Computational Screening and Exploration of Disease‐Associated Genes in Alzheimer's Disease. Issue 6 (10th January 2017)

• Record Type: Journal Article
• Authors: Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav
• Is Part Of: Journal of cellular biochemistry. Volume 118:Issue 6(2017)
• Page Start: 1471
• Page End: 1479
• Publication Date: 2017-01-10
• Subjects: ALZHEIMER'S -- SINGLE NUCLEOTIDE POLYMORPHISMS -- MUTATIONS -- PATHOGENIC -- MOLECULAR DYNAMICS; Cytochemistry -- Periodicals; 572
• DOI: 10.1002/jcb.25806
• Languages: English
• ISSNs: 0730-2312

Abstract

ABSTRACT: Alzheimer's is a neurodegenerative disease affecting large populations worldwide characterized mainly by progressive loss of memory along with various other symptoms. The foremost cause of the disease is still unclear, however various mechanisms have been proposed to cause the disease that include amyloid hypothesis, tau hypothesis, and cholinergic hypothesis in addition to genetic factors. Various genes have been known to be involved which are APOE, PSEN1, PSEN2, and APP among others. In the present study, we have used computational methods to examine the pathogenic effects of non‐synonymous single nucleotide polymorphisms (SNPs) associated with ABCA7, CR1, MS4A6A, CD2AP, PSEN1, PSEN2, and APP genes. The SNPs were obtained from dbSNP database followed by identification of deleterious SNPs and prediction of their functional impact. Prediction of disease‐associated mutations was performed and the impact of the mutations on the stability of the protein was carried out. To study the structural significance of the computationally prioritized mutations on the proteins, molecular dynamics simulation studies were carried out. On analysis, the SNPs with IDs rs76282929 ABCA7; CR1 rs55962594; MS4A6A rs601172; CD2AP rs61747098; PSEN1 rs63750231, rs63750265, rs63750526, rs63750577, rs63750687, rs63750815, rs63750900, rs63751037, rs63751163, rs63751399; PSEN2 rs63749851; and APP rs63749964, rs63750066, rs63750734, and rs63751039 were predicted to be deleterious and disease‐associated having significant structural impact on the proteins. The current study proposes a precise computational methodology for the identification of disease‐associated SNPs. J. Cell. Biochem. 118: 1471–1479, 2017. © 2016 Wiley Periodicals, Inc. Abstract : In the present study, we have used various SNP detection tools, to rank the AD‐associated deleterious nsSNPs available from the dbSNP database and to identify functionally significant nsSNPs. Furthermore, we carried out molecular dynamics (MD) simulations to gain insights into the structural impact of the identified mutations.