{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:30:21Z","timestamp":1785929421940,"version":"3.56.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study, Seq2Topt, reached a superior accuracy on ${T}_{opt}$ prediction just using protein sequences (RMSE\u2009=\u200912.26\u00b0C and R2\u2009=\u20090.57), and could capture key protein regions for enzyme ${T}_{opt}$ with multi-head attention on residues. Through case studies on thermophilic enzyme selection and predicting enzyme ${T}_{opt}$ shifts caused by point mutations, Seq2Topt was demonstrated as a promising computational tool for enzyme mining and in-silico enzyme design. Additionally, accurate deep learning predictors of enzyme optimal pH (Seq2pHopt, RMSE\u2009=\u20090.88 and R2\u2009=\u20090.42) and melting temperature (Seq2Tm, RMSE\u2009=\u20097.57\u00a0\u00b0C and R2\u2009=\u20090.64) were developed based on the model architecture of Seq2Topt, suggesting that the development of Seq2Topt could potentially give rise to a useful prediction platform of enzymes.<\/jats:p>","DOI":"10.1093\/bib\/bbaf114","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T05:09:12Z","timestamp":1741842552000},"source":"Crossref","is-referenced-by-count":23,"title":["Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1936-1223","authenticated-orcid":false,"given":"Sizhe","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Engineering Science, University of Oxford , Parks Road, OX1 3PJ, Oxford,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bozhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Division, School of Engineering, Westlake University , 310030, Hangzhou ,","place":["China"]},{"name":"Zhejiang University , 310058, Hangzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-Resources, Hubei Key Laboratory of Industrial Biotechnology, School of Life Sciences, Hubei University , 430062, Wuhan ,","place":["China"]},{"name":"Tianjin Institute of Pharmaceutical Research Co. Ltd , Tianjin Binhai New Area, 300301, Tianjin ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiren","family":"Xu","sequence":"additional","affiliation":[{"name":"Tianjin Institute of Pharmaceutical Research Co. 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