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DirectContacts2:来自高通量质谱实验的直接物理蛋白相互作用网络

Erin R Claussen¹, Miles D Woodcock-Girard¹, Samantha N Fischer¹

¹Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607.

bioRxiv : the preprint server for biology

|August 6, 2025

概括

DirectContacts2是一个新的机器学习模型,可以识别细胞内的直接蛋白质相互作用. 该工具创建了详细的蛋白质相互作用图,有助于了解细胞功能和疾病机制.

科学领域:

蛋白质组学是指蛋白质组学.
计算生物学计算生物学
结构生物学结构生物学

背景情况:

细胞功能依赖于由直接的物理相互作用形成的蛋白质复合体.
像AlphaFold这样的预测模型具有先进的交互建模,但一个完整的蛋白质组范围的地图在计算上具有挑战性.
直接蛋白相互作用的全面地图对于生物研究和药物发现至关重要.

研究的目的:

开发一个机器学习模型,DirectContacts2,用于区分直接和间接的蛋白质相互作用.
为了创建人类蛋白质组的高精度蛋白质相互作用图.
为了使疾病相关的蛋白质复合体的结构建模,并了解突变的影响.

主要方法:

开发了DirectContacts2,这是一个机器学习模型,其训练来自25,000多个质谱学实验的特征.
应用该模型分析大约2600万对人类蛋白质对.
通过像AlphaFold3.3这样的结构建模工具来整合发现.

主要成果:

在识别直接蛋白质相互作用方面,DirectContacts2显著优于以前的方法.
该模型成功识别了大约2500个新的精确结构模型.
启用了与疾病相关的复合体的结构建模,例如耳面数字综合征 (OFDS) 复合体.

结论:

DirectContacts2提供了一个非常准确的细胞蛋白线路图.
该模型提供了对由致病突变引起的疾病分子机制的见解,如OFD1.1.
这种资源将加速对蛋白相互作用的选,并促进我们对细胞过程的理解.

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