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[人工智能辅助酶分子改造的应用进展]

Pei Xu¹, Weihua Wang¹, Hongwei Ning²

¹Institutes of Physical Science and Information Technology, Anhui University, Hefei 230061, Anhui, China.

Sheng wu gong cheng xue bao = Chinese journal of biotechnology

|June 24, 2024

概括

人工智能 (AI) 正在通过实现数据驱动的分子修饰来彻底改变酶工程. 人工智能辅助的方法,特别是卷积神经网络,增强了酶特性,如热稳定性,提高了效率.

科学领域:

生物技术是生物技术.
酶工程是什么? 酶工程是什么?
计算生物学计算生物学

背景情况:

自然酶通常在活性,酶选择性和热稳定性方面表现出局限性,阻碍了它们的应用和研究.
传统的酶工程方法,如理性设计和定向进化,由于巨大的蛋白质序列空间和有限的实验数据,面临着挑战.
像人工智能这样的先进技术的出现为克服酶修饰的这些局限性提供了一个有希望的途径.

研究的目的:

审查数据采集方法和人工智能辅助分子修饰在酶工程中的应用例子.
突出AI在改善各种应用中的酶特性方面的潜力.
专注于卷积神经网络 (CNN) 方法来预测蛋白质的热稳定性.

主要方法:

探索数据驱动的酶工程方法,利用下一代测序,高通量选和蛋白质数据库.
应用人工智能辅助的统计学习模型来预测酶序列/结构-属性关系.
专注于卷积神经网络 (CNN) 架构来预测蛋白质的热稳定性.

主要成果:

人工智能辅助的方法显著提高了分子修饰的效率,通过实现数据驱动的预测和优异酶突变的选择.
卷积神经网络在预测蛋白质热稳定性方面表现出有效性,这是酶应用的关键参数.
人工智能驱动的酶工程成功示例展示了增强的酶特性.

结论:

人工智能辅助的分子改造代表了酶工程的范式转变,提供了更高的效率和精度.
数据驱动的方法,特别是用于热稳定性预测的CNN,为研究人员提供了宝贵的工具.
本综述为推进酶工程中的AI应用提供了见解和参考.

关键词:

人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.指导进化是指导进化的.酶的分子修饰是酶的分子修饰.

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