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@ARTICLE{Schneider:878447,
      author       = {Schneider, Jakob and Korshunova, Ksenia and Si Chaib,
                      Zeineb and Giorgetti, Alejandro and Alfonso-Prieto, Mercedes
                      and Carloni, Paolo},
      title        = {{L}igand {P}ose {P}redictions for {H}uman {G}
                      {P}rotein-{C}oupled {R}eceptors: {I}nsights from the
                      {A}mber-based {H}ybrid {M}olecular
                      {M}echanics/{C}oarse-{G}rained {A}pproach},
      journal      = {Journal of chemical information and modeling},
      volume       = {60},
      number       = {10},
      issn         = {1549-960X},
      address      = {Washington, DC},
      publisher    = {American Chemical Society64160},
      reportid     = {FZJ-2020-02855},
      pages        = {5103–5116},
      year         = {2020},
      abstract     = {Human G protein-coupled receptors (hGPCRs) are the most
                      frequent targets of Food and Drug Administration
                      (FDA)-approved drugs. Structural bioinformatics, along with
                      molecular simulation, can support structure-based drug
                      design targeting hGPCRs. In this context, several years ago,
                      we developed a hybrid molecular mechanics
                      (MM)/coarse-grained (CG) approach to predict ligand poses in
                      low-resolution hGPCR models. The approach was based on the
                      GROMOS96 43A1 and PRODRG united-atom force fields for the MM
                      part. Here, we present a new MM/CG implementation using,
                      instead, the Amber 14SB and GAFF all-atom potentials for
                      proteins and ligands, respectively. The new implementation
                      outperforms the previous one, as shown by a variety of
                      applications on models of hGPCR/ligand complexes at
                      different resolutions, and it is also more user-friendly.
                      Thus, it emerges as a useful tool to predict poses in
                      low-resolution models and provides insights into ligand
                      binding similarly to all-atom molecular dynamics, albeit at
                      a lower computational cost.},
      cin          = {INM-9 / IAS-5 / INM-11},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-9-20140121 / I:(DE-Juel1)IAS-5-20120330 /
                      I:(DE-Juel1)INM-11-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {32786708},
      UT           = {WOS:000586716900061},
      doi          = {10.1021/acs.jcim.0c00661},
      url          = {https://juser.fz-juelich.de/record/878447},
}