Understanding the Genetic Code

Saier, Milton H.

doi:10.1128/jb.00091-19

Cited by 19 publications

(22 citation statements)

References 88 publications

(95 reference statements)

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“…1a and b). The earliest amino acids such as glycine, alanine and glutamic acid had simple structure and could be formed in a variety of environments spontaneously, from purely chemical means, without assistance of protein molecules [20]. In The relative abundances of these ten amino acids in the order Gly > Ala > Asp > Glu > Val > Ser > Ile > Leu > Pro > Thr, correlated with the free energies of their synthesis, suggesting that thermodynamics determined their relative amounts.…”

Section: Degeneracy and Deviances Of Genetic Codementioning

confidence: 99%

“…Tryptophan has a complex structure, is comparatively rare in the protein code and hence is one of the latest additions to the code. Due to evolving anabolic pathways, when additional amino acids became available, genetic code expanded stepwise, with increasing number of codons to specify correspondingly increased number of amino acids and to eventually include all 20 common protein amino acids [20]. The complexity grew over time, so that codons were reassigned later to a related amino acid, to minimize the consequences of mutations and translational errors.…”

Section: Degeneracy and Deviances Of Genetic Codementioning

confidence: 99%

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Codon usage bias

2021

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Codon usage bias is the preferential or non-random use of synonymous codons, a ubiquitous phenomenon observed in bacteria, plants and animals. Different species have consistent and characteristic codon biases. Codon bias varies not only with species, family or group within kingdom, but also between the genes within an organism. Codon usage bias has evolved through mutation, natural selection, and genetic drift in various organisms. Genome composition, GC content, expression level and length of genes, position and context of codons in the genes, recombination rates, mRNA folding, and tRNA abundance and interactions are some factors influencing codon bias. The factors shaping codon bias may also be involved in evolution of the universal genetic code. Codon-usage bias is critical factor determining gene expression and cellular function by influencing diverse processes such as RNA processing, protein translation and protein folding. Codon usage bias reflects the origin, mutation patterns and evolution of the species or genes. Investigations of codon bias patterns in genomes can reveal phylogenetic relationships between organisms, horizontal gene transfers, molecular evolution of genes and identify selective forces that drive their evolution. Most important application of codon bias analysis is in the design of transgenes, to increase gene expression levels through codon optimization, for development of transgenic crops. The review gives an overview of deviations of genetic code, factors influencing codon usage or bias, codon usage bias of nuclear and organellar genes, computational methods to determine codon usage and the significance as well as applications of codon usage analysis in biological research, with emphasis on plants.

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Section: Degeneracy and Deviances Of Genetic Codementioning

confidence: 99%

Section: Degeneracy and Deviances Of Genetic Codementioning

confidence: 99%

Codon usage bias

2021

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“…The key factors for initiation are ribosome recruitment to the mRNA and correct positioning over the start codon, where the presence of a Kozak sequence in the 5′ UTR also increases the efficiency of translation ( Nakagawa et al, 2008 ; Hinnebusch et al, 2016 ). Elongation is mostly driven by codon usage, where ribosomes synthesize proteins by concatenating one AA per codon according to the genetic code ( Saier, 2019 ). In the termination phase, release factors terminate translation in response to stop codons and the ribosomes are recycled.…”

Section: Regulatory Mechanisms In Specific Coding and Non-coding Regionsmentioning

confidence: 99%

Learning the Regulatory Code of Gene Expression

Zrimec

Buric

Kokina

et al. 2021

Front. Mol. Biosci.

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Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.

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“…Given the position in the codon table for amino acids with similar properties, it is clear that in particular, 6/30 position two determines the properties of the amino acid [15]. For instance, all codons with T2 encode hydrophobic amino acids, while both negatively charged amino acids have A2.…”

Section: Gc Coding Vs Non-codingmentioning

confidence: 99%

“…Therefore, the overall GC content can change by using different nucleotides in the third position without affecting the proteome. Further, the codons have evolved in such a way that the general properties of the amino acids are determined mainly by the codon in position two [15].…”

Section: Introductionmentioning

confidence: 99%

The Use of GC-, Codon-, and Amino Acid-frequencies to Understand the Evolutionary Forces at a Genomic Scale

Elofsson

2019

Preprint

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It is well known that the GC content varies enormously between organisms; this is believed to be caused by a combination of mutational preferences and selective pressure. Within coding regions, the variation of GC is more substantial in position three and smaller in position one and two. Less well known is that this variation also has an enormous impact on the frequency of amino acids as their codons vary in GC content. For instance, the fraction of alanines in different proteomes varies from 1.1% to 16.5%. In general, the frequency of different amino acids correlates strongly with the number of codons, the GC content of these codons and the genomic GC contents. However, there are clear and systematic deviations from the expected frequencies. Some amino acids are more frequent than expected by chance, while others are less frequent. A plausible model to explain this is that there exist two different selective forces acting on the genes; First, there exists a force acting to maintain the overall GC level and secondly there exists a selective force acting on the amino acid level. Here, we use the divergence in amino acid frequency from what is expected by the GC content to analyze the selective pressure acting on codon frequencies in the three kingdoms of life. We find four major selective forces; First, the frequency of serine is lower than expected in all genomes, but most in prokaryotes. Secondly, there exist a selective pressure acting to balance positively and negatively charged amino acids, which results in a reduction of arginine and negatively charged amino acids. This results in a reduction of arginine and all the negatively charged amino acids. Thirdly, the frequency of the hydrophobic residues encoded by a T in the second codon position does not change with GC. Their frequency is lower in eukaryotes than in prokaryotes. Finally, some amino acids with unique properties, such as proline glycine and proline, are limited in their frequency variation.

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Understanding the Genetic Code

Cited by 19 publications

References 88 publications

Codon usage bias

Codon usage bias

Learning the Regulatory Code of Gene Expression

The Use of GC-, Codon-, and Amino Acid-frequencies to Understand the Evolutionary Forces at a Genomic Scale

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