文献类型：期刊文献

英文题名：A General Temperature-Guided Language Model to Design Proteins of Enhanced Stability and Activity

作者：Jiang, Fan[1]; Li, Mingchen[2,3]; Dong, Jiajun[4,5]; Yu, Yuanxi[1]; Sun, Xinyu[6]; Wu, Banghao[1]; Huang, Jin[1,8]; Kang, Liqi[1]; Pei, Yufeng[7]; Zhang, Liang[1]; Wang, Shaojie[4]; Xu, Wenxue[4]; Xin, Jingyao[4]; Ouyang, Wanli[2]; Fan, Guisheng[3]; Zheng, Lirong[1]; Tan, Yang[2,3]; Hu, Zhiqiang[9]; Xiong, Yi[8]; Feng, Yan[8]; Yang, Guangyu[8,10,11]; Liu, Qian[8]; Song, Jie[7]; Liu, Jia[4]; Hong, Liang[1,2,12]; Tan, Pan[1,2]

机构：[1] School of Physics and Astronomy, & Shanghai National Center for Applied Mathematics [SJTU Center], Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China; [2] Shanghai Artificial Intelligence Laboratory, Shanghai, 200030, China; [3] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200240, China; [4] Shanghai Institute for Advanced Immunochemical Studies, School of Life Sciences and Technology, ShanghaiTech University, Shanghai, 201210, China; [5] Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 XingDaoHuanBei Road, Guangdong, Guangzhou, 510005, China; [6] Department of Chemistry, University of Science and Technology of China, Anhui, Hefei, 230001, China; [7] Hangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang, Hangzhou, 310018, China; [8] School of Life Sciences and Biotechnology, & State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic, Shanghai Jiao Tong University, Shanghai, 200240, China; [9] SenseTime Research, Shanghai, 200233, China; [10] Institute of Key Biological Raw Material, Shanghai Academy of Experimental Medicine, Shanghai, 201401, China; [11] Hzymes Biotechnology Co. Ltd, Hubei, Wuhan, 430075, China; [12] Zhanjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230257903)

语种：英文

摘要：Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30-45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Remarkably, over 30% of the AI-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. Moreover, we developed an efficient and effective method based on PRIME to rapidly obtain multi-site mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering. ? 2023, CC BY-NC-SA.