Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning

It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data were used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information of the first eight codons. In this region, close to the translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI) is the key determinant of protein production. This study clearly demonstrates the key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

Automated summary

It has been found that codon usage affects translation efficiency and protein production levels. This study utilized a synonymous codon-randomized library of the red fluorescent protein coding sequence to investigate the correlations between codon usage and protein production. The results showed that the first eight codons near the translation initiation site play a crucial role in protein production, with mRNA secondary structure being a key determinant rather than Codon Adaptation Index (CAI). These findings suggest that optimizing protein production should focus on reducing mRNA secondary structure formation with the first few codons, rather than relying solely on CAI-based codon optimization strategies.