Ferreira Marcos V, Nogueira Tatiane, Rios Ricardo A, Lopes Tiago J S
Institute of Computing, Federal University of Bahia, Salvador, Brazil.
Center for Regenerative Medicine, National Center for Child Health and Development Research Institute, Tokyo, Japan.
Front Bioinform. 2023 May 10;3:1152039. doi: 10.3389/fbinf.2023.1152039. eCollection 2023.
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. 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. 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 alanine mutations, once more observing a close agreement between the and the results. Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.
血液凝固是人类和其他物种止血的重要过程。这种机制的特点是血管受伤后，十几种成分会发生分子级联反应。在这个过程中，凝血因子VIII（FVIII）是主要调节因子，可将其他成分的活性提高数千倍。从这个意义上说，即使是单个氨基酸替换也会导致A型血友病（HA），这并不奇怪。A型血友病的特征是出血不受控制，患者长期面临出血并发症的风险。尽管最近在HA的诊断和治疗方面取得了进展，但FVIII蛋白每个残基的确切作用仍不清楚。在本研究中，我们开发了一种基于图的机器学习框架，详细探索了FVIII蛋白残基形成的网络，其中每个残基是一个节点，如果两个节点在FVIII三维结构中彼此靠近，则它们相互连接。使用这个系统，我们确定了导致该疾病严重和轻度形式的特性。最后，为了推动新型重组治疗性FVIII蛋白的开发，我们调整了框架以预测300多个丙氨酸突变的活性和表达，再次观察到预测结果与实验结果之间的密切一致性。总之，本研究结果证明了基于图的分类器如何能够推动罕见病的诊断和治疗。
