Background: 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 adhere pain symptoms. Results: 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 its mutation site 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% sensitivity and 93.7% specificity [area under the receiver operating characteristics curve = 0.872]. Conclusions: 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.

Hydropathicity-Based Prediction of Pain-Causing NaV1.7 Variants / M. Xenakis, D. Kapetis, Y. Yang, M. Gerrits, J. Heijman, S. Waxman, G. Lauria, C. Faber, R. Westra, P. Lindsey, H. Smeets. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - (2021). [Epub ahead of print]

Hydropathicity-Based Prediction of Pain-Causing NaV1.7 Variants

G. Lauria;
2021

Abstract

Background: 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 adhere pain symptoms. Results: 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 its mutation site 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% sensitivity and 93.7% specificity [area under the receiver operating characteristics curve = 0.872]. Conclusions: 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; missense mutations; pain; atomic hydropathicity; computational modeling; cumulative hydropathic topology; scaling; pathogenicity prediction
Settore MED/26 - Neurologia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/822893
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