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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

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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.