The importance of proteins in biology cannot be overstated. Proteins are valuable therapeutic compounds, research reagents and industrial catalysts. Using proteins designed with GeneGPS® optimization provides the highest levels of expression, saves you time and money, while getting you quickly to meaningful results.
Using proteins with the highest levels of expression saves you time and money, while getting you quickly to meaningful results.
DNA2.0 Unlocks the Secret of Truly Optimized Gene Design
Breakthrough research by DNA2.0, funded by NSF, has identified the design principles by which codons are used to maximize protein expression. The surprising results were published in PLoS ONE.
DNA2.0 has used the results of this research to create GeneGPS with its robust gene design algorithms. These new design algorithms routinely produce 10-100 times more protein than competing methods. DNA2.0 can even test your gene for high expression.
Expression of Recombinant Proteins Depends on Codon Optimization Choice… Make the RIGHT Choice.
Expression of recombinant proteins is fundamental to modern biotechnology. Unfortunately, proteins are often difficult to express outside their original host or to over-express even within their native host. Different genes encoding the same protein can express at very different levels. Altering the coding sequence to increase protein expression is highly cost-effective, providing the re-coding is done correctly according to scientifically researched algorithms, not just the guess work or disproved theories other companies use!
DNA2.0: Scientific Research and Algorithms, NOT Guesswork
DNA2.0 has extensive expertise with machine learning algorithms for the deconvolution of multivariate data. Generally we use these to engineer proteins, designing changes to the amino acid sequence so that the protein’s properties are better suited to a desired human application. We used identical algorithms to see whether we could identify a property of the DNA sequences of our synthetic genes that correlated with expression. Although there was no correlation between expression and the CAI score of a gene, we did find a strong correlation with the use of certain codons.
Designing, synthesizing and expressing systematically varied sets of genes encoding two different proteins has enabled DNA2.0 to identify codon usage within a gene as the critical determinant of protein expression levels in E coli. This research proved that codon biases of natural genes does not help maximize expression from synthetic genes. Using a genetic algorithm to select partial least squares regression models, we found that the frequencies of about 15 critical codons, encoding 6 or 7 amino acids, were very good predictors of expression. From this data DNA2.0 has developed gene design algorithms which produce maximal protein expression.
Host Bias or High CAI? Strategies that Never Work
In the absence of experimental data, gene designs have been based either on extrapolations from what is seen in nature, or on what can be easily calculated. Codon frequencies vary significantly between different organisms, and between genes expressing high or low levels of protein within the same organism. Gene Synthesis companies assumed that they could use the codon frequencies from natural highly expressing genes to guide the choice of codons when designing synthetic genes (our competitors still do this).
- Codon Sampling makes the gene “look like” an average gene from the expression host.
- High Codon Adaptation Index (High-CAI) makes the gene “look like” a highly expressed gene from the expression host.
- RNA Structure Minimization reduces or eliminates mRNA secondary structures.
It is clear, that for both of these genes, there is no correlation at all between expression of a synthetic gene and high CAI nor its 5′ mRNA structure.
This technology is partially covered by US patents 8401798, 8126653, 7561973, and 7561972 issued to DNA2.0.
GeneGPS® Algorithms can Optimize Expression in Your System.
DNA2.0 has employed a variety of multivariate analysis strategies to identify relationships between gene design variables and protein expression. We have ongoing studies in Bacteria, Yeasts, Mammalian cells and in vivo, Plants, Fungi, Insects, and Cell-free systems. DNA2.0 is using the results of our research to create patented gene design algorithms for numerous host systems. When utilized, these new GeneGPS® design algorithms routinely produce 10-100 times more protein than competing codon optimization methods. Results from these investigations can be seen in DNA2.0 PepTalk 2015 Presentation and from the links below.
Synthetic Genes have Enormous Advantages over their Natural Counterparts
- Obtained more quickly and less expensively than conventionally cloned genes
- Simple to modify in order to facilitate downstream manipulations
- Opportunity for vastly improved protein expression
- Any sequence you can imagine is available
Not all Synthetic Genes are Created Equal
Synthetic Genes are now generally obtained more quickly and less expensively than conventionally cloned genes, but not all synthetic genes express equally well.
Better Protein Expression than Native Genes
A successfully optimized synthetic gene can cause a single polypeptide to represent 10 to 50 percent of the host’s total protein. These levels are generally between 10- and 100-fold greater than the yields from native genes. Much more protein can be expressed from a redesigned synthetic gene than from the original because native genes have evolved for balanced expression of all cellular genes, not for maximal expression of any single gene.
Save Time and Money Compared to Conventionally Cloned Genes
It costs you less to order a synthetic gene from DNA2.0 than it does to purchase the oligos and molecular biology kits, and to include the labor costs and sequencing of conventional cloning. The promise of greater protein expression delivers a major additional benefit: you spend less time and money struggling to produce a protein, leaving more energy and resources to focus on study or application of the protein itself. Your efforts are concentrated on the real goals of your research.
Freedom of Design
Gene synthesis allows you the opportunity to design, clone and express in powerful new ways, even design sequences de novo without being limited by what nature can provide.
- Optimize for recombinant protein expression in any organism using multiple codon optimization algorithms
- Remove or add restriction sites or other sequence motifs
- Recode open reading frames, check translation frames and fusion junctions
Webinar: Optimizing Gene Expression and Performance using GeneGPS™
Gene synthesis allows researchers to tailor gene sequences for optimal utility for any application. However, the relationship between gene sequence and expression is complex and depends on a wide range of sequence variables. DNA2.0 uses a systematic engineering approach, GeneGPS, to navigate these variables with a minimal number of test genes to optimize performance. Application of this approach with a variety of protein targets and expression hosts will be described.
View a pdf of the webinar slides.
Aldevron Webinar – Optimizing Gene Expression/Performance using GeneGPS™
Gene synthesis allows researchers to tailor gene sequences for optimal utility for any application. However, the relationship between gene sequence and expression is complex and depends on a wide range of sequence variables. DNA2.0 uses a systematic engineering approach, GeneGPS, to navigate these variables with a minimal number of test genes to optimize performance. Application of this approach with a variety of protein targets and expression hosts is described.
View a pdf of the DNA2.0 webinar slides.
DNA2.0 Presentations from Scientific Conferences
- PEGS, 2015: Quantitative Biology – Tools to Build Better Biology
- PepTalk, 2015: Comprehensive Engineering of Biological Systems
- PEGS, 2014: Systematic Optimization of Therapeutic Protein Production
- PepTalk, 2014: Leveraging Gene Synthesis for Systematic Optimization of Protein Production
- Autodesk Ideas Conference, 2012: Gene Synthesis + Machine Learning = BioDesign
- DNA2.0 Pfenex Boehringer Ingelheim Collaboration Case Study/White Paper