000134247 001__ 134247 000134247 005__ 20210129211615.0 000134247 0247_ $$2WOS$$aWOS:000320500000052 000134247 0247_ $$2DOI$$a10.1073/pnas.1119041110 000134247 0247_ $$2altmetric$$aaltmetric:1559780 000134247 0247_ $$2pmid$$apmid:23674685 000134247 037__ $$aFZJ-2013-02495 000134247 082__ $$a000 000134247 1001_ $$0P:(DE-Juel1)132018$$aSchröder, Gunnar$$b0$$eCorresponding author$$ufzj 000134247 245__ $$aCross-validation in cryo-EM-based structural modeling 000134247 260__ $$aWashington, DC$$bAcademy$$c2013 000134247 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1384517377_13491 000134247 3367_ $$2DataCite$$aOutput Types/Journal article 000134247 3367_ $$00$$2EndNote$$aJournal Article 000134247 3367_ $$2BibTeX$$aARTICLE 000134247 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000134247 3367_ $$2DRIVER$$aarticle 000134247 500__ $$3POF3_Assignment on 2016-02-29 000134247 520__ $$aSingle-particle cryo-electron microscopy (cryo-EM) is a powerful approach to determine the structure of large macromolecules and assemblies thereof in many cases at subnanometer resolution. It has become popular to refine or flexibly fit atomic models into density maps derived from cryo-EM experiments. These density maps are typically significantly lower in resolution than electron density maps obtained from X-ray diffraction experiments, such that the number of parameters that need to be determined is much larger than the number of experimental observables. Overfitting and misinterpretation of the density, thus, becomes a serious problem. For diffraction data a cross-validation approach has been introduced almost twenty years ago, however, no such approach has been described yet for structure refinement against cryo-EM density maps, even though the overfitting problem is, due to the lower resolution, significantly larger. We present a cross-validation approach for real-space refinement against cryo-EM density maps in analogy to cross-validation typically used in crystallography. Our approach is able to detect overfitting and allows for optimizing the choice of restraints used in the refinement. The approach is demonstrated on three protein structures with simulated data and on experimental data of the rotavirus double-layer particle. Since cross-validation requires splitting the data set into at least two independent sets, we further present an approach to quantify correlations between the structure factor sets. This analysis is also helpful for other cross-validation applications, such as refinements against diffraction data or 3D reconstructions of cryo-EM density maps. 000134247 536__ $$0G:(DE-HGF)POF2-452$$a452 - Structural Biology (POF2-452)$$cPOF2-452$$fPOF II$$x0 000134247 7001_ $$0P:(DE-Juel1)162497$$aFalkner, Benjamin$$b1$$ufzj 000134247 773__ $$0PERI:(DE-600)1461794-8$$a10.1073/pnas.1119041110$$n22$$p8930-8935$$tProceedings of the National Academy of Sciences of the United States of America$$v110 000134247 8564_ $$uhttp://www.pnas.org/content/110/22/8930.full.pdf+html 000134247 8564_ $$uhttps://juser.fz-juelich.de/record/134247/files/FZJ-2013-02495.pdf$$yRestricted 000134247 909CO $$ooai:juser.fz-juelich.de:134247$$pVDB 000134247 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132018$$aForschungszentrum Jülich GmbH$$b0$$kFZJ 000134247 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162497$$aForschungszentrum Jülich GmbH$$b1$$kFZJ 000134247 9141_ $$y2013 000134247 915__ $$0StatID:(DE-HGF)0010$$2StatID$$aJCR/ISI refereed 000134247 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR 000134247 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index 000134247 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded 000134247 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection 000134247 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List 000134247 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS 000134247 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline 000134247 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database 000134247 915__ $$0StatID:(DE-HGF)0430$$2StatID$$aNational-Konsortium 000134247 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences 000134247 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record 000134247 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews 000134247 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences 000134247 9132_ $$0G:(DE-HGF)POF3-559H$$1G:(DE-HGF)POF3-550$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lBioSoft – Fundamentals for future Technologies in the fields of Soft Matter and Life Sciences$$vAddenda$$x0 000134247 9131_ $$0G:(DE-HGF)POF2-452$$1G:(DE-HGF)POF2-450$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lBioSoft$$vStructural Biology$$x0 000134247 920__ $$lyes 000134247 9201_ $$0I:(DE-Juel1)ICS-6-20110106$$kICS-6$$lStrukturbiochemie $$x0 000134247 980__ $$ajournal 000134247 980__ $$aVDB 000134247 980__ $$aUNRESTRICTED 000134247 980__ $$aI:(DE-Juel1)ICS-6-20110106 000134247 981__ $$aI:(DE-Juel1)IBI-7-20200312