Efficiency of protein synthesis inhibition depends on tRNA and codon compositions

Rudorf, Sophia

doi:10.1371/journal.pcbi.1006979

Cited by 9 publications

(9 citation statements)

References 42 publications

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“…The competition between the aa-tRNAs results in variations in decoding times for different codon-aa-tRNA pairs ( Rudorf et al, 2014 ; Vieira et al, 2016 ; Dykeman, 2020 ), even though the rates of reactions on the decoding pathway, such as binding, GTP hydrolysis, and tRNA accommodation, are similar for different cognate aa-tRNAs ( Rodnina et al, 2005 ; Ledoux and Uhlenbeck, 2008 ). Although in bacteria, global codon usage matches the tRNA abundance, transient changes in the transcriptome composition due to transcriptional responses may shift mRNA codon bias relative to tRNA concentrations, which is predicted to have strong effects on decoding and may lead to additional, unexpected ribosome pauses ( Rudorf, 2019 ; Dykeman, 2020 ). Under conditions of rapid growth, tRNAs are almost fully charged ( Dittmar et al, 2005 ).…”

Section: Non-uniform Rate Of Translation and Translational Efficiencymentioning

confidence: 99%

Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding

et al. 2021

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Protein homeostasis of bacterial cells is maintained by coordinated processes of protein production, folding, and degradation. Translational efficiency of a given mRNA depends on how often the ribosomes initiate synthesis of a new polypeptide and how quickly they read the coding sequence to produce a full-length protein. The pace of ribosomes along the mRNA is not uniform: periods of rapid synthesis are separated by pauses. Here, we summarize recent evidence on how ribosome pausing affects translational efficiency and protein folding. We discuss the factors that slow down translation elongation and affect the quality of the newly synthesized protein. Ribosome pausing emerges as important factor contributing to the regulatory programs that ensure the quality of the proteome and integrate the cellular and environmental cues into regulatory circuits of the cell.

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Section: Non-uniform Rate Of Translation and Translational Efficiencymentioning

confidence: 99%

Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding

et al. 2021

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“…However, one of the factors which seems to have a strong influence on the translational dynamics was the composition of the transcriptome, both in its codon bias relative to tRNA concentrations and its length and total nucleotide content. The ultra sensitivity of the translational process on the tRNA abundance relative to codon bias has also been noted by Rudorf et al when Ef-Tu and TC formation reactions have been accounted for in their translational models [28,30]. However, preliminary work on reproducing the observed translational dynamics with wild-type E. Coli transcriptomes using mRNAs from predicted ORFs showed that, while matching of the tRNA abundances to codon bias enhanced translational efficiency to values closer to what is expected experimentally, additional optimization of the parameters or the overall model is needed.…”

Section: Discussionmentioning

confidence: 57%

“…https://doi.org/10.1371/journal.pcbi.1007618.g004 [28,30] which have also taken into account the effects of competition amongst newly charged tRNAs for Ef-Tu:GTP and the effects of codon bias on the overall translational process. Ribosomal density on mRNAs is transcriptome dependent.…”

Section: Biological Predictions Of the Modelmentioning

confidence: 99%

A stochastic model for simulating ribosome kinetics in vivo

Dykeman

2020

PLoS Comput Biol

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Computational modelling of in vivo protein synthesis is highly complicated, as it requires the simulation of ribosomal movement over the entire transcriptome, as well as consideration of the concentration effects from 40+ different types of tRNAs and numerous other protein factors. Here I report on the development of a stochastic model for protein translation that is capable of simulating the dynamical process of in vivo protein synthesis in a prokaryotic cell containing several thousand unique mRNA sequences, with explicit nucleotide information for each, and report on a number of biological predictions which are beyond the scope of existing models. In particular, I show that, when the complex network of concentration dependent interactions between elongation factors, tRNAs, ribosomes, and other factors required for protein synthesis are included in full detail, several biological phenomena, such as the increasing peptide elongation rate with bacterial growth rate, are predicted as emergent properties of the model. The stochastic model presented here demonstrates the importance of considering the translational process at this level of detail, and provides a platform to interrogate various aspects of translation that are difficult to study in more coarse-grained models.

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“…For example, the ribosome elongation step in the model encompasses 9 individual kinetic steps covering concentration dependant recruitment of ternary complex, GTP hydrolysis, and EF-G dependant translocation of the ribosome. Such reaction steps have been also been accounted for in similar models of translation [ 16 ], including the dependence on tRNA concentrations [ 17 ]. Similar kinetic detail is also present in the model for the initiation and recycling/termination stages, while the additional reactions involving re-charging of ternary complex and GTP/GDP exchange on GTPases are also accounted for.…”

Section: Resultsmentioning

confidence: 99%

Modelling ribosome kinetics and translational control on dynamic mRNA

Dykeman

2023

PLoS Comput Biol

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The control of protein synthesis and the overall levels of various proteins in the cell is critical for achieving homoeostasis. Regulation of protein levels can occur at the transcriptional level, where the total number of messenger RNAs in the overall transcriptome are controlled, or at the translational level, where interactions of proteins and ribosomes with the messenger RNA determine protein translational efficiency. Although transcriptional control of mRNA levels is the most commonly used regulatory control mechanism in cells, positive-sense single-stranded RNA viruses often utilise translational control mechanisms to regulate their proteins in the host cell. Here I detail a computational method for stochastically simulating protein synthesis on a dynamic messenger RNA using the Gillespie algorithm, where the mRNA is allowed to co-translationally fold in response to ribosome movement. Applying the model to the test case of the bacteriophage MS2 virus, I show that the models ability to accurately reproduce experimental measurements of coat protein production and translational repression of the viral RNA dependant RNA polymerase at high coat protein concentrations. The computational techniques reported here open up the potential to examine the infection dynamics of a ssRNA virus in a host cell at the level of the genomic RNA, as well as examine general translation control mechanisms present in polycistronic mRNAs.

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Efficiency of protein synthesis inhibition depends on tRNA and codon compositions

Cited by 9 publications

References 42 publications

Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding

Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding

A stochastic model for simulating ribosome kinetics in vivo

Modelling ribosome kinetics and translational control on dynamic mRNA

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