Research Article | Open Access
Volume 2024 |Article ID 0031 |

High-Temperature Tolerance Protein Engineering through Deep Evolution

Huanyu Chu,1,2,5 Zhenyang Tian,1,2,3,5 Lingling Hu,1,2,4 Hejian Zhang,1,2,4 Hong Chang,1,2,4 Jie Bai,1,2,4 Dingyu Liu,1,2 Lina Lu,1,2 Jian Cheng,1,2 Huifeng Jiang 1,2

1Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, P. R. China
2National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P. R. China
3Tianjin Zhonghe Gene Technology Co., LTD, Tianjin 300308, P. R. China
4College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, P. R. China
5These authors contributed equally to this work

03 Jan 2024
12 Mar 2024
03 Apr 2024


Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

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