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