推进代码子语言建模与同义代码子受约束的掩盖
James Heuschkel1,2, Laura Kingsley1, Noah Pefaur1
1Biotherapeutics Discovery Department, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT 06877, United States.
Nucleic acids research
|February 25, 2026
概括
SynCodonLM是一种新的编码器语言模型,可以将编码器和氨基酸的含义分开,以便更好地分析DNA序列. 这个模型提高了对DNA水平生物学和蛋白质表达的理解.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 当前的编码器语言模型经常将编码器的使用与氨基酸语义混合在一起,阻碍了DNA水平的生物学见解.
- 现有的模型由于混杂的语义信息,难以捕捉核酸特异性模式.
研究的目的:
- 介绍SynCodonLM,一种新的编码器语言模型,旨在解开编码器级和蛋白质级语义.
- 通过强制执行生物学上有基础的约束来实现核酸特异性模式的学习.
主要方法:
- 开发了SynCodonLM,这是一个具有约束的编码器语言模型,确保只能从同义选项中预测面具编码器,以蛋白质序列为指导.
- 实施了掩盖策略,将非同义编码子排除在 softmax 之前的预测空间之外.
- 修改了聚类,通过核酸特性而不是氨基酸身份来分组编码子.
主要成果:
- SynCodonLM成功地解开了编码子和蛋白质语义,学习了核酸特定的模式.
- 该模型通过其集群方法揭示了与DNA级别属性一致的生物结构.
- 在七个基准值中,SynCodonLM在六个对DNA级特征敏感的基准值,包括mRNA和蛋白质表达的现有模型中表现优于现有模型.
结论:
- SynCodonLM代表了对DNA序列的域特定表示学习的进步.
- 该模型为合成生物学中的序列设计和对生物过程的更深入探索提供了新的可能性.
- 这种方法提高了通过尊重生物约束来建模DNA序列的能力.
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