Hydropathicity-based prediction of pain-causing NaV1.7 variants
Makros N Xenakis1,2, Dimos Kapetis3, Yang Yang4,5
1Department of Toxicogenomics, Section Clinical Genomics, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands. mrksxenakis@gmail.com.
BMC Bioinformatics
|April 24, 2021
Summary
Predicting pain-causing NaV1.7 variants is crucial for pain disorder treatment. This study uses hydropathic effects and mutation site location to accurately identify pain-related NaV1.7 variants, aiding drug development.
Area of Science:
- Molecular Biology
- Biophysics
- Computational Biology
Background:
- NaV1.7 channel mutations are linked to human pain disorders.
- Predicting NaV1.7 variant phenotypes in silico is clinically important for pain management and drug design.
Purpose of the Study:
- To predict NaV1.7 variants causing pain based on mutation site location within the channel structure.
- To reduce the need for costly in vitro characterization of NaV1.7 variants.
Main Methods:
- Analysis of topological and scaling hydropathic characteristics around the NaV1.7 pore.
- Spatial correlation of mutation sites with hydropathic features and distance from the selectivity filter (SF).
Main Results:
- Pain-related mutation sites are located near a hydrophobic pore patch and at a specific distance from the SF.
- The method achieved 80.5% sensitivity and 93.7% specificity in differentiating pain-related from neutral NaV1.7 variants (AUC = 0.872).
Conclusions:
- Maintaining a hydrophobic NaV1.7 interior and a specific distance from the SF are vital for physiological function.
- The proposed predictive scheme offers negligible computational cost and a biophysical rationale based on hydropathicity.
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