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bioRxiv · 10.1101/2025.08.19.671089

Advancing Codon Language Modeling with Synonymous Codon Constrained Masking

Heuschkel, J.·Kingsley, L.·Pefaur, N.·Nixon, A.·Cramer, S.
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Abstract

Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.

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Heuschkel, J., Kingsley, L., Pefaur, N., Nixon, A., Cramer, S.. 2025-08-24. Advancing Codon Language Modeling with Synonymous Codon Constrained Masking. https://doi.org/10.1101/2025.08.19.671089

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