Potential Inhibitors of Methionine Aminopeptidase Type Ii Identified Via Structure-Based Pharmacophore Modeling

gdc.relation.journal Molecular Diversity en_US
dc.contributor.author Albayati, Safana
dc.contributor.author Uba, Abdullahi İbrahim
dc.contributor.author Yelekçi, Kemal
dc.date.accessioned 2021-05-12T12:27:29Z
dc.date.available 2021-05-12T12:27:29Z
dc.date.issued 2021
dc.description.abstract Methionine aminopeptidase (MetAP2) is a metal-containing enzyme that removes initiator methionine from the N-terminus of a newly synthesized protein. Inhibition of the enzyme is crucial in diminishing cancer growth and metastasis. Fumagillin—a natural irreversible inhibitor of MetAP2—and its derivatives are used as potent MetAP2 inhibitors. However, because of their adverse effects, none of them has progressed to clinical studies. In search for potential reversible inhibitors, we built structure-based pharmacophore models using the crystal structure of MetAP2 complexed with fumagillin (PDB ID: 1BOA). The pharmacophore models were validated using Gunner–Henry scoring method. The best pharmacophore consisting of 1 H-bond donor, 1 H-bond acceptor, and 3 hydrophobic features was used to conduct pharmacophore-based virtual screening of ZINC15 database against MetAP2. The top 10 compounds with pharmacophore fit values > 3.00 were selected for further analysis. These compounds were subjected to absorption, distribution, metabolism, elimination, and toxicity (ADMET) prediction and found to have druglike properties. Furthermore, molecular docking calculations was performed on these hits using AutoDock4 to predict their binding mode and binding energy. Three diverse compounds: ZINC000014903160, ZINC000040174591, and ZINC000409110720 with respective binding energy/docking scores of − 9.22, − 9.21, and −817 kcal/mol, were submitted to 100 ns (MD) simulations using Nanoscale MD (NAMD) software. The compounds showed stable binding mode over time. Therefore, they may serve as a scaffold for further computational and experimental optimization toward the design of more potent and safer MetAP2 inhibitors. en_US
dc.identifier.citationcount 3
dc.identifier.doi 10.1007/s11030-021-10221-7 en_US
dc.identifier.issn 1381-1991 en_US
dc.identifier.issn 1381-1991
dc.identifier.issn 1573-501X
dc.identifier.scopus 2-s2.0-85104136960 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/4015
dc.language.iso en en_US
dc.publisher Springer Science and Business Media Deutschland GmbH en_US
dc.relation.ispartof Molecular Diversity
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject ADMET prediction en_US
dc.subject Docking en_US
dc.subject MD simulation en_US
dc.subject MetAP2 en_US
dc.subject MetAP2 inhibitors en_US
dc.subject Structure-based pharmacophore modeling en_US
dc.title Potential Inhibitors of Methionine Aminopeptidase Type Ii Identified Via Structure-Based Pharmacophore Modeling en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Yelekçi, Kemal en_US
gdc.author.institutional Yelekçi, Kemal
gdc.author.institutional Albayati, Safana en_US
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article
gdc.description.endpage 1016
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.startpage 1005
gdc.description.volume 26
gdc.identifier.openalex W3156499578
gdc.identifier.pmid 33846894 en_US
gdc.identifier.wos WOS:000639524300001 en_US
gdc.oaire.diamondjournal false
gdc.oaire.impulse 5.0
gdc.oaire.influence 2.7980795E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Structure-based pharmacophore modeling
gdc.oaire.keywords MD simulation
gdc.oaire.keywords MetAP2 inhibitors
gdc.oaire.keywords Molecular Dynamics Simulation
gdc.oaire.keywords Aminopeptidases
gdc.oaire.keywords Docking
gdc.oaire.keywords Molecular Docking Simulation
gdc.oaire.keywords MetAP2
gdc.oaire.keywords Methionine
gdc.oaire.keywords Neoplasms
gdc.oaire.keywords Humans
gdc.oaire.keywords ADMET prediction
gdc.oaire.popularity 4.849507E-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.fwci 0.507
gdc.openalex.normalizedpercentile 0.58
gdc.opencitations.count 6
gdc.plumx.mendeley 13
gdc.plumx.scopuscites 5
gdc.scopus.citedcount 5
gdc.wos.citedcount 4
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