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@ARTICLE{Mulnaes:873040,
      author       = {Mulnaes, Daniel and Porta, Nicola and Clemens, Rebecca and
                      Apanasenko, Irina and Reiners, Jens and Gremer, Lothar and
                      Neudecker, Philipp and Smits, Sander H. J. and Gohlke,
                      Holger},
      title        = {{T}op{M}odel: {T}emplate-based protein structure prediction
                      at low sequence identity using top-down consensus and deep
                      neural networks},
      journal      = {Journal of chemical theory and computation},
      volume       = {16},
      number       = {3},
      issn         = {1549-9626},
      address      = {Washington, DC},
      reportid     = {FZJ-2020-00491},
      pages        = {1953-1967},
      year         = {2020},
      abstract     = {Knowledge of protein structures is essential to understand
                      the proteins’ functions, evolution, dynamics, stabilities,
                      interactions, and for data-driven protein- or drug-design.
                      Yet, experimental structure determination rates are far
                      exceeded by that of next-generation sequencing.
                      Computational structure prediction seeks to alleviate this
                      problem, and the Critical Assessment of protein Structure
                      Prediction (CASP) has shown the value of consensus- and
                      meta-methods that utilize complementary algorithms. However,
                      traditionally, such methods employ majority voting during
                      template selection and model averaging during refinement,
                      which can drive the model away from the native fold if it is
                      underrepresented in the ensemble. Here, we present TopModel,
                      a fully automated meta-method for protein structure
                      prediction. In contrast to traditional consensus- and
                      meta-methods, TopModel uses top-down consensus and deep
                      neural networks to select templates and identify and correct
                      wrongly modeled regions. TopModel combines a broad range of
                      state-of-the-art methods for threading, alignment and model
                      quality estimation and provides a versatile work-flow and
                      toolbox for template-based structure prediction. TopModel
                      shows a superior template selection, alignment accuracy, and
                      model quality for template-based structure prediction on the
                      CASP10-12 datasets. TopModel was validated by prospective
                      predictions of the nisin resistance protein NSR protein from
                      S. agalactiae and LipoP from C. difficile, showing far
                      better agreement with experimental data than any of its
                      constituent primary predictors. These results, in general,
                      demonstrate the utility of TopModel for protein structure
                      prediction and, in particular, show how combining
                      computational structure prediction with sparse or
                      low-resolution experimental data can improve the final
                      model.},
      cin          = {JSC / NIC / ICS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406 /
                      I:(DE-Juel1)ICS-6-20110106},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 551 - Functional Macromolecules and Complexes
                      (POF3-551) / Forschergruppe Gohlke $(hkf7_20170501)$},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-551 /
                      $G:(DE-Juel1)hkf7_20170501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31967823},
      UT           = {WOS:000519337700048},
      doi          = {10.1021/acs.jctc.9b00825},
      url          = {https://juser.fz-juelich.de/record/873040},
}