000904333 001__ 904333
000904333 005__ 20220103172054.0
000904333 0247_ $$2doi$$a10.1038/s41592-020-01051-w
000904333 0247_ $$2ISSN$$a1548-7091
000904333 0247_ $$2ISSN$$a1548-7105
000904333 0247_ $$2Handle$$a2128/29696
000904333 0247_ $$2altmetric$$aaltmetric:99412179
000904333 0247_ $$2pmid$$apmid:33542514
000904333 0247_ $$2WOS$$aWOS:000614686600012
000904333 037__ $$aFZJ-2021-05903
000904333 082__ $$a610
000904333 1001_ $$00000-0002-3261-7035$$aLawson, Catherine L.$$b0$$eCorresponding author
000904333 245__ $$aCryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge
000904333 260__ $$aLondon [u.a.]$$bNature Publishing Group$$c2021
000904333 3367_ $$2DRIVER$$aarticle
000904333 3367_ $$2DataCite$$aOutput Types/Journal article
000904333 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1640941397_14773
000904333 3367_ $$2BibTeX$$aARTICLE
000904333 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000904333 3367_ $$00$$2EndNote$$aJournal Article
000904333 520__ $$aThis paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.
000904333 536__ $$0G:(DE-HGF)POF4-5244$$a5244 - Information Processing in Neuronal Networks (POF4-524)$$cPOF4-524$$fPOF IV$$x0
000904333 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000904333 7001_ $$00000-0001-5066-7178$$aKryshtafovych, Andriy$$b1
000904333 7001_ $$00000-0001-9333-8219$$aAdams, Paul D.$$b2
000904333 7001_ $$0P:(DE-HGF)0$$aAfonine, Pavel V.$$b3
000904333 7001_ $$00000-0001-9039-8523$$aBaker, Matthew L.$$b4
000904333 7001_ $$00000-0002-1016-862X$$aBarad, Benjamin A.$$b5
000904333 7001_ $$00000-0002-8465-4823$$aBond, Paul$$b6
000904333 7001_ $$00000-0001-5307-348X$$aBurnley, Tom$$b7
000904333 7001_ $$00000-0002-8345-343X$$aCao, Renzhi$$b8
000904333 7001_ $$0P:(DE-HGF)0$$aCheng, Jianlin$$b9
000904333 7001_ $$00000-0002-3796-8352$$aChojnowski, Grzegorz$$b10
000904333 7001_ $$00000-0002-0189-1437$$aCowtan, Kevin$$b11
000904333 7001_ $$00000-0002-2390-2002$$aDill, Ken A.$$b12
000904333 7001_ $$0P:(DE-HGF)0$$aDiMaio, Frank$$b13
000904333 7001_ $$00000-0001-7024-7998$$aFarrell, Daniel P.$$b14
000904333 7001_ $$00000-0002-5080-2859$$aFraser, James S.$$b15
000904333 7001_ $$00000-0001-6653-6682$$aHerzik, Mark A.$$b16
000904333 7001_ $$00000-0003-1039-8000$$aHoh, Soon Wen$$b17
000904333 7001_ $$00000-0002-8584-5154$$aHou, Jie$$b18
000904333 7001_ $$0P:(DE-HGF)0$$aHung, Li-Wei$$b19
000904333 7001_ $$00000-0001-8781-1604$$aIgaev, Maxim$$b20
000904333 7001_ $$00000-0002-0997-8422$$aJoseph, Agnel P.$$b21
000904333 7001_ $$00000-0003-4091-6614$$aKihara, Daisuke$$b22
000904333 7001_ $$00000-0002-2721-678X$$aKumar, Dilip$$b23
000904333 7001_ $$00000-0002-5360-8947$$aMittal, Sumit$$b24
000904333 7001_ $$00000-0002-4803-0378$$aMonastyrskyy, Bohdan$$b25
000904333 7001_ $$00000-0002-6347-9587$$aOlek, Mateusz$$b26
000904333 7001_ $$00000-0002-4883-1546$$aPalmer, Colin M.$$b27
000904333 7001_ $$00000-0001-7663-9028$$aPatwardhan, Ardan$$b28
000904333 7001_ $$00000-0002-5054-5338$$aPerez, Alberto$$b29
000904333 7001_ $$00000-0002-8285-571X$$aPfab, Jonas$$b30
000904333 7001_ $$00000-0002-0848-5335$$aPintilie, Grigore D.$$b31
000904333 7001_ $$00000-0002-3311-2944$$aRichardson, Jane S.$$b32
000904333 7001_ $$00000-0002-0387-2862$$aRosenthal, Peter B.$$b33
000904333 7001_ $$00000-0002-4167-2108$$aSarkar, Daipayan$$b34
000904333 7001_ $$0P:(DE-Juel1)178773$$aSchäfer, Luisa U.$$b35
000904333 7001_ $$00000-0003-1077-5750$$aSchmid, Michael F.$$b36
000904333 7001_ $$0P:(DE-Juel1)132018$$aSchröder, Gunnar F.$$b37
000904333 7001_ $$0P:(DE-HGF)0$$aShekhar, Mrinal$$b38
000904333 7001_ $$00000-0001-7039-2589$$aSi, Dong$$b39
000904333 7001_ $$00000-0002-9000-2397$$aSingharoy, Abishek$$b40
000904333 7001_ $$00000-0002-5339-909X$$aTerashi, Genki$$b41
000904333 7001_ $$00000-0001-6384-0320$$aTerwilliger, Thomas C.$$b42
000904333 7001_ $$00000-0002-8865-0651$$aVaiana, Andrea$$b43
000904333 7001_ $$0P:(DE-HGF)0$$aWang, Liguo$$b44
000904333 7001_ $$0P:(DE-Juel1)138909$$aWang, Zhe$$b45
000904333 7001_ $$0P:(DE-HGF)0$$aWankowicz, Stephanie A.$$b46
000904333 7001_ $$0P:(DE-HGF)0$$aWilliams, Christopher J.$$b47
000904333 7001_ $$00000-0003-0496-6796$$aWinn, Martyn$$b48
000904333 7001_ $$0P:(DE-HGF)0$$aWu, Tianqi$$b49
000904333 7001_ $$00000-0001-7520-0646$$aYu, Xiaodi$$b50
000904333 7001_ $$00000-0003-0414-4776$$aZhang, Kaiming$$b51
000904333 7001_ $$00000-0002-3337-0660$$aBerman, Helen M.$$b52
000904333 7001_ $$00000-0002-8910-3078$$aChiu, Wah$$b53$$eCorresponding author
000904333 773__ $$0PERI:(DE-600)2163081-1$$a10.1038/s41592-020-01051-w$$gVol. 18, no. 2, p. 156 - 164$$n2$$p156 - 164$$tNature methods$$v18$$x1548-7091$$y2021
000904333 8564_ $$uhttps://juser.fz-juelich.de/record/904333/files/s41592-020-01051-w.pdf$$yOpenAccess
000904333 909CO $$ooai:juser.fz-juelich.de:904333$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000904333 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178773$$aForschungszentrum Jülich$$b35$$kFZJ
000904333 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132018$$aForschungszentrum Jülich$$b37$$kFZJ
000904333 9131_ $$0G:(DE-HGF)POF4-524$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5244$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vMolecular and Cellular Information Processing$$x0
000904333 9141_ $$y2021
000904333 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNAT METHODS : 2019$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)9930$$2StatID$$aIF >= 30$$bNAT METHODS : 2019$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000904333 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-02-03
000904333 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-03
000904333 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000904333 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-02-03$$wger
000904333 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-02-03
000904333 9201_ $$0I:(DE-Juel1)IBI-7-20200312$$kIBI-7$$lStrukturbiochemie$$x0
000904333 980__ $$ajournal
000904333 980__ $$aVDB
000904333 980__ $$aUNRESTRICTED
000904333 980__ $$aI:(DE-Juel1)IBI-7-20200312
000904333 9801_ $$aFullTexts