一个多模组合框架,用于最佳突变预测和计算酶工程
Ding Luo1, Huining Ji1, Baodong Hu2,3,4,5
1State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, P. R. China.
Angewandte Chemie (International ed. in English)
|December 18, 2025
概括
我们开发了GEMS,这是一个新的酶工程框架,它使用多种数据类型来预测有益的突变. 通过准确模拟复杂的蛋白质相互作用,GEMS改善了酶功能,优于现有的方法.
科学领域:
- 生物化学 生物化学
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
背景情况:
- 酶工程对于开发改进的生物催化剂至关重要.
- 传统的方法很难模拟复杂的蛋白质相互作用 (表现和远程效应).
研究的目的:
- 介绍GEMS,一种用于酶工程的新框架.
- 为了利用跨多个数据模式的集合零射击学习来预测有益的酶变体.
主要方法:
- GEMS集成了进化,结构和序列数据.
- 它使用整体零射击能力模拟了序列-结构-功能关系.
- 与最先进的方法进行了基准测试.
主要成果:
- 在排名有利的变体方面,GEMS表现出了竞争力.
- 它擅长捕捉长距离的功能约束,生成有信息的变体库.
- 应用到五种不同的酶中,GEMS发现了突变,使催化效率提高了1.1至3.2倍.
结论:
- GEMS是用于先进的酶工程的强大而通用的工具.
- 该框架有效地模拟复杂的蛋白质相互作用,以改善酶功能.
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