Synthetic gene expression in a host organism requires adjusted codon usage for higher isolation yields.
Even though codon sequence influences protein folding, current codon usage optimization algorithms do not consider protein structure.
By simulating ribosome movement on mRNA for a given codon sequence, we can predict translation time with a precision of a codon.
Explanation of slowed translation through protein structure enables us to predict translation time for an arbitrary protein.
We showed that ribosomal stalling on mRNA is driven by codon regions with higher average translation time. These are approximately 7 codons wide and result in stalling of at least two other ribosomes upstream.
A portion of ribosome stalling sites overlaps with linking regions, while we discovered more stalling sites related to hydrophobic core residues exiting the ribosome.
Utilizing features from simulated co-translational folding, we successfully created the first predictive model for translation time using protein structure.
Predicting the connection between protein structure and codon sequence enables codon usage optimization based on structure, which is not considered in current methods.
The algorithm is of key importance for the efficient expression of recombinant and de novo proteins, i.e. biological medicinal products, where high yield must be accompanied by correct protein folding for safe usage.
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