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@ARTICLE{Strodel:902424,
      author       = {Strodel, Birgit},
      title        = {{E}nergy {L}andscapes of {P}rotein {A}ggregation and
                      {C}onformation {S}witching in {I}ntrinsically {D}isordered
                      {P}roteins},
      journal      = {Journal of molecular biology},
      volume       = {433},
      number       = {20},
      issn         = {0022-2836},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-04246},
      pages        = {167182 -},
      year         = {2021},
      abstract     = {The protein folding problem was apparently solved recently
                      by the advent of a deep learning method for protein
                      structure prediction called AlphaFold. However, this program
                      is not able to make predictions about the protein folding
                      pathways. Moreover, it only treats about half of the human
                      proteome, as the remaining proteins are intrinsically
                      disordered or contain disordered regions. By definition
                      these proteins differ from natively folded proteins and do
                      not adopt a properly folded structure in solution. However
                      these intrinsically disordered proteins (IDPs) also
                      systematically differ in amino acid composition and uniquely
                      often become folded upon binding to an interaction partner.
                      These factors preclude solving IDP structures by current
                      machine-learning methods like AlphaFold, which also cannot
                      solve the protein aggregation problem, since this
                      meta-folding process can give rise to different aggregate
                      sizes and structures. An alternative computational method is
                      provided by molecular dynamics simulations that already
                      successfully explored the energy landscapes of IDP
                      conformational switching and protein aggregation in multiple
                      cases. These energy landscapes are very different from those
                      of ‘simple’ protein folding, where one energy funnel
                      leads to a unique protein structure. Instead, the energy
                      landscapes of IDP conformational switching and protein
                      aggregation feature a number of minima for different
                      competing low-energy structures. In this review, I discuss
                      the characteristics of these multifunneled energy landscapes
                      in detail, illustrated by molecular dynamics simulations
                      that elucidated the underlying conformational transitions
                      and aggregation processes.},
      cin          = {IBI-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IBI-7-20200312},
      pnm          = {5244 - Information Processing in Neuronal Networks
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5244},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34358545},
      UT           = {WOS:000713305500014},
      doi          = {10.1016/j.jmb.2021.167182},
      url          = {https://juser.fz-juelich.de/record/902424},
}