PACE-3D: A structure-aware multi-dimensional evaluation framework for codon optimization via AlphaFold2-derived metrics

Codon optimization is the cornerstone of heterologous protein expression, but current metrics, such as the Codon Adaptation Index (CAI), only focus on translation elongation rates and ignore the complex coupling between translation dynamics and co-translational folding. Therefore, despite theoretically high expression levels, optimized sequences often suffer from misfolding and aggregation. Here, we propose PACE-3D (Protein-Aware Codon Evaluation in 3D), a structure-aware evaluation framework for quantifying the compatibility between mRNA sequences and protein 3D structures. Utilizing the AlphaFold2-derived confidence metrics, pLDDT (predicted Local Distance Difference Test) and PAE (Predicted Aligned Error), we develop six multidimensional metrics to assess local rigidity-velocity matching, domain assembly synchronization, and translational rhythm smoothness, and so on. Analysis of mainstream codon optimization models based on this framework shows that while existing models successfully optimize translation rate and static mRNA structural stability, they severely neglect the dynamic requirements of co-translational folding, leading to ultimate task failure. This work bridges the gap between sequence design and structural biology, providing a universal standard for rational mRNA design in the AlphaFold era.