{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:13:04Z","timestamp":1776082384389,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Center of Excellence-International Collaboration Initiative Grant","award":["139170052"],"award-info":[{"award-number":["139170052"]}]},{"name":"West China Hospital, Sichuan University and Sichuan Science and Technology Program","award":["2023YFS0200"],"award-info":[{"award-number":["2023YFS0200"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01LM014156"],"award-info":[{"award-number":["R01LM014156"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01CA241930"],"award-info":[{"award-number":["R01CA241930"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM153822"],"award-info":[{"award-number":["R01GM153822"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2217515"],"award-info":[{"award-number":["2217515"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["2326879"],"award-info":[{"award-number":["2326879"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody\u2013antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.<\/jats:p>","DOI":"10.1093\/bib\/bbae403","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T10:04:59Z","timestamp":1724148299000},"source":"Crossref","is-referenced-by-count":10,"title":["AntiFormer: graph enhanced large language model for binding affinity prediction"],"prefix":"10.1093","volume":"25","author":[{"given":"Qing","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics , College of Medicine, , FL 32611 , USA"},{"name":"University of Florida , College of Medicine, , FL 32611 , USA"}]},{"given":"Yuzhou","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University , Chengdu 610041 , China"},{"name":"Shihezi University School of Medicine, Shihezi University , Shihezi 832003 , China"}]},{"given":"Yanfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics , College of Medicine, , FL 32611 , USA"},{"name":"University of Florida , College of Medicine, , FL 32611 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-1502","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau , Macau SAR , China"}]},{"given":"Jianguo","family":"Wen","sequence":"additional","affiliation":[{"name":"Center for Computational Systems Medicine , McWilliams School of Biomedical Informatics, , Houston, TX 77030 , USA"},{"name":"The University of Texas Health Science Center at Houston , McWilliams School of Biomedical Informatics, , Houston, TX 77030 , USA"}]},{"given":"Xiaobo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Computational Systems Medicine , McWilliams School of Biomedical Informatics, , Houston, TX 77030 , USA"},{"name":"The University of Texas Health Science Center at Houston , McWilliams School of Biomedical Informatics, , Houston, TX 77030 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4455-5302","authenticated-orcid":false,"given":"Qianqian","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics , College of Medicine, , FL 32611 , USA"},{"name":"University of Florida , College of Medicine, , FL 32611 , USA"}]}],"member":"286","published-online":{"date-parts":[[2024,8,20]]},"reference":[{"key":"2024082010042403000_ref1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3390\/antib8040055","article-title":"Antibody structure and function: the basis for engineering therapeutics","volume":"8","author":"Chiu","year":"2019","journal-title":"Antibodies"},{"key":"2024082010042403000_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12929-019-0592-z","article-title":"Development of therapeutic antibodies for the treatment of diseases","volume":"27","author":"Lu","year":"2020","journal-title":"J Biomed Sci"},{"key":"2024082010042403000_ref3","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.copbio.2019.01.012","article-title":"Why recombinant antibodies\u2014benefits and applications","volume":"60","author":"Basu","year":"2019","journal-title":"Curr Opin Biotechnol"},{"key":"2024082010042403000_ref4","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1038\/s41467-021-21463-2","article-title":"Antibody affinity maturation and plasma IgA associate with clinical outcome in hospitalized COVID-19 patients","volume":"12","author":"Tang","year":"2021","journal-title":"Nat Commun"},{"key":"2024082010042403000_ref5","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.tips.2022.12.005","article-title":"Computational and artificial intelligence-based methods for antibody development","volume":"44","author":"Kim","year":"2023","journal-title":"Trends Pharmacol Sci"},{"key":"2024082010042403000_ref6","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1038\/s41467-023-39022-2","article-title":"Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries","volume":"14","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"2024082010042403000_ref7","article-title":"Deciphering antibody affinity maturation with language models and weakly supervised learning.","author":"Ruffolo","year":"2021"},{"key":"2024082010042403000_ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2022.100513","article-title":"Deciphering the language of antibodies using self-supervised learning","volume":"3","author":"Leem","year":"2022","journal-title":"Patterns"},{"key":"2024082010042403000_ref9","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"2024082010042403000_ref10","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/pro.4205","article-title":"Observed antibody space: a diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences","volume":"31","author":"Olsen","year":"2022","journal-title":"Protein Sci"},{"key":"2024082010042403000_ref11","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1038\/s41597-022-01779-4","article-title":"A dataset comprised of binding interactions for 104,972 antibodies against a SARS-CoV-2 peptide","volume":"9","author":"Engelhart","year":"2022","journal-title":"Sci Data"},{"key":"2024082010042403000_ref12","first-page":"23","article-title":"Novel skewed usage of B-cell receptors in COVID-19 patients with various clinical presentations","volume-title":"Immunol Lett","author":"Ma","year":"2022"},{"key":"2024082010042403000_ref13","first-page":"734","article-title":"CoV-AbDab: the coronavirus antibody database","volume-title":"Bioinformatics","author":"Raybould","year":"2021"},{"key":"2024082010042403000_ref14","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":"2024082010042403000_ref15","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1093\/bioinformatics\/btt473","article-title":"lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests","volume":"29","author":"Mariani","year":"2013","journal-title":"Bioinformatics"},{"key":"2024082010042403000_ref16","doi-asserted-by":"crossref","first-page":"9250","DOI":"10.18632\/aging.204778","article-title":"High-throughput single-cell profiling of B cell responses following inactivated influenza vaccination in young and older adults","volume":"15","author":"Wang","year":"2023","journal-title":"Aging (Albany NY)"},{"key":"2024082010042403000_ref17","doi-asserted-by":"crossref","first-page":"8995","DOI":"10.1073\/pnas.1902649116","article-title":"Select sequencing of clonally expanded CD8+ T cells reveals limits to clonal expansion","volume":"116","author":"Huang","year":"2019","journal-title":"Proc Natl Acad Sci"},{"key":"2024082010042403000_ref18","doi-asserted-by":"crossref","first-page":"735-749. e738","DOI":"10.1016\/j.immuni.2019.09.001","article-title":"Germline-encoded affinity for cognate antigen enables vaccine amplification of a human broadly neutralizing response against influenza virus","volume":"51","author":"Sangesland","year":"2019","journal-title":"Immunity"},{"key":"2024082010042403000_ref19","first-page":"103","article-title":"Cross-neutralizing anti-hemagglutinin antibodies isolated from patients infected with avian influenza A (H5N1) virus","volume":"33","author":"Ying","year":"2020","journal-title":"Biomed Environ Sci"},{"key":"2024082010042403000_ref20","doi-asserted-by":"crossref","first-page":"20842","DOI":"10.1038\/srep20842","article-title":"IGHV1-69 polymorphism modulates anti-influenza antibody repertoires, correlates with IGHV utilization shifts and varies by ethnicity","volume":"6","author":"Avnir","year":"2016","journal-title":"Sci Rep"},{"key":"2024082010042403000_ref21","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.sbi.2020.11.008","article-title":"Progress toward improved understanding of antibody maturation","volume":"67","author":"Vajda","year":"2021","journal-title":"Curr Opin Struct Biol"},{"key":"2024082010042403000_ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.xpro.2023.102095","article-title":"Protocol to determine antibody affinity and concentration in complex solutions using microfluidic antibody affinity profiling","volume":"4","author":"Emmenegger","year":"2023","journal-title":"STAR Protocols"},{"key":"2024082010042403000_ref23","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1093\/protein\/gzs024","article-title":"Computer-aided antibody design","volume":"25","author":"Kuroda","year":"2012","journal-title":"Protein Eng Des Sel"},{"key":"2024082010042403000_ref24","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1038\/nbt1336","article-title":"Computational design of antibody-affinity improvement beyond in vivo maturation","volume":"25","author":"Lippow","year":"2007","journal-title":"Nat Biotechnol"},{"key":"2024082010042403000_ref25","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1002\/jps.22758","article-title":"Developability index: a rapid in silico tool for the screening of antibody aggregation propensity","volume":"101","author":"Lauer","year":"2012","journal-title":"J Pharm Sci"},{"key":"2024082010042403000_ref26","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1002\/jmr.2527","article-title":"Antibody humanization by molecular dynamics simulations\u2014in-silico guided selection of critical backmutations","volume":"29","author":"Margreitter","year":"2016","journal-title":"J Mol Recognit"},{"key":"2024082010042403000_ref27","article-title":"SiGra: single-cell spatial elucidation through an image-augmented graph transformer","volume":"14","year":"2023","journal-title":"Nat Commun"},{"key":"2024082010042403000_ref28","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad338","article-title":"SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment","volume":"24","author":"Tang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024082010042403000_ref29","article-title":"Integrating graph contextualized knowledge into pre-trained language models.","author":"He","year":"2019"},{"key":"2024082010042403000_ref30","volume-title":"Conference on Neural Information Processing Systems","author":"Lundberg","year":"2017"},{"key":"2024082010042403000_ref31","article-title":"Huggingface's transformers: state-of-the-art natural language processing.","author":"Wolf","year":"2019"},{"key":"2024082010042403000_ref32","article-title":"Semi-supervised classification with graph convolutional networks.","author":"Kipf","year":"2016"},{"key":"2024082010042403000_ref33","volume-title":"Proceedings of the AAAI conference on artificial intelligence","year":"2019"},{"key":"2024082010042403000_ref34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3617335","article-title":"Modularity-based hypergraph clustering: random hypergraph model, hyperedge-cluster relation, and computation","volume":"1","author":"Feng","year":"2023","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"2024082010042403000_ref35","doi-asserted-by":"crossref","first-page":"eabh1303","DOI":"10.1126\/sciadv.abh1303","article-title":"Generative hypergraph clustering: from blockmodels to modularity","volume":"7","author":"Chodrow","year":"2021","journal-title":"Sci Adv"},{"key":"2024082010042403000_ref36","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Jiang","year":"2021"},{"key":"2024082010042403000_ref37","article-title":"Bert: pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin","year":"2019"},{"key":"2024082010042403000_ref38","article-title":"scRepertoire: an R-based toolkit for single-cell immune receptor analysis","volume-title":"F1000Research","author":"Borcherding","year":"2020"},{"key":"2024082010042403000_ref39","first-page":"411","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume-title":"Nat Biotechnol","author":"Butler","year":"2018"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae403\/58856369\/bbae403.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae403\/58856369\/bbae403.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T10:05:20Z","timestamp":1724148320000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae403\/7736247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7,25]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae403","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,9]]},"published":{"date-parts":[[2024,7,25]]},"article-number":"bbae403"}}