首个考虑密码子对称性的mRNA语言模型，提升翻译效率预测与序列生成质量。
Equi-mRNA: Protein Translation Equivariant Encoding for mRNA Language Models
- 基于群论构建密码子旋转对称性编码，显式建模遗传密码的结构特性。
- 在表达、稳定性等任务上准确率提升约10%，生成序列真实度提高4倍。
- 适合基因工程、药物设计领域研究者，可解释性强，揭示密码子使用规律。
mRNA治疗和合成生物学的发展迫切需要能捕捉同义密码子（编码相同氨基酸的不同三联体）使用模式的模型，这些模式微妙地调控翻译效率和基因表达。现有方法虽引入密码子层面归纳偏置，但未能显式建模遗传密码固有的对称性所导致的结构关系。本文提出Equi-mRNA，首个在密码子层级具备等变性的mRNA语言模型，将同义密码子对称性显式编码为二维特殊正交群SO(2)的循环子群。通过结合群论先验、辅助等变性损失及对称感知池化，Equi-mRNA学习到具有生物学意义的表示，在多个下游任务中表现优于基线模型。在表达、稳定性及核糖开关切换等属性预测任务中，准确率最高提升约10%；在序列生成方面，其生成的mRNA在Frechet BioDistance指标下真实度提升约4倍，功能特性保留率比基线高约28%。可解释性分析显示，学习到的密码子旋转分布再现了已知的GC含量偏倚与tRNA丰度模式，为密码子使用提供了新见解。Equi-mRNA建立了一种新的生物原理驱动的mRNA建模范式，对下一代治疗药物设计具有重要意义。
原文摘要 · Abstract (English)
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.
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