Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering  

作　　者：Peng Cheng Cong Mao Jin Tang Sen Yang Yu Cheng Wuke Wang Qiuxi Gu Wei Han Hao Chen Sihan Li Yaofeng Chen Jianglin Zhou Wuju Li Aimin Pan Suwen Zhao Xingxu Huang Shiqiang Zhu Jun Zhang Wenjie Shu Shengqi Wang 

机构地区：[1]Bioinformatics Center of AMMS,Beijing,China [2]State Key Laboratory of Reproductive Medicine and Offspring Health,Women’s Hospital of Nanjing Medical University,Nanjing Maternity and Child Health Care Hospital,Nanjing Medical University,Nanjing,Jiangsu,China [3]Zhejiang Lab,Hangzhou,Zhejiang,China [4]iHuman Institute,ShanghaiTech University,Shanghai,China [5]School of Life Science and Technology,ShanghaiTech University,Shanghai,China

出　　处：《Cell Research》2024年第9期630-647,共18页细胞研究（英文版）

摘　　要：Mutations in amino acid sequences can provoke changes in protein function.Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine,but remains a fundamental challenge.To resolve this challenge,here we present Protein Mutational Effect Predictor(ProMEP),a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects.A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from~160 million proteins.ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed,enabling efficient and intelligent protein engineering.Specifically,ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA,and successfully guides the development of high-performance gene-editing tools with their engineered variants.The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04%(vs 24.66%for the wild type);and the base editing tool developed on the basis of a TadA 15-site mutant(in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA)exhibits an A-to-G conversion frequency of up to 77.27%(vs 69.80%for ABE8e,a previous TadA-based adenine base editor)with significantly reduced bystander and off-target effects compared to ABE8e.ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering.Therefore,ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins,thereby advancing studies in biomedicine and synthetic biology.

关 键 词：MODAL REPRESENTATION enable 

分 类 号：Q816[生物学—生物工程]