Abstract
Parkinson's disease (PD) is second most common motoric neurodegenerative disorder, which affects population above age 65. It is characterized by the loss of dopaminergic neurons from substantia and the presence of intracellular. SNCA is a protein encoded by 5 exons with total transcript length of 3041 bps maps on 4q21.3-q22. SNCA gene is considered to be involved in regulation of dopamine release and transport, induces fibrillization of microtubule associated protein tau, and exert neuroprotective phenotype in non-dopaminergic neurons by inhibiting both p53 expression and transactivation of proapoptotic genes leading to decreased caspase-3 activation. Polymorphisms in Alpha-synuclein (SNCA) gene have been associated with Parkinson disease. In this study, computational analysis of pathogenic SNPs of SNCA gene has been performed to identify and analyze the deletrious SNPs using bioinformatics approach. We obtained pathogenic SNPs data from dbSNP database. We employed consensus tools SIFT, PROVEAN, Condel, PolyPhen-2 to predict deleterious pathogenic nonsynonymous SNPs, Pathogenic mutants A30P (rs104893878) and G51D (rs431905511) shows deleterious by all four tools and three tools respectively. These predicted pathogenic deleterious nsSNPs are expected to have impending functional influence and may contribute in understanding the functional roles of SNCA gene associated with Parkinson disease.