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Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

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...

Leaky Scanning

Leaky Scanning

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...

Translational Regulation

Translational Regulation

Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...

Ribosome Profiling

Ribosome Profiling

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...

Initiation of Translation

Initiation of Translation

Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...

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Related Experiment Video

Updated: Jan 6, 2026

Quantitative Immunofluorescence to Measure Global Localized Translation

Quantitative Immunofluorescence to Measure Global Localized Translation

Published on: August 22, 2017

Predicting translational progress in biomedical research.

B Ian Hutchins¹, Matthew T Davis¹, Rebecca A Meseroll¹

¹Office of Portfolio Analysis, Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America.

|October 11, 2019

Summary

This summary is machine-generated.

Accelerating medical breakthroughs requires predicting which scientific papers will influence clinical research. A new machine learning system accurately identifies high-impact studies early, speeding up the translation of scientific discoveries into patient treatments.

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Last Updated: Jan 6, 2026

Quantitative Immunofluorescence to Measure Global Localized Translation

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Published on: August 22, 2017

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

Published on: May 22, 2021

Area of Science:

Biomedical research
Translational science
Scientific impact assessment

Background:

Translating fundamental scientific advances into clinical applications often takes decades.
A significant gap exists between scientific discovery and patient benefit.
Identifying early indicators of research translation is crucial for accelerating medical progress.

Purpose of the Study:

To develop a machine learning system for predicting the clinical impact of scientific publications.
To identify early signals of a paper's likelihood to be cited in future clinical trials or guidelines.
To enable real-time assessment of translational progress in biomedicine.

Main Methods:

Development of a machine learning model to analyze citation dynamics.
Utilizing postpublication data (as little as 2 years) to predict future citations by clinical articles.
Evaluating prediction accuracy using metrics such as accuracy and F1 score.

Main Results:

The machine learning system achieved 84% accuracy in predicting citations by clinical articles, significantly outperforming chance (19%).
Distinct knowledge flow patterns were identified for papers that successfully influenced clinical research versus those that did not.
Early citation data can accurately predict a paper's long-term clinical relevance.

Conclusions:

Machine learning can effectively predict the clinical impact of scientific research in near real-time.
Understanding knowledge flow trajectories aids in assessing translational potential.
This approach can help prioritize research efforts and expedite the delivery of scientific discoveries to patients.