CatIF-RL: Activity-Oriented Enzyme Sequence Design by Steered Inverse Protein Folding

Protein inverse folding models are designed to generate amino acid sequences compatible with a given backbone structure, but they are not explicitly optimized for specific biological functions. Here, we present CatIF-RL, a framework that steers a graph-based denoising diffusion inverse folding model toward designing enzyme variants with improved predicted catalytic activity. CatIF-RL first adapts the inverse folding model to enzyme structural data, then introduces activity-oriented preference signals using predicted catalytic constant ( k cat ) as the optimization objective, enabling specialization through generative data set curation and group-relative policy optimization (GRPO). This process iteratively shifts the sequence distribution toward higher predicted k cat while constraining sequence divergence to sequences that remain compatible with the input structure. On the held-out benchmark, CatIF-RL achieves an approximately 4-fold increase in predicted k cat relative to native enzymes as scored by the surrogate predictor, outperforming representative inverse-folding methods, while keeping sequence recovery (0.55) and predicted structural quality within the range of existing inverse-folding methods, and supporting motif-preserving partial sequence design. CatIF-RL establishes a practical framework for activity-oriented enzyme design and provides a generalizable strategy for steering structure-conditioned protein generation toward functional optimization.