Pharmacophore modeling and 3D-QSAR study for the design of novel α-synuclein aggregation inhibitors.

Alpha-synuclein (α-syn), as a highly soluble presynaptic protein expressed in the brain, plays an important role in recycling synaptic vesicles and regulating the synthesis, storage, and release of neurotransmitters. Accumulation of α-syn in Lewy bodies and Lewy neurites is the pathological hallmark of Parkinson's disease (PD), so inhibition of α-syn aggregation may provide a novel approach for treating PD. In this study, the 3D structure of α-syn was downloaded from Protein Data Bank (PDB ID: 2N0A). A ligand-based pharmacophore model was conducted on a set of 43 diverse α-syn ligands, and the results suggested that two hydrogen-bond acceptors, one hydrophobic group, and two aromatic rings were significant to the inhibition of α-syn aggregation. A ligand-based 3D-QSAR model was also established with good statistical significance (R2 = 0.920) and excellent predictive ability (Q2 = 0.752). Novel indolinone derivatives were designed and synthesized based on the pharmacophore model. Subsequently, the 3D-QSAR model was used to predict the inhibitory activities towards α-syn aggregation, and the actual inhibitory activities were evaluated by thioflavin-T assay in vitro with the best inhibitory activity reaching 45.08%. The fitting results indicated that the built pharmacophore and 3D-QSAR models provided better reliability and accuracy for compound modification and prediction of the activity thereof. A ligand-based pharmacophore modeling and 3D-QSAR study have been performed on a set of 43 diverse ligands for α-synuclein for the first time. Based on the best pharmacophore modeling, novel indolinone derivatives were designed and synthesized, and the inhibitory activities for α-synuclein aggregation were evaluated by thioflavin-T assay in vitro, which preliminary indicated that five pharmacophore sites (two hydrogen bond acceptors (A), a hydrophobic group (H), and two aromatic rings (R)) in compounds contribute to the inhibitory activities. In the study, the built pharmacophore modeling and 3D-QSAR provided better reliability and accuracy for compound modification and prediction of the activity thereof.