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.