In silico Analysis of the Functional and Structural Impacts of Non-synonymous Single Nucleotide Polymorphisms in the Human Paraxonase 1 Gene

• Published in: Bioautomation 2015, Vol.19 (3), p.275-286
• Main Authors: Paul, Sudip, Md. Solayman, Saha, Moumoni, Md. Sabir Hossain
• Format: Article
• Language: English
• Subjects: Bioinformatics; Energy change; Missense; PON1; Proteins; SNP
• ISSN: 1314-1902; 1314-2321

Computational approaches could help in identifying deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in a disease related gene which is a difficult and laborious task through laboratory experiments. In the present study, we analyzed the impacts of nsSNPs on structure and function of Paraxonase 1 (PON1) using different bioinformatics tools. The human PON1 protein sequence and its corresponding gene's SNP information were collected from UniProt and dbSNP databases, respectively. We utilized SIFT, Polyphen, I-Mutant 2.0, MutPred, SNP and GO, PhD-SNP and PANTHER tools in order to examine the total 39 nsSNPs occurring in the PON1 coding region. We filtered the most pathological mutations by combining the scores of the aforementioned servers and found 8 SNPs (G344C, S302L, W281C, D279Y, H134R, F120S, L90P, C42R) as deleterious and disease causing. The PDB structure of PON1 protein was obtained from RCSB Protein Data Bank (PDB ID: 1V04). The deleterious SNPs in native PON1 were introduced using Swiss-PDB Viewer package and changes in free energy were observed for six out of eight mutant structures. Two SNPs, S302L (substitution of serine to leucine at 302 position in amino acid sequence) and L90P (substitution of leucine to proline at 90 position in amino acid sequence) caused the highest energy increase amongst all. The findings implicate that these nsSNPs would be analyzed further in detail to enumerate their possible association with the protein deteriorating and disease causal potentialities.