宇宙:对蛋白质功能的直接和间接影响的位置分辨率因果模型
Jingyou Rao1, Mingsen Wang2, Matthew Howard3,4
1Department of Computer Science, UCLA, Los Angeles, CA, USA.
bioRxiv : the preprint server for biology
|August 13, 2025
概括
作为一个新的贝叶斯框架,Cosmos在多现象型深度突变扫描 (DMS) 实验中解读了复杂的蛋白质变异效应. 它区分了直接和间接的功能影响,提高了我们对分子通路的理解.
科学领域:
- 分子生物学分子生物学
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 深度突变扫描 (DMS) 揭示了蛋白质变异对多个分子功能的影响.
- 当表型通过分子路径联系在一起时,解释变异效应是复杂的,因为下游效应可以是直接的或间接的.
研究的目的:
- 介绍Cosmos,一个贝叶斯的框架,用于多现象型DMS数据中的因果推理.
- 为了使残留水平的因果推断,并区分蛋白质变异的直接和间接功能影响.
- 通过在规范上游后预测下游现象类型来提供反事实解释.
主要方法:
- "宇宙"采用贝叶斯的框架来进行残留水平的因果推断.
- 它使用位置级聚合和贝叶斯模型选择来推断因果结构.
- 该框架不需要表型特定的生物物理假设.
主要成果:
- 在多现象型DMS数据中,Cosmos有效地区分了直接和间接的功能影响.
- 对Kir2.1,PSD95-PDZ3和KRAS数据集的应用证明了它的实用性.
- 该框架成功推断了因果关系,并使反事实预测成为可能.
结论:
- 宇宙提供了一种可概括和可解释的方法,用于解开高通量蛋白功能屏幕中的因果关系.
- 它增强了对蛋白质变异如何影响分子通路的理解.
- 这一框架推进了来自DMS实验的复杂生物数据的分析.
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