Proteome-wide prediction of mode of inheritance and molecular mechanism underlying genetic diseases using structural interactomics

Ali Saadat, Jacques Fellay

Keywords: Mode of inheritance, Functional effect, Genetic diseases mechanism, Graph neural networks, Graph-of-graphs, Structural interactomics

TL;DR: We used a graph-of-graphs approach to combine protein-protein interaction network with protein structures. Then we used graph neural networks to predict mode-of-inheritance and functional effects.

Abstract: 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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 3556