As experts in synthetic DNA, we understand the importance of accurate gene synthesis and its subsequent expression, particularly in heterologous systems. A multitude of factors, including codon bias, can affect the transcriptional and translational efficiency of a gene in a host organism. GENEWIZ’s codon optimization tool considers these critical factors to produce codon optimized gene sequences for efficient expression in almost all host systems.


What is Codon Optimization?

Codons represent the genetic code that transfers information from genes to mRNA to protein. Both redundancy and evolutionary constraints, including the availability of tRNA isoacceptors, TATA box, Shine-Dalgarno sequences, and more, result in preferential usage of one codon over another for the same amino acid. This phenomenon is known as codon bias.

Codon optimization is a process used to improve gene expression and increase the translational efficiency of a gene of interest by accommodating codon bias of the host organism.

Protein Expression

Figure 1: Schematic representation of protein expression

GENEWIZ Codon Optimized Synthetic DNA

High protein expression – GENEWIZ’s algorithm analyzes a wide range of data to optimize critical parameters and significantly improve protein expression levels.
Difficult sequence normalization – Our codon optimization tool can normalize difficult sequences to remove unfavorable regions and reduce local GC content to desired percentages.
Comprehensive codon usage tables – Species-specific codon usage tables allow the tool to identify and replace low-frequency codons with high-frequency codons for all major host organisms.
Ph.D.-level expertise and support – Our dedicated team of scientists provide consultations and support for all your experimental needs, from start to finish.

How Does GENEWIZ Codon Optimization Work?

GENEWIZ’s codon optimization algorithm optimizes key parameters to stabilize DNA sequences and improve gene expression. GENEWIZ has been offering codon optimization since 2010, and our algorithm is frequently updated and improved based on empirical data to address the following parameters:

  • Codon usage bias
  • Guanine-cytosine (GC) nucleotide content
  • mRNA secondary structure and unstable motifs
  • Repeat sequences (direct repeat, inverted repeat, and dyad repeat)
  • Restriction enzyme recognition sites
  • Custom modifications (e.g. ribosome binding site modifications and RNA motif alterations)

Case Study

We performed a comparative expression analysis of wild-type (WT) and GENEWIZ optimized (GW) gene sequences transformed into E. coli. Three different protein coding genes (HSD17B4, DNA pol, and hRad51) were selected for codon optimization. Protein expression level for WT and GENEWIZ optimized sequences are shown in adjacent lanes. For each protein, GENEWIZ codon optimized sequences resulted in significantly higher protein expression levels compared to wild-type sequences.

For each protein, GENEWIZ codon-optimized
sequences resulted in significantly
higher protein expression levels
compared to wild-type sequences
when expressed in E. coli.

Protein Expression

GENEWIZ Citations

  1. Fry L, Bastos R, Stone B, et al.(2019). Gene gun DNA immunization of cattle induces humoral and CD4 T-cell-mediated immune responses against the Theileria parva polymorphic immunodominant molecule. Vaccine, 37(12), pp.1546-1553. doi: 10.1016/j.vaccine.2019.02.009
  2. Li D, Fu G, Tu R, et al. (2019). High-efficiency expression and secretion of human FGF21 in Bacillus subtilis by intercalation of a mini-cistron cassette and combinatorial optimization of cell regulatory components. Microbial Cell Factories, 18(1). doi: 10.1186/s12934-019-1066-4
  3. Katoli P, Godbole A, Romanowski M, et al. (2018). Full-length myocilin protein is purified from mammalian cells as a dimer. Protein Expression and Purification, 147, pp.38-48. doi: 10.1016/j.pep.2018.02.008
  4. You M, Yang Y, Zhong C, et al. (2018). Efficient mAb production in CHO cells with optimized signal peptide, codon, and UTR. Applied Microbiology and Biotechnology, 102(14), pp.5953-5964. doi: 10.1007/s00253-018-8986-5


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