Equi-mRNA: Protein Translation Equivariant Encoding for mRNA Language Models

TL;DR

Equi-mRNA is a novel equivariant mRNA language model that explicitly encodes codon symmetries, improving prediction accuracy, sequence realism, and interpretability for mRNA therapeutics and synthetic biology.

Contribution

It introduces the first codon-level equivariant model leveraging group theory to encode genetic symmetries, outperforming baseline models in various tasks.

Findings

Up to 10% accuracy improvement in property prediction tasks.

Approximately 4x more realistic mRNA sequence generation.

Recapitulates known biological biases and offers new insights.

Abstract

The growing importance of mRNA therapeutics and synthetic biology highlights the need for models that capture the latent structure of synonymous codon (different triplets encoding the same amino acid) usage, which subtly modulates translation efficiency and gene expression. While recent efforts incorporate codon-level inductive biases through auxiliary objectives, they often fall short of explicitly modeling the structured relationships that arise from the genetic code's inherent symmetries. We introduce Equi-mRNA, the first codon-level equivariant mRNA language model that explicitly encodes synonymous codon symmetries as cyclic subgroups of 2D Special Orthogonal matrix (SO(2)). By combining group-theoretic priors with an auxiliary equivariance loss and symmetry-aware pooling, Equi-mRNA learns biologically grounded representations that outperform vanilla baselines across multiple axes. On downstream property-prediction tasks including expression, stability, and riboswitch switching Equi-mRNA delivers up to approximately 10% improvements in accuracy. In sequence generation, it produces mRNA constructs that are up to approximately 4x more realistic under Frechet BioDistance metrics and approximately 28% better preserve functional properties compared to vanilla baseline. Interpretability analyses further reveal that learned codon-rotation distributions recapitulate known GC-content biases and tRNA abundance patterns, offering novel insights into codon usage. Equi-mRNA establishes a new biologically principled paradigm for mRNA modeling, with significant implications for the design of next-generation therapeutics.