文献类型：期刊文献

英文题名：Artificial Intelligence-Powered Construction of a Microbial Optimal Growth Temperature Database and Its Impact on Enzyme Optimal Temperature Prediction

作者：Wang, Xiaotao[1,2];Zong, Yuwei[2];Zhou, Xuanjie[2];Xu, Li[2];He, Wei[1];Quan, Shu[1,3]

机构：[1]East China Univ Sci & Technol, Shanghai Collaborat Innovat Ctr Biomfg SCICB, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Shanghai Jiao Tong Univ, Zhangjiang Inst Adv Study, Shanghai 201203, Peoples R China

年份：2024

卷号：128

期号：10

起止页码：2281

外文期刊名：JOURNAL OF PHYSICAL CHEMISTRY B

收录：;EI(收录号：20241015695044);WOS:【SCI-EXPANDED(收录号:WOS:001179760200001)】；

基金：We thank Elsevier for providing us with remote access to their API and text resources. We also express our gratitude to Chi Zhang and Yingchao Zhang for critical suggestions and their assistance on manuscript preparation, Mencius Meng and Xin Huang for helpful discussions. This work was supported by the Undergraduate Training Program on Innovation and Entrepreneurship grant 202210251039 (to X.W., Y.Z., X.Z., and L.X.) and the National Natural Science Foundation of China (NSFC) grants 32222049 (to S.Q.), 32201043 (to W.H.).

语种：英文

外文关键词：Catalysis - Database systems - Forecasting - Growth temperature

摘要：Accurate prediction of enzyme optimal temperature (Topt) is crucial for identifying enzymes suitable for catalytic functions under extreme bioprocessing conditions. The optimal growth temperature (OGT) of microorganisms serves as a key indicator for estimating enzyme Topt, reflecting an evolutionary temperature balance between enzyme-catalyzed reactions and the organism's growth environments. Existing OGT databases, collected from culture collection centers, often fall short as culture temperature does not precisely represent the OGT. Models trained on such databases yield inadequate accuracy in enzyme Topt prediction, underscoring the need for a high-quality OGT database. Herein, we developed AI-based models to extract the OGT information from the scientific literature, constructing a comprehensive OGT database with 1155 unique organisms and 2142 OGT values. The top-performing model, BioLinkBERT, demonstrated exceptional information extraction ability with an EM score of 91.00 and an F1 score of 91.91 for OGT. Notably, applying this OGT database in enzyme Topt prediction achieved an R (2) value of 0.698, outperforming the R- 2 value of 0.686 obtained using culture temperature. This emphasizes the superiority of the OGT database in predicting the enzyme Topt and underscores its pivotal role in identifying enzymes with optimal catalytic temperatures.