seq2ribo:机器学习和模拟的结构意识集成,以从RNA序列中预测核糖体位置配置文件
bioRxiv : the preprint server for biology
|February 23, 2026
概括
seq2ribo可以从单独的mRNA序列中准确地预测核糖体位置. 这种新的框架通过改善没有基因组背景的翻译效率预测来增强mRNA疫苗设计.
科学领域:
- 计算生物学 计算生物学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 核糖体动力学对蛋白质表达至关重要,但如核糖体分析 (Ribo-seq) 等当前的方法需要基因组背景,限制了合成生物学中的应用.
- 现有的模拟模型,如完全不对称的简单排除过程 (TASEP),通过只关注码头延长时间,过度简化了翻译.
研究的目的:
- 开发一种新的计算框架,seq2ribo,用于预测只使用mRNA序列作为输入的核糖体A位点位置.
- 通过克服现有方法的局限性,为诸如信使RNA (mRNA) 疫苗之类的应用程序进行新的序列设计.
主要方法:
- 实施了混合模拟和机器学习方法,将结构意识的TASEP (sTASEP) 与精制抛光器模型相结合.
- sTASEP 结合了编码器等待时间和结构特征 (局部角度,基点配对,定位桶) 来模型翻译.
- 抛光器模型改进了由stasep模拟的核糖体分布.
主要成果:
- seq2ribo在各种细胞类型中实现了高保真性核糖体位置预测,超过了现有的基线.
- 与仅使用序列方法相比,显著减少了转录级别错误 (高达35.8%) 和结构错误 (43.3%-97.3%).
- 与实验翻译效率 (高达0.795斯皮尔曼) 和蛋白质表达 (0.689斯皮尔曼) 取得了高相关性.
结论:
- seq2ribo提供了一种强大的,仅用于序列的工具,用于预测核糖体动力学,推进合成生物学和合理的mRNA序列设计.
- 该框架消除了对表达级数据或基因组上下文的需求,为新型应用优化mRNA序列提供了便利.
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