EvoZymePro-Cat:一种蛋白质 - 配体 - 感知深度学习框架,用于预测酶功能的突变效应
Ran Xu1, Xinkang Li1, Jianan Sui1
1Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
ACS synthetic biology
|December 21, 2025
概括
EvoZymePro-Cat (EZPro-Cat) 是一个深度学习平台,用于选酶突变物. 它准确地预测了相对的酶活性,改善了酶的发现和定向进化.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算生物学和生物信息学
- 机器学习在化学中的应用
背景情况:
- 酶设计是复杂的,因为它拥有庞大的序列空间和相互依赖.
- 预测酶突变活性是传统方法的挑战.
- 精确的酶突变查对于生物催化剂至关重要.
研究的目的:
- 开发一个深度学习平台,EvoZymePro-Cat (EZPro-Cat),用于高效的酶突变查.
- 通过对对比框架克服绝对活动预测的局限性.
- 通过改进功能概况来增强酶发现和定向进化.
主要方法:
- 开发了EvoZymePro-Cat (EZPro-Cat),这是一个集序列,结构和连接物数据的深度学习平台.
- 利用一对对比框架来预测相对突变活动优势.
- 使用ESM1b进行蛋白序列编码,并使用Molt5/MACCS进行连接体表示.
- 集成的结构特征和进化特征与蛋白质 - 配体相互作用的双线性注意力机制.
主要成果:
- 双对比框架在识别改进的酶突变物方面表现出卓越的表现.
- 在深度突变扫描数据集上使用几次射击学习策略实现了高预测精度 (AUC 0.908).
- 该模型有效地捕捉了催化过程中的远程分子间相互作用.
- 在预测酶动力学 (Km, kcat) 突变效应方面表现出卓越的表现.
结论:
- EvoZymePro-Cat (EZPro-Cat) 为酶功能分析提供了一种机械和实用的解决方案.
- 该平台有助于高效的酶发现和定向进化.
- 双对比方法克服了绝对活动预测中的系统错误.
- 集成的多模式表示增强了对蛋白内变异函数的理解.
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