Computational Pipeline to probe NaV1.7 gain-of-functions variants in neuropathic painful syndromes

TL;DR

This paper presents a computational pipeline combining homology modeling, network theory, and machine learning to analyze NaV1.7 variants in patients with painful syndromes, aiding in candidate selection for electrophysiological testing and clinical insights.

Contribution

The study introduces a novel integrated computational pipeline specifically designed for analyzing NaV1.7 variants, improving candidate selection for experimental validation.

Findings

Pipeline effectively identifies candidate variants for electrophysiology.

Optimal template conditions enhance model reliability.

Potential clinical applications in pain 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 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.