LAUSR

Significance Designing efficient enzymes could contribute to a sustainable future. Current computational approaches, including physics-based and machine learning–based design, have not led to a robust enzyme design. Predicting enzyme catalytic power is the crucial step for enzyme design. Here, we found that the properties of enzymes are correlated in a nontrivial way with their evolutionary information. For the active site region and the more distant region, the statistical energy obtained from the maximum entropy model for enzyme homologs is strongly correlated with enzyme catalytic power and stability, respectively. The findings here could be used to understand enzyme catalysis and evolution. Combining the present approach with physics-based computer modeling can provide a potent tool for enzyme design. Although computational enzyme design is of great importance, the advances utilizing physics-based approaches have been slow, and further progress is urgently needed. One promising direction is using machine learning, but such strategies have not been established as effective tools for predicting the catalytic power of enzymes. Here, we show that the statistical energy inferred from homologous sequences with the maximum entropy (MaxEnt) principle significantly correlates with enzyme catalysis and stability at the active site region and the more distant region, respectively. This finding decodes enzyme architecture and offers a connection between enzyme evolution and the physical chemistry of enzyme catalysis, and it deepens our understanding of the stability–activity trade-off hypothesis for enzymes. Overall, the strong correlations found here provide a powerful way of guiding enzyme design.