> Genes & Molecular Sequences > Nucleotide Sequence > Codon

Codon

Codon: A triplet of adjacent nucleotides in a nucleic acid molecule that specifies a particular amino acid or signals the termination of protein synthesis.
Codons are the basic units of the genetic code, determining the sequence of amino acids in polypeptide chains.
Codon optimization is a crucial process in synthetic biology and biotechnology, enhanceing protein expression and recombinant DNA research.
PubCompare.ai's AI-driven comparison platform simplifies the codon optimization process, helping researchers locate the best protocols and products to improve reproducibility and accuracy.

Most cited protocols related to «Codon»

Reverse Translation and Codon Alignment

The server requires a multiple sequence alignment of proteins and the corresponding DNA sequences as input. The internal action of the program can be divided into three main steps: (i) upload the protein sequence alignment and DNA sequences, (ii) reverse translation, i.e. conversion of the protein sequences into the corresponding DNA sequences in the form of regular expression patterns and (iii) generation of the codon alignment. In the second step, each protein sequence is converted into DNA sequence of a regular expression. For example, a short peptide sequence, MDP, is reverse-translated into a regular expression pattern of the DNA sequence as (A(U∣T)G)(GA(U∣T∣C∣Y))(CC.). For frame shifts, we adapted the notation used in GeneWise (6 (link)): if an insertion or deletion is found in the coding region, it is represented by the number of nucleic acid residues at that site instead of an amino acid code. For example, M2P indicates that there is 1 nt deletion between methionine and proline. With this notation, it is easy to convert the peptide sequence into a regular expression pattern, in this case (A(U∣T)G)..(CC.). After converting into a regular expression pattern, the input DNA sequence is searched with the pattern to obtain the corresponding coding region. Unmatched DNA sequence regions are discarded. The pattern matching has been designed to be tolerant of mismatches. This was achieved by extending 10 amino acid regular expression matches in both directions until the entire coding region of the input DNA sequence is covered. The regions between the extended fragments and those not covered by the extension are taken as mismatches, and reported, if any, in the output. In the third step, the protein sequence alignment is converted into the corresponding codon alignment by replacing each amino acid residue with the corresponding codon sequence.

Suyama M., Torrents D, & Bork P. (2006). PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Research, 34(Web Server issue), W609-W612.

Publication 2006

Amino Acids Amino Acid Sequence Codon Deletion Mutation DNA Sequence Exons Methionine Nucleic Acids Peptides Proline Proteins Reading Frames Sequence Alignment

Detecting Lineage-Specific Selection Using MEME

The fitting of MEME to an alignment of coding sequences proceeds in three stages:
First, the codon model with an alignment-wide is fitted to the data using parameter estimates under a GTR nucleotide model as initial values. Although in some cases nucleotide branch lengths may be a good approximation to codon branch lengths [23] (link), [24] (link), recent results indicate that in other instances, nucleotide models can significantly underestimate branch lengths and possibly bias downstream inference [25] . The resulting maximum likelihood estimates, and , for each branch , are used in the site-by-site analyses in the next two steps. Thus we are assuming that the relative branch length and mutational bias parameters are shared across sites and are well approximated by those estimated under a simpler codon model. However, the absolute branch lengths also depend on the site- and model-specific rate parameters below.
Second, at each site, we first fit the alternative random effects model of lineage-specific selective pressure with two categories of : and (unrestricted). The probability ( in equation 1) that branch is evolving with , is , and the complementary probability that it is evolving with is . By equation 1, the phylogenetic likelihood at a site, marginalized over all possible joint assignments of , is equivalent to computing the standard likelihood function with the following mixture transition matrix for each branch :
Consequently, the alternative substitution model includes four parameters for each site, inferred jointly from all branches of the tree: and . These form the fixed effects component of the model. Estimating separately for each site accounts for the site-to-site variability in synonymous substitution rates [26] (link).
Lastly, at every site, we fit the model from the previous step, but with : our null model. Using simulated data, we determined that an appropriate asymptotic test statistic for testing most worst-case null of of is a mixture of and (see Text S1). Mixture statistics of this form often arise in hypothesis testing where model parameters take values on the boundaries of the parameter space, and closed-form expressions for mixing coefficients are difficult to obtain [27] .
Throughout the manuscript, we compare MEME to the fixed effects likelihood approach, introduced in [24] (link) (see Text S1 for motivation). The procedure used by FEL differs from MEME in that a single pair of rates are fitted at each site (no variation over branches) in Step 2, and the test in Step 3 is to determine if . Positive selection is inferred by FEL when and the p-value derived from the LRT is significant, based on the asymptotic distribution.

Murrell B., Wertheim J.O., Moola S., Weighill T., Scheffler K, & Kosakovsky Pond S.L. (2012). Detecting Individual Sites Subject to Episodic Diversifying Selection. PLoS Genetics, 8(7), e1002764.

Full text: Click here

Publication 2012

BAD protein, human Codon Joints Motivation Mutation Nucleotides Pressure Sequence Alignment Trees

Identifying Mut-Driver Genes Through Mutation Patterns

If mutation frequency, corrected for mutation context, gene length, and other parameters, cannot reliably identify modestly mutated driver genes, what can? In our experience, the best way to identify Mut-driver genes is through their pattern of mutation rather than through their mutation frequency. The patterns of mutations in well-studied oncogenes and tumor suppressor genes are highly characteristic and nonrandom. Oncogenes are recurrently mutated at the same amino acid positions, whereas tumor suppressor genes are mutated through protein-truncating alterations throughout their length (Fig. 4 and table S2A).
On the basis of these mutation patterns rather than frequencies, we can determine which of the 18,306 mutated genes containing a total of 404,863 subtle mutations that have been recorded in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (30 (link)) are Mut-driver genes and whether they are likely to function as oncogenes or tumor suppressor genes. To be classified as an oncogene, we simply require that >20% of the recorded mutations in the gene are at recurrent positions and are missense (see legend to table S2A). To be classified as a tumor suppressor gene, we analogously require that >20% of the recorded mutations in the gene are inactivating. This “20/20 rule” is lenient in that all well-documented cancer genes far surpass these criteria (table S2A).
The following examples illustrate the value of the 20/20 rule. When IDH1 mutations were first identified in brain tumors, their role in tumorigenesis was unknown (2 (link), 31 (link)). Initial functional studies suggested that IDH1 was a tumor suppressor gene and that mutations inactivated this gene (32 (link)). However, nearly all of the mutations in IDH1 were at the identical amino acid, codon 132 (Fig. 4). As assessed by the 20/20 rule, this distribution unambiguously indicated that IDH1 was an oncogene rather than a tumor suppressor gene, and this conclusion was eventually supported by biochemical experiments (33 (link), 34 (link)). Another example is provided by mutations in NOTCH1. In this case, some functional studies suggested that NOTCH1 was an oncogene, whereas others suggested it was a tumor suppressor gene (35 (link), 36 (link)). The situation could be clarified through the application of the 20/20 rule to NOTCH1 mutations in cancers. In “liquid tumors” such as lymphomas and leukemias, the mutations were often recurrent and did not truncate the predicted protein (37 (link)). In squamous cell carcinomas, the mutations were not recurrent and were usually inactivating (38 (link)–40 (link)). Thus, the genetic data clearly indicated that NOTCH1 functions differently in different tumor types. The idea that the same gene can function in completely opposite ways in different cell types is important for understanding cell signaling pathways.

Vogelstein B., Papadopoulos N., Velculescu V.E., Zhou S., Diaz LA J.r, & Kinzler K.W. (2013). Cancer Genome Landscapes. Science (New York, N.Y.), 339(6127), 1546-1558.

Publication 2013

Amino Acids Ataxia Telangiectasia Mutated Proteins Brain Neoplasms Cells Codon Cosmic composite resin Diploid Cell Gene, Cancer Genes Leukemia Lymphoma Malignant Neoplasms Mutation Neoplasms Neoplastic Cell Transformation Oncogenes Proteins Reproduction Signal Transduction Pathways Squamous Cell Carcinoma Tumor Suppressor Genes

Codon Adaptation Index Optimization

The algorithm is based on the calculation of the CAI (10 (link)). Each codon is given a weight with respect to the subset of highly expressed genes defined for the considered organism. The so-called relative adaptiveness of a codon is defined as:

w_{i} = \frac{f_{i}}{f_{max (i)}}

where f_i is the frequency of a codon (i) and f_max(i) is the frequency of the codon most often used to code for the considered amino acid in the subset of highly expressed genes.
The CAI for a gene ‘g’ can be calculated according to Equation 2:

{CAI}_{g} = {(\prod_{i = 1}^{N} w_{i})}^{1 / N}

where N is the number of codons in a gene ‘g’ without the initiation and stop codons.
The calculation of the relative adaptiveness for all genomes in the PRODORIC database was made in advance. The subset of highly expressed genes for each organism was defined by applying the algorithm proposed by Carbone et al. (13 (link)). The algorithm is based on the assumption that in each genome there is a set of genes with high codon bias. The algorithm is iterative and reduces the set of genes (initially all genes of an organism) during each iteration until only 1% of genes remain with the highest codon bias of the initial set of genes.
The optimization of a given sequence splits into two parts. First, the sequence is examined whether it is either a correct gene sequence or a correct amino acid sequence. Subsequently, depending on the type of sequence, it is translated into an amino acid sequence. The second step is to translate the amino acid sequence into a gene sequence by using the codons that got the highest relative adaptiveness for the amino acid in question. In this way, every amino acid of the sequence is replaced until the whole sequence is retranslated.

Grote A., Hiller K., Scheer M., Münch R., Nörtemann B., Hempel D.C, & Jahn D. (2005). JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Research, 33(Web Server issue), W526-W531.

Publication 2005

Acclimatization Amino Acids Amino Acid Sequence Codon Codon, Terminator Codon Bias Genes Genes, vif Genome

Dual Bacterial-Archaeal Heuristic Models for Genome Annotation

Linear trends in frequencies of nucleotides in the three codon positions with respect to genome GC content have been observed to be different in bacteria and archaea (Table 1). Therefore, two distinct heuristic models could be built, one for bacterial and another for archaeal sequences. Notably, no pre-processing is needed to identify a domain of life the short sequence fragment represents. The bacterial and archaeal heuristic models can be used in the GeneMark.hmm algorithm simultaneously (Figure 5), similarly to the simultaneous use of typical and atypical gene models (30 (link)). A protein-coding region, if present in the sequence, is supposed to be recognized by either bacterial or archaeal model.
Figure 5.

Hidden states diagram of the generalized hidden Markov model (HMM) used in the GeneMark.hmm algorithm; this is the case of using bacterial and archaeal model pair (a similar diagram would be valid for use of mesophilic and thermophilic model pair).

Alternatively, all prokaryotic species could be divided into mesophilic and thermophilic (310 mesophilic and 47 thermophilic in our reference set of sequenced genomes). Then, application of regression analysis of nucleotide frequencies in the three codon positions produced once again two distinct sets of 12 linear functions (Table 1). The two heuristic models (built for mesophiles and thermophiles) could also be used simultaneously in GeneMark.hmm. However, such a dual model seems to be less effective for practical use, as the temperature of a microbiome habitat is supposed to be known and one of the models could be chosen a priori.
In the Results section, we designate the model pairs by suffix BA or TM, e.g. 3-3BA stands for use a pair of bacterial and archaeal models derived by the third-order polynomial approximation of triplet frequencies.

Zhu W., Lomsadze A, & Borodovsky M. (2010). Ab initio gene identification in metagenomic sequences. Nucleic Acids Research, 38(12), e132.

Publication 2010

Archaea Bacteria Codon Genome Microbiome Nucleotides Open Reading Frames Prokaryotic Cells Triplets

Most recents protocols related to «Codon»

Biosynthesis of Bisbenzylisoquinoline Alkaloids in Engineered Yeast

Not available on PMC !

Example 5

FIG. 16 illustrates (A) a biosynthetic scheme for conversion of L-tyrosine to bisBlAs and (B) yeast strains engineered to biosynthesize bisBlAs, in accordance with embodiments of the invention. In particular, FIG. 16 illustrates (A) a pathway that is used to produce bisBlAs berbamunine and guattegaumerine. FIG. 16 provides the use of the enzymes ARO9, aromatic aminotransferase; ARO10, phenylpyruvate decarboxlase; TyrH, tyrosine hydroxylase; DODC, DOPA decarboxylase; NCS, norcoclaurine synthase; 6OMT, 6-O-methyltransferase; CNMT, coclaurine N-methyltransferase; CYP80A1, cytochrome P450 80A1; CPR, cytochrome P450 NADPH reductase. Of the metabolites provided in FIG. 16, 4-HPA, 4-HPP, and L-tyrosine are naturally synthesized in yeast. Other metabolites that are shown in FIG. 16 are not naturally produced in yeast.

In examples of the invention, a bisBIA-producing yeast strain, that produces bisBlAs such as those generated using the pathway illustrated in (A), is engineered by integration of a single construct into locus YDR514C. Additionally, FIG. 16 provides (B) example yeast strains engineered to synthesize bisBlAs. Ps6OMT, PsCNMT, PsCPR, and BsCYP80A1 were integrated into the yeast genome at a single locus (YDR514C). Each enzyme was expressed from a constitutive promoter. The arrangement and orientation of gene expression cassettes is indicated by arrows in the schematic. These strains convert (R)- and (S)-norcoclaurine to coclaurine and then to N-methylcoclaurine. In one example, the strains may then conjugate one molecule of (R)—N-methylcoclaurine and one molecule of (S)—N-methylcoclaurine to form berbamunine. In another example, the strains may conjugate two molecules of (R)—N-methylcoclaurine to form guattegaumerine. In another example, the strains may conjugate one molecule of (R)—N-methylcoclaurine and one molecule of (S)-coclaurine to form 2′-norberbamunine. In another embodiment, the strain may be engineered to supply the precursors (R)- and (S)-norcoclaurine from L-tyrosine, as provided in FIG. 5.

The construct includes expression cassettes for P. somniferum enzymes 6OMT and CNMT expressed as their native plant nucleotide sequences. A third enzyme from P. somniferum, CPR, is codon optimized for expression in yeast. The PsCPR supports the activity of a fourth enzyme, Berberis stolonifera CYP80A1, also codon optimized for expression in yeast. The expression cassettes each include unique yeast constitutive promoters and terminators. Finally, the integration construct includes a LEU2 selection marker flanked by loxP sites for excision by Cre recombinase.

A yeast strain expressing Ps6OMT, PsCNMT, BsCYP80A1, and PsCPR is cultured in selective medium for 16 hours at 30° C. with shaking. Cells are harvested by centrifugation and resuspended in 400 μL breaking buffer (100 mM Tris-HCl, pH 7.0, 10% glycerol, 14 mM 2-mercaptoethanol, protease inhibitor cocktail). Cells are physically disrupted by the addition of glass beads and vortexing. The liquid is removed and the following substrates and cofactors are added to start the reaction: 1 mM (R,S)-norcoclaurine, 10 mM S-adenosyl methionine, 25 mM NADPH. The crude cell lysate is incubated at 30° C. for 4 hours and then quenched by the 1:1 addition of ethanol acidified with 0.1% acetic acid. The reaction is centrifuged and the supernatant analyzed by liquid chromatography mass spectrometry (LC-MS) to detect bisBlA products berbamunine, guattegaumerine, and 2′-norberbamunine by their retention and mass/charge.

US11859225B2. Methods of producing epimerases and benzylisoquinoline alkaloids (2024-01-02). The Board of Trustees of the Leland Stanford Junior University [US]. Inventors: Christina D. Smolke [US], Stephanie Galanie [US], Isis Trenchard [US], Catherine Thodey [US], Yanran Li [US].

Full text: Click here

Patent 2024

2-Mercaptoethanol 3-phenylpyruvate Acetic Acid Allopurinol Anabolism Barberry Base Sequence berbamunine Buffers Cells Centrifugation coclaurine Codon Cre recombinase Culture Media Cytochrome P450 Dopa Decarboxylase enzyme activity Enzymes Ethanol Gene Expression Genome Glycerin guatteguamerine higenamine Liquid Chromatography Mass Spectrometry Methyltransferase NADP NADPH-Ferrihemoprotein Reductase norcoclaurine synthase Plants Protease Inhibitors Retention (Psychology) S-adenosyl-L-methionine coclaurine N-methyltransferase S-Adenosylmethionine Saccharomyces cerevisiae Strains Transaminases Tromethamine Tyrosine Tyrosine 3-Monooxygenase

Optimizing Receptor Expression

Not available on PMC !

Example 4

With a view to optimising expression of the receptor, the following were tested: (a) inclusion of a scaffold attachment region (SAR) into the cassette; (b) inclusion of chicken beta hemoglobin chromatin insulator (CHS4) into the 3′LTR and (c) codon optimization of the open reading frame (FIG. 6a). It was shown that inclusion of a SAR improved the nature of expression as did codon-optimization while the CHS4 had little effect (FIG. 6b). Combining SAR and codon-optimization improved expression additively (FIG. 6c)

US11879016B2. Chimeric antigen receptor (2024-01-23). AUTOLUS LIMITED [GB]. Inventors: Martin Pulé [GB], John Anderson [GB], Simon Thomas [GB].

Full text: Click here

Patent 2024

Chickens Chromatin Codon hemoglobin B Matrix Attachment Regions

Codon Optimization for Potential Vaccine

The last stage of the current study was the codon optimization for the designed potential vaccine where we employed the JCAT server for this purpose (Grote et al., 2005 (link)). Here, we selected E. coli k-12 strain as the expression organism as it is frequently used in gene cloning experiments (the first stage for wet lab validation of the current potential vaccine). The codon adaptation index (CAI), a value that is calculated by the server, gives an estimation for the constructed potential vaccine to be expressed in E. coli k-12.

Gouda A.M., Soltan M.A., Abd-Elghany K., Sileem A.E., Elnahas H.M., Ateya M.A., Elbatreek M.H., Darwish K.M., Bogari H.A., Lashkar M.O., Aldurdunji M.M., Elhady S.S., Ahmad T.A, & Said A.M. (2023). Integration of immunoinformatics and cheminformatics to design and evaluate a multitope vaccine against Klebsiella pneumoniae and Pseudomonas aeruginosa coinfection. Frontiers in Molecular Biosciences, 10, 1123411.

Full text: Click here

Publication 2023

Acclimatization Codon Dichelobacter nodosus Escherichia coli Genes Strains Vaccines

Comprehensive library of single-codon substitutions

We constructed libraries of all the possible single-codon substitutions in NDM-1, CAT-I, and aadB using inverse PCR with mutagenic oligonucleotides as described in previous work (Mehlhoff and Ostermeier 2020 (link)). The oligonucleotides contained a NNN degenerative codon targeted to each codon within the three genes. We constructed the library in three regions for NDM-1, three regions for CAT-I, and two regions for aadB due to the read length constraints of Illumina MiSeq. We estimated a minimum of 50,000 transformants would be necessary for each region to have a high probability of having nearly all possible single-codon substitutions (Bosley and Ostermeier 2005 (link)). For each region, we repeated the transformation and pooled the resulting colonies until we had an excess of 100,000 transformants. We recovered each library from the LB-agar plates using LB media and glycerol before making aliquots for storage at −80° C.

Mehlhoff J.D, & Ostermeier M. (2023). Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations. Molecular Biology and Evolution, 40(3), msad038.

Publication 2023

Agar Chloramphenicol O-Acetyltransferase Codon DNA Library Genes Glycerin Inverse PCR Mutagenesis Oligonucleotides

Constructing and Evaluating Antibiotic Resistance Mutants

We constructed a total of 34 mutants across the three genes consisting of 12 CAT-I mutants, 13 NDM-1 mutants, and 9 aadB mutants. We used inverse PCR to introduce the mutations. We also used inverse PCR to construct a control plasmid, pSKunk1-ΔGene, which had the coding region of the studied antibiotic resistance genes deleted.
For the C26D and C26S mutants in NDM-1, we found that an IS4-like element ISVsa5 family transposase insertion would occur within the NDM-1 gene during the six hours of induced monoculture growth (supplementary Text, Supplementary Material online). We made two synonymous mutations within the 5′-GCTGAGC-3′ insertion site that fully overlapped codons 23 and 24 to reduce transposase insertion and get an accurate measure of the collateral fitness effects for the C26D and C26S mutations. The new sequence was 5′-GTTATCA-3′. Inverse PCR was used to introduce these synonymous mutations. All mutant plasmids were transformed into NEB 5-alpha LacI^q electrocompetent cells.

Mehlhoff J.D, & Ostermeier M. (2023). Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations. Molecular Biology and Evolution, 40(3), msad038.

Publication 2023

Antibiotic Resistance, Microbial Chloramphenicol O-Acetyltransferase Codon Genes Inverse PCR Mutation Pancreatic alpha Cells Plasmids Silent Mutation Transposase

Top products related to «Codon»

Geneart by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom, France, Italy, New Zealand

The GeneArt is a laboratory equipment product designed for genetic engineering and molecular biology applications. It provides tools and functionalities for DNA synthesis, assembly, and modification. The core function of the GeneArt is to enable researchers and scientists to create and manipulate genetic constructs for various experimental and research purposes.

Lipofectamine 2000 by Thermo Fisher Scientific

Sourced in United States, China, Germany, United Kingdom, Canada, Japan, France, Italy, Switzerland, Australia, Spain, Belgium, Denmark, Singapore, India, Netherlands, Sweden, New Zealand, Portugal, Poland, Israel, Lithuania, Hong Kong, Argentina, Ireland, Austria, Czechia, Cameroon, Taiwan, Province of China, Morocco

Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.

Q5 site directed mutagenesis kit by New England Biolabs

Sourced in United States, United Kingdom, Germany, China, Canada, Singapore, Japan, Morocco, France

The Q5 Site-Directed Mutagenesis Kit is a laboratory tool designed for introducing precise mutations into DNA sequences. It provides a streamlined workflow for generating site-specific changes in plasmid or linear DNA templates.

In fusion hd cloning kit by Takara Bio

Sourced in United States, Japan, China, France, Germany, Canada

The In-Fusion HD Cloning Kit is a versatile DNA assembly method that allows for the rapid and precise seamless cloning of multiple DNA fragments. The kit provides a high-efficiency, directional cloning solution for a wide range of applications.

T4 dna ligase by New England Biolabs

Sourced in United States, China, United Kingdom, Germany, Japan, France, Canada, Morocco, Switzerland, Australia

T4 DNA ligase is an enzyme that catalyzes the formation of phosphodiester bonds between adjacent 3'-hydroxyl and 5'-phosphate termini in DNA. It is commonly used in molecular biology for the joining of DNA fragments.

Pcdna3 by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Japan, Canada, France, Sweden, Netherlands, Italy, Portugal, Spain, Australia, Denmark

The PcDNA3.1 is a plasmid vector used for the expression of recombinant proteins in mammalian cells. It contains a powerful human cytomegalovirus (CMV) promoter, which drives high-level expression of the inserted gene. The vector also includes a neomycin resistance gene for selection of stable transfectants.

Gblock by Integrated DNA Technologies

Sourced in United States, Belgium, Canada, Singapore

GBlocks are synthetic DNA fragments designed for a variety of molecular biology applications. They are double-stranded, sequence-verified DNA segments that can be used as building blocks for gene assembly, cloning, and other genetic engineering techniques.

Geneart gene synthesis by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom

GeneArt Gene Synthesis is a laboratory equipment product that enables the custom synthesis of DNA sequences. It provides a platform for the design, assembly, and production of synthetic genes, allowing researchers to create custom genetic constructs for various applications.

Gibson assembly by New England Biolabs

Sourced in United States, United Kingdom, Denmark, France

Gibson Assembly is a molecular biology technique used for the seamless cloning and assembly of multiple DNA fragments. It allows the joining of DNA sequences with high efficiency, without the need for restriction enzymes or ligase. The core function of Gibson Assembly is to enable the rapid and precise construction of recombinant DNA molecules from multiple overlapping DNA fragments.

In fusion cloning by Takara Bio

Sourced in United States, Japan, France, China, United Kingdom

In-Fusion cloning is a seamless DNA assembly method that enables the rapid and efficient insertion of DNA fragments into any vector. It utilizes an enzyme-based system to join DNA fragments with overlapping sequences, allowing for the creation of recombinant plasmids without the need for restriction enzymes or ligase.

What is a codon and why is it important in biology?

A codon is a triplet of three adjacent nucleotides in a nucleic acid molecule, such as DNA or RNA, that specifies a particular amino acid or signals the termination of protein synthesis. Codons are the basic units of the genetic code, determining the sequence of amino acids in polypeptide chains. Codon optimization is a crucial process in synthetic biology and biotechnology, enhancing protein expression and recombinant DNA research.

How can PubCompare.ai help with codon optimization?

PubCompare.ai's AI-driven comparison platform can simplify the codon optimization process by helping researchers locate the best protocols and products to improve reproducibility and accuracy. The platform allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. This can help you identify the most effective protocols related to codon optimization for your specific research goals, and the platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for your needs.

What are some common challenges with codon optimization?

Some common challenges with codon optimization include ensuring high levels of protein expression, minimizing the risk of inclusion bodies, and maintaining the stability and functionality of the expressed protein. Codon bias, where certain codons are used more frequently than others in a particular organism, can also be a challenge that needs to be addressed through careful codon selection and optimization.

Are there different types or variations of codons?

Yes, there are different types and variations of codons. In addition to the standard 64 codons that specify amino acids, there are also stop codons, which signal the termination of protein synthesis. Additionally, some organisms may have slightly different codon usage patterns or even alternative genetic codes, which can impact codon optimization efforts. Researchers need to be aware of these variations when designing their codon optimization strategies.

What are some specific applications of codon optimization?

Codon optimization has a wide range of applications in biotechnology and synthetic biology. It is commonly used to enhance the production of recombinant proteins, such as therapeutic antibodies or enzymes, in heterologous host organisms. Codon optimization can also be employed in the design of synthetic genes or gene circuits, as well as in the development of viral vectors for gene therapy. Additionally, codon optimization plays a key role in the field of metabolic engineering, where it is used to optimize the expression of genes involved in the production of valuable compounds.

More about "Codon"

Codons are the fundamental building blocks of the genetic code, comprising a triplet of adjacent nucleotides that specify a particular amino acid or signal the termination of protein synthesis.
This process of codon optimization is crucial in synthetic biology and biotechnology, as it enhances protein expression and recombinant DNA research.
Codon optimization involves the strategic selection and arrangement of codons to maximize the efficiency of protein production in a given host organism.
This is particularly important when working with heterologous genes, where the codon usage patterns of the source and host organisms may differ significantly.
Tools like GeneArt, a leading provider of custom DNA synthesis and gene optimization services, can help streamline the codon optimization process.
Similarly, popular reagents such as Lipofectamine 2000 (a transfection reagent), the Q5 Site-Directed Mutagenesis Kit (for introducing targeted mutations), and the In-Fusion HD Cloning Kit (for seamless DNA assembly) can all contribute to improving the accuracy and reproducibility of codon optimization experiments.
Additionally, the use of plasmid vectors like pcDNA3.1, as well as synthetic DNA fragments like GBlocks and GeneArt Gene Synthesis, can further enhance the efficiency and flexibility of codon optimization workflows.
Techniques like Gibson Assembly and In-Fusion cloning, which enable seamless DNA assembly, can also play a key role in optimizing codon usage and gene expression.
By leveraging these tools and techniques, researchers can simplify the codon optimization process, leading to more accurate and reproducible results in their recombinant DNA research and synthetic biology applications.