Expanding Anfinsen's principle: contributions of synonymous codon selection to rational protein design.
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ABSTRACT:
SUBMITTER: Sander IM
PROVIDER: S-EPMC3959793 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recently there has been increasing evidence that purifying selection occurs among synonymous codons in mammalian genes. This selection appears to be a consequence of either cis-regulatory motifs, such as exonic splicing enhancers (ESEs), or mRNA secondary structures, being superimposed on the coding sequence of the gene. We have developed a program to identify regions likely to be enriched for such motifs by searching for extended regions of extreme codon conservation between homologous genes of related species. Here we present the results of applying this approach to five mammalian species (human, chimpanzee, mouse, rat and dog). Even with very conservative selection criteria, we find over 200 regions of extreme codon conservation, ranging in length from 60 to 178 codons. The regions are often found within genes involved in DNA-binding, RNA-binding or zinc-ion-binding. They are highly depleted for synonymous single nucleotide polymorphisms (SNPs) but not for non-synonymous SNPs, further indicating that the observed codon conservation is being driven by negative selection. Forty-three percent of the regions overlap conserved alternative transcript isoforms and are enriched for known ESEs. Other regions are enriched for TpA dinucleotides and may contain conserved motifs/structures relating to mRNA stability and/or degradation. We anticipate that this tool will be useful for detecting regions enriched in other classes of coding-sequence motifs and structures as well.
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Project description:A key goal in molecular evolution is to extract mechanistic insights from signatures of selection. A case study is codon usage, where despite many recent advances and hypotheses, two longstanding problems remain: the relative contribution of selection and mutation in determining codon frequencies and the relative contribution of translational speed and accuracy to selection. The relevant targets of selection--the rate of translation and of mistranslation of a codon per unit time in the cell--can only be related to mechanistic properties of the translational apparatus if the number of transcripts per cell is known, requiring use of gene expression measurements. Perhaps surprisingly, different gene-expression data sets yield markedly different estimates of selection. We show that this is largely due to measurement noise, notably due to differences between studies rather than instrument error or biological variability. We develop an analytical framework that explicitly models noise in expression in the context of the population-genetic model. Estimates of mutation and selection strength in budding yeast produced by this method are robust to the expression data set used and are substantially higher than estimates using a noise-blind approach. We introduce per-gene selection estimates that correlate well with previous scoring systems, such as the codon adaptation index, while now carrying an evolutionary interpretation. On average, selection for codon usage in budding yeast is weak, yet our estimates show that genes range from virtually unselected to average per-codon selection coefficients above the inverse population size. Our analytical framework may be generally useful for distinguishing biological signals from measurement noise in other applications that depend upon measurements of gene expression.
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Project description:Translation of mRNA into protein is a unidirectional information flow process. Analysing the input (mRNA) and output (protein) of translation, we find that local protein structure information is encoded in the mRNA nucleotide sequence. The Coding Sequence and Structure (CSandS) database developed in this work provides a detailed mapping between over 4000 solved protein structures and their mRNA. CSandS facilitates a comprehensive analysis of codon usage over many organisms. In assigning translation speed, we find that relative codon usage is less informative than tRNA concentration. For all speed measures, no evidence was found that domain boundaries are enriched with slow codons. In fact, genes seemingly avoid slow codons around structurally defined domain boundaries. Translation speed, however, does decrease at the transition into secondary structure. Codons are identified that have structural preferences significantly different from the amino acid they encode. However, each organism has its own set of 'significant codons'. Our results support the premise that codons encode more information than merely amino acids and give insight into the role of translation in protein folding.
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Project description:Eighteen of the 20 amino acids are each encoded by more than one synonymous codon. Due to differential transfer RNA supply within the cell, synonymous codons are not used with equal frequency, a phenomenon termed codon usage bias (CUB). Previous studies have demonstrated that CUB of endogenous genes trans-regulates the translational efficiency of other genes. We hypothesized similar effects for CUB of exogenous genes on host translation, and tested it in the case of viral infection, a common form of naturally occurring exogenous gene translation. We analysed public Ribo-Seq datasets from virus-infected yeast and human cells and showed that virus CUB trans-regulated tRNA availability, and therefore the relative decoding time of codons. Manipulative experiments in yeast using 37 synonymous fluorescent proteins confirmed that an exogenous gene with CUB more similar to that of the host would apply decreased translational load on the host per unit of expression, whereas expression of the exogenous gene was elevated. The combination of these two effects was that exogenous genes with CUB overly similar to that of the host severely impeded host translation. Finally, using a manually curated list of viruses and natural and symptomatic hosts, we found that virus CUB tended to be more similar to that of symptomatic hosts than that of natural hosts, supporting a general deleterious effect of excessive CUB similarity between virus and host. Our work revealed repulsion between virus and host CUBs when they are overly similar, a previously unrecognized complexity in the coevolution of virus and host.
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Project description:The different triplets encoding the same amino acid, termed as synonymous codons, are not equally abundant in a genome. Factors such as G + C% and tRNA are known to influence their abundance in a genome. However, the order of the nucleotide in each codon per se might also be another factor impacting on its abundance values. Of the synonymous codons for specific amino acids, some are preferentially used in the high expression genes that are referred to as the 'optimal codons' (OCs). In this study, we compared OCs of the 18 amino acids in 221 species of bacteria. It is observed that there is amino acid specific influence for the selection of OCs. There is also influence of phylogeny in the choice of OCs for some amino acids such as Glu, Gln, Lys and Leu. The phenomenon of codon bias is also supported by the comparative studies of the abundance values of the synonymous codons with same G + C. It is likely that the order of the nucleotides in the triplet codon is also perhaps involved in the phenomenon of codon usage bias in organisms.
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Project description:Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.
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Project description:Messenger RNA (mRNA) stability substantially impacts steady-state gene expression levels in a cell. mRNA stability is strongly affected by codon composition in a translation-dependent manner across species, through a mechanism termed codon optimality. We have developed iCodon (www.iCodon.org), an algorithm for customizing mRNA expression through the introduction of synonymous codon substitutions into the coding sequence. iCodon is optimized for four vertebrate transcriptomes: mouse, human, frog, and fish. Users can predict the mRNA stability of any coding sequence based on its codon composition and subsequently generate more stable (optimized) or unstable (deoptimized) variants encoding for the same protein. Further, we show that codon optimality predictions correlate with both mRNA stability using a massive reporter library and expression levels using fluorescent reporters and analysis of endogenous gene expression in zebrafish embryos and/or human cells. Therefore, iCodon will benefit basic biological research, as well as a wide range of applications for biotechnology and biomedicine.
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Project description:MotivationMost amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding.ResultsWe model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding.Availability and implementationhttps://nd.edu/∼cone/CodonUsage/.Supplementary informationSupplementary data are available at Bioinformatics online.
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