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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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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 1, 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. shaobinlx@gmail.com.

Nature Communications
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

Riboformer, a deep learning tool, standardizes ribosome profiling data to reveal how gene sequences affect protein production. It helps identify disease-related translation issues and sequence motifs causing ribosome stalling.

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genomics

Background:

  • Cellular proteostasis relies on accurate translation elongation, with disruptions linked to various diseases.
  • Ribosome profiling offers genome-wide translation measurements but struggles to differentiate biological signals from technical noise.
  • Identifying sequence-specific determinants of translation dysregulation is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To introduce Riboformer, a deep learning framework for modeling context-dependent translation dynamics.
  • To accurately predict ribosome densities at codon resolution using a transformer architecture.
  • To provide a method for standardizing ribosome profiling data and identifying sequence-based translation regulatory elements.

Main Methods:

  • Developed Riboformer, a deep learning framework utilizing the transformer architecture.
  • Trained Riboformer on an unbiased ribosome profiling dataset.
  • Applied Riboformer to correct artifacts in new datasets and combined it with in silico mutagenesis.

Main Results:

  • Riboformer accurately predicts ribosome densities and corrects experimental artifacts in ribosome profiling data.
  • The framework revealed subtle differences in synonymous codon translation and identified a translation elongation bottleneck.
  • Riboformer successfully identified sequence motifs associated with ribosome stalling in aging and viral infection contexts.

Conclusions:

  • Riboformer offers a context-aware and interpretable approach for analyzing ribosome profiling data.
  • The tool aids in standardizing translation dynamics measurements and elucidating the regulatory basis of translation kinetics.
  • Riboformer facilitates the identification of sequence determinants contributing to translation dysregulation in disease and biological processes.