{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T01:36:03Z","timestamp":1769045763529,"version":"3.49.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62173204"],"award-info":[{"award-number":["62173204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62173204"],"award-info":[{"award-number":["62173204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62173204"],"award-info":[{"award-number":["62173204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-024-05961-w","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:02:05Z","timestamp":1730908925000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of antibody-antigen interaction based on backbone aware with invariant point attention"],"prefix":"10.1186","volume":"25","author":[{"given":"Miao","family":"Gu","sequence":"first","affiliation":[]},{"given":"Weiyang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"5961_CR1","doi-asserted-by":"publisher","unstructured":"Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet T-S, Flem-Karlsen K, Frank R, Mehta BB, Vu MH. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. In: MAbs, vol. 14, p. 2008790. Taylor & Francis. https:\/\/doi.org\/10.1080\/19420862.2021.2008790","DOI":"10.1080\/19420862.2021.2008790"},{"key":"5961_CR2","doi-asserted-by":"publisher","unstructured":"Makowski EK, Kinnunen PC, Huang J, Wu LN, Smith MD, Wang TX, Desai AA, Streu CN, Zhang YL, Zupancic JM, Schardt JS, Linderman JJ, Tessier PM. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space. Nat Commun 2022;13(1) https:\/\/doi.org\/10.1038\/s41467-022-31457-3","DOI":"10.1038\/s41467-022-31457-3"},{"key":"5961_CR3","doi-asserted-by":"publisher","unstructured":"Adolf-Bryfogle J, Kalyuzhniy O, Kubitz M, Weitzner BD, Hu XZ, Adachi Y, Schief WR, Dunbrack RL. Rosettaantibodydesign (rabd): A general framework for computational antibody design. Plos Comput Biol 2018;14(4) https:\/\/doi.org\/10.1093\/bioinformatics\/btac016","DOI":"10.1093\/bioinformatics\/btac016"},{"issue":"11","key":"5961_CR4","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1038\/s42256-022-00553-w","volume":"4","author":"J Zhang","year":"2022","unstructured":"Zhang J, Du YS, Zhou PF, Ding JR, Xia S, Wang Q, Chen FY, Zhou M, Zhang XM, Wang WF, Wu HY, Lu L, Zhang ST. Predicting unseen antibodies\u2019 neutralizability via adaptive graph neural networks. Nat Mach Intell. 2022;4(11):964\u201376. https:\/\/doi.org\/10.1038\/s42256-022-00553-w.","journal-title":"Nat Mach Intell"},{"key":"5961_CR5","doi-asserted-by":"publisher","unstructured":"Chen G, Zhang S, Ma X, Wilson G, Zong R, Fu Q. Antibody mimics for precise identification of proteins based on molecularly imprinted polymers: Developments and prospects. Chem Eng J 448, 148115 (2023) https:\/\/doi.org\/10.1016\/j.cej.2023.148115","DOI":"10.1016\/j.cej.2023.148115"},{"issue":"8","key":"5961_CR6","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.cels.2023.04.009","volume":"14","author":"EK Makowski","year":"2023","unstructured":"Makowski EK, Chen HT, Tessier PM. Simplifying complex antibody engineering using machine learning. Cell Syst. 2023;14(8):667\u201375. https:\/\/doi.org\/10.1016\/j.cels.2023.04.009.","journal-title":"Cell Syst"},{"key":"5961_CR7","doi-asserted-by":"crossref","unstructured":"Wilman W, Wr\u00f3bel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Mlokosiewicz J, Rouyan A, Satlawa T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Briefings Bioinf 2022;23(4)","DOI":"10.1093\/bib\/bbac267"},{"issue":"1","key":"5961_CR8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1093\/bib\/bbv027","volume":"17","author":"R Esmaielbeiki","year":"2016","unstructured":"Esmaielbeiki R, Krawczyk K, Knapp B, Nebel J-C, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform. 2016;17(1):117\u201331. https:\/\/doi.org\/10.1093\/bib\/bbv027.","journal-title":"Brief Bioinform"},{"issue":"20","key":"5961_CR9","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1093\/bioinformatics\/btab321","volume":"37","author":"Q Hou","year":"2021","unstructured":"Hou Q, Stringer B, Waury K, Capel H, Haydarlou R, Xue F, Abeln S, Heringa J, Feenstra KA. Serendip-ce: sequence-based interface prediction for conformational epitopes. Bioinformatics. 2021;37(20):3421\u20137. https:\/\/doi.org\/10.1093\/bioinformatics\/btab321.","journal-title":"Bioinformatics"},{"key":"5961_CR10","doi-asserted-by":"publisher","unstructured":"Chiu ML, Goulet DR, Teplyakov A, Gilliland GL. Antibody structure and function: The basis for engineering therapeutics. Antibodies 8(4) (2019) https:\/\/doi.org\/10.1016\/j.heliyon.2023.e15032","DOI":"10.1016\/j.heliyon.2023.e15032"},{"issue":"13","key":"5961_CR11","doi-asserted-by":"publisher","first-page":"3996","DOI":"10.1093\/bioinformatics\/btaa263","volume":"36","author":"S Pittala","year":"2020","unstructured":"Pittala S, Bailey-Kellogg C. Learning context-aware structural representations to predict antigen and antibody binding interfaces. Bioinformatics. 2020;36(13):3996\u20134003. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa263.","journal-title":"Bioinformatics"},{"key":"5961_CR12","doi-asserted-by":"publisher","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: A review of methods and applications. AI open 1, 2020;57\u201381. https:\/\/doi.org\/10.1007\/s11042-010-0645-5","DOI":"10.1007\/s11042-010-0645-5"},{"issue":"4","key":"5961_CR13","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1093\/bioinformatics\/btab762","volume":"38","author":"Y Myung","year":"2022","unstructured":"Myung Y, Pires DEV, Ascher DB. Csm-ab: graph-based antibody-antigen binding affinity prediction and docking scoring function. Bioinformatics. 2022;38(4):1141\u20133. https:\/\/doi.org\/10.1093\/bioinformatics\/btab762.","journal-title":"Bioinformatics"},{"issue":"2","key":"5961_CR14","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1093\/bioinformatics\/btab660","volume":"38","author":"C Schneider","year":"2022","unstructured":"Schneider C, Buchanan A, Taddese B, Deane CM. Dlab: deep learning methods for structure-based virtual screening of antibodies. Bioinformatics. 2022;38(2):377\u201383. https:\/\/doi.org\/10.1093\/bioinformatics\/btab660.","journal-title":"Bioinformatics"},{"key":"5961_CR15","doi-asserted-by":"publisher","unstructured":"Fischman S, Ofran Y. Computational design of antibodies. Curr Op Struct Biol 2018;51:56\u2013162. https:\/\/doi.org\/10.1016\/j.sbi.2018.04.007","DOI":"10.1016\/j.sbi.2018.04.007"},{"issue":"1","key":"5961_CR16","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1186\/s12859-023-05562-z","volume":"24","author":"Y Yuan","year":"2023","unstructured":"Yuan Y, Chen Q, Mao J, Li G, Pan X. Dg-affinity: predicting antigen-antibody affinity with language models from sequences. BMC Bioinf. 2023;24(1):430. https:\/\/doi.org\/10.1186\/s12859-023-05562-z.","journal-title":"BMC Bioinf"},{"issue":"6","key":"5961_CR17","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1038\/s41551-021-00699-9","volume":"5","author":"DM Mason","year":"2021","unstructured":"Mason DM, Friedensohn S, Weber CR, Jordi C, Wagner B, Meng SM, Ehling RA, Bonati L, Dahinden J, Gainza P, Correia BE, Reddy ST. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat Biomed Eng. 2021;5(6):600. https:\/\/doi.org\/10.1038\/s41551-021-00699-9.","journal-title":"Nat Biomed Eng"},{"key":"5961_CR18","doi-asserted-by":"publisher","unstructured":"Lim YW, Adler AS, Johnson DS. Predicting antibody binders and generating synthetic antibodies using deep learning. In: MAbs, vol. 14, p. 2069075. Taylor & Francis. https:\/\/doi.org\/10.1080\/19420862.2022.2069075","DOI":"10.1080\/19420862.2022.2069075"},{"key":"5961_CR19","doi-asserted-by":"publisher","unstructured":"Huang Y, Zhang ZD, Zhou Y. Abagintpre: A deep learning method for predicting antibody-antigen interactions based on sequence information. Front Immunol 13 (2022) https:\/\/doi.org\/10.3389\/fimmu.2022.1053617","DOI":"10.3389\/fimmu.2022.1053617"},{"issue":"11","key":"5961_CR20","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1093\/protein\/gzp055","volume":"22","author":"XB Wang","year":"2009","unstructured":"Wang XB, Wu LY, Wang YC, Deng NY. Prediction of palmitoylation sites using the composition of k-spaced amino acid pairs. Protein Eng Des Select. 2009;22(11):707\u201312. https:\/\/doi.org\/10.1093\/protein\/gzp055.","journal-title":"Protein Eng Des Select"},{"issue":"4","key":"5961_CR21","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TNB.2014.2352454","volume":"14","author":"LY Wei","year":"2015","unstructured":"Wei LY, Liao MH, Gao X, Zou Q. An improved protein structural classes prediction method by incorporating both sequence and structure information. IEEE Trans Nanobiosci. 2015;14(4):339\u201349. https:\/\/doi.org\/10.1109\/TNB.2014.2352454.","journal-title":"IEEE Trans Nanobiosci"},{"key":"5961_CR22","doi-asserted-by":"publisher","unstructured":"Huang Y, Wuchty S, Zhou Y, Zhang ZD. Sgppi: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network. Briefings Bioinf 24(2) (2023) https:\/\/doi.org\/10.1093\/bib\/bbad020","DOI":"10.1093\/bib\/bbad020"},{"issue":"7873","key":"5961_CR23","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Z\u00eddek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with alphafold. Nature. 2021;596(7873):583. https:\/\/doi.org\/10.1038\/s41586-021-03819-2.","journal-title":"Nature"},{"issue":"D1","key":"5961_CR24","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1093\/nar\/gkab1061","volume":"50","author":"M Varadi","year":"2022","unstructured":"Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, Z\u00eddek A, Green T, Tunyasuvunakool K, Petersen S, Jumper J, Clancy E, Green R, Vora A, Lutfi M, Figurnov M, Cowie A, Hobbs N, Kohli P, Kleywegt G, Birney E, Hassabis D, Velankar S. Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50(D1):439\u201344. https:\/\/doi.org\/10.1093\/nar\/gkab1061.","journal-title":"Nucleic Acids Res"},{"key":"5961_CR25","doi-asserted-by":"publisher","unstructured":"Edgar RC, Batzoglou SJCoisb. Multiple sequence alignment 2006;16(3):368\u2013373. https:\/\/doi.org\/10.1186\/1471-2105-5-113","DOI":"10.1186\/1471-2105-5-113"},{"key":"5961_CR26","doi-asserted-by":"publisher","unstructured":"Cohen T, Halfon M, Schneidman-Duhovny D. Nanonet: Rapid and accurate end-to-end nanobody modeling by deep learning at sub angstrom resolution. Front Immunol 13 (2022) https:\/\/doi.org\/10.3389\/fimmu.2022.958584","DOI":"10.3389\/fimmu.2022.958584"},{"issue":"1","key":"5961_CR27","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1038\/s41592-021-01365-3","volume":"19","author":"DT Jones","year":"2022","unstructured":"Jones DT, Thornton JM. The impact of alphafold2 one year on. Nat Methods. 2022;19(1):15\u201320. https:\/\/doi.org\/10.1038\/s41592-021-01365-3.","journal-title":"Nat Methods"},{"issue":"7","key":"5961_CR28","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1093\/bioinformatics\/btac016","volume":"38","author":"B Abanades","year":"2022","unstructured":"Abanades B, Georges G, Bujotzek A, Deane CM. Ablooper: fast accurate antibody cdr loop structure prediction with accuracy estimation. Bioinformatics. 2022;38(7):1877\u201380. https:\/\/doi.org\/10.1093\/bioinformatics\/btac016.","journal-title":"Bioinformatics"},{"key":"5961_CR29","doi-asserted-by":"publisher","unstructured":"Ruffolo JA, Sulam J, Gray JJ. Antibody structure prediction using interpretable deep learning. Patterns 3(2) (2022) https:\/\/doi.org\/10.1016\/j.patter.2021.100406","DOI":"10.1016\/j.patter.2021.100406"},{"issue":"6","key":"5961_CR30","doi-asserted-by":"publisher","first-page":"6896","DOI":"10.1109\/TPAMI.2020.3007032","volume":"45","author":"Z Huang","year":"2023","unstructured":"Huang Z, Wang X, Wei Y, Huang L, Shi H, Liu W, Huang TS. Ccnet: Criss-cross attention for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2023;45(6):6896\u2013908. https:\/\/doi.org\/10.1109\/TPAMI.2020.3007032.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"5961_CR31","doi-asserted-by":"publisher","first-page":"5634","DOI":"10.1038\/S41596-021-00628-9","volume":"16","author":"ZY Du","year":"2021","unstructured":"Du ZY, Su H, Wang WK, Ye LS, Wei H, Peng ZL, Anishchenko I, Baker D, Yang JY. The trrosetta server for fast and accurate protein structure prediction. Nat Protoc. 2021;16(12):5634\u201351. https:\/\/doi.org\/10.1038\/S41596-021-00628-9.","journal-title":"Nat Protoc"},{"issue":"1","key":"5961_CR32","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1038\/s41467-023-38063-x","volume":"14","author":"JA Ruffolo","year":"2023","unstructured":"Ruffolo JA, Chu L-S, Mahajan SP, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun. 2023;14(1):2389. https:\/\/doi.org\/10.1038\/s41467-023-38063-x.","journal-title":"Nat Commun"},{"key":"5961_CR33","unstructured":"Ruffolo JA, Gray JJ, Sulam J. Deciphering antibody affinity maturation with language models and weakly supervised learning (2021). arXiv preprint arXiv:2112.07782"},{"key":"5961_CR34","doi-asserted-by":"publisher","unstructured":"Gligorijevic V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, Chandler C, Taylor BC, Fisk IM, Vlamakis H, Xavier RJ, Knight R, Cho K, Bonneau R. Structure-based protein function prediction using graph convolutional networks. Nat Commun 12(1) (2021) https:\/\/doi.org\/10.1101\/786236","DOI":"10.1101\/786236"},{"issue":"1","key":"5961_CR35","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","volume":"38","author":"QM Yuan","year":"2022","unstructured":"Yuan QM, Chen JW, Zhao HY, Zhou YQ, Yang YD. Structure-aware protein-protein interaction site prediction using deep graph convolutional network. Bioinformatics. 2022;38(1):125\u201332. https:\/\/doi.org\/10.1093\/bioinformatics\/btab643.","journal-title":"Bioinformatics"},{"key":"5961_CR36","doi-asserted-by":"publisher","unstructured":"Jha K, Saha S, Singh H. Prediction of protein-protein interaction using graph neural networks. Sci Rep 12(1) (2022) https:\/\/doi.org\/10.1038\/s41598-022-12201-9","DOI":"10.1038\/s41598-022-12201-9"},{"key":"5961_CR37","doi-asserted-by":"publisher","unstructured":"Demolombe V, Brevern AG, Felicori L, NGuyen C, Avila RA, Valera L, Jardin-Watelet B, Lavigne G, Lebreton A, Molina F et al. Pepop 2.0: new approaches to mimic non-continuous epitopes. BMC Bioinf 2019;20:1\u201314. https:\/\/doi.org\/10.1186\/s12859-019-2867-5","DOI":"10.1186\/s12859-019-2867-5"},{"key":"5961_CR38","doi-asserted-by":"publisher","unstructured":"Lau AM, Kandathil SM, Jones DT. Merizo: a rapid and accurate protein domain segmentation method using invariant point attention. Nat Commun 14(1) (2023) https:\/\/doi.org\/10.1038\/s41467-023-43934-4","DOI":"10.1038\/s41467-023-43934-4"},{"issue":"2","key":"5961_CR39","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1093\/bioinformatics\/btv552","volume":"32","author":"J Dunbar","year":"2016","unstructured":"Dunbar J, Deane CM. Anarci: antigen receptor numbering and receptor classification. Bioinformatics. 2016;32(2):298\u2013300. https:\/\/doi.org\/10.1093\/bioinformatics\/btv552.","journal-title":"Bioinformatics"},{"issue":"D1","key":"5961_CR40","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/nar\/gkt1043","volume":"42","author":"J Dunbar","year":"2014","unstructured":"Dunbar J, Krawczyk K, Leem J, Baker T, Fuchs A, Georges G, Shi JY, Deane CM. Sabdab: the structural antibody database. Nucleic Acids Res. 2014;42(D1):1140\u20136. https:\/\/doi.org\/10.1093\/nar\/gkt1043.","journal-title":"Nucleic Acids Res"},{"issue":"23","key":"5961_CR41","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu L, Niu B, Zhu Z, Wu S, Li W. Cd-hit: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150\u20132. https:\/\/doi.org\/10.1093\/bioinformatics\/bts565.","journal-title":"Bioinformatics"},{"key":"5961_CR42","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1002\/0471250953.bi0203s00","volume":"1","author":"JD Thompson","year":"2003","unstructured":"Thompson JD, Gibson TJ, Higgins DG. Multiple sequence alignment using clustalw and clustalx. Curr Protoc Bioinf. 2003;1:2\u20133. https:\/\/doi.org\/10.1002\/0471250953.bi0203s00.","journal-title":"Curr Protoc Bioinf"},{"issue":"5","key":"5961_CR43","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1101\/2020.05.15.077313","volume":"37","author":"MIJ Raybould","year":"2021","unstructured":"Raybould MIJ, Kovaltsuk A, Marks C, Deane CM. Cov-abdab: the coronavirus antibody database. Bioinformatics. 2021;37(5):734\u20135. https:\/\/doi.org\/10.1101\/2020.05.15.077313.","journal-title":"Bioinformatics"},{"key":"5961_CR44","doi-asserted-by":"publisher","unstructured":"Mitchell LS, Colwell LJ. Comparative analysis of nanobody sequence and structure data. Proteins: Structure, Function, and Bioinformatics 2018;86(7):697\u2013706. https:\/\/doi.org\/10.1002\/prot.25497","DOI":"10.1002\/prot.25497"},{"issue":"10","key":"5961_CR45","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","volume":"26","author":"A Altmann","year":"2010","unstructured":"Altmann A, Tolo\u015fi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340\u20137. https:\/\/doi.org\/10.1093\/bioinformatics\/btq134.","journal-title":"Bioinformatics"},{"issue":"6","key":"5961_CR46","doi-asserted-by":"publisher","first-page":"2018234118","DOI":"10.1073\/pnas.2018234118","volume":"118","author":"NB Rego","year":"2021","unstructured":"Rego NB, Xi E, Patel AJ. Identifying hydrophobic protein patches to inform protein interaction interfaces. Proc Natl Acad Sci. 2021;118(6):2018234118. https:\/\/doi.org\/10.1073\/pnas.2018234118.","journal-title":"Proc Natl Acad Sci"},{"key":"5961_CR47","doi-asserted-by":"publisher","unstructured":"Burbach SM, Briney B. Improving antibody language models with native pairing. Patterns 0, 100967 (2024) https:\/\/doi.org\/10.1016\/j.patter.2024.100967","DOI":"10.1016\/j.patter.2024.100967"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05961-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-05961-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05961-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T16:02:38Z","timestamp":1730908958000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-05961-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,6]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["5961"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-05961-w","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,6]]},"assertion":[{"value":"18 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no Conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"348"}}