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@ARTICLE{Schmidt:862493,
      author       = {Schmidt, Denis and Boehm, Markus and McClendon, Christopher
                      L. and Torella, Rubben and Gohlke, Holger},
      title        = {{C}osolvent-enhanced {S}ampling and {U}nbiased
                      {I}dentification of {C}ryptic {P}ockets {S}uitable for
                      {S}tructure-based {D}rug {D}esign},
      journal      = {Journal of chemical theory and computation},
      volume       = {15},
      number       = {5},
      issn         = {1549-9626},
      address      = {Washington, DC},
      reportid     = {FZJ-2019-02799},
      pages        = {3331–3343},
      year         = {2019},
      abstract     = {Modulating protein activity with small molecules binding to
                      cryptic pockets offers great opportunities to overcome
                      hurdles in drug design. Cryptic sites are atypical binding
                      sites in proteins that are closed in the absence of a
                      stabilizing ligand and are thus inherently difficult to
                      identify. Many studies have proposed methods to predict
                      cryptic sites. However, a general approach to prospectively
                      sample open conformations of these sites and to identify
                      cryptic pockets in an unbiased manner suitable for
                      structure-based drug design remains elusive. Here, we
                      describe an all-atom, explicit cosolvent, molecular dynamics
                      (MD) simulations-based workflow to sample the open states of
                      cryptic sites and identify opened pockets, in a manner that
                      does not require a priori knowledge about these sites.
                      Furthermore, the workflow relies on a target-independent
                      parameterization that only distinguishes between binding
                      pockets for peptides or small-molecules. We validated our
                      approach on a diverse test set of seven proteins with
                      crystallographically determined cryptic sites. The known
                      cryptic sites were found among the three highest-ranked
                      predicted cryptic sites, and an open site conformation was
                      sampled and selected for most of the systems.
                      Crystallographic ligand poses were well reproduced by
                      docking into these identified open conformations for five of
                      the systems. When the fully open state could not be
                      reproduced, we were still able to predict the location of
                      the cryptic site, or identify other cryptic sites that could
                      be retrospectively validated with knowledge of the protein
                      target. These characteristics render our approach valuable
                      for investigating novel protein targets without any prior
                      information.},
      cin          = {JSC / NIC / ICS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406 /
                      I:(DE-Juel1)ICS-6-20110106},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / Forschergruppe Gohlke $(hkf7_20170501)$},
      pid          = {G:(DE-HGF)POF3-511 / $G:(DE-Juel1)hkf7_20170501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30998331},
      UT           = {WOS:000468242900048},
      doi          = {10.1021/acs.jctc.8b01295},
      url          = {https://juser.fz-juelich.de/record/862493},
}