Multistate Enzyme Design Enables Efficient and Stereoselective Multistep Catalysis

Abstract

Enzymes catalyze multistep reactions by stabilizing successive transition states within well organized, yet dynamic active sites. However, computational enzyme design typically targets a single transition state using rigid active-site models. Here, we introduce multistate enzyme design, which uses conformational ensembles to optimize active sites across an entire reaction coordinate. Applied to a de novo Morita--Baylis--Hillmanase, multistate enzyme design outperformed conventional single-state design, with the most active variant achieving >100-fold higher bi-substrate catalytic efficiency and surpassing an extensively optimized enzyme from directed evolution in both efficiency and enantioselectivity. Structural and kinetic analyses revealed that multistate design preserved catalytic preorganization and conformational plasticity, distributed stabilization across the reaction coordinate and avoided kinetic bottlenecks created by single-state optimization. By contrast, single-state design compromised preorganization, destabilized upstream states and shifted rate limitation away from the targeted transition state. Multistate enzyme design provides a framework for designing catalytic landscapes rather than static active sites, opening a route to efficient de novo enzymes for complex multistep chemistry.