# Proteome-wide prediction of the mode of inheritance and molecular mechanisms underlying genetic diseases using structural interactomics

## TL;DR

This paper presents a new method using protein networks and structures to predict how genetic diseases are inherited and their molecular causes.

## Contribution

A novel graph-of-graphs approach combining structural interactomics and graph neural networks for proteome-wide disease inheritance prediction.

## Key findings

- The method predicts mode of inheritance and functional effects of genetic variants across autosomal proteins.
- Feature attribution provides biological insights into disease mechanisms.
- Proteome-wide predictions are publicly available for broad use.

## 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.

•A graph-of-graphs approach integrates multi-scale protein information•Graph neural networks predict both mode of inheritance and functional mechanisms•Feature attribution reveals biological insights into genetic disease mechanisms•Models were applied to all autosomal proteins, and predictions are publicly available

A graph-of-graphs approach integrates multi-scale protein information

Graph neural networks predict both mode of inheritance and functional mechanisms

Feature attribution reveals biological insights into genetic disease mechanisms

Models were applied to all autosomal proteins, and predictions are publicly available

molecular interaction; molecular network; neural networks; quantitative genetics