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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
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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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Initiation of Translation02:33

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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.
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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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Transfer RNA Synthesis02:36

Transfer RNA Synthesis

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One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
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Translation in Prokaryotes

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Prokaryote translation is a complex, highly coordinated process that converts genetic information from mRNA into functional proteins. It involves three stages: initiation, elongation, and termination, each facilitated by specific molecular components.Initiation of TranslationThe process begins with the assembly of the ribosomal subunits and initiation factors on the mRNA. In bacteria, the 30S ribosomal subunit recognizes the Shine-Dalgarno sequence in the mRNA, a conserved region upstream of...
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Updated: Sep 16, 2025

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems
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UTRGAN: learning to generate 5' UTR sequences for optimized translation efficiency and gene expression.

Sina Barazandeh1,2, Furkan Ozden3, Ahmet Hincer4

  • 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States.

Bioinformatics Advances
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

UTRGAN, a novel Generative Adversarial Network model, designs synthetic 5' untranslated regions (UTRs) for enhanced protein expression. This AI-driven approach significantly boosts translation efficiency and ribosome load for synthetic biology applications.

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Area of Science:

  • Synthetic Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • The 5' untranslated region (UTR) of mRNA is critical for controlling protein expression levels and stability.
  • Optimizing UTR sequences is essential for high and stable protein production in synthetic biological systems.
  • Existing UTR sequences are often patented, necessitating the development of novel, high-performance alternatives.

Purpose of the Study:

  • To develop a computational model for generating novel 5' UTR sequences with improved properties.
  • To optimize generated UTR sequences for enhanced target gene expression, ribosome load, and translation efficiency.
  • To provide a publicly available tool for designing synthetic UTRs for biological applications.

Main Methods:

  • Utilized a Generative Adversarial Network (GAN) framework named UTRGAN to generate 5' UTR sequences.
  • Implemented an optimization procedure to enhance predicted protein expression, ribosome load, and translation efficiency.
  • Validated generated UTR sequences by comparing their predicted properties and experimental translation rates against known UTRs.

Main Results:

  • UTRGAN-generated UTRs demonstrated up to five-fold higher predicted expression and a 34-fold higher predicted translation efficiency compared to initial sequences.
  • Sequences exhibited increased similarity to known regulatory motifs, including internal ribosome entry sites and Kozak sequences.
  • In vitro experiments confirmed higher translation rates for TNF-α protein using UTRGAN-designed UTRs compared to the high-capacity Beta Globin 5' UTR.

Conclusions:

  • UTRGAN provides an effective AI-driven method for designing synthetic 5' UTRs with significantly enhanced translational properties.
  • The model's ability to mimic natural UTR characteristics and optimize for specific expression metrics offers a valuable tool for synthetic biology.
  • The open-source release of UTRGAN and its dataset facilitates further research and application in optimizing protein expression systems.