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Integrating protein language models and automatic biofoundry for enhanced protein evolution

Zhang Qiang,Chen Wanyi,Qin Ming,Wang Yuhao,Pu Zhongji,Ding Keyan,Liu Yuyue,Zhang Qunfeng,Li Dongfang,Li Xinjia,Zhao Yu,Yao Jianhua,Huang Lei,Wu Jianping,Yang Lirong,Chen Huajun,Yu Haoran

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.