000600207 001__ 600207
000600207 005__ 20250715173207.0
000600207 0247_ $$2doi$$a10.1016/j.str.2023.05.002
000600207 0247_ $$2ISSN$$a0969-2126
000600207 0247_ $$2ISSN$$a1878-4186
000600207 0247_ $$2altmetric$$aaltmetric:149087915
000600207 0247_ $$2pmid$$apmid:37253357
000600207 0247_ $$2WOS$$aWOS:001042128600001
000600207 0247_ $$2openalex$$aopenalex:W4378674714
000600207 037__ $$aPUBDB-2023-07781
000600207 041__ $$aEnglish
000600207 082__ $$a540
000600207 1001_ $$00000-0003-2311-2155$$aReggiano, Gabriella$$b0
000600207 245__ $$aResidue-level error detection in cryoelectron microscopy models
000600207 260__ $$aCambridge, Mass.$$bCell Press$$c2023
000600207 3367_ $$2DRIVER$$aarticle
000600207 3367_ $$2DataCite$$aOutput Types/Journal article
000600207 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1706708307_2355238
000600207 3367_ $$2BibTeX$$aARTICLE
000600207 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000600207 3367_ $$00$$2EndNote$$aJournal Article
000600207 520__ $$aBuilding accurate protein models into moderate resolution (3–5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local backbone errors in protein structures built into cryo-EM maps by combining local fit-to-density with deep-learning-derived structural information. MEDIC is validated on a set of 28 structures that were subsequently solved to higher resolutions, where we identify the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to fix over 100 errors in 12 deposited structures and to identify errors in 4 refined AlphaFold predictions with 80% precision and 60% recall. As modelers more frequently use deep learning predictions as a starting point for refinement and rebuilding, MEDIC’s ability to handle errors in structures derived from hand-building and machine learning methods makes it a powerful tool for structural biologists.
000600207 536__ $$0G:(DE-HGF)POF4-633$$a633 - Life Sciences – Building Blocks of Life: Structure and Function (POF4-633)$$cPOF4-633$$fPOF IV$$x0
000600207 588__ $$aDataset connected to CrossRef, Journals: bib-pubdb1.desy.de
000600207 693__ $$0EXP:(DE-MLZ)NOSPEC-20140101$$5EXP:(DE-MLZ)NOSPEC-20140101$$eNo specific instrument$$x0
000600207 7001_ $$0P:(DE-H253)PIP1021411$$aLugmayr, Wolfgang$$b1
000600207 7001_ $$aFarrell, Daniel$$b2
000600207 7001_ $$0P:(DE-H253)PIP1021412$$aMarlovits, Thomas$$b3
000600207 7001_ $$00000-0002-7524-8938$$aDiMaio, Frank$$b4$$eCorresponding author
000600207 773__ $$0PERI:(DE-600)2031189-8$$a10.1016/j.str.2023.05.002$$gVol. 31, no. 7, p. 860 - 869.e4$$n7$$p860-869.e4$$tStructure$$v31$$x0969-2126$$y2023
000600207 8564_ $$uhttps://bib-pubdb1.desy.de/record/600207/files/1-s2.0-S0969212623001582-main.pdf$$yRestricted
000600207 8564_ $$uhttps://bib-pubdb1.desy.de/record/600207/files/1-s2.0-S0969212623001582-main.pdf?subformat=pdfa$$xpdfa$$yRestricted
000600207 909CO $$ooai:bib-pubdb1.desy.de:600207$$pVDB
000600207 9101_ $$0I:(DE-H253)_CSSB-20140311$$6P:(DE-H253)PIP1021411$$aCentre for Structural Systems Biology$$b1$$kCSSB
000600207 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1021411$$aDeutsches Elektronen-Synchrotron$$b1$$kDESY
000600207 9101_ $$0I:(DE-H253)_CSSB-20140311$$6P:(DE-H253)PIP1021412$$aCentre for Structural Systems Biology$$b3$$kCSSB
000600207 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1021412$$aDeutsches Elektronen-Synchrotron$$b3$$kDESY
000600207 9131_ $$0G:(DE-HGF)POF4-633$$1G:(DE-HGF)POF4-630$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lVon Materie zu Materialien und Leben$$vLife Sciences – Building Blocks of Life: Structure and Function$$x0
000600207 9141_ $$y2023
000600207 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2023-08-24$$wger
000600207 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-24
000600207 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-08-24
000600207 9201_ $$0I:(DE-H253)CSSB-UKE-TM-20210520$$kCSSB-UKE-TM$$lCSSB-UKE-TM$$x0
000600207 980__ $$ajournal
000600207 980__ $$aVDB
000600207 980__ $$aI:(DE-H253)CSSB-UKE-TM-20210520
000600207 980__ $$aUNRESTRICTED