Integrating protein language models and automatic biofoundry for enhanced protein evolution

• 全文链接

整合蛋白质语言模型和自动生物铸造以增强蛋白质进化

关键词：

来源：

Nature Communications

全文链接：

//agri.nais.net.cn/topic/downloadFile/2316c250-5d6d-450b-8866-59ba38455f6f

来源地址：

https://www.nature.com/articles/s41467-025-56751-8

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：

2025-02-11

摘要：

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.