Integrating protein language models and automatic biofoundry for enhanced protein evolution

Qiang Zhang, Wanyi Chen, Ming Qin, Yuhao Wang, Zhongji Pu, Keyan Ding, Yuyue Liu, Qunfeng Zhang, Dongfang Li, Xinjia Li, Yu Zhao, Jianhua Yao, Lei Huang, Jianping Wu, Lirong Yang, Huajun Chen () and Haoran Yu ()
Additional contact information
Qiang Zhang: Zhejiang University
Wanyi Chen: Zhejiang University
Ming Qin: Zhejiang University
Yuhao Wang: Zhejiang University
Zhongji Pu: Xianghu Laboratory
Keyan Ding: ZJU-Hangzhou Global Scientific and Technological Innovation Centre
Yuyue Liu: ZJU-Hangzhou Global Scientific and Technological Innovation Centre
Qunfeng Zhang: Zhejiang University
Dongfang Li: ZJU-Hangzhou Global Scientific and Technological Innovation Centre
Xinjia Li: Xianghu Laboratory
Yu Zhao: Shenzhen
Jianhua Yao: Shenzhen
Lei Huang: Zhejiang University
Jianping Wu: Zhejiang University
Lirong Yang: Zhejiang University
Huajun Chen: Zhejiang University
Haoran Yu: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.

Date: 2025
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DOI: 10.1038/s41467-025-56751-8

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