Prot2RNA: Reward-Guided Discrete Diffusion for Protein-Conditioned CDS Design

A single protein can be encoded by many synonymous mRNA coding sequences (CDSs), whose composition affects RNA structure, stability, and protein output. We introduce **Prot2RNA**, a protein-conditioned discrete diffusion model that learns a conditional distribution over natural human CDSs. Motivated by the role of RNA structure in protein output, we shift this conditional distribution toward sequences with increased predicted structural stability using normalized minimum free energy (MFE) as a non-differentiable sequence-level reward, through reinforcement finetuning across diverse proteins (**Prot2RNA-RFT**), followed by test-time reinforcement learning for a fixed target (**Prot2RNA-TTRL**). Prot2RNA-RFT consistently shifts normalized MFE in the intended direction across held-out proteins, while Prot2RNA-TTRL further adapts the policy for a single NanoLuc protein. In HEK293T cells, the selected NanoLuc design achieves the highest measured reporter output among the tested constructs, with 39% and 71% higher output than GEMORNA and LinearDesign, respectively. These results demonstrate that a learned protein-conditioned CDS prior can be effectively optimized toward a sequence-level reward using reinforcement learning, and when adapted to a single target, yield experimentally validated designs that outperform established CDS-design methods.