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. 2024 Feb 23;19(2):e0297367.
doi: 10.1371/journal.pone.0297367. eCollection 2024.

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene

Sana Waheed  1 , Kainat Ramzan  2 , Sibtain Ahmad  3 , Muhammad Saleem Khan  1 , Muhammad Wajid  1 , Hayat Ullah  4 , Ali Umar  1 , Rashid Iqbal  5 , Riaz Ullah  6 , Ahmed Bari  7
Affiliations

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene

Sana Waheed et al. PLoS One. .

Erratum in

Abstract

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Entire workflow for nsSNPs screening in the SCN9A gene using computational tools.
Fig 2
Fig 2. Pie chart distribution of Single nucleotide polymorphisms (SNPs) in SCN9A gene.
Fig 3
Fig 3
Prediction of functional consequences of nsSNPs by A) SIFT and B) PolyPhen.
Fig 4
Fig 4. Pie chart displaying the prevalence of deleterious missense mutations.
Evaluation of 15 In silico tools reveals the percentage and numerical quantity of deleterious nsSNPs.
Fig 5
Fig 5. Procheck-RAMACHANDRAN plot of the native SCN9A predicted model.
Fig 6
Fig 6. Interaction of protein ligands with typical SCN9A and mutant D1778N, F1782V, L1802P, M939V, and V1311M.
See this image and copyright information in PMC

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