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

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.