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Article Abstract

Usher syndrome is an inherited condition that causes hearing and visual impairments, along with vestibular dysfunction, due to mutations in various genes, including , which codes for the scaffold protein SANS, essential for proper sensory function. This study employed a computational approach in order to analyze the potential impact of missense SNPs in . We started by curating and filtering SNPs from the Ensembl database, followed by a variety of computational prediction methods, such as SIFT, PolyPhen-2, MetaLR, BayesDel_addAF, and MutationTaster, to identify the pathogenic impact of the nsSNPs. Then, we used CUPSAT, DUET, I-stable, I-Mutant, MUpro, and E-SNPs&GO to assess the stability of the altered proteins. To determine their conservation state, we used NCBI BLASTP. Out of 499 missense SNPs, only 5 (L396P, L426F, G434W, R436Q, and R446Q) were identified as most impactful and were subjected to molecular dynamics (MD) simulations (RMSD, RMSF, Rg, PCA, and FEL) to fully understand how these variations affect the dynamic behavior of our protein. Lastly, we conducted a Wilcoxon rank-sum test on RMSD values of the MD simulations. This analysis provided important insight into how the deleterious SNPs impact the protein's structural stability. This computational study provides a framework for identifying potentially deleterious mutations, understanding the pathological foundation of the pathology, and guiding future experimental research.

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http://dx.doi.org/10.1080/07391102.2025.2553345DOI Listing

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Computational study of deleterious missense SNPs in the gene implicated in Usher syndrome.

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Usher syndrome is an inherited condition that causes hearing and visual impairments, along with vestibular dysfunction, due to mutations in various genes, including , which codes for the scaffold protein SANS, essential for proper sensory function. This study employed a computational approach in order to analyze the potential impact of missense SNPs in . We started by curating and filtering SNPs from the Ensembl database, followed by a variety of computational prediction methods, such as SIFT, PolyPhen-2, MetaLR, BayesDel_addAF, and MutationTaster, to identify the pathogenic impact of the nsSNPs.

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