Saadat Ali, Fellay Jacques
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
iScience. 2025 Jun 4;28(7):112812. doi: 10.1016/j.isci.2025.112812. eCollection 2025 Jul 18.
Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, while autosomal recessive diseases are primarily linked to loss-of-function variants. In this study, we introduce a graph-of-graphs approach that leverages protein-protein interaction networks and high-resolution protein structures to predict the mode of inheritance of diseases caused by variants in autosomal genes and to classify dominant-associated proteins based on their functional effect. Our approach integrates graph neural networks, structural interactomics, and topological network features to provide proteome-wide predictions, thus offering a scalable method for understanding genetic disease mechanisms.
遗传疾病可根据其遗传模式和潜在分子机制进行分类。常染色体显性疾病通常由导致功能丧失、功能获得或显性负效应的DNA变异引起，而常染色体隐性疾病主要与功能丧失变异有关。在本研究中，我们引入了一种图中图方法，该方法利用蛋白质-蛋白质相互作用网络和高分辨率蛋白质结构来预测常染色体基因变异导致的疾病的遗传模式，并根据其功能效应对显性相关蛋白质进行分类。我们的方法整合了图神经网络、结构相互作用组学和拓扑网络特征，以提供全蛋白质组预测，从而为理解遗传疾病机制提供了一种可扩展的方法。
