Figure 2 - uploaded by Naresh Kandakatla
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Normal mode analysis of c-alpha carbon atom of native and mutant NPC1. (A) Native, (B) R1186C, (C) S940L, (D) R958Q, (E) I1061T.
Source publication
Niemann-Pick disease type C1 (NPC1), caused by mutations of NPC1 gene, is an inherited lysosomal lipid
storage disorder. Loss of functional NPC1 causes the accumulation of free cholesterol (FC) in endocytic organelles that
comprised the characteristics of late endosomes and/or lysosomes. In this study we analyzed the pathogenic effect of 103
nsSNPs...
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... relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. In WEBnm analysis we observed mode 7 which has lower deformation energy than other mode. Native and mutant fluctuation in mode 7 is shown in Fig. 2(A)-(E). The mutant structures showed more motions and flexibility behavior than native structure. It confirms that due to mutation, NPC1 structure became more flexible in nature and because of this structural flexibility it may lose the correct function and leads to cause Niemann-Pick disease type C1. Our analysis suggests the most ...
Citations
... studies focused on variants of one or more genes belonging to several genetic disorders. While some studies aimed to investigate variants in multiple disorders and a single gene (Arshad et al., 2018;Islam et al., 2019;Porto et al., 2015;Shen et al., 2006) or multiple gene and a single disorder (Masoodi et al., 2013;Pandey et al., 2019), others aimed to analyze variants in a single gene and disorder (Abdul Samad et al., 2016;Chitrala and Yeguvapalli, 2014;Kandakatla et al., 2014;Khan et al., 2013;Kumar et al., 2013;Nagarajan et al., 2020;Naveed et al., 2016Naveed et al., , 2017Owji et al., 2020;Ponzoni et al., 2020a;Sang et al., 2017;Yadegari and Majidzadeh, 2019). Interpretations from these studies are generally related to individual genes, while some general consequences could also be derived from them. ...
Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.
Wolfram syndrome (WS) is a rare, progressive, neurodegenerative disorder that has an autosomal recessive pattern of inheritance. The gene for WS, wolfram syndrome 1 gene (WFS1), is located on human chromosome 4p16.1 and encodes a transmembrane protein. To date, approximately 230 mutations in WFS1 have been confirmed, in which nonsynonymous single nucleotide polymorphisms (nsSNPs) are the most common forms of genetic variation. Nonetheless, there is poor knowledge on the relationship between SNP genotype and phenotype in other nsSNPs of the WFS1 gene. Here, we analysed 395 nsSNPs associated with the WFS1 gene using different computational methods and identified 20 nsSNPs to be potentially pathogenic. Furthermore, to identify the amino acid distributions and significances of pathogenic nsSNPs in the protein of WFS1, its transmembrane domain was constructed by the TMHMM server, which suggested that mutations outside of the TMhelix could have more effects on protein function. The predicted pathogenic mutations for the nsSNPs of the WFS1 gene provide an excellent guide for screening pathogenic mutations.
