{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T20:54:21Z","timestamp":1777064061541,"version":"3.51.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["DBI-2321666"],"award-info":[{"award-number":["DBI-2321666"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"US Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0019749"],"award-info":[{"award-number":["DE-SC0019749"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute, National Institute of Allergy and Infectious Diseases"},{"name":"National Institute of General Medical Sciences of the National Institutes of Health","award":["R01GM157729"],"award-info":[{"award-number":["R01GM157729"]}]},{"name":"DOE NERSC","award":["DE-SC0019749"],"award-info":[{"award-number":["DE-SC0019749"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence\/deep learning (AI\/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Herein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI\/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code available at https:\/\/github.com\/bioinsilico\/rcsb-embedding-search. Source code DOI: https:\/\/doi.org\/10.6084\/m9.figshare.30546698.v1. Benchmark datasets DOI: https:\/\/doi.org\/10.6084\/m9.figshare.30546650.v1. Web server prototype available at: http:\/\/embedding-search.rcsb.org\/.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag058","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:46:49Z","timestamp":1769777209000},"source":"Crossref","is-referenced-by-count":2,"title":["Multi-scale structural similarity embedding search across entire proteomes"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5593-735X","authenticated-orcid":false,"given":"Joan","family":"Segura","sequence":"first","affiliation":[{"name":"Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego , San Diego, CA 92093,","place":["United 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, San Diego, CA 92093,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen K","family":"Burley","sequence":"additional","affiliation":[{"name":"Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego , San Diego, CA 92093,","place":["United States"]},{"name":"Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey , Piscataway, NJ 08854,","place":["United States"]},{"name":"Rutgers Cancer Institute , Rutgers, , New Brunswick, NJ 08901,","place":["United States"]},{"name":"The State University of New Jersey , Rutgers, , New Brunswick, NJ 08901,","place":["United States"]},{"name":"Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey , Piscataway, NJ 08854,","place":["United States"]},{"name":"Rutgers Artificial 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