人工智能驱动的酶工程:新兴模型和下一代生物技术应用
Mohd Faheem Khan1, Mohd Tasleem Khan2
1UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8 Dublin, Ireland.
Molecules (Basel, Switzerland)
|January 10, 2026
概括
人工智能 (AI) 正在彻底改变酶工程,超越传统方法. 人工智能可以为医学和工业的新应用提供精确,数据驱动的酶设计.
科学领域:
- 生物技术和生物化学工程 生物技术和生物化学工程
- 计算生物学和生物信息学
- 合成生物学 合成生物学
背景情况:
- 传统的酶工程受到劳动密集型,昂贵和范围狭窄的方法的限制.
- 人工智能 (AI) 为快速,精确和数据驱动的酶设计提供了一个范式转变.
- 新兴的人工智能工具对于推动生物技术,医学和工业过程至关重要.
研究的目的:
- 在酶工程中合成和呈现新兴的AI方法.
- 要突出从经验到预测的过渡,计算引导的酶设计.
- 展示AI在创造具有增强功能的新型生物催化剂中的作用.
主要方法:
- 机器学习和深度学习模型 (例如AlphaFold2,ESM-2) 用于预测酶结构,稳定性和功能.
- 用于 de novo 酶序列设计的生成模型 (例如,ProteinGAN).
- 强化学习用于优化突变选择和功能预测.
- 混合人工智能-实验工作流程,将预测建模与高通量选集成.
主要成果:
- 人工智能准确地预测了酶特性,促进了合理的突变和优化.
- 生成型模型可以创建具有定制活动的酶.
- 人工智能加速合成酶 ("合成酶") 的发展,用于非自然反应.
- 人工智能集成通过反合成和途径建模来增强代谢和过程优化.
结论:
- 人工智能方法正在定义下一代酶工程.
- 这种方法可以创建可持续,高效和功能多样化的生物催化剂.
- 人工智能驱动的酶工程扩大了制药,生物燃料和环境修复领域的应用.
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