在混合空间中加速发现高活性酶纳米混合体与并行贝叶斯优化
Yu Liu1,2, Haoyang Hu3, Yueheng Han3
1Key Lab for Industrial Biocatalysis, Ministry of Education, Department of Chemical Engineering, Tsinghua University, Beijing, China.
Nature communications
|March 7, 2026
概括
人工智能通过机器学习工作流加速酶固定. 这种方法有效地发现纳米载体,增强工业应用的酶活性.
科学领域:
- 生物技术是生物技术.
- 蛋白质工程是指蛋白质工程.
- 机器学习 机器学习
背景情况:
- 酶固定对于工业应用至关重要,但由于扩散限制,通常会降低活性.
- 优化固定载体通常需要广泛的试错实验.
- 脆弱的生物大分子需要保护性载体,以便在恶劣条件下保持稳定.
研究的目的:
- 开发一种以机器学习为指导的工作流程,以加速酶纳米混合体的发现.
- 为特定的酶和反应优化纳米载体,增强催化活性.
- 为了克服传统的试错方法在酶固定化的局限性.
主要方法:
- 使用并行混合空间贝叶斯优化 (PHBO) 实现机器学习工作流.
- 利用先前的知识,机器学习和代反来实现数据效率优化.
- 在有限的实验预算内探索一个巨大的反应空间 (超过10^7个实验).
主要成果:
- 实现了对葡萄糖氧化酶100%的活性恢复.
- 证明了90%的活动恢复对catalase.
- 获得了Candida南极洲脂酶B的79%活动恢复.
- 在多种不同的酶系统中成功识别了高活性酶纳米混合体.
结论:
- 数据效率优化显著加速了酶纳米混合体的发现.
- PHBO算法和ML指导的工作流对各种酶系统有效.
- 这种方法提高了工业生物催化剂的酶稳定性和活性.
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