Modeling Protein Activities and Mutations with Graph Neural Networks: Insights into Hemophilia

Hemophilia is a genetic disorder characterized by mutations in coagulation factor proteins, resulting in impaired blood coagulation. Despite recent advances in diagnosing and treating Hemophilia, the precise function of each residue within such proteins remains unclear. In this work, aiming to provide a crucial step to addressing this complex problem, we present a systematic approach that organizes the coagulation factor proteins into graphs to be later modeled by novel Graph Neural Networks (GNN). In our graphs, nodes represent amino acids, which can be connected by proximity in the three-dimensional structure. Our experiments on FVIII (coagulation factor VIII) proteins, responsible for causing Hemophilia A, presented significant results in predicting severe and mild forms of the disease. Moreover, focused on supporting the advance of novel recombinant therapeutic FVIII proteins, we adapted our study to predict the activity and expression of more than 300 in vitro alanine mutations. Besides innovating the form in which hemophilia is organized and modeled, this work presents a GNN architecture specifically designed to optimize the classification of FVIII proteins. Our results emphasize how graph-based approaches support a deeper understanding of Hemophilia A, thus providing a solid scientific basis for further research in this field.