eprintid: 10108756
rev_number: 18
eprint_status: archive
userid: 608
dir: disk0/10/10/87/56
datestamp: 2020-09-02 11:31:45
lastmod: 2021-10-04 00:11:21
status_changed: 2020-09-02 11:31:45
type: article
metadata_visibility: show
creators_name: Jafaripour, SS
creators_name: Gharaghani, S
creators_name: Nazarshodeh, E
creators_name: Haider, S
creators_name: Saboury, AA
title: In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D10
divisions: G09
keywords: Aggregation inhibitors, Alpha-synuclein (α-syn), Pharmacophore-based repositioning
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2020-10
date_type: published
official_url: https://doi.org/10.1016/j.compbiolchem.2020.107308
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1798147
doi: 10.1016/j.compbiolchem.2020.107308
pii: S1476-9271(19)31088-6
lyricists_name: Shozeb, Syed
lyricists_id: SMSSH06
actors_name: Shozeb, Syed
actors_id: SMSSH06
actors_role: owner
full_text_status: public
publication: Computational Biology and Chemistry
volume: 88
article_number: 107308
event_location: England
citation:        Jafaripour, SS;    Gharaghani, S;    Nazarshodeh, E;    Haider, S;    Saboury, AA;      (2020)    In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation.                   Computational Biology and Chemistry , 88     , Article 107308.  10.1016/j.compbiolchem.2020.107308 <https://doi.org/10.1016/j.compbiolchem.2020.107308>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10108756/3/Shozeb_In%20silico%20drug%20repositioning%20of%20FDA-approved%20drugs%20to%20predict%20new%20inhibitors%20for%20alpha-synuclein%20aggregation_AAM.pdf