蛋白质工程使用可变的自由能量近似值
Evgenii Lobzaev1,2, Michael A Herrera1, Martyna Kasprzyk1
1School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
Nature communications
|December 1, 2024
概括
使用生成深度学习的蛋白质工程得到了PREVENT的推进,这是一个设计稳定和功能蛋白质变体的新型模型. 这种方法加速了新蛋白质的产生,成功率很高.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 蛋白质工程传统上依赖于定向进化 (DE),这是一个耗时的方法.
- 生成型深度学习模型可以设计新的蛋白质变体,但往往缺乏热力学稳定性,产生非功能性蛋白质.
研究的目的:
- 引入一个深度学习模型,PREVENT,用于生成热力学稳定和功能性蛋白质变体.
- 评估PREVENT在设计大肠杆菌N-乙-L-谷氨酸激酶 (EcNAGK) 变体中的疗效.
主要方法:
- 通过变异frEe eNergyapproximaTion (PREVENT) 开发的蛋白质工程,是一种生成的深度学习模型.
- 在蛋白质序列和结构数据集上训练PREVENT,以学习热力学景观.
- 应用PREVENT来设计EcNAGK酶的变体.
主要成果:
- 预防成功地产生了40种EcNAGK的变种.
- 85%的设计变体是功能性的.
- 55%的功能变异表现出与野生类型酶相比的生长速度,即使有多达9个突变.
结论:
- 与传统方法相比,PREVENT在蛋白质工程中提供了显著的加速.
- 该模型产生稳定和功能蛋白质的能力表明了蛋白质设计的有希望的新方向.
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