Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression

Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA vaccines, yet it remains a hard problem. The design space is discrete and combinatorially large, precluding gradient-based methods, and existing tools rely on heuristic proxies (e.g., Codon Adaptation Index or GC-content) that poorly capture true protein expression. We introduce Latent-Space Codon Optimization (LSCO), which recasts this discrete problem as a continuous one by mapping sequences into the latent space of a pretrained mRNA language model, enabling efficient gradient-based search. LSCO combines four components: a data-driven expression objective from an uncertainty-aware deep ensemble, whose epistemic uncertainty steers the search away from out-of-distribution designs; a Minimum-Free-Energy regularizer for structural stability; a naturalness prior from a protein-to-codon back-translation model; and constrained decoding for protein fidelity. On a real-world, wet-lab antibody expression dataset, LSCO attains the highest predicted expression among the compared methods under its native predictor and two evaluators held out from optimization, outperforming naturalness-driven and deep generative RNA foundation models, while retaining suitable biophysical properties. Notably, these gains do not come from reproducing conventional codon-bias heuristics. Ablations further show that search in the pretrained latent space outperforms direct input-space optimization, both on the guiding expression surrogate and under held-out evaluators.