{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:28:23Z","timestamp":1770780503114,"version":"3.50.0"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Investigator Grant from the National Health and Medical Research Council (NHMRC) of Australia","award":["GNT1174405"],"award-info":[{"award-number":["GNT1174405"]}]},{"name":"Victorian Government's OIS Program. Y.M. was supported by the Melbourne Research Scholarship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Understanding antibody\u2013antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody\u2013antigen binding affinity by modelling interaction interfaces as graph-based signatures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>CSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>CSM-AB is freely available as a user-friendly web interface and API at http:\/\/biosig.unimelb.edu.au\/csm_ab\/datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab762","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T20:13:13Z","timestamp":1635797593000},"page":"1141-1143","source":"Crossref","is-referenced-by-count":59,"title":["CSM-AB: graph-based antibody\u2013antigen binding affinity prediction and docking scoring function"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6763-9808","authenticated-orcid":false,"given":"Yoochan","family":"Myung","sequence":"first","affiliation":[{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne, VIC, Australia"},{"name":"Systems and Computational Biology, Bio21 Institute, University of Melbourne , Melbourne, VIC, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-2119","authenticated-orcid":false,"given":"Douglas E V","family":"Pires","sequence":"additional","affiliation":[{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne, VIC, Australia"},{"name":"Systems and Computational Biology, Bio21 Institute, University of Melbourne , Melbourne, VIC, Australia"},{"name":"School of Computing and Information Systems, University of Melbourne , Melbourne, VIC, Australia"},{"name":"School of Chemistry and Molecular Biosciences, University Of Queensland, St Lucia, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2948-2413","authenticated-orcid":false,"given":"David B","family":"Ascher","sequence":"additional","affiliation":[{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne, VIC, Australia"},{"name":"Systems and Computational Biology, Bio21 Institute, University of Melbourne , Melbourne, VIC, Australia"},{"name":"School of Chemistry and Molecular Biosciences, University Of Queensland, St Lucia, QLD, Australia"}]}],"member":"286","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"2023020108511765100_btab762-B1","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1186\/s12859-018-2449-y","article-title":"Benchmarking of different molecular docking methods for protein\u2013peptide docking","volume":"19","author":"Agrawal","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023020108511765100_btab762-B2","doi-asserted-by":"crossref","first-page":"e0161879","DOI":"10.1371\/journal.pone.0161879","article-title":"DockQ: quality measure for protein\u2013protein docking models","volume":"11","author":"Basu","year":"2016","journal-title":"PLoS One"},{"key":"2023020108511765100_btab762-B3","first-page":"606","author":"Guest","year":"2021"},{"key":"2023020108511765100_btab762-B4","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1002\/prot.22830","article-title":"Protein-protein docking benchmark version 4.0","volume":"78","author":"Hwang","year":"2010","journal-title":"Proteins"},{"key":"2023020108511765100_btab762-B5","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.jmb.2007.08.027","article-title":"Exploring the capacity of minimalist protein interfaces: interface energetics and affinity maturation to picomolar KD of a single-domain antibody with a flat paratope","volume":"373","author":"Koide","year":"2007","journal-title":"J. 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