Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease-related wild-type alpha-synuclein and six pathogenic mutants

alpha-Synuclein is a central player in Parkinson's disease (PD) pathology. Various point mutations in alpha-synuclein have been identified to alter the protein-phospholipid binding behavior and cause PD. Therefore, exploration of alpha-synuclein-phospholipid interaction is important for understanding the PD pathogenesis and helping the early diagnosis of PD. Herein, a phospholipid-decorated liquid crystal (LC)-aqueous interface is constructed to investigate the binding between alpha-synucleins (wild-type and six familial mutant A30P, E46K, H50Q, G51D, A53E and A53T) and phospholipid. The application of deep learning analyzes and reveals distinct LC signatures generated by the binding of alpha-synuclein and phospholipid. This system allows for the identification of single point mutant alpha-synucleins with an average accuracy of 98.3 +/- 1.3% in a fast and efficient manner. We propose that this analytical methodology provides a new platform to understand alpha-synuclein-lipid interactions, and can be potentially developed for easy identification of alpha-synuclein mutations in common clinic.