codonGPT:基于生成语言模型的强化学习可以实现可扩展的mRNA设计
Binita Rajbanshi1, Anuj Guruacharya1
1Nanil Therapeutics Inc., Canada.
Nucleic acids research
|December 19, 2025
概括
我们开发了codonGPT,这是用于信使RNA (mRNA) 设计的生成语言模型. 该模型与强化学习 (RL) 结合,优化mRNA序列,以改善生物应用中的表达,稳定性和GC含量.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 合成生物学 合成生物学
背景情况:
- 强化学习 (RL) 在工程设计中已经确立,但在生物应用中尚未得到充分探索.
- 目前生物学的生成语言模型主要侧重于DNA,RNA或蛋白质,忽视传递 RNA (mRNA).
- 缺乏mRNA的生成模型阻碍了治疗和合成生物学的可扩展设计.
研究的目的:
- 引入第一个生成语言模型,codonGPT,专门训练mRNA序列.
- 开发一种用于mRNA设计的方法,作为一个受约束的语言建模任务.
- 用RL和codonGPT来演示mRNA序列优化.
主要方法:
- 从模型生物中专门在338,417个mRNA序列上训练了codonGPT.
- 引入了一个推断时间掩盖方法来限制mRNA的同名序列.
- 应用强化学习 (RL) 到codonGPT以优化基于生物约束 (表达,稳定性,GC含量) 的mRNA序列.
主要成果:
- 开发了codonGPT,这是mRNA的第一个生成语言模型.
- 对HLA-A和ACTB基因进行了成功的mRNA序列优化.
- 验证了优化GFP和β-乳酸酶等记者基因的方法.
结论:
- codonGPT代表了mRNA的生成建模的重大进步.
- 基于RL的优化框架可以为各种生物应用量身定制的mRNA设计.
- 这项工作为通过优化mRNA序列的增强疗法,合成生物学和蛋白质工程铺平了道路.
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