一种生成语言模型解码了mRNA设计的编码子选择的上下文约束
bioRxiv : the preprint server for biology
|June 4, 2025
概括
一个新的语言模型Trias通过从数据中学习复杂的编码子使用规则来优化合成mRNA序列. 它提高了mRNA稳定性和蛋白质输出,优于现有的工具.
科学领域:
- 计算生物学和生物信息学
- 分子生物学和遗传学
- 合成生物学 合成生物学
背景情况:
- 遗传密码的退化允许同名的编码子,在蛋白质编码基因中产生序列多样性.
- 子的选择影响mRNA功能和蛋白质的产生,这对于推进mRNA技术至关重要.
- 现有的编码子优化方法无法捕捉编码子使用中的复杂上下文模式.
研究的目的:
- 开发一种新的语言模型,Trias,用于理解和预测上下文依赖的代码子使用.
- 为了生成符合生物约束的特定物种的编码子序列,并提高mRNA性能.
- 为优化合成mRNA设计提供数据驱动的框架.
主要方法:
- 在数以百万计的真核生物编码序列上训练了一个编码解码语言模型 (Trias).
- 在序列数据中整合了本地和全球依赖关系,以学习codon使用规则.
- 评估了Trias产生的序列与mRNA稳定性,核糖体负载和蛋白质输出的实验测量.
主要成果:
- 在没有明确的蛋白质表达训练的情况下,Trias学会了复杂的编码子使用模式.
- 来自Trias的生成序列和得分与mRNA稳定性和蛋白质输出的实验测量有很强的相关性.
- 在产生高表达性编码子序列变异方面,Trias在商业编码子优化工具上表现出色.
结论:
- 特里亚斯为合成mRNA设计的编码子优化提供了一种强大的数据驱动的方法.
- 该模型提供了对控制子选择的分子和进化原理的见解.
- 这一框架推进了合成mRNA的设计,提高了稳定性和蛋白质表达.
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