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@INPROCEEDINGS{Berg:874409,
      author       = {Berg, Andrej and Peter, Christine},
      title        = {{C}onformational {A}nalysis of {D}ual-{S}cale {S}imulations
                      of {U}biquitin {C}hains},
      volume       = {50},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2020-01419},
      series       = {Publication Series of the John von Neumann Institute for
                      Computing (NIC) NIC Series},
      pages        = {137 - 146},
      year         = {2020},
      comment      = {NIC Symposium 2020},
      booktitle     = {NIC Symposium 2020},
      abstract     = {The analysis of large-scale simulations of (bio)molecular
                      systems generated on high performance computer (HPC)
                      clusters poses a challenge on its own due to the sheer
                      amount of high-dimensional data. To make sense of these data
                      and extract relevant information, techniques such as
                      dimensionality reduction and clustering are used. They can
                      be applied to characterise the sampling of conformational
                      phase space as well as to bridge between simulations on
                      different levels of resolution in multiscale setups. Here,
                      we present an approach to analyse long-timescale simulations
                      and to characterise conformational ensembles of
                      flexibly-linked multidomain proteins using the example of
                      differently covalently conjugated ubiquitin chains. We have
                      analysed exhaustive coarse grained (CG) and atomistic
                      simulations with the help of collective variables (CVs) that
                      are particularly suitable to describe the mutual orientation
                      of different subunits and the protein-protein interfaces
                      between them. These data have been further processed through
                      different dimensionality reduction techniques (relying on
                      multidimensional-scaling like approaches as well as neural
                      network autoencoders). The resulting low-dimensional maps
                      have been used for the characterisation of conformational
                      states and the quantitative comparison of conformational
                      free energy landscapes (from simulations at different levels
                      of resolution as well as of different chain types). With
                      this multiscale simulation and analysis approach it is
                      possible to identify characteristic properties of ubiquitin
                      chains in solution which can be subsequently correlated with
                      experimentally observed linkage- and chain length-specific
                      behaviour.},
      month         = {Feb},
      date          = {2020-02-27},
      organization  = {NIC Symposium 2020, Jülich (Germany),
                       27 Feb 2020 - 28 Feb 2020},
      cin          = {NIC},
      cid          = {I:(DE-Juel1)NIC-20090406},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/874409},
}