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From DNA to Protein03:06

From DNA to Protein

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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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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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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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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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Related Experiment Video

Updated: Jun 12, 2025

Residue-specific Incorporation of Noncanonical Amino Acids into Model Proteins Using an Escherichia coli Cell-free Transcription-translation System
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A generative language model decodes contextual constraints on codon choice for mRNA design.

Marjan Faizi1, Helen Sakharova2, Liana F Lareau1,2,3,4

  • 1California Institute for Quantitative Biosciences, University of California, Berkeley, CA 94720, USA.

Biorxiv : the Preprint Server for Biology
|June 4, 2025
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Summary

Trias, a new language model, optimizes synthetic mRNA sequences by learning complex codon usage rules from data. It improves mRNA stability and protein output, outperforming existing tools.

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Area of Science:

  • Computational Biology and Bioinformatics
  • Molecular Biology and Genetics
  • Synthetic Biology

Background:

  • The genetic code's degeneracy allows synonymous codons, creating sequence diversity in protein-coding genes.
  • Codon choice influences mRNA function and protein production, critical for advancing mRNA technologies.
  • Existing codon optimization methods fail to capture complex contextual patterns in codon usage.

Purpose of the Study:

  • To develop a novel language model, Trias, for understanding and predicting context-dependent codon usage.
  • To generate species-specific codon sequences that adhere to biological constraints and enhance mRNA performance.
  • To provide a data-driven framework for optimizing synthetic mRNA design.

Main Methods:

  • Trained an encoder-decoder language model (Trias) on millions of eukaryotic coding sequences.
  • Integrated local and global dependencies in sequence data to learn codon usage rules.
  • Evaluated Trias's generated sequences against experimental measurements of mRNA stability, ribosome load, and protein output.

Main Results:

  • Trias learned complex codon usage patterns without explicit training on protein expression.
  • Generated sequences and scores from Trias strongly correlated with experimental measures of mRNA stability and protein output.
  • Trias outperformed commercial codon optimization tools in producing high-expression codon sequence variants.

Conclusions:

  • Trias offers a powerful, data-driven approach to codon optimization for synthetic mRNA design.
  • The model provides insights into the molecular and evolutionary principles governing codon choice.
  • This framework advances the design of synthetic mRNA with improved stability and protein expression.