000888465 001__ 888465
000888465 005__ 20210415193201.0
000888465 0247_ $$2doi$$a10.1063/5.0015398
000888465 0247_ $$2ISSN$$a0021-9606
000888465 0247_ $$2ISSN$$a1089-7690
000888465 0247_ $$2ISSN$$a1520-9032
000888465 0247_ $$2Handle$$a2128/26555
000888465 0247_ $$2pmid$$a32872878
000888465 0247_ $$2WOS$$aWOS:000566895600003
000888465 037__ $$aFZJ-2020-04933
000888465 082__ $$a530
000888465 1001_ $$0P:(DE-Juel1)177673$$aPeter, Emanuel K.$$b0
000888465 245__ $$aCORE-MD, a path correlated molecular dynamics simulation method
000888465 260__ $$aMelville, NY$$bAmerican Institute of Physics$$c2020
000888465 3367_ $$2DRIVER$$aarticle
000888465 3367_ $$2DataCite$$aOutput Types/Journal article
000888465 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1618497881_13388
000888465 3367_ $$2BibTeX$$aARTICLE
000888465 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000888465 3367_ $$00$$2EndNote$$aJournal Article
000888465 520__ $$aWe present an enhanced Molecular Dynamics (MD) simulation method, which is free from the requirement of a priori structural information of the system. The technique is capable of folding proteins with very low computational effort and requires only an energy parameter. The path correlated MD (CORE-MD) method uses the autocorrelation of the path integral over the reduced action and propagates the system along the history dependent path correlation. We validate the new technique in simulations of the conformational landscapes of dialanine and the TrpCage mini-peptide. We find that the novel method accelerates the sampling by three orders of magnitude and observe convergence of the conformational sampling in both cases. We conclude that the new method is broadly applicable for the enhanced sampling in MD simulations. The CORE-MD algorithm reaches a high accuracy compared with long time equilibrium MD simulations.
000888465 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000888465 536__ $$0G:(DE-Juel1)hkf6_20200501$$aForschergruppe Schug (hkf6_20200501)$$chkf6_20200501$$fForschergruppe Schug$$x1
000888465 588__ $$aDataset connected to CrossRef
000888465 7001_ $$00000-0002-9801-9273$$aShea, Joan-Emma$$b1
000888465 7001_ $$0P:(DE-Juel1)173652$$aSchug, Alexander$$b2$$eCorresponding author
000888465 773__ $$0PERI:(DE-600)1473050-9$$a10.1063/5.0015398$$gVol. 153, no. 8, p. 084114 -$$n8$$p084114 -$$tThe journal of chemical physics$$v153$$x1089-7690$$y2020
000888465 8564_ $$uhttps://juser.fz-juelich.de/record/888465/files/5.0015398.pdf$$yPublished on 2020-08-26. Available in OpenAccess from 2021-08-26.
000888465 909CO $$ooai:juser.fz-juelich.de:888465$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000888465 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177673$$aForschungszentrum Jülich$$b0$$kFZJ
000888465 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173652$$aForschungszentrum Jülich$$b2$$kFZJ
000888465 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000888465 9132_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000888465 9141_ $$y2020
000888465 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000888465 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ CHEM PHYS : 2018$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0430$$2StatID$$aNational-Konsortium$$d2020-09-05$$wger
000888465 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2020-09-05
000888465 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2020-09-05$$wger
000888465 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-09-05
000888465 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000888465 9201_ $$0I:(DE-Juel1)NIC-20090406$$kNIC$$lJohn von Neumann - Institut für Computing$$x1
000888465 980__ $$ajournal
000888465 980__ $$aVDB
000888465 980__ $$aI:(DE-Juel1)JSC-20090406
000888465 980__ $$aI:(DE-Juel1)NIC-20090406
000888465 980__ $$aUNRESTRICTED
000888465 9801_ $$aFullTexts