通过深度网络幻觉进行新型蛋白质设计
Ivan Anishchenko1,2, Samuel J Pellock1,2, Tamuka M Chidyausiku1,2
1Department of Biochemistry, University of Washington, Seattle, WA, USA.
Nature
|December 2, 2021
概括
研究人员使用深度神经网络从随机序列设计新型蛋白质. 实验验证证了新型功能性蛋白质的成功生成,进步了新型蛋白质设计.
科学领域:
- 计算生物学
- 蛋白质工程
- 深度学习
背景情况:
- 深度神经网络通过分析氨基酸序列来预测蛋白质结构.
- 这些网络产生全新的蛋白序列和结构的潜力在很大程度上仍未被探索.
研究的目的:
- 调查为蛋白质结构预测而训练的深度神经网络是否可用于设计新型功能性蛋白质.
- 探索与已知的天然蛋白质无关的蛋白质的新设计.
主要方法:
- 使用trRosetta网络来预测随机氨基酸序列之间的残余距离图.
- 采用蒙特卡洛序列空间采样来优化对背景平均值的预测距离分布.
- 合成了设计序列的基因,在大肠杆菌中表达蛋白质,并分析了它们的结构.
主要成果:
- 产生具有多样性预测结构的新型蛋白序列.
- 在129个设计的蛋白质中,成功表达和净化了27个,产生单分散样本.
- 确定了三种设计的蛋白质的3D结构 (通过X射线结晶学和NMR),这些结构与计算模型密切匹配.
结论:
- 经过结构预测训练的深度神经网络可以被反转为新的蛋白质设计.
- 这种方法对传统的基于物理的方法提供了强大的补充,用于创建具有新功能的蛋白质.
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