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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Regulated mRNA Transport02:22

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Translation01:31

Translation

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA
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Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation.

Yun-Bo Zhao, J Krishnan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 22, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances a Probabilistic Boolean model for mRNA translation, enabling exact probability calculations without simulations. The improved model accurately reflects stochastic processes and aids in analyzing translation complexities.

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    Area of Science:

    • Computational Biology
    • Molecular Biology
    • Systems Biology

    Background:

    • mRNA translation is a complex, regulated process crucial for protein synthesis.
    • Existing models use deterministic or stochastic formalisms, each with limitations.
    • A Probabilistic Boolean modelling framework was previously introduced for mRNA translation.

    Purpose of the Study:

    • To extend the Probabilistic Boolean model for mRNA translation by incorporating random sequential and parallel update rules.
    • To demonstrate the model's effectiveness in handling biological complexities and performing parameter sensitivity analysis.
    • To provide a robust platform for understanding mRNA translation by bridging existing modeling approaches.

    Main Methods:

    • Extension of a Probabilistic Boolean modelling framework.
    • Incorporation of random sequential and parallel update rules.
    • Comparison of model analysis results with TASEP (Totally Asymmetric Simple Exclusion Process) model simulations.

    Main Results:

    • The extended model effectively accommodates static and dynamic biological complexities.
    • The model's parameter sensitivity analysis aligns with TASEP simulation outcomes.
    • The framework preserves the stochastic nature of mRNA translation and allows exact probability calculations.

    Conclusions:

    • The enhanced Probabilistic Boolean model offers a powerful, simulation-free approach to analyze mRNA translation.
    • This methodology provides new analytical tools and bridges gaps between existing modeling techniques.
    • The study contributes to a more robust understanding and modeling of the mRNA translation process.