{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:54:20Z","timestamp":1769849660196,"version":"3.49.0"},"reference-count":66,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,12,21]],"date-time":"2017-12-21T00:00:00Z","timestamp":1513814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672541"],"award-info":[{"award-number":["61672541"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007836","name":"Shanghai Key Laboratory of Intelligent Information Processing","doi-asserted-by":"publisher","award":["IIPL-2014-002"],"award-info":[{"award-number":["IIPL-2014-002"]}],"id":[{"id":"10.13039\/100007836","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein\u2013RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein\u2013RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we describe a computational method, PrabHot (Prediction of protein\u2013RNA binding hot spots), that can effectively detect hot spot residues on protein\u2013RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein\u2013RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The PrabHot webserver is freely available at http:\/\/denglab.org\/PrabHot\/.<\/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\/btx822","type":"journal-article","created":{"date-parts":[[2017,12,20]],"date-time":"2017-12-20T04:34:56Z","timestamp":1513744496000},"page":"1473-1480","source":"Crossref","is-referenced-by-count":91,"title":["Computational identification of binding energy hot spots in protein\u2013RNA complexes using an ensemble approach"],"prefix":"10.1093","volume":"34","author":[{"given":"Yuliang","family":"Pan","sequence":"first","affiliation":[{"name":"School of Software, Central South University, Changsha, China"}]},{"given":"Zixiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha, China"}]},{"given":"Weihua","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo, China"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Changsha, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}]}],"member":"286","published-online":{"date-parts":[[2017,12,21]]},"reference":[{"key":"2023012713022098700_btx822-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped blast and psi-blast: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023012713022098700_btx822-B2","doi-asserted-by":"crossref","first-page":"1135.","DOI":"10.1016\/j.jmb.2004.10.055","article-title":"Network analysis of protein structures identifies functional residues","volume":"344","author":"Amitai","year":"2004","journal-title":"J. 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