TL;DR
EvoMOBO is established as a modular framework for multi-objective protein engineering using experimental or mechanism-derived labels using simulation-derived mechanistic descriptors, with experiments reserved for final validation.
This work introduces ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics.
A machine-learning-assisted enzyme-engineering (MLEE) workflow that adds substrate-specific functional information to htFuncLib through an initial screening and sequencing round that may bypass the need for transition-state models and reduce the effort required for obtaining high-activity variants.
This review examines current computational strategies for exploring constrained protein fitness landscapes, including sequence-derived evolutionary descriptors, structural fitness assessment, energetic evaluation, and integrated multi-parameter scoring.
MULTI-evolve is a model guided, universal, targeted installation of multimutants framework that rapidly designs hyperactive multimutant proteins and improves the identi fi cation of productive mutations compared with individual PLMs alone.
This work introduces Evolutionary Driven Bayesian Optimization (EA-BO), a surrogate-based framework designed for efficient exploration under strict evaluation budgets and demonstrates that EA-BO provides data-efficient strategy for locating promising docking regions when computational cost limit traditional approaches.
This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.