{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T10:22:51Z","timestamp":1789813371349,"version":"4.0.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003653","name":"Korea National Institute of Health","doi-asserted-by":"publisher","award":["2024-ER-0801-01"],"award-info":[{"award-number":["2024-ER-0801-01"]}],"id":[{"id":"10.13039\/501100003653","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00414964"],"award-info":[{"award-number":["RS-2024-00414964"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research Foundation of Korea Basic Science Research Programs","award":["2021R1C1C1005184"],"award-info":[{"award-number":["2021R1C1C1005184"]}]},{"name":"National Research Foundation of Korea Basic Science Research Programs","award":["2020M3A9H5104237"],"award-info":[{"award-number":["2020M3A9H5104237"]}]},{"name":"National Research Foundation of Korea Basic Science Research Programs","award":["2020R1A5A1019023"],"award-info":[{"award-number":["2020R1A5A1019023"]}]},{"name":"Korea Health Technology R&D"},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI23C026400"],"award-info":[{"award-number":["HI23C026400"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS-2023-00304637"],"award-info":[{"award-number":["RS-2023-00304637"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The identification of protective antigens is fundamentally constrained by sparse annotations and pervasive label uncertainty in reverse vaccinology. Protective antigen\u2013antibody interactions are mediated by a limited subset of surface-accessible residues that form spatially coherent epitopes. This motivates modeling antigenicity at the residue level, where 3D structure provides critical context for functional immune recognition. Moreover, antigen datasets are inherently positive unlabeled: unannotated proteins may contain hidden positives, making reliable negatives difficult to obtain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present PU-GRAIL, a graph neural network framework that integrates protein language model embeddings with predicted 3D structures under positive-unlabeled learning. Trained and evaluated on three benchmark datasets, PU-GRAIL achieves competitive performance compared with existing methods. Importantly, the model\u2019s attention mechanism enables residue-level interpretation, identifying putative epitope regions that correspond to experimentally validated antibody-binding sites. Beyond standard benchmarks, we demonstrate practical utility through (i) severity-associated antigenicity patterns in SARS-CoV-2 patient cohorts, and (ii) proteome-wide vaccine candidate prioritization across 11 bacterial species.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The PU-GRAIL software is available at https:\/\/github.com\/jaeminjj\/PU-GRAIL.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag263","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:34:14Z","timestamp":1783442054000},"source":"Crossref","is-referenced-by-count":1,"title":["PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7922-1034","authenticated-orcid":false,"given":"Jaemin","family":"Jeon","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul 08826,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangwook","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Medicine, Seoul National University , Seoul 03080,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0675-4244","authenticated-orcid":false,"given":"Inuk","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Kyungpook National University , Daegu 41566,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4586-5062","authenticated-orcid":false,"given":"Kwangsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Medicine, Seoul National University , Seoul 03080,","place":["Republic of Korea"]},{"name":"Department of Transdisciplinary Medicine, Seoul National University Hospital , Seoul 03080,","place":["Republic of Korea"]},{"name":"Center for Data Science, Healthcare AI Research Institute, Seoul National University Hospital , Seoul 03080,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5332-2658","authenticated-orcid":false,"given":"Jinki","family":"Yeom","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , Seoul 08826,","place":["Republic of Korea"]},{"name":"Department of Biomedical Science, College of Medicine, Seoul National University , Seoul 08826,","place":["Republic of Korea"]},{"name":"Department of Microbiology and Immunology, Seoul National University , Seoul 08826,","place":["Republic of Korea"]},{"name":"Cancer Research Institute, Seoul National University , Seoul 03080,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"2026071008255259100_btag263-B1","doi-asserted-by":"crossref","first-page":"535","DOI":"10.2471\/BLT.08.053819","article-title":"WHO and UNICEF estimates of national infant immunization coverage: methods and processes","volume":"87","author":"Burton","year":"2009","journal-title":"Bull World Health Organ"},{"key":"2026071008255259100_btag263-B2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.3389\/fimmu.2019.00113","article-title":"Comparison of open-source reverse vaccinology programs for bacterial vaccine antigen discovery","volume":"10","author":"Dalsass","year":"2019","journal-title":"Front Immunol"},{"key":"2026071008255259100_btag263-B3","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.3389\/fimmu.2018.01369","article-title":"Predicting HLA CD4 immunogenicity in human populations","volume":"9","author":"Dhanda","year":"2018","journal-title":"Front Immunol"},{"key":"2026071008255259100_btag263-B4","doi-asserted-by":"crossref","first-page":"709","DOI":"10.3390\/vaccines8040709","article-title":"Bacterial immunogenicity prediction by machine learning methods","volume":"8","author":"Dimitrov","year":"2020","journal-title":"Vaccines (Basel)"},{"key":"2026071008255259100_btag263-B5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/1471-2105-8-4","article-title":"Vaxijen: a server for prediction of protective antigens, tumour antigens and subunit vaccines","volume":"8","author":"Doytchinova","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2026071008255259100_btag263-B6","doi-asserted-by":"crossref","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":"2026071008255259100_btag263-B7","doi-asserted-by":"crossref","first-page":"109975","DOI":"10.1016\/j.isci.2024.109975","article-title":"Sars-cov-2 envelope protein regulates innate immune tolerance","volume":"27","author":"Geanes","year":"2024","journal-title":"iScience"},{"key":"2026071008255259100_btag263-B8","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/s41579-021-00573-0","article-title":"Sars-cov-2 variants, spike mutations and immune escape","volume":"19","author":"Harvey","year":"2021","journal-title":"Nat Rev Microbiol"},{"key":"2026071008255259100_btag263-B9","doi-asserted-by":"crossref","first-page":"btaf080","DOI":"10.1093\/bioinformatics\/btaf080","article-title":"Epicpred: predicting phenotypes driven by epitope-binding TCRS using attention-based multiple instance learning","volume":"41","author":"Jeon","year":"2025","journal-title":"Bioinformatics"},{"key":"2026071008255259100_btag263-B10","doi-asserted-by":"crossref","first-page":"465","DOI":"10.5483\/BMBRep.2022.55.9.077","article-title":"Establishment of the large-scale longitudinal multi-omics dataset in covid-19 patients: data profile and biospecimen","volume":"55","author":"Jo","year":"2022","journal-title":"BMB Rep"},{"key":"2026071008255259100_btag263-B11","doi-asserted-by":"crossref","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":"2026071008255259100_btag263-B12","doi-asserted-by":"crossref","first-page":"elaf008","DOI":"10.1093\/bfgp\/elaf008","article-title":"Virusimmu: a novel ensemble machine learning approach for viral immunogenicity prediction","volume":"24","author":"Li","year":"2025","journal-title":"Brief Funct Genomics"},{"key":"2026071008255259100_btag263-B13"},{"key":"2026071008255259100_btag263-B14","doi-asserted-by":"crossref","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":"2026071008255259100_btag263-B15","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1186\/1471-2105-14-S4-S3","article-title":"Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics","volume":"14","author":"Lo","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026071008255259100_btag263-B16","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1021\/acsomega.4c07147","article-title":"Paprec: a pipeline for antigenicity prediction comparison methods across bacteria","volume":"10","author":"Martins","year":"2025","journal-title":"ACS Omega"},{"key":"2026071008255259100_btag263-B17","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1517\/14712598.2.8.895","article-title":"Reverse vaccinology: a genome-based approach for vaccine development","volume":"2","author":"Masignani","year":"2002","journal-title":"Expert Opin Biol Ther"},{"key":"2026071008255259100_btag263-B18","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.1093\/bioinformatics\/btaa119","article-title":"Vaxign-ml: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens","volume":"36","author":"Ong","year":"2020","journal-title":"Bioinformatics"},{"key":"2026071008255259100_btag263-B19","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.cell.2006.02.019","article-title":"Translating innate immunity into immunological memory: implications for vaccine development","volume":"124","author":"Pulendran","year":"2006","journal-title":"Cell"},{"key":"2026071008255259100_btag263-B20","first-page":"41"},{"key":"2026071008255259100_btag263-B21","doi-asserted-by":"crossref","first-page":"105401","DOI":"10.1016\/j.compbiomed.2022.105401","article-title":"Vaxi-dl: a web-based deep learning server to identify potential vaccine candidates","volume":"145","author":"Rawal","year":"2022","journal-title":"Comput Biol Med"},{"key":"2026071008255259100_btag263-B22","doi-asserted-by":"crossref","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":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026071008255259100_btag263-B23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/1471-2164-6-79","article-title":"Bcipep: a database of b-cell epitopes","volume":"6","author":"Saha","year":"2005","journal-title":"BMC Genomics"},{"key":"2026071008255259100_btag263-B24","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1038\/s41467-021-21879-w","article-title":"Deeptcr is a deep learning framework for revealing sequence concepts within t-cell repertoires","volume":"12","author":"Sidhom","year":"2021","journal-title":"Nat Commun"},{"key":"2026071008255259100_btag263-B25","doi-asserted-by":"crossref","first-page":"D339","DOI":"10.1093\/nar\/gky1006","article-title":"The immune epitope database (iedb): 2018 update","volume":"47","author":"Vita","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026071008255259100_btag263-B26","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/1472-6750-6-35","article-title":"Nerve: new enhanced reverse vaccinology environment","volume":"6","author":"Vivona","year":"2006","journal-title":"BMC Biotechnol"},{"key":"2026071008255259100_btag263-B27","doi-asserted-by":"publisher","author":"Wu","DOI":"10.1101\/2022.07.21.500999"},{"key":"2026071008255259100_btag263-B28","doi-asserted-by":"crossref","first-page":"D1073","DOI":"10.1093\/nar\/gkq944","article-title":"Protegen: a web-based protective antigen database and analysis system","volume":"39","author":"Yang","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2026071008255259100_btag263-B29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s13040-025-00476-3","article-title":"Identification of severity related mutation hotspots in sars-cov-2 using a density-based clustering approach","volume":"18","author":"Youn","year":"2025","journal-title":"BioData Min"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/Supplement_1\/btag263\/68709591\/btag263.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/Supplement_1\/btag263\/68709591\/btag263.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:26:01Z","timestamp":1783686361000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag263\/8726352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"references-count":29,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2026,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag263","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,7]]},"published":{"date-parts":[[2026,7,1]]},"article-number":"btag263"}}