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

Adibvafa Fallahpour, Vincent Gureghian, Guillaume J. Filion (), Ariel B. Lindner () and Amir Pandi ()
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Adibvafa Fallahpour: Vector Institute for Artificial Intelligence
Vincent Gureghian: Quantitative and Synthetic Biology
Guillaume J. Filion: University of Toronto Scarborough; Department of Biological Science
Ariel B. Lindner: Quantitative and Synthetic Biology
Amir Pandi: Quantitative and Synthetic Biology

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58588-7

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