High-Temperature Tolerance Protein Engineering through Deep Evolution

• 全文链接

深度进化的耐高温蛋白工程

关键词：

来源：

BIODESIGN RESEARCH

全文链接：

//agri.nais.net.cn/topic/downloadFile/8b88f00f-9ea2-4f45-a09e-cacc654810bb

来源地址：

https://spj.science.org/doi/10.34133/bdr.0031

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-04-03

摘要：

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.