Computational association in parkinson's disease SNPs with brain structural and functional alterations

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Parkinson's disease (PD) is a prevalent neurodegenerative disorder with increasing global incidence.
• Epigenetic factors and specific genes (SNCA, LRRK2, NURR1, ATP13A2, GSK3B, Parkin, PINK1, DJ-1, UCHL1) are implicated in PD pathogenesis.
• Identifying deleterious genetic variants is crucial for understanding PD progression.

Purpose of the Study:

• To identify single nucleotide polymorphisms (SNPs) with high deleterious effects on nine key PD-associated proteins.
• To evaluate potential therapeutic strategies by screening FDA-approved drugs against identified genetic variants.

Main Methods:

• Utilized over 13 computational tools for sequence, structural, and functional analyses of SNPs.
• Employed Frustrometer, NetSurf 3.0, and xProtCAS servers for screening deleterious SNPs.
• Performed protein modeling, structural and STRING analyses, binding site analysis, molecular docking, and molecular dynamics simulations.

Main Results:

• Identified highly deleterious missense variants in major PD-associated genes.
• One FDA-approved drug demonstrated high binding affinity across all targeted mutant proteins.
• Molecular dynamics simulations confirmed the compound's stability and interaction with targets.

Conclusions:

• The study provides novel insights into genetic variants contributing to Parkinson's disease.
• Identified drug compound shows promise as a potential therapeutic agent for PD.
• Findings support further research into personalized medicine approaches for PD treatment.