Abstract / Summary
Parkinson’s disease is pathologically characterized by the misfolding, aggregation, and transsynaptic propagation of α-synuclein, encoded by the SNCA gene. Here, we review publicly accessible genomic and structural bioinformatics workflows-variant databases, mutation conservation scoring, classical pathogenecity predictors, and protein structure prediction and analysis tools-. Using canonical familial mutations (p.A53T, p.A30P, p.E46K, p.H50Q, and p.G51D/p.A53E), we systematically benchmark in silico approaches against experimental biophysical, clinical, and in vivo phenotypes. Clearly, traditional sequence-based predictors disagree widely and miss sequestered state dynamic activities including charge inversion, liquid-liquid phase separation, and lipid binding deficiency. Conversely, bioinformatics workflows that integrate evolutionary insights, atomistic simulation, and full long-response protein dynamics increasingly provide the highest concordance with experiments. Finally, we synthesize these methods into a practical, costeffective testing workflow to allow resource-limited labs to diagnose mutations of uncertain significance in synucleinopathies.