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

ABSTRACT: 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

SUBMITTER: Nieuwkoop T 

PROVIDER: S-EPMC10018363 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature