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@ARTICLE{Illig:889720,
      author       = {Illig, Alexander-Maurice and Strodel, Birgit},
      title        = {{P}erformance of {M}arkov {S}tate {M}odels and {T}ransition
                      {N}etworks on {C}haracterizing {A}myloid {A}ggregation
                      {P}athways from {MD} {D}ata},
      journal      = {Journal of chemical theory and computation},
      volume       = {16},
      number       = {12},
      issn         = {1549-9626},
      address      = {Washington, DC},
      reportid     = {FZJ-2021-00342},
      pages        = {7825 - 7839},
      year         = {2020},
      abstract     = {Molecular dynamic (MD) simulations are animportant tool for
                      studying protein aggregation processes, whichplay a central
                      role in a number of diseases including Alzheimer’sdisease.
                      However, MD simulations produce large amounts of
                      data,requiring advanced methods to extract mechanistic
                      insight into theprocess under study. Transition networks
                      (TNs) provide anelegant method to identify (meta)stable
                      states and the transitionsbetween them from MD simulations.
                      Here, we apply two differentmethods to generate TNs for
                      protein aggregation: Markov statemodels (MSMs), which are
                      based on kinetic clustering the statespace, and TNs using
                      conformational clustering. The similaritiesand differences
                      of both methods are elucidated for the aggregationof the
                      fragment Aβ16−22 of the Alzheimer’s amyloid-β peptide.
                      Ingeneral, both methods perform excellently in identifying
                      the main aggregation pathways. The strength of MSMs is that
                      they providea rather coarse and thus simply to interpret
                      picture of the aggregation process. Conformation-sorting
                      TNs, on the other hand,outperform MSMs in uncovering
                      mechanistic details. We thus recommend to apply both methods
                      to MD data of proteinaggregation in order to obtain a
                      complete picture of this process. As part of this work, a
                      Python script called ATRANET forautomated TN generation
                      based on a correlation analysis of the descriptors used for
                      conformational sorting is made publiclyavailable.},
      cin          = {IBI-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {553 - Physical Basis of Diseases (POF3-553)},
      pid          = {G:(DE-HGF)POF3-553},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33233894},
      UT           = {WOS:000598208600044},
      doi          = {10.1021/acs.jctc.0c00727},
      url          = {https://juser.fz-juelich.de/record/889720},
}