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用多式模式深度表示学习指导蛋白质工程的突变效应的零射击预测

Peng Cheng¹, Cong Mao², Jin Tang³

¹Bioinformatics Center of AMMS, Beijing, China.

|July 5, 2024

概括

蛋白质突变效应预测器 (ProMEP) 使用深度学习准确预测突变效应,使蛋白质工程更快. 这个工具指导了改进的基因编辑技术的开发,如TnpB和TadA变体.

科学领域:

生物技术和生物信息学
计算生物学计算生物学
分子生物学分子生物学

背景情况:

预测氨基酸突变的功能影响对于生物技术和生物医学至关重要.
准确且无监督的突变效应预测仍然是一个重大挑战.
现有的方法通常需要多个序列对齐,这限制了它们的适用性.

研究的目的:

介绍蛋白质突变效应预测器 (ProMEP),一种用于零射击预测突变效应的新方法.
为全面的序列和结构上下文学习开发一个多模式的深度表示学习模型.
为了证明ProMEP在指导蛋白质工程中提高基因编辑工具的能力.

主要方法:

开发了ProMEP,这是一个通用的,无多次序对齐的方法,用于突变效应预测.
采用了一种在约1.6亿个蛋白质上训练的多模式深度表示学习模型.
利用ProMEP预测TnpB和TadA基因编辑酶突变的后果.

主要成果:

ProMEP在突变效应预测方面实现了最先进的性能,并显著提高了速度.
工程TnpB变种显示增强的基因编辑效率 (例如,74.04%的5位突变者与24.66%的野生类型).
开发了基于TadA的基础编辑器,具有高的A-to-G转换频率 (高达77.27%) 和减少的目标外影响.

结论:

ProMEP提供了一种强大而有效的方法来预测蛋白质突变效应.
该方法成功指导了高性能基因编辑工具的工程.
ProMEP促进了蛋白质空间的探索和合成生物学和生物医学中的实际蛋白质设计.

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