进化规模的酶学使生物化学常数在多峰触媒景观中的预测成为可能
Duncan F Muir1,2, Garrison P R Asper1, Pascal Notin3,4
1Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA.
bioRxiv : the preprint server for biology
|November 1, 2024
概括
绘制酶序-催化场景的地图对于酶的功能和设计至关重要. 这项研究揭示了不同的催化峰值,并挑战了适应假设,使得更好的酶工程成为可能.
科学领域:
- 生物化学 生物化学
- 酶动力学 酶动力学
- 蛋白质工程是一种蛋白质工程.
背景情况:
- 绘制酶序-催化场景的地图对于理解酶的功能,进化和设计至关重要.
- 腺酸酶 (ADK) 作为研究这些景观的模型酶.
研究的目的:
- 量化地绘制ADK的序列催化场景.
- 剖析ADK序列催化场景的拓,航行性和机械基础.
- 挑战有关酶适应的现有假设,特别是有关热友性酶的假设.
主要方法:
- 扩展微流体平台,用于高通量测量催化常量 (kcat和KM).
- 对数百个自然存在的ADK序列和突变物的分析.
- 集成深度学习模型与定制高通量动态数据进行预测建模.
主要成果:
- 在ADK景观中识别明显的催化峰,按结构动机组织.
- 证明热友酶不一定比中友酶慢,挑战了长期存在的假设.
- 开发半监督模型,在预测ADK序列的催化参数方面显著优于现有方法.
结论:
- 这项研究提供了一个强大的策略,用于剖析整个进化过程中的酶序列催化场景.
- 开发的特定家族模型可以准确预测催化常数,进步酶工程和功能预测.
- 这些发现为酶适应和进化提供了新的见解.
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