{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T03:18:26Z","timestamp":1792120706200,"version":"4.4.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004063","name":"Knut and Alice Wallenberg Foundation","doi-asserted-by":"publisher","award":["SNIC 2021\/5\u2013297"],"award-info":[{"award-number":["SNIC 2021\/5\u2013297"]}],"id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>AlphaFold has transformed structural biology with an unprecedented accuracy in modelling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that lack strong co-evolutionary information, such as antibody\u2013antigen (Ab\u2013Ag) complexes. One of the fundamental issues is that AF3 often generates accurate predictions, but fails to reliably distinguish them from the much larger set of incorrect ones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address this, we propose ABAG-Rank, a deep neural network that provides an efficient and robust solution for model selection of Ab\u2013Ag interactions from a pool of structural ensembles predicted with AlphaFold. Built on the permutation-invariant DeepSets architecture, ABAG-Rank can process variable-sized ensembles of structural decoys and is directly applicable to prediction settings in which the number of candidates may vary. We train a model on a redundancy-reduced set of all known antibody\u2013antigen complexes and find that simple geometric descriptors, along with confidence scores from AlphaFold, provide rich information about interface quality without requiring intensive physics-based calculations. Our experiments demonstrate that ABAG-Rank significantly outperforms AF3 internal scoring and the ranking performance of existing deep learning baselines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>Source code can be found at: https:\/\/github.com\/tadteo\/ABAG-Rank or on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.21132090<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag663","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T08:11:12Z","timestamp":1788509472000},"source":"Crossref","is-referenced-by-count":2,"title":["ABAG-Rank: improving model selection of AlphaFold antibody\u2013antigen complexes by learning to rank"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2201-5129","authenticated-orcid":false,"given":"Matteo","family":"Tadiello","sequence":"first","affiliation":[{"name":"Department of biochemistry and biophysics (DBB) and SciLifeLab, Stockholm University , Stockholm, 171 65,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marko","family":"Ludaic","sequence":"additional","affiliation":[{"name":"Department of biochemistry and biophysics (DBB) and SciLifeLab, Stockholm University , Stockholm, 171 65,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vsevolod","family":"Viliuga","sequence":"additional","affiliation":[{"name":"Department of biochemistry and biophysics (DBB) and SciLifeLab, Stockholm University , Stockholm, 171 65,","place":["Sweden"]},{"name":"Max Planck Institute for Polymer Research (MPIP) , Mainz, 55128,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7115-9751","authenticated-orcid":false,"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[{"name":"Department of biochemistry and biophysics (DBB) and SciLifeLab, Stockholm University , Stockholm, 171 65,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"2026091823065963600_btag663-B1","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2026091823065963600_btag663-B2","doi-asserted-by":"publisher","first-page":"e0161879","DOI":"10.1371\/journal.pone.0161879","article-title":"Dockq: a quality measure for protein\u2013protein 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