Study of Enzyme Catalytic Mechanism Based on Statistical Energy Analysis Using Serine Hydrolases as an Example

With the increasing popularity of artificial intelligence methods and structural bioinformatics tools, such as AlphaFold, cultivating students' ability to conduct quantitative analysis using big data on protein structures has become a new challenge in biochemistry education. This paper constructs a teaching case based on a serine hydrolase, integrating the principles of statistical mechanics, the Dunbrack backbone-dependent rotamer library, the Boltzmann probability-energy relationship, and transition state theory. Students are guided through the entire process, from structural dataset acquisition and conformational probability distribution analysis to catalytic rate enhancement prediction, all within a Jupyter Notebook environment. Students analyze the χ₁ dihedral angle distribution of the catalytic serine (Ser195) in different binding states (apo, substrate analog GSA, and transition state analog TSA), calculate pseudo-energies corresponding to conformational preferences, and derive catalytic acceleration from ΔE₀. This case not only strengthens students' database searching, statistical analysis, and scripting skills, but also deepens their understanding of the enzyme catalytic mechanism of "ground state destabilization," laying a methodological foundation for future research in protein design and computational enzyme engineering.