{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:19:21Z","timestamp":1754144361257,"version":"3.41.2"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20321","62272490"],"award-info":[{"award-number":["U23A20321","62272490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013RNA interactions play a pivotal role in biological processes and disease mechanisms, with hotspot residues being critical for targeted drug design. Traditional experimental methods for identifying hotspot residues are often inefficient and expensive. Moreover, many existing prediction methods rely heavily on high-resolution structural data, which may not always be available. Consequently, there is an urgent need for an accurate and efficient sequence-based computational approach for predicting hotspot residues in protein\u2013RNA complexes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we introduce DeepHotResi, a sequence-based computational method designed to predict hotspot residues in protein\u2013RNA complexes. DeepHotResi leverages a pretrained protein language model to predict protein structure and generate an amino acid contact map. To enhance feature representation, DeepHotResi integrates the Squeeze-and-Excitation (SE) module, which processes diverse amino acid-level features. Next, it constructs an amino acid feature network from the contact map and SE-module-derived features. Finally, DeepHotResi employs a graph attention network to model hotspot residue prediction as a graph node classification task. Experimental results demonstrate that DeepHotResi outperforms state-of-the-art methods, effectively identifying hotspot residues in protein\u2013RNA complexes with superior accuracy on the test set.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and dataset are available at https:\/\/github.com\/Q1DT\/DeepHotResi.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf197","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:02Z","timestamp":1752584582000},"page":"i466-i474","source":"Crossref","is-referenced-by-count":0,"title":["Precise prediction of hotspot residues in protein\u2013RNA complexes using graph attention networks and pretrained protein language models"],"prefix":"10.1093","volume":"41","author":[{"given":"Siyuan","family":"Shen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University , Urumqi, 830046,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1949-167X","authenticated-orcid":false,"given":"Zhijian","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University , Urumqi, 830046,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyu","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xinjiang Institute of Engineering , Urumqi, 830023,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2869-1619","authenticated-orcid":false,"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha, 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