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@ARTICLE{Schrder:134247,
      author       = {Schröder, Gunnar and Falkner, Benjamin},
      title        = {{C}ross-validation in cryo-{EM}-based structural modeling},
      journal      = {Proceedings of the National Academy of Sciences of the
                      United States of America},
      volume       = {110},
      number       = {22},
      address      = {Washington, DC},
      publisher    = {Academy},
      reportid     = {FZJ-2013-02495},
      pages        = {8930-8935},
      year         = {2013},
      abstract     = {Single-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.},
      cin          = {ICS-6},
      ddc          = {000},
      cid          = {I:(DE-Juel1)ICS-6-20110106},
      pnm          = {452 - Structural Biology (POF2-452)},
      pid          = {G:(DE-HGF)POF2-452},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000320500000052},
      pubmed       = {pmid:23674685},
      doi          = {10.1073/pnas.1119041110},
      url          = {https://juser.fz-juelich.de/record/134247},
}