IN SILICO PREDICTION OF DELETERIOUS AND NON-DELETERIOUS nsSNPs IN CFTR GENE VARIANTS
DOI:
https://doi.org/10.22159/ijpps.2016v8i12.14737Keywords:
Cystic fibrosis, CFTR protein, SIFT, PolyPhen2, I-Mutant2, Homology modelAbstract
Objective: The major objective of the study was to carry out comparative bioinformatics analyses to identify different nsSNPs that were predicted to be deleterious or damaging to the structure and functions of CFTR protein causing cystic fibrosis.
Methods: The CFTR gene variants (nsSNPs) and their related protein sequences from Homo sapiens were subjected to computational analyses using the following bioinformatics tools (a) SIFT: a sequence-homology based prediction tool that can be used to distinguish between the intolerant from tolerant SNP changes. (b) PolyPhen2: a structure and sequence-based physical and comparison tool to study the impact of amino acid substitution on the structure and function of human proteins and (c) I-Mutant2: to predict the protein stability changes arising due to single point mutations.
Results: SIFT, PolyPhen2, and I-Mutant2 analyses indicated that 21 out of 108 nsSNPs were identified to be common that were strongly predicted to be deleterious and damaging for CTFR protein in cystic fibrosis conditions. Most of the substitutions in the CFTR protein contained the amino acids valine followed by cysteine and proline respectively. Homology modeling carried out to determine if any of these nsSNPs had a role in changing the conformation of CFTR protein drastically. Homology modeling of selected nsSNP variants indicated that these substitutions,however did not change the overall CFTR protein structure but predicted to cause severe damaging changes to the phenotypes of CFTR protein. Results indicated that multiple bioinformatics tools are needed to predictthe effect of substitutions and these prediction tools need to be analyzed more into detail and common determination factors are required to predict a nsSNP to be deleterious or damaging to the overall functioning of the CFTR protein.
Conclusion: Multiple bioinformatics tools are in fact the need of the hour to establish if a strong relationship between nsSNPs that could alter the protein stability and cause a deleterious or damaging phenotypic change to the individual with cystic fibrosis involving the CFTR protein.
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Welsh MJ, Tusui LC, Boat TF, Beaudet AI. In: Scriver CR, Beaudet AI, Sly WS, Valle D. eds. Cystic Fibrosis: The Metabolic and Molecular Basis of Inherited Disease. New York: McGraw-Hill Book Co; 1995. p. 3799–876.
Rommens JM, Iannuzzi MC, Kerem B, Drumm ML, Melmer G, Dean M, et al. Identification of the cystic fibrosis gene: chromosome walking and jumping. Science 1989;245:1059–65.
Gabriela MR, Alonso RP, Iris D. XV-2c and KM.19 haplotype analysis in Chilean patients with cystic fibrosis and unknown CFTR gene mutations. Biol Res 2007;40:223-9.
Gibson LE, Cooke RE. A test for concentration of electrolytes in sweat in cystic fibrosis of the pancreas utilizing pilocarpine by iontophoresis. Pediatrics 1959;23:545–9.
Schüler D, Sermet-Gaudelus I, Wilschanski M. Basic protocol for transepithelial nasal potential difference measurements. J Cystic Fibrosis 2004;3:151–5.
Venter JC, Adams MD, Myers EW. The sequence of the human genome. Science 2001;291:1304-51.
Lander ES, Linton LM, Birren B. Initial sequencing and analysis of the human genome. Nature 2001;409:860–921.
Sherry ST, Ward MH, Kholodov. dbSNP: the NCBI database of genetic variation. Nucl Acids Res 2001;29:308–11.
Lohitesh K, Tushar B, Alok Kumar B, Ramanathan K, Shanthi V. In silico investigation of missense mutations in succinate dehydrogenase complex-5 gene using different genomic algorithms. Asian J Pharm Clin Res 2015;8:189-92.
Kesavan Sabitha, Ahmad Kodous, Thangarajan Rajkumar. Computational analysis of mutations in really interesting new gene finger domain and brca1 c terminus domain of breast cancer susceptibility gene. Asian J Pharm Clin Res 2016;9:96-102.
Arnold K, Bordoli L, Kopp J, Schwede T. The swiss-model workspace: a web-based environment for protein structure homology modeling. Bioinformatics 2006;22:195-201.
Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucl Acids Res 2003;31:3812–4.
Adzhubei I A, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010;7:248-9.
Capriotti E, Fariselli P, Casadio R. I-Mutant2.0:Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005;33:306-10.
Guex N, Peitsch MC. Swiss-model and the swiss-Pdb viewer: an environment for comparative protein modeling. Electro-phoresis 1997;18:2714-23.
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-402.
George Priya Doss C, Rajasekaran R, Sudandiradoss C, Ramanathan K, Purohit R, Sethumadhavan R. A novel computational and structural analysis of nsSNPs in CFTR gene. Genomic Med 2008;2:23–32.
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