In silico studies of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of NRL gene

Divanshu, G.; Lekshmi, Maya; Shanthi, V.

doi:10.1007/s13721-014-0059-9

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Therefore, detection of functionally deleterious nsSNPs in TP63 protein may provide important information regarding multiple diseases, particularly different types of cancers. Different computational tools for predicting deleterious SNPs and their role in protein function, stability and structure maintenance are extensively used [9][10][11][12] . Researchers have determined several harmful nsSNPs of human CHK2 gene using computational tools to predict the structural and functional impact on the protein 13 .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Akter

Hossain

Hosen

et al. 2021

Preprint

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Single Nucleotide Polymorphisms (SNPs) help to understand the phenotypic variations in humans. Numerous studies have examined the association of SNPs with various complex diseases. Researchers have identified the association of SNPs of genes through Genome-wide association study (GWAS). A number of GWAS have identified a loci located in the TP63 gene to be significantly associated with the risk of urinary bladder cancer. However, there is not any study characterizing the SNPs located at the TP63 gene for their functional and structural significance. Hence, the study aimed to comprehensively characterize SNPs in the human TP63 gene for their functional and structural significance. We investigated and evaluated the genomic variations affecting the expression, structure, and function of the TP63 protein. The study systematically retrieved nsSNPs information for the TP63 gene from the dbSNP database. We screened and analyzed both nsSNPs and non-coding SNPs in TP63 protein using a wide variety of computational tools to find the risk of pathogenicity. A total of 17 nsSNPs were identified using the 13 bioinformatics tools (i.e., SIFT, CADD, PROVEAN, PolyPhen2, PANTHER, PhD-SNP, SNP&GO, I-Mutant 2.0, ClinVar, Mutpred2, ConSurf, HOPE, and Mutation 3D) along with domain analysis. These pathogenic mutations cause a decrease in protein stability according to I-Mutant2.0. HOPE predicted 17 SNPs to have significant effect on TP63 protein structure and function. 12 nsSNPs were found in highly conserved position in TP63 inferring the damaging effect on the structure and function of the protein. Swiss PDB Viewer showed loss of hydrogen bonds and increased energy due to the SNPs. Molecular docking showed the reduction of the binding affinity of proteins for DNA and loss of hydrogen bonds. Six non-coding SNPs were found in miRNA binding sites in gene showing the effect on protein regulation using PolymiRTS and five non-coding SNPs were identified in single tissue expression quantitative trait loci (eQTL) in lung tissue, heart tissue (LV), and cerebral hemisphere (Brain) according to GTEx portal. The characterization of nsSNPs and non-coding SNPs will support researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Akter

Hossain

Hosen

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Different computational tools and approaches to predict deleterious SNPs and their roles in protein stability, function, and structure are extensively used [ 16 , 17 , 18 , 19 ]. One study investigated the nsSNPs of the human CHK2 gene using computational tools to predict the structural and functional impacts on the protein [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

et al. 2021

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Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

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Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensivein silicoapproach

Akter

Roy

Tonmoy

et al. 2021

Journal of Biomolecular Structure and Dynamics

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In silico studies of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of NRL gene

Cited by 3 publications

References 23 publications

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Comprehensive Characterization of the Coding and Non-coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensivein silicoapproach

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