An In silico Analysis of Deleterious Single Nucleotide Polymorphisms of Human Lysozyme C Gene

Background: Single nucleotide polymorphisms (SNPs) play a critical role in influencing a person's susceptibility to diseases and in determining how an individual reacts to various treatment options. It is crucial to differentiate and characterize damaging SNPs from neutral ones and the aim of this study was to predict the deleterious SNPs of the lysozyme C (LYZ C) gene via an in silico analysis. LYZ C is an important antimicrobial peptide capable of damaging the peptidoglycan layer of bacteria leading to osmotic shock and cell death.

Methods: The missense nonsynonymous SNPs (nsSNPs) of the LYZ C gene were subjected to different computational tools- SIFT, PolyPhen v2, SNAP, PROVEAN, PhD-SNP, and SNPs & GO. Deleterious SNPs as predicted by these tools were examined by I-Mutant 3.0 and ConSurf. GeneMANIA and STRING tools were used to study the interaction network of the LYZ C gene. The impact of variations on the structural characteristics of the protein was studied by HOPE analysis. The structures of variants and wild types were predicted by the SWISS-MODEL web server and the TM-align tool was used to predict the root mean square deviation (RMSD) and template modeling (TM) scores.

Results: Eight missense nsSNPs (T88N, I74T, F75I, D67H, W82R, D85H, R80C, and R116S) of the LYZ C gene were found to be potentially deleterious. I-mutant 3.0 determined the variants that decreased the stability of the protein. ConSurf predicted rs121913547, rs121913549, and rs387906536 nsSNPs to be conserved. Interaction network tools showed that LYZ C protein interacted with lactoferrin (LTF). HOPE tool analyzed differences in physicochemical properties between wild type and variants. TM-align tool predicted the alignment score and the protein folding was found to be identical. PYMOL was used to visualize the superimposition of variants over wild types.

Conclusion: The present study ascertained the deleterious missense nsSNPs of the LYZ C gene and could be used in further experimental analysis. These high-risk nsSNPs could be used as molecular targets for diagnostic and therapeutic interventions.