{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:17:15Z","timestamp":1772813835811,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technology Research and Development Program of China","award":["2022YFA0911800"],"award-info":[{"award-number":["2022YFA0911800"]}]},{"name":"Major Science and Technology Project of Hubei Province","award":["2024BAA008"],"award-info":[{"award-number":["2024BAA008"]}]}],"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>The prediction of enzyme kinetic parameters is crucial for screening enzymes with high catalytic efficiency and desired characteristics to catalyze natural or non-natural reactions. Data-driven machine learning models have been explored to reduce experimental cost and speed up the enzyme design process. However, the prediction performance is still subject to significant limitations due to the variance in sequence similarity between training and testing datasets. In this work, we introduce DEKP, an integrated deep learning approach enzyme kinetic parameter prediction. It leverages pretrained models of protein sequences and incorporates enhanced graph neural networks that provide comprehensive representation of protein structural features. This novel approach can effectively alleviate the performance degradation caused by sequence similarity variation. Moreover, it provides sensitive detection of changes in catalytic efficiency due to enzyme mutations. Experiments validate that DEKP outperforms existing models in predicting enzyme kinetic parameters. This work is expected to significantly improve the performance of the enzyme screening process and provide a robust tool for enzyme-directed evolution research.<\/jats:p>","DOI":"10.1093\/bib\/bbaf187","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:24:33Z","timestamp":1745522673000},"source":"Crossref","is-referenced-by-count":7,"title":["DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1049-3067","authenticated-orcid":false,"given":"Yizhen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]}]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]},{"name":"Key Laboratory of Intelligent Sensing System and Security , Hubei University, Ministry of Education, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]},{"name":"Hubei Key Laboratory of Big Data Intelligent Analysis and Application , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]}]},{"given":"Yanyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]},{"name":"Key Laboratory of Intelligent Sensing System and Security , Hubei University, Ministry of Education, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]},{"name":"Hubei Key Laboratory of Big Data Intelligent Analysis and Application , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]}]},{"given":"Yujia","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science , Hubei University, No. 368 Youyi Road, 430062 Wuhan,","place":["China"]}]},{"given":"Daniyal","family":"Alghazzawi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology (FCIT) , 3599 King Abdulaziz University (KAU), Unit 3600, Jeddah 22254-7653,","place":["Saudi Arabia"]}]}],"member":"286","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"2025042415242696200_ref1","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1042\/ETLS20200257","article-title":"Revolutionizing enzyme engineering through artificial intelligence and machine learning","volume":"5","author":"Singh","year":"2021","journal-title":"Emerg Top Life Sci"},{"key":"2025042415242696200_ref2","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1021\/acscentsci.3c01275","article-title":"Opportunities and challenges for machine learning-assisted enzyme engineering","volume":"10","author":"Yang","year":"2024","journal-title":"ACS Central 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