Volume 34, No 9, Sep 2024

ISSN: 1001-0602 
EISSN: 1748-7838 2018 
impact factor 17.848* 
(Clarivate Analytics, 2019)

Volume 34 Issue 9, September 2024: 630-647   |  Open Access

ORIGINAL ARTICLES

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

Peng Cheng^1,† , Cong Mao^2,† , Jin Tang^3,† , Sen Yang^1,† , Yu Cheng² , Wuke Wang³ , Qiuxi Gu² , Wei Han³ , Hao Chen² , Sihan Li² , Yaofeng Chen¹ , Jianglin Zhou¹ , Wuju Li¹ , Aimin Pan³ , Suwen Zhao^4,5 , Xingxu Huang^3,5 , Shiqiang Zhu^3,* , Jun Zhang^2,* , Wenjie Shu^1,* , Shengqi Wang^1,*

¹Bioinformatics Center of AMMS, Beijing, China
²State 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
³Zhejiang Lab, Hangzhou, Zhejiang, China
⁴iHuman Institute, ShanghaiTech University, Shanghai, China
⁵School of Life Science and Technology, ShanghaiTech University, Shanghai, China
^†These authors contributed equally: Peng Cheng, Cong Mao, Jin Tang, Sen Yang
Correspondence: Shiqiang Zhu(zhusq@zhejianglab.edu.cn)Jun Zhang(zhang_jun@njmu.edu.cn)Wenjie Shu(shuwj@bmi.ac.cn)Shengqi Wang(sqwang@bmi.ac.cn)

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.

https://doi.org/10.1038/s41422-024-00989-2

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