Prediction of hemophilia A severity using a small-input machine-learning framework

Abstract Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracr...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tiago J. S. Lopes, Ricardo Rios, Tatiane Nogueira, Rodrigo F. Mello
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fb1b7a4cc993467793807c51e24d59c8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fb1b7a4cc993467793807c51e24d59c8
record_format	dspace
spelling	oai:doaj.org-article:fb1b7a4cc993467793807c51e24d59c82021-12-02T16:53:20ZPrediction of hemophilia A severity using a small-input machine-learning framework10.1038/s41540-021-00183-92056-7189https://doaj.org/article/fb1b7a4cc993467793807c51e24d59c82021-05-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00183-9https://doaj.org/toc/2056-7189Abstract Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.Tiago J. S. LopesRicardo RiosTatiane NogueiraRodrigo F. MelloNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tiago J. S. Lopes Ricardo Rios Tatiane Nogueira Rodrigo F. Mello Prediction of hemophilia A severity using a small-input machine-learning framework
description	Abstract Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.
format	article
author	Tiago J. S. Lopes Ricardo Rios Tatiane Nogueira Rodrigo F. Mello
author_facet	Tiago J. S. Lopes Ricardo Rios Tatiane Nogueira Rodrigo F. Mello
author_sort	Tiago J. S. Lopes
title	Prediction of hemophilia A severity using a small-input machine-learning framework
title_short	Prediction of hemophilia A severity using a small-input machine-learning framework
title_full	Prediction of hemophilia A severity using a small-input machine-learning framework
title_fullStr	Prediction of hemophilia A severity using a small-input machine-learning framework
title_full_unstemmed	Prediction of hemophilia A severity using a small-input machine-learning framework
title_sort	prediction of hemophilia a severity using a small-input machine-learning framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fb1b7a4cc993467793807c51e24d59c8
work_keys_str_mv	AT tiagojslopes predictionofhemophiliaaseverityusingasmallinputmachinelearningframework AT ricardorios predictionofhemophiliaaseverityusingasmallinputmachinelearningframework AT tatianenogueira predictionofhemophiliaaseverityusingasmallinputmachinelearningframework AT rodrigofmello predictionofhemophiliaaseverityusingasmallinputmachinelearningframework
_version_	1718382895756738560

Prediction of hemophilia A severity using a small-input machine-learning framework

Ejemplares similares