In this perspective, we describe how we arrived at a framework of ensemble–function analyses to quantitatively dissect enzyme catalysis and biological function more broadly. Serine proteases are described in biochemistry textbooks to illustrate enzyme mechanisms, yet those descriptions do not explain how these enzymes achieve their ~ 10 12 ‐fold rate enhancements. Moving away from the classic descriptions of ‘catalytic triad’ and ‘oxyanion hole’, we returned to the basic physical and chemical interactions in serine protease active sites and identified molecular features that enable a highly efficient reaction path on the enzymes, compared to the uncatalyzed reaction. We then leveraged principles from statistical mechanics to quantify the contributions from each catalytic feature. Combining the contributions from each feature in a ‘catalytic ledger’ provided a quantitative accounting of serine protease catalysis. These analyses revealed previously unrecognized catalytic interactions that are destabilizing in the reaction's ground state—unfavorable bond rotamers, shorter‐than‐ideal distances, and suboptimal hydrogen bonds—each of which is relieved in the transition state, thereby lowering the barrier to reaction. Analogous catalytic features are found in over 30 different protease and nonprotease enzymes spread across 12 structural folds, suggesting that nature has taken advantage of these strategies multiple times in different contexts. In the future, ensemble–function analyses can be used to derive quantitative mechanistic models for other enzymes, to dissect allostery, and to ascertain how molecular machines operate. Ensemble–function also provides a powerful educational approach by linking the complex behavior of biomolecules to the simple chemical and physical principles that are taught in undergraduate classes.
Herschlag et al. (2026) studied this question.