A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A

Frontiers in Bioinformatics

Bioinformatics

Tiago J. S. Lopes¹  Tatiane Nogueira²  Ricardo A. Rios²  Marcos V. Ferreira² 

[1] Center for Regenerative Medicine, National Center for Child Health and Development Research Institute, Tokyo, Japan;Institute of Computing, Federal University of Bahia, Salvador, Brazil;

关键词: protein structure;    machine learning;    bioinformatics;    residue network;    FVII;    FVIIIa;    graph neural network;   

DOI  :  10.3389/fbinf.2023.1152039

 received in 2023-01-27, accepted in 2023-04-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)—a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications.Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure.Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results.Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

【 授权许可】

Unknown   
Copyright © 2023 Ferreira, Nogueira, Rios and Lopes.