GraphLOGIC: Lethality prediction of osteogenesis imperfecta on type I collagen by a mechanics-informed graph neural network.

Collagen plays a crucial role in human bodies and has a significant presence in connective tissues. As such, the impact of collagen mutations can be devastating. Osteogenesis imperfecta (OI), a rare genetic disease affecting 1 in every 15,000 to 20,000 people, is one such example characterized by brittle bones. Severe cases of OI could lead to prenatal death. Previous studies have provided insights into the impact of mutations on collagen molecules and predictions of lethality. However, these discussions have focused mainly on mutations in the α1 chain, and some mutation types exhibit poor predictive performance. Coverage of α2 mutations is also limited. We propose a method to predict the risk of lethality for OI-inducing mutations, where a novel mechanics-informed graph representation of the collagen fibril is proposed based on full atomistic simulations to encode sequential and structural information. The method demonstrated improved accuracy in predicting the risk of lethality associated with mutations occurring on both α1 and α2chains. We also found a correlation between the sequences and the predicted OI lethality with the use of a variant of the Grad-CAM technique, where the results agree well with previous studies. Our findings provide insights into the molecular mechanism of collagen on OI lethality.