Discovery of High Affinity Ligands for Beta(2)-Adrenergic Receptor Through Pharmacophore-Based High-Throughput Virtual Screening and Docking

dc.contributor.author Yakar, Rüya
dc.contributor.author Akten, Ebru Demet
dc.date.accessioned 2019-06-27T08:02:49Z
dc.date.available 2019-06-27T08:02:49Z
dc.date.issued 2014
dc.description.abstract Novel high affinity compounds for human beta(2)-adrenergic receptor (beta(2)-AR) were searched among the clean drug-like subset of ZINC database consisting of 9928465 molecules that satisfy the Lipinski's rule of five. The screening protocol consisted of a high-throughput pharmacophore screening followed by an extensive amount of docking and rescoring. The pharmacophore model was composed of key features shared by all five inactive states of beta(2)-AR in complex with inverse agonists and antagonists. To test the discriminatory power of the pharmacophore model a small-scale screening was initially performed on a database consisting of 117 compounds of which 53 antagonists were taken as active inhibitors and 64 agonists as inactive inhibitors. Accordingly 7.3% of the ZINC database subset (729413 compounds) satisfied the pharmacophore requirements along with 44 antagonists and 17 agonists. Afterwards all these hit compounds were docked to the inactive apo form of the receptor using various docking and scoring protocols. Following each docking experiment the best pose was further evaluated based on the existence of key residues for antagonist binding in its vicinity. After final evaluations based on the human intestinal absorption (HIA) and the blood brain barrier (BBB) penetration properties 62 hit compounds have been clustered based on their structural similarity and as a result four scaffolds were revealed. Two of these scaffolds were also observed in three high affinity compounds with experimentally known K-i values. Moreover novel chemical compounds with distinct structures have been determined as potential beta(2)-AR drug candidates. (C) 2014 Elsevier Inc. All rights reserved. en_US]
dc.identifier.doi 10.1016/j.jmgm.2014.07.007 en_US
dc.identifier.issn 1093-3263
dc.identifier.issn 1873-4243
dc.identifier.scopus 2-s2.0-84906234617 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/690
dc.identifier.uri https://doi.org/10.1016/j.jmgm.2014.07.007
dc.language.iso en en_US
dc.publisher Elsevier Science Inc en_US
dc.relation.ispartof Journal of Molecular Graphics and Modelling
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Virtual screening en_US
dc.subject Pharmacophore modeling en_US
dc.subject Beta(2)-Adrenergic receptor en_US
dc.subject Docking en_US
dc.subject Scoring en_US
dc.title Discovery of High Affinity Ligands for Beta(2)-Adrenergic Receptor Through Pharmacophore-Based High-Throughput Virtual Screening and Docking en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Akten, Ebru Demet en_US
gdc.bip.impulseclass C5
gdc.bip.influenceclass C5
gdc.bip.popularityclass C5
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Biyoinformatik ve Genetik Bölümü en_US
gdc.description.endpage 160
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q2
gdc.description.startpage 148 en_US
gdc.description.volume 53 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W1988821653
gdc.identifier.pmid 25137647 en_US
gdc.identifier.wos WOS:000343631800016 en_US
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.diamondjournal false
gdc.oaire.impulse 4.0
gdc.oaire.influence 2.759796E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Virtual screening
gdc.oaire.keywords Binding Sites
gdc.oaire.keywords Docking
gdc.oaire.keywords High-Throughput Screening Assays
gdc.oaire.keywords Protein Structure, Tertiary
gdc.oaire.keywords Molecular Docking Simulation
gdc.oaire.keywords Small Molecule Libraries
gdc.oaire.keywords Beta(2)-Adrenergic receptor
gdc.oaire.keywords Adrenergic beta-2 Receptor Antagonists
gdc.oaire.keywords Drug Discovery
gdc.oaire.keywords Humans
gdc.oaire.keywords Receptors, Adrenergic, beta-2
gdc.oaire.keywords Scoring
gdc.oaire.keywords Pharmacophore modeling
gdc.oaire.keywords Protein Binding
gdc.oaire.popularity 1.50721E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 0303 health sciences
gdc.oaire.sciencefields 03 medical and health sciences
gdc.openalex.collaboration National
gdc.openalex.fwci 0.71730198
gdc.openalex.normalizedpercentile 0.69
gdc.opencitations.count 6
gdc.plumx.crossrefcites 5
gdc.plumx.mendeley 26
gdc.plumx.scopuscites 5
gdc.relation.journal Journal of Molecular Graphics and Modelling
gdc.scopus.citedcount 5
gdc.virtual.author Akdoğan, Ebru Demet
gdc.wos.citedcount 5
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