大学化学 >> 2026, Vol. 41 >> Issue (9): 373-381.doi: 10.12461/PKU.DXHX202508066

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基于统计能量分析的酶催化机理探究：以丝氨酸水解酶为例

王莹莹, 于洋   

  1. 北京理工大学化学与化工学院, 北京 102488
  • 收稿日期:2025-08-19 录用日期:2025-09-30 发布日期:2026-09-01
  • 通讯作者: 于洋 E-mail:yangyu1@bit.edu.cn Yang Yu
  • 基金资助:
    国家自然科学基金(22578032)

Study of enzyme catalytic mechanism based on statistical energy analysis: taking serine hydrolases as an example

Yingying Wang, Yang Yu   

  1. School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 102488, China
  • Received:2025-08-19 Accepted:2025-09-30 Published:2026-09-01
  • Contact: Yang Yu E-mail:yangyu1@bit.edu.cn

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摘要： 随着以AlphaFold为代表的人工智能方法和结构生物信息学工具的普及，如何在生物化学教学中培养学生利用蛋白质结构大数据开展定量分析的能力成为新的挑战。本文构建了一个以丝氨酸水解酶为模型的教学案例，将统计力学原理、氨基酸残基旋转异构体库、玻尔兹曼概率-能量关系及酶催化过渡态理论有机结合，指导学生通过分析丝氨酸水解酶不同状态(无配体状态、底物类似物、过渡态类似物)蛋白质结构中催化残基的二面角分布，计算构象偏好对应的统计能量，并由统计能量变化推算酶催化加速倍数。本案例不仅强化了学生的数据库检索、统计分析与编程能力，还加深了对“基态去稳定化”这一酶催化机制的理解，为未来参与蛋白质设计与计算酶工程研究打下了基础。

关键词: 丝氨酸水解酶, 二面角分布, 统计能量, 基态去稳定化, 过渡态

Abstract: The widespread adoption of artificial intelligence methods and structural bioinformatics tools, exemplified by AlphaFold, presents new challenges in biochemistry education regarding how to cultivate students' ability to perform quantitative analysis using big data on protein structures. This study develops an instructional case centered on serine hydrolases, which synergistically combines statistical mechanics principles, amino acid residue rotamer libraries, the Boltzmann probability-energy relationship, and transition state theory in enzyme catalysis. The pedagogical approach guides students to analyze dihedral angle distributions of catalytic residues across different serine hydrolase states (ligand-free, substrate analogs, and transition state analogs), compute the corresponding statistical energies of conformational preferences, and estimate catalytic acceleration factors from statistical energy changes. This teaching framework not only enhances students' competencies in database retrieval, statistical analysis, and programming, but also provides deeper mechanistic insights into ground state destabilization as a fundamental enzyme catalytic strategy, thereby establishing a foundation for future research in protein design and computational enzyme engineering.

Key words: Serine hydrolase, Dihedral angle distribution, Statistical energy, Ground state destabilization, Transition state