使用深度学习进行光酶的新设计
Andy Hsien-Wei Yeh1,2,3, Christoffer Norn4,5, Yakov Kipnis4,5,6
1Department of Biochemistry, University of Washington, Seattle, WA, USA. hsyeh@ucsc.edu.
Nature
|February 22, 2023
概括
研究人员开发了一种深度学习方法来设计新型酶,为生物医学应用创造具有高活性和特异性的人工光酶.
科学领域:
- 生物化学
- 计算生物学
- 蛋白质工程
背景情况:
- 由于蛋白质结构稀缺和复杂的序列结构关系,De novo酶设计传统上面临限制.
- 之前的方法难以产生具有所需的催化功能和特定基质结合口袋的酶.
研究的目的:
- 开发一种深度学习方法来生成具有多种口袋形状的理想化蛋白质支架.
- 使用这些工程支架设计具有高催化活性和基质特异性的新型人工化酶.
主要方法:
- 采用深度学习的"全家族幻觉"策略来产生众多理想化的蛋白质结构.
- 设计的人工光酶向合成光素基质:二甲和二氧化.
- 为精确定位催化残留物和基质相互作用而优化了活性位点的几何结构.
主要成果:
- 成功设计出具有选择性氧化化学发光的人工光酶,用于二甲和二氧化.
- 开发了一种高活性 (kcat/Km = 10^6 M^-1s^-1) 和热稳定的 (Tm > 95°C) 人工化酶.
- 与原生 luciferases 相比,实现了显著更高的基质特异性.
结论:
- 深度学习幻觉方法可以从头开始创建高活性和特定的生物催化剂.
- 这一里程碑进步了计算性酶设计,为产生多种酶铺平了道路,
- 设计的酶具有广泛的生物医学和生物技术应用潜力.
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