{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:09:42Z","timestamp":1792735782034,"version":"4.5.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S024093\/1"],"award-info":[{"award-number":["EP\/S024093\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49\u2009\u00c5, which drops to 2.05\u2009\u00c5 when considering only its 75% most confident predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/oxpig\/ABlooper.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac016","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T15:15:04Z","timestamp":1643296504000},"page":"1877-1880","source":"Crossref","is-referenced-by-count":161,"title":["ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8712-533X","authenticated-orcid":false,"given":"Brennan","family":"Abanades","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Georges","sequence":"additional","affiliation":[{"name":"Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Bujotzek","sequence":"additional","affiliation":[{"name":"Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-2252","authenticated-orcid":false,"given":"Charlotte M","family":"Deane","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"2023020109003262800_btac016-B1","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"2023020109003262800_btac016-B2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2023020109003262800_btac016-B3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nrd.2017.227","article-title":"Next generation antibody drugs: pursuit of the \u2018high-hanging fruit\u2019","volume":"17","author":"Carter","year":"2018","journal-title":"Nat. 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