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Related Concept Videos

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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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Initiation of Translation02:33

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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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.
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Updated: Jul 31, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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Riboformer: A Deep Learning Framework for Predicting Context-Dependent Translation Dynamics.

Bin Shao1,2, Jiawei Yan3, Jing Zhang4

  • 1Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.

Biorxiv : the Preprint Server for Biology
|May 10, 2023
PubMed
Summary
This summary is machine-generated.

Riboformer, a deep learning tool, models translation dynamics to correct ribosome profiling data. It identifies sequence features causing protein synthesis issues in diseases like aging and viral infections.

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genomics

Background:

  • Cellular proteostasis relies on accurate translation elongation; disruptions link to various diseases.
  • Ribosome profiling offers genome-scale translation measurement but struggles with technical artifacts and identifying sequence determinants.
  • Understanding translation dynamics is crucial for disease research and therapeutic development.

Approach:

  • Developed Riboformer, a deep learning framework utilizing transformer architecture to model context-dependent translation dynamics.
  • Riboformer predicts ribosome densities at codon resolution, enabling artifact correction and analysis of subtle translational variations.
  • Integrated Riboformer with in silico mutagenesis for identifying sequence motifs driving ribosome stalling.

Key Points:

  • Riboformer accurately predicts ribosome densities and corrects experimental artifacts in ribosome profiling data.
  • The tool reveals nuanced differences in synonymous codon translation and identifies protein synthesis bottlenecks.
  • Identified sequence motifs contributing to ribosome stalling in aging and viral infection contexts.

Conclusions:

  • Riboformer provides a context-aware, interpretable method for standardizing ribosome profiling data.
  • Elucidates the regulatory underpinnings of translation kinetics and sequence-dependent regulation.
  • Offers a powerful approach for dissecting translational dysregulation in disease states.