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CodonTransformer: a multispecies codon optimizer using context-aware neural networks

Author

Listed:
  • Adibvafa Fallahpour

    (Vector Institute for Artificial Intelligence
    University of Toronto Scarborough; Department of Biological Science)

  • Vincent Gureghian

    (Quantitative and Synthetic Biology
    Institut de Biologie Paris-Seine)

  • Guillaume J. Filion

    (University of Toronto Scarborough; Department of Biological Science)

  • Ariel B. Lindner

    (Quantitative and Synthetic Biology
    Institut de Biologie Paris-Seine
    Biofoundry Alliance Sorbonne Université)

  • Amir Pandi

    (Quantitative and Synthetic Biology
    Institut de Biologie Paris-Seine
    Biofoundry Alliance Sorbonne Université)

Abstract

Degeneracy in the genetic code allows many possible DNA sequences to encode the same protein. Optimizing codon usage within a sequence to meet organism-specific preferences faces combinatorial explosion. Nevertheless, natural sequences optimized through evolution provide a rich source of data for machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all domains of life. The model demonstrates context-awareness thanks to its Transformers architecture and to our sequence representation strategy that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with minimum negative cis-regulatory elements. This work introduces the strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a codon optimization framework with a customizable open-access model and a user-friendly Google Colab interface.

Suggested Citation

  • Adibvafa Fallahpour & Vincent Gureghian & Guillaume J. Filion & Ariel B. Lindner & Amir Pandi, 2025. "CodonTransformer: a multispecies codon optimizer using context-aware neural networks," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58588-7
    DOI: 10.1038/s41467-025-58588-7
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