Predicting mutation-disease associations through protein interactions via deep learning

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet-lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule networks and Graph topology networks with multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and in order to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by Swiss-model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset and proposes CGM to predict the protein mutations-disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.