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To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error\u2009=\u20090.88, R-squared\u2009=\u20090.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.<\/jats:p>","DOI":"10.1093\/bib\/bbad506","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T11:40:10Z","timestamp":1702467610000},"source":"Crossref","is-referenced-by-count":37,"title":["DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates"],"prefix":"10.1093","volume":"25","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 , OX1 3PJ , United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simiao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Radcliffe 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