Towards de novo enzyme design with multistate Gibbs sampling

Enzyme catalysis is fundamentally multistate, yet most computational design methods optimize proteins around a single catalytic geometry. We introduce Multistate Gibbs Sampling (MuGS), a framework for designing one sequence to support multiple catalytic states by alternating between motif conditioned structure prediction and shared sequence updates. We find that Gibbs sampling improves atomic motif scaffolding in the single state setting, then extend to multiple states by curating an initial benchmark of experimentally characterized enzymes (MAME) and validate on a five state serine hydrolase catalytic cycle. Across all, MuGS refines and rescues existing designs and can also generate multistate compatible sequences de novo. These results suggest a path toward enzyme design that treats catalytic trajectories, rather than isolated states, as the primary object of design.