{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:29Z","timestamp":1772138069471,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union Horizon 2020","award":["823830"],"award-info":[{"award-number":["823830"]}]},{"name":"European Union Horizon 2020","award":["101093290"],"award-info":[{"award-number":["101093290"]}]},{"name":"EGI-ACE","award":["101017567"],"award-info":[{"award-number":["101017567"]}]},{"name":"Netherlands e-Science Center","award":["027.020.G13"],"award-info":[{"award-number":["027.020.G13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Antibody\u2212antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein\u2212protein docking tools to use machine learning generated input structures for information-driven docking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody\u2212antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code of HADDOCK3 is freely available at github.com\/haddocking\/haddock3. The code to generate and analyse the data is available at github.com\/haddocking\/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org\/labs\/32\/1139) at the SBGRID data repository.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae583","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T07:21:23Z","timestamp":1727421683000},"source":"Crossref","is-referenced-by-count":21,"title":["Towards the accurate modelling of antibody\u2212antigen complexes from sequence using machine learning and information-driven docking"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2691-712X","authenticated-orcid":false,"given":"Marco","family":"Giulini","sequence":"first","affiliation":[{"name":"Bijvoet Centre for Biomolecular Research, Faculty of Science\u2014Chemistry, Utrecht University , Utrecht CH 3584,","place":["The Netherlands"]}]},{"given":"Constantin","family":"Schneider","sequence":"additional","affiliation":[{"name":"Exscientia Plc , Oxford OX4 4GE,","place":["United Kingdom"]}]},{"given":"Daniel","family":"Cutting","sequence":"additional","affiliation":[{"name":"Exscientia Plc , Oxford OX4 4GE,","place":["United Kingdom"]}]},{"given":"Nikita","family":"Desai","sequence":"additional","affiliation":[{"name":"Exscientia Plc , Oxford OX4 4GE,","place":["United Kingdom"]}]},{"given":"Charlotte M","family":"Deane","sequence":"additional","affiliation":[{"name":"Exscientia Plc , Oxford OX4 4GE,","place":["United Kingdom"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7369-1322","authenticated-orcid":false,"given":"Alexandre M J J","family":"Bonvin","sequence":"additional","affiliation":[{"name":"Bijvoet Centre for Biomolecular Research, Faculty of Science\u2014Chemistry, Utrecht University , Utrecht CH 3584,","place":["The 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