首  页
密码子优化及验证服务   首页 > 技术服务 > PAE的技术服务

PAE Optimizer is a well-proven codon optimizing tool to gene expression as shown in the figures.

In most cases, genes not visibly expressed before optimization can produce dramatically large bands after optimization as shown on SDS-PAGE electrophoresis.

All 10 optimized constructs are extensively expressed while non optimized prototypic construct is not visible. Fig.1. is an example.

In the most cases, PAE Optimizer is able to produce one eighth highly expressed constructs even the non-optimized prototypic construct is invisible as shown by verification tests. Up to 80 folds increase compared to the prototype is not the limit of PAE Optimizer. Please see Fig.1 to Fig.3.

 

Service Price:

300 RMB/100bp(provide one optimized sequence. Calculated as 100bp for terms less than 100bp)

350 RMB/100bp(provide eight optimized sequences. Calculated as 100bp for terms less than 100bp)

Optimization Technology at PAE

For over 20 years, the scientists at PAE pay close attention to all the factors affecting gene expression, including but not limited to promoter, regulation elements, codon optimization, etc. PAE set up its Optimizer that integrated its extensively employed molecular evolution technology and codon optimization and confirmed several factors other than the above mentioned that affect gene expression. Based on these solid experience, PAE’s senior bioinformatics scientists build up effective codon optimization logarithm and developed the software- PAE Optimizer.

Laboratory experience showed that this software can output sequences with dramatically improved expression by from 10 to 80 folds.

Some examples show that optimized sequences is strongly expressed and amount up 27~35% of the host total protein even though the expression of their prototypes is invisible on SDS-PAGE.

Several hundred successful optimized projects showed that PAE Optimizer fully meets the predetermined target and has dominant advantages over the most commercially available optimization software. PAE is very pleased to share all functions of the optimizer with you and helps you solve the problems related to gene optimization.

Features
· Algorithms integrates over-20 years experience of its team in molecular evolution and gene expression, and enables high success rate in codon optimization. About one eighth fr
· om a tested pool are of good expression level even its prototype DNA sequence is of invisible expression as shown in SDS-PAGE.
· Special algorithms for fully human monoclonal antibody integrated all the published data concerning gene expression, including but not limited to those codons impacting expression level.
· Significant increase in protein expression up to 80-fold compared to the prototype gene DNA sequence.
· Handling ability for even the most difficult sequences such as highly repeated sequences, extremely long sequences.
· Comprehensive usage tables of many species are ready for optimization of all of the popular hosts, such as CHO, HEK293, COS, Pichia, Sf9, etc.
· Interface is designed for expansion its function.
Backgrounds about Condon Optimization

While the ability to make increasingly long stretches of DNA efficiently and at lower prices is a technological driver of this field, increasingly attention is being focused on improving the design of genes for specific purposes. Early in the genome sequencing era, gene synthesis was used as an (expensive) source of cDNA's that were predicted by genomic or partial cDNA information but were difficult to clone. As higher quality sources of sequence verified cloned cDNA have become available, this practice has become less urgent.

Producing large amounts of protein from gene sequences (or at least the protein coding regions of genes, the open reading frame) found in nature can sometimes prove difficult and is a problem of sufficient impact that scientific conferences have been devoted to the topic. Many of the most interesting proteins sought by molecular biologist are normally regulated to be expressed in very low amounts in wild type cells. Redesigning these genes offers a means to improve gene expression in many cases. Rewriting the open reading frame is possible because of the degeneracy of the genetic code. Thus it is possible to change up to about a third of the nucleotides in an open reading frame and still produce the same protein. The available number of alternate designs possible for a given protein is astronomical. For a typical protein sequence of 300 amino acids there are over 10150 codon combinations that will encode an identical protein. Using optimization methods such as replacing rarely used codons with more common cod
ons sometimes have a dramatic effects. Further optimizations such as removing RNA secondary structures can also be included. At least in the case of E. 11coli, protein expression is maximized by predominantly using codons corresponding to tRNA's that retain amino acid charging during starvation. Computer programs are written to perform these, and other simultaneous optimizations are used to handle the enormous complexity of the task. A well optimized gene can improve protein expression 2 to 10 folds, and in some cases more than 100 fold improvements have been reported. Because of the large numbers of nucleotide changes made to the original DNA sequence, the only practical way to create the newly designed genes is to use gene synthesis.

Codon usage bias

Codon usage bias refers to differences in the frequency of occurrence of synonymous codons in coding DNA. A codon is a series of three nucleotides (triplets) that encodes a specific amino acid residue in a polypeptide chain or for the termination of translation (stop codons).

There are 64 different codons (61 codons encoding for amino acids plus 3 stop codons) but only 20 different translated amino acids. The overabundance in the number of codons allows many amino acids to be encoded by more than one codon. Because of such redundancy it is said that the genetic code is degenerate. Different organisms often show particular preferences for one of the several codons that encode the same amino acid- that is, a greater frequency of one will be found than expected by chance. How such preferences arise is a much debated area of molecular evolution.

It is generally acknowledged that codon preferences reflect a balance between mutational biases and natural selection for translational optimization. Optimal codons in fast-growing microorganisms, like Escherichia coli or Saccharomyces cerevisiae (baker's yeast), reflect the composition of their respective genomic tRNA pool. It is thought that optimal codons help to achieve faster translation rates and high accuracy. As a result of these factors, translational selection is expected to be stronger in highly expressed genes, as is indeed the case for the above-mentioned organisms. In other organisms that do not show high growing rates or that present small genomes, codon usage optimization is normally absent, and codon preferences are determined by the characteristic mutational biases seen in that particular genome. Examples of this are Homo sapiens (human) and Helicobacter pylori. Organisms that show an intermediate level of codon usage optimization include Drosophila melanogaster (fruit fly),Caenorhabditis el
egans (nematode worm) or Arabidopsis thaliana (thale cress).

The nature of the codon usage-tRNA optimization has been fiercely debated. It is not clear whether codon usage drives tRNA evolution or vice versa. At least one mathematical model has been developed where both codon-usage and tRNA-expression co-evolve in feedback fashion (i.e., codons already present in high frequencies drive up the expression of their corresponding tRNAs, and tRNAs normally expressed at high levels drive up the frequency of their corresponding codons), however this model does not seem to yet have experimental confirmation. Another problem is that the evolution of tRNA genes has been a very inactive area of research.

Whatever methods used for optimization, only those verified via experiments are useful. Verification of the optimized coding sequences in an appropriate host is the sole way to obtain useful results. PAE provides Verification Service for this purpose.

Criteria and Contents of verification
Gene Synthesis: Synthesize the gene based on the optimized sequence and obtain the target coding sequence. The price for gene sythesis, pls refer to Gene Synthesis Price.
Sequences to be tested 8 Optimized Sequences /Group  
Total cell number used to test ≥1*10  
Available hosts CHO, HEK293, Petunia, NSO, COS, carrot, SF9, Brassicaceae, Pichia, Arabidopsis thaliana, E. coli, Zebra fish, Chlorella  
Available Hosts and Price
Hosts Available RMB/Groupp Hosts Available RMB/Group
CHO、HEK293 5000 Petunia 5000
NSO、COS 5000 carrot 5000
SF9 5000 Brassicaceae 5000
Pichia 3000 Arabidopsis thaliana 5000
E. coli 2000 Petunia 5000
Zebra Fish 6500 Chlorella 3000
* Service can be provided for hosts other than the above If customer can provide a host and its culture technique.

版权所有 无锡拼搏官网app有限公司 苏ICP备12045354 
地址:无锡 • 惠山 •  智慧路33号华清创意园7栋601室   电话:0510-81819585 手机:189 5157 6701 QQ:2642 166682