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CodonTransformer: a multispecies codon optimizer using context-aware neural networks
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DOI：10.1038/s41467-025-58588-7.png)
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Abstract
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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.
Keywords:
USAGE BIAS
TRANSLATION
EXPRESSION
INDEX
Journal
IF:
15.7
Papers:
9.4W
Citations:
91.2W
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Cited Papers
Large language models generate functional protein sequences across diverse families.png)
NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveCodon usage bias from tRNA's point of view: Redundancy, specialization, and efficient decoding for translation optimizationGENOME RESEARCH
OAAI
GENOME RESEARCHIF5.5
OAAIShareSaveNonoptimal Codon Usage Is Critical for Protein Structure and Function of the Master General Amino Acid Control Regulator CPC-1MBIOOAAI
MBIOIF4.7
OAAIShareSaveShareSaveeRF1 mediates codon usage effects on mRNA translation efficiency through premature termination at rare codonsNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveShareSave
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