使用基于进化的建模和结构质量评估进行新型Cas9 PAM交互域的计算设计
Cyril Malbranke1,2, William Rostain2, Florence Depardieu2
1Laboratory of Physics of the Ecole Normale Superieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Paris, France.
PLoS computational biology
|November 17, 2023
概括
我们开发了一种新的蛋白质设计方法,将机器学习和基于物理的建模结合起来. 这种方法成功地产生了功能性的Cas9变体,其中一些表现出比野生类型更好的活性.
科学领域:
- 蛋白质工程和计算生物学
- 机器学习在生物信息学中的应用.
- 结构生物学和生物物理学
背景情况:
- 蛋白质设计需要整合各种数据源,包括功能测试,进化数据和物理原理.
- 现有的方法往往难以有效地将这些信息类型结合起来,以实现强大的蛋白质工程.
- Cas9蛋白的DNA结合域对于序列和功能优化来说是一个具有挑战性的目标.
研究的目的:
- 开发和验证蛋白质设计的混合计算方法.
- 利用机器学习和基于物理的建模来探索蛋白质序列空间.
- 为了设计Cas9蛋白的DNA识别域的功能变体.
主要方法:
- 利用受限制的博尔兹曼机器 (RBM) 来从进化数据中学习蛋白质序列家族模型.
- 在RBM培训期间使用半监督学习来纳入稀缺的功能 (实验) 数据.
- 集成了一种实证力场方法 (FoldX) 来指导蛋白质表示空间的探索.
- 将这种方法应用于Cas9蛋白的DNA结合域,并通过实验验证设计的变体.
主要成果:
- 产生了71种蛋白质变体,具有不同的RBM和FoldX能量配置文件.
- 在71种设计的变体中,有21种表现出功能性,这表明蛋白质设计成功.
- 与野生类型的氨基酸差异高达20%的序列保留了功能.
- 与野生类型的Cas9蛋白相比,六种设计的变体表现出增强的活性.
结论:
- 机器学习和基于物理的建模策略的结合对于蛋白质设计是有效的.
- 这种混合方法可以在保持功能的同时探索广泛的蛋白质序列空间.
- 改进的Cas9变体的成功工程突出了这种方法在蛋白质工程和生物技术中的潜力.
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