Structural and Functional Insights into PINK1 Variants in Parkinson’s Disease
Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by progressive motor dysfunction and neurodegeneration of dopaminergic neurons. Genetic mutations play a significant role in its etiology, and among the implicated genes, PINK1 has emerged as a crucial factor in maintaining mitochondrial health. This study focuses on the identification and analysis of deleterious non-synonymous single-nucleotide polymorphisms (nsSNPs) in the kinase domain of PINK1, using computational approaches to unravel their pathogenic potential.
Role of PINK1 in Mitochondrial Dysfunction
PINK1, a mitochondrial-targeted serine/threonine kinase, is vital for cellular defense against stress-induced mitochondrial damage. Mutations in this gene impair mitochondrial quality control and are strongly associated with autosomal recessive Parkinsonism. Investigating the molecular mechanisms by which pathogenic SNPs alter PINK1 structure and function can provide valuable insights into disease pathogenesis.
Bioinformatics and Deep Learning in SNP Analysis
Advanced computational approaches, including bioinformatics pipelines and deep learning models, were employed to predict damaging nsSNPs in PINK1. These tools offer powerful methods to filter high-risk mutations, prioritize variants, and understand their structural and functional consequences. Such integrative computational strategies accelerate biomarker discovery and enhance precision in genetic studies of PD.
Protein–Protein Interaction and Docking Analysis
To evaluate the impact of pathogenic SNPs on PINK1 interactions, protein–protein interaction mapping and molecular docking studies were conducted. Alterations in binding affinity and interaction dynamics provide insights into how specific variants may disrupt signaling pathways and compromise mitochondrial integrity. These findings highlight the importance of SNP-driven structural disruptions in disease progression.
Molecular Dynamics Simulations
Molecular dynamics (MD) simulations were carried out to observe the stability, conformational flexibility, and dynamic behavior of PINK1 variants over time. The simulations revealed that mutations such as C166R, E240K, D362N, D362Y, and C388R significantly destabilize the protein, supporting their classification as high-risk variants. This dynamic perspective strengthens the structural predictions made by docking and bioinformatics analyses.
Implications for Diagnostics and Drug Development
The identification of pathogenic nsSNPs in the PINK1 kinase domain contributes to understanding the molecular basis of Parkinson’s disease and opens new avenues for diagnostics and targeted therapeutics. These findings may serve as potential biomarkers for early disease detection and guide the development of novel drugs aimed at restoring PINK1 function or compensating for its loss.
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