Jeongmu Kim  Giltae Song

10.5626/JOK.2026.53.2.131

codon optimization Protein expression mRNA multi-objective Transformer

Codon optimization is a crucial technique for enhancing protein expression, particularly important in fields like gene therapy and vaccine development. However, traditional approaches based on codon frequency often overlook the structural stability of mRNA. In practice, optimizing mRNA codons is a multi-objective challenge that must balance expression efficiency and structural stability, while also considering long-range contextual dependencies within extended sequences, making it computationally complex. To tackle this issue, we propose TACO, a deep learning model that combines 1D-CNN and Transformer architectures. TACO optimizes both the Codon Adaptation Index (CAI) and Minimum Free Energy (MFE) through contrastive learning and a joint loss function. We evaluated the model using datasets from Homo sapiens, Escherichia coli, and Saccharomyces cerevisiae. The results show that TACO consistently outperforms existing BiLSTM-based models and popular commercial optimization tools. These findings underscore the potential of our approach as an AI-driven framework for sequence optimization under biologically constrained multi-objective conditions.