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There are lots of different codon optimization approaches out there, and a lot of different qualitative reasons to want optimization (e.g., organism codon bias, GC content, avoiding secondary structure).

One may also want to avoid optimization, however, in order to increase reusability of a component, comparability with prior results, or opportunities for error.

I'd like to know how much I'm likely giving up if I decide not to codon optimize. For example, in my typical work giving up 10% is likely to be lost in the noise, but giving up 2x is worth thinking carefully about, and giving up 10x means it's a must-have.

I know "it depends", and there's a lot of qualitative information out there, but is there any way to get a good rough-order-of-magnitude estimate of the typical expression boost from codon optimization?

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    $\begingroup$ The "it depends" might hint toward a nice opportunity for a bioinformatics tool as prior data should allow to make an informed prediction. Maybe biostars.org or bioinformatics.stackexchange.com would be able to help on a related question, whether there already is a tool to predict boost based on custom sequences and/or expression host. $\endgroup$
    – tsttst
    Commented Mar 17, 2021 at 15:06

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I think that there is a problem with how well current codon optimization algorithms actually "optimize" protein expression. I am not aware of any algorithms that work beyond a heuristic level and so for an arbitrary protein, one cannot predict the expression boost that optimization will provide - sometimes yield is significantly decreased. This review covers some of the complexities of optimization really nicely https://doi.org/10.1016/j.molcel.2020.09.014.

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