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

Autor:	Nieuwkoop T; Laboratory of Microbiology, Wageningen University, Wageningen, Stippeneng 4, 6708 WE, The Netherlands., Terlouw BR; Bioinformatics Group, Wageningen University, Wageningen, Droevendaalsesteeg 1, 6708 PB, The Netherlands., Stevens KG; Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, The Netherlands.; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands., Scheltema RA; Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, The Netherlands.; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands., de Ridder D; Bioinformatics Group, Wageningen University, Wageningen, Droevendaalsesteeg 1, 6708 PB, The Netherlands., van der Oost J; Laboratory of Microbiology, Wageningen University, Wageningen, Stippeneng 4, 6708 WE, The Netherlands., Claassens NJ; Laboratory of Microbiology, Wageningen University, Wageningen, Stippeneng 4, 6708 WE, The Netherlands.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2023 Mar 21; Vol. 51 (5), pp. 2363-2376.
DOI:	10.1093/nar/gkad035
Abstrakt:	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. (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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