{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T03:03:29Z","timestamp":1789095809555,"version":"build-2803163510"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Data and Computing in Natural Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on \u223c525\u2002000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method\u2019s performance. We find that StrucTFactor significantly (P-value &amp;lt; 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Data and source code are available at https:\/\/github.com\/lieboldj\/StrucTFactor and on our website at https:\/\/apps.cosy.bio\/StrucTFactor<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae762","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T07:18:13Z","timestamp":1736320693000},"source":"Crossref","is-referenced-by-count":6,"title":["Transcription factor prediction using protein 3D secondary 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22761,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3722-9429","authenticated-orcid":false,"given":"Janina","family":"Geiser","sequence":"additional","affiliation":[{"name":"Institute for Computational Systems Biology, Universit\u00e4t Hamburg , Hamburg 22761,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5783-0054","authenticated-orcid":false,"given":"Stefan","family":"Kurtz","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics, Informatics and Natural Sciences, ZBH\u2014Center for Bioinformatics, Universit\u00e4t Hamburg , Hamburg 22761,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0282-0462","authenticated-orcid":false,"given":"Jan","family":"Baumbach","sequence":"additional","affiliation":[{"name":"Institute for Computational Systems Biology, 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