000890311 001__ 890311
000890311 005__ 20240625095127.0
000890311 0247_ $$2doi$$a10.1007/s00894-020-04470-w
000890311 0247_ $$2ISSN$$a0948-5023
000890311 0247_ $$2ISSN$$a1610-2940
000890311 0247_ $$2pmid$$a32748063
000890311 0247_ $$2WOS$$aWOS:000555990300001
000890311 037__ $$aFZJ-2021-00884
000890311 041__ $$aEnglish
000890311 082__ $$a540
000890311 1001_ $$0P:(DE-HGF)0$$aGuaitoli, Valentina$$b0
000890311 245__ $$aA computational strategy to understand structure-activity relationship of 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma
000890311 260__ $$aHeidelberg$$bSpringer$$c2020
000890311 3367_ $$2DRIVER$$aarticle
000890311 3367_ $$2DataCite$$aOutput Types/Journal article
000890311 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1666862219_21995
000890311 3367_ $$2BibTeX$$aARTICLE
000890311 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000890311 3367_ $$00$$2EndNote$$aJournal Article
000890311 500__ $$aUnfortunately, the authors do not have a copy of the submitted version anymore
000890311 520__ $$aWe followed a comprehensive computational strategy to understand and eventually predict the structure-activity relationship ofthirty-three 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1receptor related to Ewing sarcoma. The quantitative structure-activity relationship model showed that the inhibitory potency iscorrelated with the molar volume, a steric descriptor and the net charge calculated value on atom C1 (q1) and N4 (q4) of thepharmacophore, all of them appearing to give a positive contribution to the inhibitory activity. According to experimental andcalculated values, the most potent compoundwould be 3-[4-(azetidin-2-ylmethyl) cyclohexyl]-1-[3-(benzyloxy) phenyl] imidazo[1,5-α]pyrazin-8-amine (compound 23). Docking was used to guess important residues involved in the ATP-competitive inhibitoryactivity. It was validated by 200 ns of molecular dynamics (MD) simulation using improved linear interaction energy (LIE)method. MD of previously preferred structures by docking shows that the most potent ligand could establish hydrogen bondswith the ATP-binding site of the receptor, and the Ser979 and Ser1059 residues contribute favourably to the binding stability ofcompound 23.MDsimulation also gave arguments about the chemical structure of the compound 23 being able to fit in the ATPbindingpocket, expecting to remain stable into it during the entire simulation and allowing us to hint the significant contributionexpected to be given by electrostatic and hydrophobic interactions to the ligand-receptor complex stability. This computationalcombined strategy here described could represent a useful and effective prime approach to guide the identification of tyrosinekinase inhibitors as new lead compounds.
000890311 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000890311 588__ $$aDataset connected to CrossRef
000890311 7001_ $$0P:(DE-HGF)0$$aAlvarez-Ginarte, Yoanna María$$b1
000890311 7001_ $$0P:(DE-HGF)0$$aMontero-Cabrera, Luis Alberto$$b2$$eCorresponding author
000890311 7001_ $$0P:(DE-HGF)0$$aBencomo-Martínez, Alberto$$b3
000890311 7001_ $$0P:(DE-HGF)0$$aBadel, Yoana Pérez$$b4
000890311 7001_ $$0P:(DE-Juel1)165199$$aGiorgetti, Alejandro$$b5$$ufzj
000890311 7001_ $$0P:(DE-HGF)0$$aSuku, Eda$$b6
000890311 773__ $$0PERI:(DE-600)1284729-x$$a10.1007/s00894-020-04470-w$$gVol. 26, no. 8, p. 222$$n8$$p222$$tJournal of molecular modeling$$v26$$x0948-5023$$y2020
000890311 8564_ $$uhttps://juser.fz-juelich.de/record/890311/files/Guaitoli2020_Article_AComputationalStrategyToUnders.pdf$$yRestricted
000890311 909CO $$ooai:juser.fz-juelich.de:890311$$pVDB
000890311 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165199$$aForschungszentrum Jülich$$b5$$kFZJ
000890311 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000890311 9132_ $$0G:(DE-HGF)POF4-524$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5241$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vMolecular and Cellular Information Processing$$x0
000890311 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-10-13$$wger
000890311 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2020-10-13$$wger
000890311 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ MOL MODEL : 2018$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-10-13
000890311 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-10-13
000890311 920__ $$lyes
000890311 9201_ $$0I:(DE-Juel1)IAS-5-20120330$$kIAS-5$$lComputational Biomedicine$$x0
000890311 9201_ $$0I:(DE-Juel1)INM-9-20140121$$kINM-9$$lComputational Biomedicine$$x1
000890311 980__ $$ajournal
000890311 980__ $$aVDB
000890311 980__ $$aI:(DE-Juel1)IAS-5-20120330
000890311 980__ $$aI:(DE-Juel1)INM-9-20140121
000890311 980__ $$aUNRESTRICTED