深度生成优化mRNA编码子序列,以提高mRNA翻译和治疗疗效
Yupeng Li1, Fan Wang1, Jiaqi Yang1
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Nature communications
|November 12, 2025
概括
深度学习工具RiboDecode优化传递 RNA (mRNA) 序列,以显著提高蛋白质的生产. 这种人工智能驱动的方法增强了mRNA疗法,从而导致更强大,更有效的治疗方法.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 使者RNA (mRNA) 疗法具有显著的潜力,但由于蛋白质表达效率低下而受到阻碍.
- 优化mRNA序列对于提高治疗疗效和降低剂量要求至关重要.
研究的目的:
- 介绍RiboDecode,这是一个深度学习框架,用于设计增强蛋白质翻译的mRNA序列.
- 证明RiboDecode在各种应用中提高mRNA治疗功效和效率的能力.
主要方法:
- 开发了RiboDecode,使用大规模的核糖体分析数据和生成序列探索.
- 通过在不同基因和细胞环境中的in silico分析验证了RiboDecode的预测准确性.
- 在体外和体内测试,以评估蛋白质表达的改善和治疗结果.
主要成果:
- RiboDecode在体外显著增强了蛋白质表达,超过了现有的方法.
- 在体内研究显示,流感mRNA的抗体反应增加了十倍,神经生长因子mRNA的降低剂量相当于神经保护.
- 该框架在修改的mRNA格式 (例如m1Ψ修改的圆形mRNA) 和细胞环境中表现出强大的性能.
结论:
- RiboDecode代表了一种数据驱动,上下文意识的mRNA序列设计方法,从传统的基于规则的方法转变.
- 这一框架使得开发更强效和剂量有效的mRNA疗法成为可能.
- RiboDecode对于在各种医学领域推进基于mRNA的治疗方法具有广泛的适用性.
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