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 protei...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.展开更多
基金supported by the National Key R&D Program of China(2022YFC2106000)Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIP-KJGG-009-02,TSBICIP-CXRC-015,and TSBICIP-CXRC-003)+1 种基金the CAS Project for Young Scientists in Basic Research(YSBR-072-4)the National Natural Youth Science Foundation of China(32201187).
文摘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.
