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A COVID-19 drug repurposing strategy through quantitative homological similarities using a topological data analysis-based framework

Título : A COVID-19 drug repurposing strategy through quantitative homological similarities using a topological data analysis-based framework
Autor : Pérez Moraga, Raúl
Forés Martos, Jaume
Suay García, Beatriz
Duval, Jean Louis
Falcó Montesinos, Antonio
Climent Bataller, Joan
Materias: SARS-CoV-2 (Virus) - Farmacoterapia.Medicamentos - Reciclado.COVID-19 - Farmacoterapia.COVID-19 (Disease) - Chemotherapy.Homology theory.Topology in pharmacology.Topología en farmacología.SARS-CoV-2 (Virus) - Chemotherapy.Homología, Teoría de.Drugs - Recycling.
Editorial : MDPI
Citación : Pérez-Moraga, R., Forés-Martos, J., Suay-García, B., Duval, J. L., Falcó, A. & Climent, J. (2021). A COVID-19 drug repurposing strategy through quantitative homological similarities using a topological data analysis-based framework. Pharmaceutics, vol. 13, i. 4 (02 apr.), art. 488. DOI: https://doi.org/10.3390/pharmaceutics13040488
Resumen : Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.
Descripción : Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1999-4923/13/4/488
Este artículo de investigación pertenece al número especial "In Silico Strategies for Prospective Drug Repositionings".
URI : http://hdl.handle.net/10637/13588
Derechos: http://creativecommons.org/licenses/by/4.0/deed.es
ISSN : 1999-4923 (Electrónico)
Fecha de publicación : 2-abr-2021
Centro : Universidad Cardenal Herrera-CEU
Aparece en las colecciones: Dpto. Matemáticas, Física y Ciencias Tecnológicas





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