Functional protein design and enhancement with ontology reinforcement iteration

The discrepancy between computational modeling and experimental performance remains a major challenge in protein engineering. We present ORI (Ontology Reinforcement Iteration), a scalable framework integrating ontology-conditioned decoding with reinforcement learning from experimental feedback (RLWF). ORI leverages structured ontologies as semantic prompts to impose multi-level constraints, enabling controllable and interpretable protein generation. A closed-loop iterative workflow—comprising generation, experimental measurement, and model updating—enables continuous optimization under real-world objectives. We demonstrate ORI’s practical applicability through diverse tasks, including enzymatic activity optimization, thermal stability enhancement, and multifunctional protein engineering. Using this framework, we engineer variants with substantial improvements over natural baselines, such as a lysozyme with 100-fold higher activity, a chitinase stable at 85 °C, and dual-function enzymes exhibiting both lysozyme and chitinase activities. These results establish ORI as a robust technical platform for efficient, multi-objective protein engineering in real-world experimental settings. Protein engineering is often hindered by the gap between modelling and experiment. Here, authors present ORI, a closed-loop computational framework to iteratively optimize proteins, achieving 100-fold higher activity, enhanced thermal stability, and the design of multifunctional enzymes.