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

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 o

SUBMITTER: Toffano AA 

PROVIDER: S-EPMC7578092 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature