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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...
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tRNA Activation

Aminoacyl-tRNA synthetases are present in both eukaryotes and bacteria. Though eukaryotes have 20 different aminoacyl-tRNA synthetases to couple to 20 amino acids, many bacteria do not have genes for all of these aminoacyl-tRNA synthetases. Despite this, they still use all 20 amino acids to synthesize their proteins. For instance, some bacteria do not have the gene encoding the enzyme that couples glutamine with its partner tRNA. In these organisms, one enzyme adds glutamic acid to all of the...
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...

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Related Experiment Video

Updated: Jun 11, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Transforming mRNA drug design with AI: From UTR and codon optimization to coordinated design.

Yuqi Shi1, Chuanlong Zeng1, Xia Sheng1

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.

Journal of Advanced Research
|June 9, 2026
PubMed
Summary

Artificial Intelligence (AI) is revolutionizing messenger RNA (mRNA) drug design by enabling complex sequence optimization for stability and efficiency. This review explores AI frameworks for mRNA engineering, moving towards coordinated design for improved therapeutic development.

Keywords:
AI-assisted mRNA DesignCDS OptimizationGenerative ModelsRepresentation LearningUTR DesignUTR-CDS Coordinated Design

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Cell Based Assays of SINEUP Non-coding RNAs That Can Specifically Enhance mRNA Translation

Published on: February 1, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Drug Discovery

Background:

  • Messenger RNA (mRNA) drug design requires optimizing sequence elements for stability, translation, and immunogenicity.
  • Traditional methods struggle with the complex, high-dimensional nature of mRNA sequence-function relationships.
  • Artificial Intelligence (AI) offers a powerful approach to decode these relationships and engineer mRNA precisely.

Purpose of the Study:

  • To systematically review the data infrastructure, evaluation metrics, and AI algorithms for mRNA design.
  • To categorize AI methodologies, focusing on representation learning and generative design for mRNA.
  • To examine applications in untranslated region (UTR) and codon sequence (CDS) optimization, including coordinated UTR-CDS design.

Main Methods:

  • Review of AI methodologies including discriminative and generative models (e.g., Large Language Models).
  • Analysis of approaches for sequence representation and de novo design.
  • Examination of strategies for optimizing mRNA sub-regions (UTR, CDS) and integrated frameworks.

Main Results:

  • AI has evolved from predicting properties to enabling de novo mRNA sequence design.
  • A shift towards coordinated design strategies addressing cross-regional dependencies is noted.
  • Current methods are largely sequential or modular, with a need for end-to-end global models.

Conclusions:

  • Major challenges include the generalization gap and model interpretability.
  • Future directions involve multimodal foundation models and multi-objective optimization.
  • Bridging the gap between computational design and clinical translatability is crucial for mRNA therapeutics.