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@ARTICLE{Beton:600655,
      author       = {Beton, Joseph and Cragnolini, Tristan and Kaleel, Manaz and
                      Mulvaney, Thomas and Sweeney, Aaron and Topf, Maya},
      title        = {{I}ntegrating model simulation tools and cryo‐electron
                      microscopy},
      journal      = {Wiley interdisciplinary reviews / Computational Molecular
                      Science},
      volume       = {13},
      number       = {3},
      issn         = {1759-0876},
      address      = {Malden, MA},
      publisher    = {Wiley-Blackwell},
      reportid     = {PUBDB-2023-08078},
      pages        = {e1642},
      year         = {2023},
      abstract     = {The power of computer simulations, including
                      machine-learning, has become an inseparable part of
                      scientific analysis of biological data. This has
                      significantly impacted the field of cryogenic electron
                      microscopy (cryo-EM), which has grown dramatically since the
                      “resolution-revolution.” Many maps are now solved at
                      3–4 Å or better resolution, although a significant
                      proportion of maps deposited in the Electron Microscopy Data
                      Bank are still at lower resolution, where the positions of
                      atoms cannot be determined unambiguously. Additionally,
                      cryo-EM maps are often characterized by a varying local
                      resolution, partly due to conformational heterogeneity of
                      the imaged molecule. To address such problems, many
                      computational methods have been developed for cryo-EM map
                      reconstruction and atomistic model building. Here, we review
                      the development in algorithms and tools for building models
                      in cryo-EM maps at different resolutions. We describe
                      methods for model building, including rigid and flexible
                      fitting of known models, model validation, small-molecule
                      fitting, and model visualization. We provide examples of how
                      these methods have been used to elucidate the structure and
                      function of dynamic macromolecular machines.This article is
                      categorized under: Structure and Mechanism > Molecular
                      Structures Structure and Mechanism > Computational
                      Biochemistry and Biophysics Software > Molecular Modeling},
      cin          = {CSSB-LIV/UKE-MT},
      ddc          = {540},
      cid          = {$I:(DE-H253)CSSB-LIV_UKE-MT-20220525$},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000888382800001},
      doi          = {10.1002/wcms.1642},
      url          = {https://bib-pubdb1.desy.de/record/600655},
}