{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:03:19Z","timestamp":1783108999198,"version":"3.54.6"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":12,"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\/S003002\/1"],"award-info":[{"award-number":["EP\/S003002\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/wells-wood-research\/PDBench<\/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\/btad027","type":"journal-article","created":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T01:26:21Z","timestamp":1673659581000},"source":"Crossref","is-referenced-by-count":19,"title":["PDBench: evaluating computational methods for protein-sequence design"],"prefix":"10.1093","volume":"39","author":[{"given":"Leonardo V","family":"Castorina","sequence":"first","affiliation":[{"name":"School of Informatics, University of Edinburgh , 10 Crichton Street, Newington , Edinburgh EH8 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rokas","family":"Petrenas","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, University of Edinburgh , Roger Land Building , Edinburgh EH9 3FF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kartic","family":"Subr","sequence":"additional","affiliation":[{"name":"School of Informatics, University of Edinburgh , 10 Crichton Street, Newington , Edinburgh EH8 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1243-3105","authenticated-orcid":false,"given":"Christopher W","family":"Wood","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, University of Edinburgh , Roger Land Building , Edinburgh EH9 3FF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"2023012312151811100_btad027-B1","article-title":"Single-sequence protein structure prediction using language models from deep learning","author":"Chowdhury","year":"2021","journal-title":"bioRxiv"},{"key":"2023012312151811100_btad027-B2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1146\/annurev.biochem.77.062906.171838","article-title":"Macromolecular modeling with rosetta","volume":"77","author":"Das","year":"2008","journal-title":"Annu. 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