Evolution‐Inspired Multi‐Objective Bayesian Optimization for Protein Engineering

Protein engineering requires efficient navigation of vast sequence spaces under limited evaluation budgets, especially when multiple properties must be optimized simultaneously. We developed evolution‐inspired multi‐objective Bayesian optimization (EvoMOBO), an active‐learning framework that integrates path‐dependent sequence generation, global candidate competition, and explicit multi‐objective optimization, enabling efficient prioritization of high‐value variants without exhaustive library enumeration. Compared with state‐of‐the‐art methods on complete steroid receptor DNA‐binding domain (DBD) and ParD3 antitoxin landscapes, EvoMOBO achieved greater target‐region enrichment while maintaining sequence diversity across two‐ and three‐objective tasks. In the DBD landscape, optimization guided by simulation‐derived geometric descriptors enriched variants with satisfactory experimental performance without experimental labels. Building on this validation, we applied EvoMOBO to two enzyme‐engineering tasks using mechanistic descriptors, with experiments reserved for final validation. For an old yellow enzyme from Geobacillus kaustophilus , probabilities of substrate binding and reaction preorganization guided optimization of a non‐native oxidative dehydrogenation, yielding a variant that increased substrate conversion from 17.5% to 95%. For a formate oxidase from Aspergillus oryzae , stability‐related descriptors identified aggregation‐resistant variants that nearly doubled diethyl phthalate degradation in a photoenzymatic cascade. Together, these results demonstrate robust multi‐objective optimization by EvoMOBO and show that mechanism‐derived descriptors can effectively guide protein engineering toward experimentally improved functions.