In Silico Evaluation of the Potential Association of the Pathogenic Mutations of Alpha Synuclein Protein with Induction of Synucleinopathies

Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson’s disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used...

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Bibliographic Details
Published in:	Diseases Vol. 11; no. 3; p. 115
Main Authors:	Elnageeb, Mohamed E, Elfaki, Imadeldin, Adam, Khalid M, Ahmed, Elsadig Mohamed, Elkhalifa, Elkhalifa M, Abuagla, Hytham A, Ahmed, Abubakr Ali Elamin Mohamed, Ali, Elshazali Widaa, Eltieb, Elmoiz Idris, Edris, Ali M
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-09-2023 MDPI
Subjects:	Aggregates Algorithms alpha-synuclein (α-Syn) protein Alzheimer's disease Amino acids Analysis Bioinformatics Chromosomes Conserved sequence Degeneration Gene mapping Genetic aspects Genetic screening Health aspects Machine learning Membrane proteins Molecular dynamics Movement disorders Mutation Mutation (Biology) Nervous system Neural coding Neurodegenerative diseases Parkinson's disease Parkinson’s disease (PD) Physicochemical properties Protein structure Proteins Risk factors Single nucleotide polymorphisms Single-nucleotide polymorphism SNCA gene non-synonymous single nucleotide polymorphisms (nsSNPs) Structure-function relationships Support vector machines Synuclein synucleinopathies Saudi Arabia
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Summary:	Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson’s disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used a range of computational algorithms such as sequence conservation, structural analysis, physicochemical properties, and machine learning. The sequence of the SNCA gene was analyzed, resulting in the mapping of 42,272 SNPs that are classified into different functional categories. A total of 177 nsSNPs were identified within the coding region; there were 20 variants that may influence the α-Syn protein structure and function. This identification was made by employing different analytical tools including SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, I-Mut, and HOPE. Three mutations, V82A, K80E, and E46K, were selected for further examinations due to their spatial positioning within the α-Syn as determined by PyMol. Results indicated that these mutations may affect the stability and function of α-Syn. Then, a molecular dynamics simulation was conducted for the SNCA wildtype and the four mutant variants (p.A18G, p.V82A, p.K80E, and p.E46K). The simulation examined temperature, pressure, density, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg). The data indicate that the mutations p.V82A, p.K80E, and p.E46K reduce the stability and functionality of α-Syn. These findings highlight the importance of understanding the impact of nsSNPs on α-syn structure and function. Our results required verifications in further protein functional and case–control studies. After being verified these findings can be used in genetic testing for the early diagnosis of PD, the evaluation of the risk factors, and therapeutic approaches.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2079-9721 2079-9721
DOI:	10.3390/diseases11030115