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通过使用蛋白质语言模型组合多个突变来优化酶热稳定性

Jiahao Bian^1,2, Pan Tan^3,4, Ting Nie^1,2

¹State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China.

|January 2, 2025

概括

本研究介绍了一种人工智能策略,有效地结合有益的突变来提高酶的热稳定性,克服复杂的遗传相互作用的挑战,并加速工业应用的蛋白质工程.

科学领域:

蛋白质工程是指蛋白质工程.
生物技术是生物技术.
计算生物学计算生物学

背景情况:

酶的热稳定性对于工业应用和蛋白质科学至关重要.
目前用于增强酶热稳定的方法,如理性设计和随机突变发生,在组合突变中与复杂的表皮性相互作用作斗争.
优化酶通常需要漫长,代的设计过程.

研究的目的:

通过重组有益的单点突变,开发一种人工智能辅助的策略,以高效地设计酶热稳定性.
为了应对高阶组合突变的表观症的挑战.
创建一个更快,更有效的酶设计框架.

主要方法:

利用了肌酸酶热稳定性数据,包括单个到四重突变.
采用温度导向蛋白质语言模型 (Pro-PRIME) 来学习表观特征.
使用人工智能模型设计和生成组合突变物.

主要成果:

在两个设计回合后,在获得50个具有优越热稳定的组合突变物时实现了100%的成功率.
开发了一种突变 (13M4) 具有13种突变,近乎野生类型的活动,化温度增加10.19°C,在58°C时半衰期延长约655倍.
成功建模了包括符号和协同效应在内的高阶表征,并使用动态交叉相关矩阵方法阐明了机制.

结论:

这种人工智能辅助的策略有效地促进了对酶热稳定性有益突变的重组.
该Pro-PRIME模型有效地捕捉了高阶突变的复杂表观效应.
这个框架为蛋白质导向进化和热稳定酶的设计提供了一个强大的工具.

关键词:

组合突变的组合突变.创氨酸酶是什么意思史诗主义就是一种史诗主义.蛋白质语言模型热稳定性热稳定性

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