Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Alberto A. Toffano, Giacomo Chiarot, Stefano Zamuner, Margherita Marchi, Erika Salvi, Stephen G. Waxman, Catharina G. Faber, Giuseppe Lauria, Achille Giacometti, Marta Simeoni

Research output: Contribution to journalArticlepeer-review

Abstract

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.

Original languageEnglish
Article number17930
JournalScientific Reports
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 1 2020

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes'. Together they form a unique fingerprint.

Cite this