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@ARTICLE{AlfonsoPrieto:1024690,
      author       = {Alfonso-Prieto, Mercedes and Capelli, Riccardo},
      title        = {{M}achine {L}earning-based {M}odeling of {O}lfactory
                      {R}eceptors in their {I}nactive {S}tate: {H}uman {OR}51{E}2
                      as a {C}ase {S}tudy},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {FZJ-2024-02363},
      year         = {2023},
      note         = {Preprint publicly available; peer-reviewed version was
                      published in Journal of Chemical Information and Modeling
                      (doi: 10.1021/acs.jcim.3c00380) as open access paper},
      abstract     = {Atomistic-level investigation of olfactory receptors (ORs)
                      is a challenging task due to the experimental/computational
                      difficulties in the structural determination/prediction for
                      members of this family of G-protein coupled receptors. Here
                      we have developed a protocol that performs a series of
                      molecular dynamics simulations from a set of structures
                      predicted de novo by recent machine learning algorithms and
                      apply it to a well-studied receptor, the human OR51E2. Our
                      study demonstrates the need for simulations to refine and
                      validate such models. Furthermore, we demonstrate the need
                      for the sodium ion at a binding site near D2.50 and E3.39 to
                      stabilize the inactive state of the receptor. Considering
                      the conservation of these two acidic residues across human
                      ORs, we surmise this requirement also applies to the other
                      ∼400 members of this family.},
      cin          = {IAS-5 / INM-9},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524) / DFG project 291198853 - FOR 2518: Funktionale
                      Dynamik von Ionenkanälen und Transportern - DynIon -
                      (291198853) / DFG project 329460521 - Protonentransfer und
                      Substraterkennung in SLC17-Transportern (329460521)},
      pid          = {G:(DE-HGF)POF4-5241 / G:(GEPRIS)291198853 /
                      G:(GEPRIS)329460521},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.02.22.529484},
      url          = {https://juser.fz-juelich.de/record/1024690},
}