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@ARTICLE{Sengupta:872771,
      author       = {Sengupta, Ushnish and Carballo-Pacheco, Martín and
                      Strodel, Birgit},
      title        = {{A}utomated {M}arkov state models for molecular dynamics
                      simulations of aggregation and self-assembly},
      journal      = {The journal of chemical physics},
      volume       = {150},
      number       = {11},
      issn         = {1089-7690},
      address      = {Melville, NY},
      publisher    = {American Institute of Physics},
      reportid     = {FZJ-2020-00249},
      pages        = {115101 -},
      year         = {2019},
      abstract     = {Markov state models have become popular in the
                      computational biochemistry and biophysics communities as a
                      technique for identifying stationary and kinetic information
                      of protein dynamics from molecular dynamics simulation data.
                      In this paper, we extend the applicability of automated
                      Markov state modeling to simulation data of molecular
                      self-assembly and aggregation by constructing collective
                      coordinates from molecular descriptors that are invariant to
                      permutations of molecular indexing. Understanding molecular
                      self-assembly is of critical importance if we want to deepen
                      our understanding of neurodegenerative diseases where the
                      aggregation of misfolded or disordered proteins is thought
                      to be the main culprit. As a proof of principle, we
                      demonstrate our Markov state model technique on simulations
                      of the KFFE peptide, a subsequence of Alzheimer’s
                      amyloid-β peptide and one of the smallest peptides known to
                      aggregate into amyloid fibrils in vitro. We investigate the
                      different stages of aggregation up to tetramerization and
                      show that the Markov state models clearly map out the
                      different aggregation pathways. Of note is that disordered
                      and β-sheet oligomers do not interconvert, leading to
                      separate pathways for their formation. This suggests that
                      amyloid aggregation of KFFE occurs via ordered aggregates
                      from the very beginning. The code developed here is freely
                      available as a Jupyter notebook called TICAgg, which can be
                      used for the automated analysis of any self-assembling
                      molecular system, protein, or otherwise},
      cin          = {ICS-6 / JARA-HPC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)ICS-6-20110106 / $I:(DE-82)080012_20140620$},
      pnm          = {553 - Physical Basis of Diseases (POF3-553) / Aggregation
                      of Functional Amyloids $(jara0095_20140501)$},
      pid          = {G:(DE-HGF)POF3-553 / $G:(DE-Juel1)jara0095_20140501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30901988},
      UT           = {WOS:000462014500035},
      doi          = {10.1063/1.5083915},
      url          = {https://juser.fz-juelich.de/record/872771},
}