In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation. (October 2020)

• Record Type: Journal Article
• Authors: Jafaripour, Sedighe Sadat; Gharaghani, Sajjad; Nazarshodeh, Elmira; Haider, Shozeb; Saboury, Ali Akbar
• Is Part Of: Computational biology and chemistry. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: Alpha-synuclein (α-syn) -- Aggregation inhibitors -- Pharmacophore-based repositioning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107308
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The first report to identify drugs for repositioning against alpha-synuclein aggregation inhibition. 3D ligand-based pharmacophore model for alpha-synuclein inhibition developed. Key pharmacophore features consist of three HBD, two HBA, and two AR. New compounds identified as potential alpha-synuclein inhibitors. Abstract: One of the hallmarks of Parkinson's disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates.