通过主动学习增强序列活动映射和人工金属酶的演变
Tobias Vornholt1,2, Mojmír Mutný3, Gregor W Schmidt1
1Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.
ACS central science
|July 29, 2024
概括
机器学习通过预测蛋白质功能来加速酶工程. 这一新管道将选与主动机器学习相结合,以高效地开发用于可持续化学的人工金属酶.
科学领域:
- 生物催化剂是一种生物催化剂.
- 蛋白质工程是指蛋白质工程.
- 机器学习在化学中的应用
背景情况:
- 酶工程对于可持续的生物经济至关重要,但通常是低效的,依赖于机会.
- 目前的方法很难在实践的实验约束下有效地建模大型蛋白质序列空间.
研究的目的:
- 开发和验证一个综合管道,结合大规模选和主动机器学习,以提高酶工程效率.
- 使用开发的管道设计一种新型的人造金属酶 (ArM),用于新的胺化反应.
主要方法:
- 实施了整合实验室自动化,下一代测序和主动机器学习 (高斯过程回归) 的管道.
- 为数千种ARM变种生成了序列活动数据,包括一个探索性选轮和对实验噪声的计算.
- 利用高斯过程回归来建模序列活动格局并指导代选.
主要成果:
- 在识别改进的酶变体的成功率上实现了数量级的增加.
- 通过以数据为基础的选证明了对实验资源的有效使用.
- 成功设计了一种人工金属酶,用于具有挑战性的胺化反应.
结论:
- 集成的活跃机器学习和大规模选管道显著提高了酶工程的效率和成功率.
- 这种方法加速了新型生物催化剂的开发,为可持续的化学和生物经济应用提供了广泛的实用性.
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