8/3/2023 0 Comments Snapgene codon optimizationAmong these indexes, the CAI is the primary index used to predict gene expression level because it indicates the extent to which the coding sequence represents the usage of codons in an organism 25. As a consequence, their indexes for codon optimization mainly include the codon adaptation index (CAI) 21, the frequency of relative synonymous codon usage 22, the codon bias index 23, optimal codon usage 7, and effective codon number 24. In the industry, many biotechnology companies perform codon optimization, such as ThermoFisher ( and Genewiz ( whose methods are based on the aforementioned strategies and empirical indexes. This strategy has been recognized as the best way to optimize codons. In addition, a strategy is proposed to adjust the original codon sequence to match the natural distribution of the host codons 13, 17, 18, 19, the goal of which is to preserve the slow translation regions that are important for protein folding 9, 10, 20. Most optimization strategies use codons with host bias to replace less frequently occurring codons 13, 14, 15, 16. Various codon optimization strategies have been developed by using a range of quantitative methods to generate different mRNA sequences, which can result in different levels of final protein expression. In heterologous expression systems, to maximize protein expression from the DNA sequence of the original species in the host, codon optimization improves the translation efficiency of a target gene 12 by converting the DNA sequence of nucleotides of one species to that of another, such as converting human sequences to bacterial or yeast sequences, plant sequences to human sequences, and fungal sequences to yeast sequences. ![]() Therefore, codon optimization for microorganisms is an essential part of gene synthesis. In gene synthesis, codon optimization involves recombination based on different criteria without changing the sequence of the amino acid 9 and can promote expression of the recombinant gene in different host organisms 9, 10, 11. Furthermore, codon optimization is the most critical determinant of increasing protein expression 8. Rare codons tend to reduce the rate of translation and even cause translation errors 7. Thus, the expression levels of proteins are highly correlated with codon usage bias. The frequency of codons in a DNA sequence is positively correlated with the corresponding tRNA in a species, and the tRNA concentration determines the number of amino acids available for protein translation extension, which in turn affects the efficiency of protein synthesis 5, 6. Moreover, the codon usage bias of genes differs significantly among different functions.Ĭodon usage bias has a complex effect on protein expression levels when recombinant proteins are heterologously expressed 4. While the usage probabilities of synonymous codons are not the same during protein synthesis, a species or a gene typically prefers to use one or several specific synonymous codons called optimal codons, and this phenomenon is known as codon usage bias 3. Codons that encode the same amino acid are called synonymous codons. The codon is the basic unit of correspondence between nucleic acids carrying information and proteins carrying information and is also the basic link for information transfer in vivo. With the rapid development of biotechnology, heterologous expression has been utilized to generate recombinant proteins for use in vaccines and pharmaceuticals 1, 2. The results show that our method for enhancing protein expression is efficient and competitive. In addition to the comparison of the codon adaptation index, protein expression experiments for plasmodium falciparum candidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. ![]() Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. In this paper, we propose a novel codon optimization method based on deep learning. The existing optimization methods are based on biological indexes. Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary.
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