{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T11:12:48Z","timestamp":1783768368168,"version":"3.55.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["2R35GM138146"],"award-info":[{"award-number":["2R35GM138146"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI2208679"],"award-info":[{"award-number":["DBI2208679"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>xBind is an interactive, freely accessible, and fully configurable webserver for large language model (LLM)-enabled cross-molecular protein binding-site prediction. xBind leverages LLM embeddings from the ESM-2 model together with sequence- and structure-derived features to predict protein\u2013protein, protein\u2013DNA, and protein\u2013RNA binding sites using symmetry-aware deep graph neural networks. The input to xBind is either a single-chain protein sequence in FASTA format or a monomer protein structure in PDB or mmCIF format and it outputs predicted residue-level binding sites of the input protein with its pre-selected interaction partner. The customizable xBind web interface provides: (i) choice of interaction partners including protein\u2013protein, protein\u2013DNA, and protein\u2013RNA; (ii) on-the-fly AlphaFold-based protein structure prediction for sequence-only inputs; (iii) on-demand selection of the likelihood threshold for calibrating structure-aware binding site annotations; (iv) interactive and interpretable web-based results, including sequence and structural visualizations and plots of residue-level binding likelihoods with user-adjustable threshold calibration; and (v) extensive help information for usage and results interpretation through a web-based tutorial and guide. xBind is freely available at\u00a0https:\/\/fusion.cs.vt.edu\/xBind.<\/jats:p>","DOI":"10.1093\/nar\/gkag425","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T11:42:39Z","timestamp":1776944559000},"page":"W337-W342","source":"Crossref","is-referenced-by-count":0,"title":["xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction"],"prefix":"10.1093","volume":"54","author":[{"given":"Xinyu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Virginia Tech , Blacksburg, VA 24061 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyue","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech , Blacksburg, VA 24061 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumit","family":"Tarafder","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech , Blacksburg, VA 24061 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9630-0141","authenticated-orcid":false,"given":"Debswapna","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech , Blacksburg, VA 24061 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"2026071106373998300_B1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1073\/pnas.93.1.13","article-title":"Principles of protein\u2013protein interactions","volume":"93","author":"Jones","year":"1996","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026071106373998300_B2","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1073\/pnas.0409522102","article-title":"Conserved patterns of protein interaction in multiple species","volume":"102","author":"Sharan","year":"2005","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026071106373998300_B3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1038\/nature07348","article-title":"Stepwise chromatin remodelling by a cascade of transcription initiation of non-coding RNAs","volume":"456","author":"Hirota","year":"2008","journal-title":"Nature"},{"key":"2026071106373998300_B4","doi-asserted-by":"publisher","first-page":"7364","DOI":"10.1093\/nar\/gkq617","article-title":"Genomic repertoires of DNA-binding transcription factors across the tree of life","volume":"38","author":"Charoensawan","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2026071106373998300_B5","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1038\/nature11503","article-title":"Structure-based prediction of protein\u2013protein interactions on a genome-wide scale","volume":"490","author":"Zhang","year":"2012","journal-title":"Nature"},{"key":"2026071106373998300_B6","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1126\/science.aaw6718","article-title":"Protein interaction networks revealed by proteome coevolution","volume":"365","author":"Cong","year":"2019","journal-title":"Science"},{"key":"2026071106373998300_B7","doi-asserted-by":"publisher","first-page":"e1011435","DOI":"10.1371\/journal.pcbi.1011435","article-title":"E(3) equivariant graph neural networks for robust and accurate protein\u2013protein interaction site prediction","volume":"19","author":"Roche","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2026071106373998300_B8","doi-asserted-by":"publisher","first-page":"e27","DOI":"10.1093\/nar\/gkae039","article-title":"EquiPNAS: improved protein\u2013nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks","volume":"52","author":"Roche","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2026071106373998300_B9","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-1-0716-4623-6_9","article-title":"Advances in language-model-informed protein\u2013nucleic acid binding site prediction","volume":"2941","author":"Tarafder","year":"2025","journal-title":"Methods Mol Biol"},{"key":"2026071106373998300_B10","doi-asserted-by":"publisher","first-page":"9012","DOI":"10.1038\/s41467-025-64512-w","article-title":"PLM-interact: extending protein language models to predict protein\u2013protein interactions","volume":"16","author":"Liu","year":"2025","journal-title":"Nat Commun"},{"key":"2026071106373998300_B11","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1038\/s41467-021-21636-z","article-title":"Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences","volume":"12","author":"Green","year":"2021","journal-title":"Nat Commun"},{"key":"2026071106373998300_B12","doi-asserted-by":"publisher","first-page":"100275","DOI":"10.1016\/j.crmeth.2022.100275","article-title":"Illuminating the dark protein\u2013protein interactome","volume":"2","author":"Sharifi\u00a0Tabar","year":"2022","journal-title":"Cell Rep Methods"},{"key":"2026071106373998300_B13","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1038\/s41467-023-36720-9","article-title":"Evaluating native-like structures of RNA\u2013protein complexes through the deep learning method","volume":"14","author":"Zeng","year":"2023","journal-title":"Nat Commun"},{"key":"2026071106373998300_B14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10969-011-9110-6","article-title":"Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures","volume":"12","author":"Ghersi","year":"2011","journal-title":"J Struct Funct Genomics"},{"key":"2026071106373998300_B15","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/s41592-022-01490-7","article-title":"ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction","volume":"19","author":"Tubiana","year":"2022","journal-title":"Nat Methods"},{"key":"2026071106373998300_B16","doi-asserted-by":"publisher","first-page":"bbae162","DOI":"10.1093\/bib\/bbae162","article-title":"A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond","volume":"25","author":"Jia","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026071106373998300_B17","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2026071106373998300_B18","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2026071106373998300_B19","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"2026071106373998300_B20","doi-asserted-by":"publisher","first-page":"e2303499120","DOI":"10.1073\/pnas.2303499120","article-title":"The transformative power of transformers in protein structure prediction","volume-title":"Proc Natl Acad Sci USA","author":"Moussad","year":"2023"},{"key":"2026071106373998300_B21","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2020","journal-title":"Nat Methods"},{"key":"2026071106373998300_B22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.09844","article-title":"E(n) equivariant Graph Neural Networks","author":"Satorras","year":"2021"},{"key":"2026071106373998300_B23","doi-asserted-by":"publisher","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume-title":"Proc Natl Acad Sci USA","author":"Rives","year":"2021"},{"key":"2026071106373998300_B24","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026071106373998300_B25","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.08219","article-title":"Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds","author":"Thomas","year":"2018"},{"key":"2026071106373998300_B26","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.cels.2021.05.017","article-title":"Learning the protein language: evolution, structure, and function","volume-title":"Cell Syst","author":"Bepler","year":"2021"},{"key":"2026071106373998300_B27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2006.15222","article-title":"BERTology meets biology: interpreting attention in protein language models","author":"Vig","year":"2021"},{"key":"2026071106373998300_B28","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1006\/jmbi.1997.1232","article-title":"Analysis of protein\u2013protein interaction sites using surface patches","volume":"272","author":"Jones","year":"1997","journal-title":"J Mol Biol"},{"key":"2026071106373998300_B29","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1006\/jmbi.1997.1233","article-title":"Prediction of protein\u2013protein interaction sites using patch analysis","volume":"272","author":"Jones","year":"1997","journal-title":"J Mol Biol"},{"key":"2026071106373998300_B30","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","article-title":"Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network","volume":"38","author":"Yuan","year":"2021","journal-title":"Bioinformatics"},{"key":"2026071106373998300_B31","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1093\/nar\/gkab044","article-title":"GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues","volume":"49","author":"Xia","year":"2021","journal-title":"Nucleic Acids Res"}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/54\/W1\/W337\/68210703\/gkag425.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/54\/W1\/W337\/68210703\/gkag425.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:37:51Z","timestamp":1783766271000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/54\/W1\/W337\/8667315"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":31,"journal-issue":{"issue":"W1","published-print":{"date-parts":[[2026,7,11]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkag425","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,7,7]]},"published":{"date-parts":[[2026,5,5]]}}}