{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T12:39:53Z","timestamp":1776170393690,"version":"3.50.1"},"reference-count":82,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["32070662"],"award-info":[{"award-number":["32070662"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61832019"],"award-info":[{"award-number":["61832019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["32030063"],"award-info":[{"award-number":["32030063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein\u2013protein\/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The datasets and source codes can be accessed at https:\/\/github.com\/WeiLab-Biology\/DeepProSite. The proposed DeepProSite can be accessed at https:\/\/inner.wei-group.net\/DeepProSite\/.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad718","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T20:13:18Z","timestamp":1701202398000},"source":"Crossref","is-referenced-by-count":86,"title":["DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4644-1464","authenticated-orcid":false,"given":"Yitian","family":"Fang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai 200040, China"},{"name":"Peng Cheng Laboratory , Shenzhen 518055, China"}]},{"given":"Yi","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University , Jinan, Shandong 250100, China"}]},{"given":"Qin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}]},{"given":"Zhixiang","family":"Ren","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory , Shenzhen 518055, China"}]},{"given":"Qianmu","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4200-7502","authenticated-orcid":false,"given":"Dong-Qing","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai 200040, China"},{"name":"Peng Cheng Laboratory , Shenzhen 518055, China"}]}],"member":"286","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"2023121721544223500_btad718-B1","article-title":"Sequence and structure based deep learning models for the identification of peptide binding sites","volume":"33","author":"Abdin","year":"2020","journal-title":"Adv Neural Inf Process 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