Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

Èric Catalina-Hernández, Mario López-Martín, David Masnou-Sánchez, Marco Martins, Victor A Lorenz-Fonfria, Francesc Jiménez-Altayó, Ute A Hellmich, Hitoshi Inada, Antonio Alcaraz, Yuji Furutani, Alfons Nonell-Canals, Jose Luis Vázquez-Ibar, Carmen Domene, Rachelle Gaudet, Alex Perálvarez-Marín

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

2 Citas (Scopus)

Resumen

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.
Idioma originalInglés
Páginas (desde-hasta)473-482
Número de páginas10
PublicaciónComputational and Structural Biotechnology Journal
Volumen23
Fecha en línea anticipada29 dic 2023
DOI
EstadoPublicación electrónica previa a su impresión - 29 dic 2023

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