Department of Toxicogenomics, Section Clinical Genomics, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
Research School for Mental Health and Neuroscience (MHeNS), Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands.
BMC Bioinformatics. 2021 Apr 23;22(1):212. doi: 10.1186/s12859-021-04119-2.
Mutation-induced variations in the functional architecture of the NaV1.7 channel protein are causally related to a broad spectrum of human pain disorders. Predicting in silico the phenotype of NaV1.7 variant is of major clinical importance; it can aid in reducing costs of in vitro pathophysiological characterization of NaV1.7 variants, as well as, in the design of drug agents for counteracting pain-disease symptoms.
In this work, we utilize spatial complexity of hydropathic effects toward predicting which NaV1.7 variants cause pain (and which are neutral) based on the location of corresponding mutation sites within the NaV1.7 structure. For that, we analyze topological and scaling hydropathic characteristics of the atomic environment around NaV1.7's pore and probe their spatial correlation with mutation sites. We show that pain-related mutation sites occupy structural locations in proximity to a hydrophobic patch lining the pore while clustering at a critical hydropathic-interactions distance from the selectivity filter (SF). Taken together, these observations can differentiate pain-related NaV1.7 variants from neutral ones, i.e., NaV1.7 variants not causing pain disease, with 80.5[Formula: see text] sensitivity and 93.7[Formula: see text] specificity [area under the receiver operating characteristics curve = 0.872].
Our findings suggest that maintaining hydrophobic NaV1.7 interior intact, as well as, a finely-tuned (dictated by hydropathic interactions) distance from the SF might be necessary molecular conditions for physiological NaV1.7 functioning. The main advantage for using the presented predictive scheme is its negligible computational cost, as well as, hydropathicity-based biophysical rationalization.
NaV1.7 通道蛋白功能结构的突变诱导变异与广泛的人类疼痛障碍有关。NaV1.7 变体的表型预测具有重要的临床意义；它可以帮助降低体外 NaV1.7 变体病理生理学特征的成本，以及设计对抗疼痛疾病症状的药物。
在这项工作中，我们利用疏水性效应的空间复杂性，根据 NaV1.7 结构中对应突变位点的位置，预测哪些 NaV1.7 变体引起疼痛（哪些是中性的）。为此，我们分析了 NaV1.7 孔周围原子环境的拓扑和缩放疏水性特征，并研究了它们与突变位点的空间相关性。我们表明，与疼痛相关的突变位点占据了接近孔内疏水区的结构位置，同时聚集在与选择性过滤器 (SF) 的关键疏水力相互作用距离处。总之，这些观察结果可以将与疼痛相关的 NaV1.7 变体与中性变体（即不会引起疼痛疾病的 NaV1.7 变体）区分开来，其灵敏度为 80.5%，特异性为 93.7%[接收者操作特性曲线下的面积=0.872]。
我们的研究结果表明，保持 NaV1.7 内部疏水区的完整性，以及与 SF 保持精细调节的（由疏水力相互作用决定）距离，可能是 NaV1.7 生理功能的必要分子条件。使用所提出的预测方案的主要优点是其计算成本低，以及基于疏水性的生物物理合理化。
