CodonRL:使用示范引导的强化学习优化多目标Codon序列
bioRxiv : the preprint server for biology
|February 23, 2026
概括
作为一种新的强化学习框架,CodonRL优化了mRNA序列,以提高翻译效率和稳定性. 它通过学习结构前置和实现用户控制的多目标权衡,优于现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 合成生物学 合成生物学
- 生物信息学是一种生物信息学.
背景情况:
- 优化mRNA序列的翻译效率,RNA稳定性和组合性质是复杂的,因为巨大的搜索空间和交互的目标.
- 现有的方法,如动态编程,难以扩展,而深度生成模型需要广泛的训练数据,强化学习面临延迟奖励和大行动空间的挑战.
研究的目的:
- 开发一个强化学习框架,CodonRL,以实现高效和可定制的mRNA序列优化.
- 解决现有方法的局限性,将结构先验纳入,并在推理过程中实现多目标权衡.
主要方法:
- 科登RL利用强化学习与高效的折叠反和示范引导的重复来学习mRNA设计的结构先验.
- 它使用LinearFold在训练期间快速计算中间奖励,并使用ViennaRNA进行最终评估.
- 基于里程碑的中间奖励和专家序列热身加速了融合,并在长期优化中处理延迟反.
主要成果:
- 与GEMORNA相比,CodonRL在55种人类蛋白质上表现优越.
- 平均达到9.5%更高的编码子适应指数 (CAI),25.4千卡/mol更有利的最小自由能量 (MFE) 和3.4%更低的尿素含量.
- 在匹配约束下,超过90%的基准蛋白质中改善了子稳定系数 (CSC).
结论:
- CodonRL提供了一个强大的框架来设计mRNA序列,以提高翻译效率,结构稳定性和降低免疫性.
- 该方法允许在推断时进行持续的客观重量调整,在mRNA设计中提供灵活性.
- CodonRL代表了计算mRNA设计的重大进步,特别是在训练数据稀缺的场景中.
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