Efficient Codon Optimization with Motif Engineering

Condon, Anne; Thachuk, Chris

doi:10.1007/978-3-642-25011-8_27

Cited by 6 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization of codon usage allows improving the translational efficiency of foreign proteins by replacing the codons which are rarely found in the host organism [ 28 ], and it has been introduced into many heterologous systems [ 29 - 31 ]. As we found in this study, the optimal codons of B. mori are all ended by either G or C. This phenomenon is interesting and important to enhance the expression level of foreign proteins in B. mori.…”

Section: Resultsmentioning

confidence: 99%

Non-uniqueness of factors constraint on the codon usage in Bombyx mori

et al. 2015

View full text Add to dashboard Cite

BackgroundThe analysis of codon usage is a good way to understand the genetic and evolutionary characteristics of an organism. However, there are only a few reports related with the codon usage of the domesticated silkworm, Bombyx mori (B. mori). Hence, the codon usage of B. mori was analyzed here to reveal the constraint factors and it could be helpful to improve the bioreactor based on B. mori.ResultsA total of 1,097 annotated mRNA sequences from B. mori were analyzed, revealing there is only a weak codon bias. It also shows that the gene expression level is related to the GC content, and the amino acids with higher general average hydropathicity (GRAVY) and aromaticity (Aromo). And the genes on the primary axis are strongly positively correlated with the GC content, and GC3s. Meanwhile, the effective number of codons (ENc) is strongly correlated with codon adaptation index (CAI), gene length, and Aromo values. However, the ENc values are correlated with the second axis, which indicates that the codon usage in B. mori is affected by not only mutation pressure and natural selection, but also nucleotide composition and the gene expression level. It is also associated with Aromo values, and gene length. Additionally, B. mori has a greater relative discrepancy in codon preferences with Drosophila melanogaster (D. melanogaster) or Saccharomyces cerevisiae (S. cerevisiae) than with Arabidopsis thaliana (A. thaliana), Escherichia coli (E. coli), or Caenorhabditis elegans (C. elegans).ConclusionsThe codon usage bias in B. mori is relatively weak, and many influence factors are found here, such as nucleotide composition, mutation pressure, natural selection, and expression level. Additionally, it is also associated with Aromo values, and gene length. Among them, natural selection might play a major role. Moreover, the “optimal codons” of B. mori are all encoded by G and C, which provides useful information for enhancing the gene expression in B. mori through codon optimization.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1596-z) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultsmentioning

confidence: 99%

Non-uniqueness of factors constraint on the codon usage in Bombyx mori

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In order to take into account the nucleotide sequences, such as codon pairs and forbidden sequence motifs, that span continuous multiple codons in our DP, we introduce the parameter

[14] . The

is the minimum amino acid length necessary to compute the score vector functions.…”

Section: Methodsmentioning

confidence: 99%

“…The time (

) and space (

) complexities of unicriterion DP codon optimization have already been analyzed in [14] , where the parameter corresponding to the parameter k of the present study is treated as a constant. By (i) replacing the max operation in the unicriterion DP codon optimization by the merge algorithm for two sets of non-dominated score vectors, (ii) treating k as a constant, and (iii) assuming that the largest set

has K elements, the bicriteria codon optimization algorithm requires

in both the time and space complexities, where the merging of the non-dominated solutions is performed at every amino acid position.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to these heuristics, methods that use exact approaches have also been applied to codon optimization. Condon and Thachuk [14] developed a dynamic programming (DP) algorithm designed to obtain optimal solutions for CDS design problems in which both the codon usage frequency score, codon adaptation index (CAI) [15] , and the numbers of forbidden and desired sequence motifs are taken into account by introducing a priority based order for these objective functions (OFs). CCTool is another DP implementation for codon optimization, which optimizes the codon context of a coding sequence [16] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

COSMO: A dynamic programming algorithm for multicriteria codon optimization

Taneda

Asai

2020

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-and-bound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA.

show abstract

“…In addition to the strategies considered to eliminate rare codons, there also exist parameters with important impacts on protein expression, such as the GC content 26 , RNA secondary structure, cleavage sites, restriction endonuclease sites, repeats and certain added or deleted motifs 27 , 28 . Many websites and software incorporate codon optimization algorithms with various determinants, such as DNA Works 29 , Optimizer 30 , GeMS 31 , Gene Designer 14 , Gene Designer Synthetic 32 , ThermoFisher and Genewiz.…”

Section: Introductionmentioning

confidence: 99%

Codon optimization with deep learning to enhance protein expression

Liang

Zhong

et al. 2020

Sci Rep

127

View full text Add to dashboard Cite

Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. 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. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. 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. The results show that our method for enhancing protein expression is efficient and competitive.

show abstract

Efficient Codon Optimization with Motif Engineering

Cited by 6 publications

References 17 publications

Non-uniqueness of factors constraint on the codon usage in Bombyx mori

Non-uniqueness of factors constraint on the codon usage in Bombyx mori

COSMO: A dynamic programming algorithm for multicriteria codon optimization

Codon optimization with deep learning to enhance protein expression

Contact Info

Product

Resources

About