从传统到人工智能驱动:用于生物催化剂的智能酶工程的演变
Feifei Lv1, Jiaxing Zhang2, Shengping You1
1Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China.
Biotechnology advances
|December 24, 2025
概括
酶工程使用人工智能,特别是机器学习 (ML) 和深度学习 (DL),以增强酶设计. 这些先进的方法改进了传统的策略,如定向进化,使生物催化剂更有效和可预测.
科学领域:
- 生物技术和生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质的工程.
背景情况:
- 酶工程优化了使用复杂技术系统的酶功能和应用.
- 关键领域包括理解序列-结构-功能关系,健身景观和监管.
- 已建立的方法包括定向进化,理性/半理性设计,残留共进化和de novo设计.
研究的目的:
- 审查传统的酶工程策略.
- 引入最新的机器学习 (ML) 和深度学习 (DL) 在酶工程中的应用.
- 突出AI在生物催化剂中的变革潜力.
主要方法:
- 审查已建立的酶工程技术:定向进化,理性/半理性设计,残留共进化和新设计.
- 整合和应用ML/DL模型来增强现有方法.
- 分析AI在预测突变效应和探索序列空间方面的作用.
主要成果:
- ML和DL显著提高了传统酶工程方法的效率.
- 人工智能有助于克服诸如选效率和逃离当地最佳健身等局限性.
- 人工智能为酶设计提供了强大的工具,提高了可预测性和效率.
结论:
- 人工智能支持的酶工程承诺转向可预测和高效的酶设计的范式转变.
- 未来的发展包括多式联运基础模型和标准化数据库,以提高可靠性.
- 人工智能为推进生物催化剂提供了精确而强大的解决方案.
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