Directed Evolution in Codon Space

Abstract

Directed evolution is commonly used in protein engineering, where mature molecules are routinely improved through iterative local search of amino acid space. Here, we extend this principle to coding DNA. We developed a language-model-guided framework that iteratively refined industry-optimized coding sequences of clinical-stage therapeutics through synonymous exploration of codon space. Across 23 antibody-based therapeutics, SynCodonLM-guided refinement significantly increased recombinant expression in CHO cells for 17 molecules (74% responder rate), without significant compromise of product-quality or biophysical attributes. Moreover, changes in model likelihood predicted expression gains more effectively than heuristic statistical or mRNA-structure descriptors, despite no explicit expression objective. Codon-level likelihood also tracked temporal progression in influenza A H1N1 sequences, indicating the model captures evolutionary signal. These results show that even production-optimized sequences retain accessible fitness in synonymous codon space, establishing directed evolution as a practical strategy to improve biologic expression, a key manufacturing bottleneck, without altering protein sequence.