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@ARTICLE{Schffler:1047546,
      author       = {Schäffler, Moritz and Wales, David J. and Strodel, Birgit},
      title        = {{E}nergy {L}andscape and {K}inetic {A}nalysis of
                      {M}olecular {D}ynamics {S}imulations for {I}ntrinsically
                      {D}isordered {P}roteins},
      journal      = {The journal of physical chemistry / B},
      volume       = {129},
      number       = {44},
      issn         = {1520-6106},
      address      = {Washington, DC},
      publisher    = {Americal Chemical Society},
      reportid     = {FZJ-2025-04372},
      pages        = {11430-11440},
      year         = {2025},
      abstract     = {Understanding the conformational dynamics of biomolecules
                      requires methods that go beyond structural sampling and
                      provide a quantitative description of thermodynamics and
                      kinetics. For intrinsically disordered proteins (IDPs),
                      energy landscape characterization is particularly crucial to
                      unravel their complex conformational behavior. Here, we
                      present a comprehensive protocol for analyzing molecular
                      dynamics (MD) simulations in terms of energy landscapes,
                      metastable states, and transition pathways. Our approach is
                      based on the distribution of reciprocal interatomic
                      distances (DRID) for dimensionality reduction, followed by
                      clustering and kinetic modeling. Free energy surfaces and
                      transition state barriers are computed directly from the
                      simulation data and visualized using disconnectivity graphs.
                      The method integrates two Python packages, DRIDmetric and
                      freenet, with standard energy landscape tools based on
                      kinetic transition networks, including PATHSAMPLE and
                      disconnectionDPS. We demonstrate this workflow for
                      simulations of the intrinsically disordered,
                      aggregation-prone Alzheimer’s amyloid-β peptide in
                      physiologically relevant environments. This modular
                      framework offers a robust and interpretable way to extract
                      thermodynamic and kinetic insights from MD data and is
                      especially valuable for characterizing the diverse
                      conformational states of IDPs.},
      cin          = {IBI-7},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1021/acs.jpcb.5c05390},
      url          = {https://juser.fz-juelich.de/record/1047546},
}