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Benefits of codon optimization

Planning to express a gene in an heterologous system? Learn how rebalancing codon usage is important for optimizing protein expression. While there are no known methods to predict protein expression, as numerous factors contribute to ultimate protein yield, codon optimization plays a critical role.

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Why rebalance codon usage?

The genetic code is composed of 64 codons with only 21 amino acid and “stop” assignments. Therefore, degeneracy is inherently designed into translation. Preferential usage of particular codons varies by organism. For example, leucine is specified by 6 distinct codons, some of which are rarely used. By rebalancing codon usage within a reading frame, preferred leucine codons are selected over rarely used codons. This is thought to increase the yield of heterologous expression [1]. The free, online IDT Codon Optimization Tool can help you rebalance codon usage for a sequence from one species to that for the organism chosen for expression.

How the Codon Optimization Tool works

The Codon Optimization Tool was written using a codon sampling strategy [2] in which the reading frame is recoded based on the frequencies of each codon’s usage in the new organism.

As an example, codon optimizations of sequences that will be expressed in human cell lines assign the phenylalanine codon UUU 46% and UUC 54% of the time (see Table 1, amino acid F). In addition, codons with <10% frequency are eliminated, with remaining codons renormalized to 100%. For example, codons CUA and UUA designate leucine (amino acid L), but are rarely used. Thus, they would not be assigned, and the remaining codons for leucine (UUG, CUU, CUC, and CUG) would be renormalized to 100%.

D-SB16PS-codon-F1

Table 1. Human codon table [8]. Stars (*) denote stop codons.

Will codon optimization affect protein expression?

There is currently no known method that is predictive of protein expression, although the concept of the codon adaptation index has proven most predictive for expression by E. coli [3,4]. While codon optimization can improve expression, it does not provide a guarantee. The amount of increase in protein expression through codon optimization will vary, depending on the particular protein and organism. Additionally, expression levels can be influenced by many other factors, including tRNA copies [5], mRNA stability [6], protein folding kinetics [7], protein stability, protein transport, toxicity of the protein within the expression cell environment, and a host of other factors that vary for each protein and organism. As a result, any particular optimization demands experimental verification.

Access IDT’s free, online Codon Optimization Tool and get started.

We also provide a step-by-step tutorial for using the Codon Optimization Tool.

Contact our Genes Support Group

If you are working with an organism not listed in the Codon Optimization Tool’s Organism list, or do not see the information you need, contact our Genes Support group at: genes@idtdna.com. We can accept non-standard optimizations that fall outside of the rules used by the tool (design fee may apply).

References

  1. Plotkin JB, Kudla G. (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet, 12:32–42.
  2. Robison K. (2009) Omics! Omics! Available at http://omicsomics.blogspot.com/2009/04/is-codon-optimization-bunk.html.
  3. Sharp PM, Li WH. (1987) The codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucl Acids Res, 15(3):1281–1295.
  4. Khalili M, Soleyman MR, et al. (2015) High-level expression and purification of soluble bioactive recombinant human heparin-binding epidermal growth factor in Escherichia coli. Cell Biol Int, 39(7):858–864.
  5. Cannarozzi G, Schraudolph NN, et al. (2010) A role for codon order in translation dynamics. Cell, 141:355–367.
  6. Wang Y, Liu CL, et al. (2002) Precision and functional specificity in mRNA decay. Proc Natl Acad Sci USA, 99:5860–5865.
  7. Zhang G, Ignatova Z. (2011) Folding at the birth of the nascent chain: coordinating translation with co-translational folding. Curr Opin Struct Biol, 21:25–31.
  8. Data for Table 1 taken from www.kazusa.or.jp/codon/. Accessed Apr 15, 2016.

Author(s)

Sam Shen, PhD, Product Support Specialist, IDT

Published Apr 27, 2016
Revised/updated Jun 10, 2020

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