhou","year":"2019","journal-title":"Genome Biol."},{"key":"2022070423581404600_B3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s13059-016-1037-6","article-title":"An expanded evaluation of protein function prediction methods shows an improvement in accuracy","volume":"17","author":"Jiang","year":"2016","journal-title":"Genome Biol."},{"key":"2022070423581404600_B4","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.1093\/bioinformatics\/btz115","article-title":"Finding enzyme cofactors in Protein Data Bank","volume":"35","author":"Mukhopadhyay","year":"2019","journal-title":"Bioinformatics"},{"key":"2022070423581404600_B5","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1002\/prot.21741","article-title":"Evolution of binding sites for zinc and calcium ions playing structural roles","volume":"71","author":"Torrance","year":"2008","journal-title":"Proteins"},{"key":"2022070423581404600_B6","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.csbj.2020.02.008","article-title":"Exploring the computational methods for protein\u2013ligand binding site prediction","volume":"18","author":"Zhao","year":"2020","journal-title":"Comput. 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