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Related Concept Videos

Improving Translational Accuracy02:07

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...
Improving Translational Accuracy02:07

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...
Transgenic Organisms00:53

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Transgenic Organisms00:53

Transgenic Organisms

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The Central Dogma01:20

The Central Dogma

The central dogma explains the flow of genetic information from DNA nucleotides to the amino acid sequence of proteins.
RNA is the Missing Link Between DNA and Proteins
In the early 1900s, scientists discovered that DNA stores all the information needed for cellular functions and that proteins perform most of these functions. However, the mechanisms of converting genetic information into functional proteins remained unknown for many years. Initially, it was believed that a single gene is...
Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA molecules by RNA...

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Related Experiment Video

Updated: Jul 7, 2026

Engineering Oncogenic Heterozygous Gain-of-Function Mutations in Human Hematopoietic Stem and Progenitor Cells
12:04

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Published on: March 10, 2023

Enhancing protein expression in humans through codon optimization with transformer and contrastive learning.

Juseong Kim1,2, Jeongmu Kim1,2, Jae-Wook Lee3

  • 1Division of Artificial Intelligence, Pusan National University, Busan 46241, South Korea.

Molecular Therapy. Nucleic Acids
|July 6, 2026
PubMed
Summary

COformer, a deep learning framework, optimizes messenger RNA (mRNA) codons for enhanced protein expression. This novel approach improves mRNA stability and translation efficiency for better vaccine and therapeutic development.

Keywords:
Homo sapiensMT: Bioinformaticscodon optimizationcontrastive learningdeep learningprotein expressionstability

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Area of Science:

  • Biotechnology
  • Molecular Biology
  • Bioinformatics

Background:

  • Messenger RNA (mRNA) therapeutics, including vaccines and drugs, have gained prominence following COVID-19 successes.
  • Effective mRNA therapeutics require optimized translation efficiency and mRNA stability.
  • Current codon optimization methods rely on frequency-based heuristics, lacking scalability and neglecting sequence context.

Purpose of the Study:

  • To introduce COformer, a deep learning framework for advanced codon optimization.
  • To improve protein expression in *Homo sapiens* cells by enhancing translation efficiency and mRNA stability.
  • To overcome limitations of traditional codon optimization techniques.

Main Methods:

  • Developed COformer, a deep learning framework integrating convolutional neural networks (CNNs) and transformers.
  • Incorporated contrastive learning to align codon representations with amino acid identities.
  • Trained the model on sequences optimized using commercial tools.

Main Results:

  • COformer captures codon-level features and sequence contexts influencing protein expression.
  • The model demonstrated improved mRNA stability and translation efficiency.
  • In vitro validation confirmed enhanced protein expression using COformer-generated codon choices.

Conclusions:

  • COformer offers a scalable and context-aware deep learning approach for mRNA codon optimization.
  • The framework significantly enhances protein expression, advancing mRNA therapeutic development.
  • This method represents a significant improvement over traditional codon optimization strategies.