scholarly journals OSTIR: open source translation initiation rate prediction

2021 ◽  
Vol 6 (64) ◽  
pp. 3362
Author(s):  
Cameron Roots ◽  
Alexandra Lukasiewicz ◽  
Jeffrey Barrick
Keyword(s):  
Open Source ◽  
Translation Initiation ◽  
Initiation Rate ◽  
Rate Prediction

scholarly journals Competition between translation initiation factor eIF5 and its mimic protein 5MP determines non-AUG initiation rate genome-wide

2017 ◽  
Vol 45 (20) ◽  
pp. 11941-11953 ◽  
Author(s):  
Leiming Tang ◽  
Jacob Morris ◽  
Ji Wan ◽  
Chelsea Moore ◽  
Yoshihiko Fujita ◽  
...  
Keyword(s):  
Translation Initiation ◽  
Translation Initiation Factor ◽  
Initiation Factor ◽  
Initiation Rate ◽  
Genome Wide

scholarly journals Kinetic modeling predicts a role for ribosome collisions at elongation stall sites in bacteria

2016 ◽  
Author(s):  
Michael Ferrin ◽  
Arvind R. Subramaniam
Keyword(s):  
Translation Initiation ◽  
Amino Acid Starvation ◽  
Fine Tuning ◽  
Initiation Rate ◽  
Translation Elongation ◽  
Kinetic Rate ◽  
Inverse Approach ◽  
Ribosome Stalling ◽  
Traffic Jams ◽  
Reporter Mrna

AbstractRibosomes can stall during translation elongation in bacteria and eukaryotes. To identify mechanisms by which ribosome stalling affects expression of the encoded protein, we develop an inverse approach that combines computational modeling with systematic perturbations of translation initiation rate, the number of stall sites, and the distance between stall sites on a reporter mRNA. By applying this approach to ribosome stalls caused by amino acid starvation in the bacteriumEscherichia coli, we find that our measurements are quantitatively inconsistent with two widely used kinetic models for stalled ribosomes: ribosome traffic jams that block initiation, and abortive (premature) termination of stalled ribosomes. To account for this discrepancy, we consider a model in which collision from a trailing ribosome causes abortive termination of the stalled ribosome. This collision-stimulated abortive termination model provides a better fit to measured protein synthesis rates from our reporter library, and is consistent with observed ribosome densities near stall sites. Analysis of this model further predicts that ribosome collisions can selectively stimulate abortive termination of stalled ribosomes without fine-tuning of kinetic rate parameters. Thus ribosome collisions may serve as a robust timer for translational quality control pathways to recognize stalled ribosomes.

scholarly journals An automated model test system for systematic development and improvement of gene expression models

2017 ◽  
Author(s):  
Alexander C. Reis ◽  
Howard M. Salis
Keyword(s):  
Gene Expression ◽  
Model Test ◽  
Translation Initiation ◽  
Test System ◽  
Biophysical Model ◽  
Genetic Circuits ◽  
Initiation Rate ◽  
Information Theoretic ◽  
New Information ◽  
Genetic Systems

ABSTRACTGene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

scholarly journals Effect of Brain Ischemia and Reperfusion on the Localization of Phosphorylated Eukaryotic Initiation Factor 2α

1997 ◽  
Vol 17 (12) ◽  
pp. 1291-1302 ◽  
Author(s):  
Donald J. DeGracia ◽  
Jonathon M. Sullivan ◽  
Robert W. Neumar ◽  
Sarah S. Alousi ◽  
Katie R. Hikade ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Translation Initiation ◽  
Initiation Factor ◽  
Eukaryotic Initiation Factor ◽  
Initiation Rate ◽  
Western Blots ◽  
Α Subunit ◽  
Neuronal Nuclei ◽  
Initiation Factor 2 ◽  
Brain Reperfusion

Postischemic brain reperfusion is associated with a substantial and long-lasting reduction of protein synthesis in selectively vulnerable neurons. Because the overall translation initiation rate is typically regulated by altering the phosphorylation of serine 51 on the α-subunit of eukaryotic initiation factor 2 (eIF-2α), we used an antibody specific to phosphorylated eIF-2α [eIF-2(αP)] to study the regional and cellular distribution of eIF-2(αP) in normal, ischemic, and reperfused rat brains. Western blots of brain postmitochondrial supernatants revealed that ~1% of all eIF-2α is phosphorylated in controls, eIF-2(αP) is not reduced by up to 30 minutes of ischemia, and eIF-2(αP) is increased ~20-fold after 10 and 90 minutes of reperfusion. Immunohistochemistry shows localization of eIF-2(αP) to astrocytes in normal brains, a massive increase in eIF-2(αP) in the cytoplasm of neurons within the first 10 minutes of reperfusion, accumulation of eIF-2(αP) in the nuclei of selectively vulnerable neurons after 1 hour of reperfusion, and morphology suggesting pyknosis or apoptosis in neuronal nuclei that continue to display eIF-2(αP) after 4 hours of reperfusion. These observations, together with the fact that eIF-2(αP) inhibits translation initiation, make a compelling case that eIF-2(αP) is responsible for reperfusion-induced inhibition of protein synthesis in vulnerable neurons.

scholarly journals Circular RNA, the Key for Translation

2020 ◽  
Vol 21 (22) ◽  
pp. 8591
Author(s):  
Anne-Catherine Prats ◽  
Florian David ◽  
Leila H. Diallo ◽  
Emilie Roussel ◽  
Florence Tatin ◽  
...  
Keyword(s):  
Translation Initiation ◽  
Translational Control ◽  
Mrna Translation ◽  
Circular Rna ◽  
Initiation Factor ◽  
Circular Rnas ◽  
Strong Impact ◽  
Initiation Rate ◽  
New Family ◽  
Independent Translation

It was thought until the 1990s that the eukaryotic translation machinery was unable to translate a circular RNA. However internal ribosome entry sites (IRESs) and m6A-induced ribosome engagement sites (MIRESs) were discovered, promoting 5′ end-independent translation initiation. Today a new family of so-called “noncoding” circular RNAs (circRNAs) has emerged, revealing the pivotal role of 5′ end-independent translation. CircRNAs have a strong impact on translational control via their sponge function, and form a new mRNA family as they are translated into proteins with pathophysiological roles. While there is no more doubt about translation of covalently closed circRNA, the linearity of canonical mRNA is only theoretical: it has been shown for more than thirty years that polysomes exhibit a circular form and mRNA functional circularization has been demonstrated in the 1990s by the interaction of initiation factor eIF4G with poly(A) binding protein. More recently, additional mechanisms of 3′–5′ interaction have been reported, including m6A modification. Functional circularization enhances translation via ribosome recycling and acceleration of the translation initiation rate. This update of covalently and noncovalently closed circular mRNA translation landscape shows that RNA with circular shape might be the rule for translation with an important impact on disease development and biotechnological applications.

scholarly journals Extrinsic noise acts to lower protein production at higher translation initiation rates

2020 ◽  
Author(s):  
Rati Sharma
Keyword(s):  
Translation Initiation ◽  
Protein Production ◽  
Translation Termination ◽  
Mrna Translation ◽  
Intrinsic Noise ◽  
Stochastic Simulations ◽  
Residence Times ◽  
Extrinsic Noise ◽  
Initiation Rate ◽  
The Individual

Any cellular process at the microscopic level is governed by both extrinsic and intrinsic noise. In this article, we incorporate extrinsic noise in a model of mRNA translation and carry out stochastic simulations of the same. We then evaluate various statistics related to the residence time of the ribosome on the mRNA and subsequent protein production. We also study the effect of slow codons. From our simulations, we show that noise in the translation initiation rate rather than the translation termination rate acts to significantly broaden the distribution of mRNA residence times near the membrane. Further, the presence of slow codons acts to increase the mean residence times. However, this increase also depends on the number and position of the slow codons on the lattice. We also show that the the slow codons act to mask any effect from the extrinsic noise themselves. Our results have implications towards a better understanding of the role the individual components play during the translation process.

scholarly journals Kinetic modeling predicts a stimulatory role for ribosome collisions at elongation stall sites in bacteria

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Michael A Ferrin ◽  
Arvind R Subramaniam
Keyword(s):  
Escherichia Coli ◽  
Quality Control ◽  
Protein Synthesis ◽  
Translation Initiation ◽  
Computational Analysis ◽  
Fine Tuning ◽  
Initiation Rate ◽  
Kinetic Rate ◽  
Ribosome Stalling ◽  
Traffic Jams

Ribosome stalling on mRNAs can decrease protein expression. To decipher ribosome kinetics at stall sites, we induced ribosome stalling at specific codons by starving the bacterium Escherichia coli for the cognate amino acid. We measured protein synthesis rates from a reporter library of over 100 variants that encoded systematic perturbations of translation initiation rate, the number of stall sites, and the distance between stall sites. Our measurements are quantitatively inconsistent with two widely-used kinetic models for stalled ribosomes: ribosome traffic jams that block initiation, and abortive (premature) termination of stalled ribosomes. Rather, our measurements support a model in which collision with a trailing ribosome causes abortive termination of the stalled ribosome. In our computational analysis, ribosome collisions selectively stimulate abortive termination without fine-tuning of kinetic rate parameters at ribosome stall sites. We propose that ribosome collisions serve as a robust timer for translational quality control pathways to recognize stalled ribosomes.

scholarly journals Translation Initiation Rate Determines the Impact of Ribosome Stalling on Bacterial Protein Synthesis

2014 ◽  
Vol 289 (41) ◽  
pp. 28160-28171 ◽  
Author(s):  
Steven J. Hersch ◽  
Sara Elgamal ◽  
Assaf Katz ◽  
Michael Ibba ◽  
William Wiley Navarre
Keyword(s):  
Protein Synthesis ◽  
Translation Initiation ◽  
Bacterial Protein ◽  
Initiation Rate ◽  
Ribosome Stalling ◽  
Bacterial Protein Synthesis ◽  
The Impact

scholarly journals Experiments with Snails Add to Our Knowledge about the Role of aPKC Subfamily Kinases in Learning

2019 ◽  
Vol 20 (9) ◽  
pp. 2117 ◽  
Author(s):  
Chesnokova ◽  
Zuzina ◽  
Bal ◽  
Vinarskaya ◽  
Roshchin ◽  
...  
Keyword(s):  
Translation Initiation ◽  
Ampa Receptors ◽  
Land Snail ◽  
Behavioral Experiments ◽  
Initiation Rate ◽  
C1 Domain ◽  
Sequence Encoding ◽  
Protein Kinase Mζ ◽  
First Time

Protein kinase Mζ is considered important for memory formation and maintenance in different species, including invertebrates. PKMζ participates in multiple molecular pathways in neurons, regulating translation initiation rate, AMPA receptors turnover, synaptic scaffolding assembly, and other processes. Here, for the first time, we established the sequence of mRNA encoding PKMζ homolog in land snail Helix lucorum. We annotated important features of this mRNA: domains, putative capping sites, translation starts, and splicing sites. We discovered that this mRNA has at least two isoforms, and one of them lacks sequence encoding C1 domain. C1 deletion may be unique for snail because it has not been previously found in other species. We performed behavioral experiments with snails, measured expression levels of identified isoforms, and confirmed that their expression correlates with one type of learning.

scholarly journals Fine-tuning biosensor dynamic range based on rational design of cross-ribosome-binding sites in bacteria

2020 ◽  
Author(s):  
Nana Ding ◽  
Shenghu Zhou ◽  
Zhenqi Yuan ◽  
Xiaojuan Zhang ◽  
Jing Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Deep Learning ◽  
Translation Initiation ◽  
Rational Design ◽  
Dynamic Range ◽  
Regulatory Elements ◽  
Fine Tuning ◽  
Initiation Rate ◽  
Ribosome Binding

ABSTRACTCurrently, predictive translation tuning of regulatory elements to the desired output of transcription factor based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e., fold change in gene expression between the presence and absence of inducer) by adjusting the translation initiation rate of the transcription factor and reporter. However, existing genetically encoded biosensors generally suffer from unpredictable translation tuning of regulatory elements to dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation initiation rate, protein folding and dynamic range, and presented a rational design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). A library containing 24,000 semi-rationally designed cRBSs was constructed using DNA microarray, and was divided into five sub-libraries through fluorescence-activated cell sorting. To explore the relationship between cRBSs and dynamic range, we established a classification model with the cRBSs and average dynamic range of five sub-libraries to accurately predict the dynamic range of biosensors based on convolutional neural network in deep learning. Thus, this work provides a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

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