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一个有条件的蛋白质扩散模型产生了具有增强活性的人工可编程内核酶序列

Bingxin Zhou^1,2, Lirong Zheng^3,4, Banghao Wu^1,5

¹Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.

|September 9, 2024

概括

这项研究介绍了CPDiffusion,这是一种用于生成新型功能蛋白的深度学习模型. 人工智能模型成功地创造了具有增强DNA裂变活性的阿尔戈诺特蛋白,展示了生物催化剂设计的新途径.

科学领域:

蛋白质工程是一种蛋白质工程.
计算生物学是一种计算生物学.
生物技术是生物技术.

背景情况:

对新型生物催化剂的需求需要先进的蛋白质工程方法.
深度学习为设计具有定制功能的蛋白质提供了潜力.

研究的目的:

开发和验证CPDiffusion,一种条件蛋白质扩散模型,用于生成功能蛋白质序列.
创建具有增强功能的多样化蛋白序列,包括特定的结构和保存残留约束.

主要方法:

使用条件蛋白质扩散模型 (CPDiffusion) 进行*in silico*蛋白质序列生成.
应用CPDiffusion以基于野生类型 (WT) 模板 (KmAgo和PfAgo) 生成人工阿尔戈诺特 (Ago) 蛋白序列.
将二次结构和保存的氨基酸等蛋白质特定条件纳入生成过程中.

主要成果:

从WT模板中生成了具有显著偏差 (高达400个氨基酸) 的人工Ago蛋白序列.
实验验证证了大多数生成的KmAgo和PfAgo蛋白质中的DNA裂变活性.
许多产生的蛋白质与各自的WT对应物相比,表现出优越的DNA裂变活性.

结论:

在单个步骤中,CPDiffusion有效地产生具有复杂结构和增强功能的新型蛋白质序列.
该模型成功地捕获了保存的残留物和序列特征,而无需广泛的标记训练数据.
这种由人工智能驱动的方法促进了*in silico*设计和选的酶,改进了多域分子结构和复杂的功能.

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