{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T09:10:05Z","timestamp":1755853805600,"version":"3.44.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T00:00:00Z","timestamp":1754697600000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62302291"],"award-info":[{"award-number":["62302291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants in a costly and time-consuming manner. Emerging deep learning solutions offer an alternative by modeling antibody structures to predict binding affinity changes. However, they heavily depend on high-quality complex structures, which are frequently unavailable in practice. Therefore, we propose ProtAttBA, a deep learning model that predicts binding affinity changes based solely on the sequence information of antibody-antigen complexes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>ProtAttBA employs a pre-training phase to learn protein sequence patterns, following a supervised training phase using labeled antibody-antigen complex data to train a cross-attention-based regressor for predicting binding affinity changes. We evaluated ProtAttBA on three open benchmarks under different conditions. Compared to both sequence- and structure-based prediction methods, our approach achieves competitive performance, demonstrating notable robustness, especially with uncertain complex structures. Notably, our method possesses interpretability from the attention mechanism. We show that the learned attention scores can identify critical residues with impacts on binding affinity. This work introduces a rapid and cost-effective computational tool for antibody engineering, with the potential to accelerate the development of novel therapeutic antibodies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source codes and data are available at https:\/\/github.com\/code4luck\/ProtAttBA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf446","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T22:15:49Z","timestamp":1755036949000},"source":"Crossref","is-referenced-by-count":0,"title":["Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5210-1769","authenticated-orcid":false,"given":"Chen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237,","place":["China"]}]},{"given":"Mingchen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7261-1705","authenticated-orcid":false,"given":"Yang","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237,","place":["China"]}]},{"given":"Wenrui","family":"Gou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237,","place":["China"]}]},{"given":"Guisheng","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology , Shanghai 200237,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-9766","authenticated-orcid":false,"given":"Bingxin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai 200240,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,8,9]]},"reference":[{"key":"2025082204563234600_btaf446-B1","first-page":"26831","article-title":"Are transformers more robust than CNNs?","volume":"34","author":"Bai","year":"2021","journal-title":"Adv Neural Info Proc Syst"},{"key":"2025082204563234600_btaf446-B2","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1038\/nri2747","article-title":"Strategies and challenges for the next generation of therapeutic antibodies","volume":"10","author":"Beck","year":"2010","journal-title":"Nat Rev Immunol"},{"key":"2025082204563234600_btaf446-B3","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.cbpa.2010.11.020","article-title":"Optimizing non-natural protein function with directed evolution","volume":"15","author":"Brustad","year":"2011","journal-title":"Curr Opin Chem Biol"},{"key":"2025082204563234600_btaf446-B4","doi-asserted-by":"crossref","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","article-title":"Multifaceted protein\u2013protein interaction prediction based on siamese residual RCNN","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B5","doi-asserted-by":"crossref","first-page":"btae447","DOI":"10.1093\/bioinformatics\/btae447","article-title":"Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models","volume":"40","author":"Cuturello","year":"2024","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B6","doi-asserted-by":"crossref","first-page":"W333","DOI":"10.1093\/nar\/gkt450","article-title":"Beatmusic: prediction of changes in protein\u2013protein binding affinity on mutations","volume":"41","author":"Dehouck","year":"2013","journal-title":"Nucleic Acids Res"},{"year":"2023","author":"Elnaggar","key":"2025082204563234600_btaf446-B7"},{"key":"2025082204563234600_btaf446-B8","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"},{"first-page":"8946","year":"2022","author":"Hsu","key":"2025082204563234600_btaf446-B9"},{"key":"2025082204563234600_btaf446-B10","doi-asserted-by":"crossref","first-page":"btae563","DOI":"10.1093\/bioinformatics\/btae563","article-title":"Structure-inclusive similarity based directed gnn: a method that can control information flow to predict drug\u2013target binding affinity","volume":"40","author":"Huang","year":"2024","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B11","doi-asserted-by":"crossref","first-page":"bbae304","DOI":"10.1093\/bib\/bbae304","article-title":"AttABseq: an attention-based deep learning prediction method for antigen\u2013antibody binding affinity changes based on protein sequences","volume":"25","author":"Jin","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025082204563234600_btaf446-B12","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":"2025082204563234600_btaf446-B13","doi-asserted-by":"crossref","first-page":"13863","DOI":"10.1021\/acscatal.3c02743","article-title":"Machine learning-guided protein engineering","volume":"13","author":"Kouba","year":"2023","journal-title":"ACS Catal"},{"key":"2025082204563234600_btaf446-B14","doi-asserted-by":"crossref","first-page":"btae579","DOI":"10.1093\/bioinformatics\/btae579","article-title":"MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning","volume":"41","author":"Li","year":"2024","journal-title":"Bioinformatics"},{"year":"2024","author":"Li","key":"2025082204563234600_btaf446-B15"},{"year":"2025","author":"Li","key":"2025082204563234600_btaf446-B16"},{"key":"2025082204563234600_btaf446-B17","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":"2025082204563234600_btaf446-B18","doi-asserted-by":"crossref","first-page":"e1009284","DOI":"10.1371\/journal.pcbi.1009284","article-title":"Deep geometric representations for modeling effects of mutations on protein-protein binding affinity","volume":"17","author":"Liu","year":"2021","journal-title":"PLoS Comput Biol"},{"year":"2019","author":"Loshchilov","key":"2025082204563234600_btaf446-B19"},{"key":"2025082204563234600_btaf446-B20","doi-asserted-by":"crossref","first-page":"btae103","DOI":"10.1093\/bioinformatics\/btae103","article-title":"Hiresist: a database of HIV-1 resistance to broadly neutralizing antibodies","volume":"40","author":"Misra","year":"2024","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B21","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1093\/bioinformatics\/bts489","article-title":"Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models","volume":"28","author":"Moal","year":"2012","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B22","doi-asserted-by":"crossref","first-page":"W469","DOI":"10.1093\/nar\/gkw458","article-title":"MCSM-AB: a web server for predicting antibody\u2013antigen affinity changes upon mutation with graph-based signatures","volume":"44","author":"Pires","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2025082204563234600_btaf446-B23","doi-asserted-by":"crossref","first-page":"10870","DOI":"10.1021\/acs.jpclett.3c02679","article-title":"Geometric graph learning to predict changes in binding free energy and protein thermodynamic stability upon mutation","volume":"14","author":"Rana","year":"2023","journal-title":"J Phys Chem Lett"},{"key":"2025082204563234600_btaf446-B24","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":"2025082204563234600_btaf446-B25","doi-asserted-by":"crossref","first-page":"W382","DOI":"10.1093\/nar\/gki387","article-title":"The foldx web server: an online force field","volume":"33","author":"Schymkowitz","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2025082204563234600_btaf446-B26","doi-asserted-by":"crossref","first-page":"e2122954119","DOI":"10.1073\/pnas.2122954119","article-title":"Deep learning guided optimization of human antibody against sars-cov-2 variants with broad neutralization","volume":"119","author":"Shan","year":"2022","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025082204563234600_btaf446-B27","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1002\/pro.2829","article-title":"Ab-bind: antibody binding mutational database for computational affinity predictions","volume":"25","author":"Sirin","year":"2016","journal-title":"Protein Sci"},{"key":"2025082204563234600_btaf446-B28","doi-asserted-by":"crossref","first-page":"127063","DOI":"10.1016\/j.neucom.2023.127063","article-title":"Roformer: enhanced transformer with rotary position embedding","volume":"568","author":"Su","year":"2024","journal-title":"Neurocomputing"},{"key":"2025082204563234600_btaf446-B29","doi-asserted-by":"crossref","first-page":"i401","DOI":"10.1093\/bioinformatics\/btaf189","article-title":"From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model","volume":"41","author":"Tan","year":"2025","journal-title":"Bioinformatics"},{"journal-title":"Advances in Neural Information Processing Systems","article-title":"End-to-end learning on 3D protein structure for interface prediction","author":"Townshend","key":"2025082204563234600_btaf446-B30"},{"key":"2025082204563234600_btaf446-B31","doi-asserted-by":"crossref","first-page":"bbab228","DOI":"10.1093\/bib\/bbab228","article-title":"Lstm-phv: prediction of human-virus protein\u2013protein interactions by lstm with word2vec","volume":"22","author":"Tsukiyama","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025082204563234600_btaf446-B32","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1038\/s41591-023-02483-5","article-title":"Deep-learning-enabled protein\u2013protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution","volume":"29","author":"Wang","year":"2023","journal-title":"Nat Med"},{"key":"2025082204563234600_btaf446-B33","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s42256-020-0149-6","article-title":"A topology-based network tree for the prediction of protein\u2013protein binding affinity changes following mutation","volume":"2","author":"Wang","year":"2020","journal-title":"Nat Mach Intell"},{"year":"2024","author":"Xiao","key":"2025082204563234600_btaf446-B34"},{"key":"2025082204563234600_btaf446-B35","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.jmb.2016.11.022","article-title":"Bindprofx: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts","volume":"429","author":"Xiong","year":"2017","journal-title":"J Mol Biol"},{"key":"2025082204563234600_btaf446-B36","doi-asserted-by":"crossref","first-page":"btae364","DOI":"10.1093\/bioinformatics\/btae364","article-title":"HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer","volume":"40","author":"Xu","year":"2024","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B37","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1093\/bioinformatics\/btab533","article-title":"Transfer learning via multi-scale convolutional neural layers for human\u2013virus protein\u2013protein interaction prediction","volume":"37","author":"Yang","year":"2021","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B38","doi-asserted-by":"crossref","first-page":"e7126","DOI":"10.7717\/peerj.7126","article-title":"An integration of deep learning with feature embedding for protein\u2013protein interaction prediction","volume":"7","author":"Yao","year":"2019","journal-title":"PeerJ"},{"key":"2025082204563234600_btaf446-B39","doi-asserted-by":"crossref","first-page":"i418","DOI":"10.1093\/bioinformatics\/btae232","article-title":"Ddaffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure","volume":"40","author":"Yu","year":"2024","journal-title":"Bioinformatics"},{"key":"2025082204563234600_btaf446-B40","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s41564-020-00824-5","article-title":"Spike-specific circulating t follicular helper cell and cross-neutralizing antibody responses in COVID-19-convalescent individuals","volume":"6","author":"Zhang","year":"2021","journal-title":"Nat Microbiol"},{"key":"2025082204563234600_btaf446-B41","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1002\/mlf2.12157","article-title":"Protein engineering in the deep learning era","volume":"3","author":"Zhou","year":"2024","journal-title":"mLife"},{"key":"2025082204563234600_btaf446-B42","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1038\/s41421-024-00728-2","article-title":"A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity","volume":"10","author":"Zhou","year":"2024","journal-title":"Cell Discov"},{"key":"2025082204563234600_btaf446-B43","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.1021\/acs.jcim.4c00036","article-title":"Protein engineering with lightweight graph denoising neural networks","volume":"64","author":"Zhou","year":"2024","journal-title":"J Chem Inf Model"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf446\/63996215\/btaf446.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf446\/63996215\/btaf446.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf446\/63996215\/btaf446.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T08:56:44Z","timestamp":1755853004000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf446\/8229597"}},"subtitle":[],"editor":[{"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf446","relation":{},"ISSN":["1367-4811"],"issn-type":[{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2025,8]]},"published":{"date-parts":[[2025,8]]},"article-number":"btaf446"}}