论文标题
探测NAV1.7的计算渠道，神经性疼痛综合征中的功能障碍变体
Computational Pipeline to probe NaV1.7 gain-of-functions variants in neuropathic painful syndromes
论文作者
论文摘要
由于数据可用性庞大和前所未有的技术发展，机器学习和图理论技术对神经科学的应用在过去十年中引起了人们的兴趣。他们的工作来研究编码基因的突变变化对调节可激发细胞膜的蛋白质的作用，该蛋白会在电生理水平上评估其生物学相关性，可以提供有用的预测线索。我们通过实施专用的计算管道，赋予慢性疼痛综合症患者的钠通道NAV1.7亚基的变体分析，从而在慢性疼痛综合症患者中发现了这种概念。通过测试不同原点和序列身份的三个模板，我们为其使用提供了最佳条件。我们的发现揭示了我们计算管道在支持细胞电生理测定和潜在临床应用中选择候选物的有用性。
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.
