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@ARTICLE{Ferraro:890174,
      author       = {Ferraro, Mariarosaria and Moroni, Elisabetta and Ippoliti,
                      Emiliano and Rinaldi, Silvia and Sanchez-Martin, Carlos and
                      Rasola, Andrea and Pavarino, Luca F. and Colombo, Giorgio},
      title        = {{M}achine {L}earning of {A}llosteric {E}ffects: {T}he
                      {A}nalysis of {L}igand-{I}nduced {D}ynamics to {P}redict
                      {F}unctional {E}ffects in {TRAP}1},
      journal      = {The journal of physical chemistry / B},
      volume       = {125},
      number       = {1},
      issn         = {1520-5207},
      address      = {Washington, DC},
      publisher    = {Soc.},
      reportid     = {FZJ-2021-00763},
      pages        = {101 - 114},
      year         = {2021},
      abstract     = {Allosteric molecules provide a powerful means to modulate
                      protein function. However, the effect of such ligands on
                      distal orthosteric sites cannot be easily described by
                      classical docking methods. Here, we applied machine learning
                      (ML) approaches to expose the links between local dynamic
                      patterns and different degrees of allosteric inhibition of
                      the ATPase function in the molecular chaperone TRAP1. We
                      focused on 11 novel allosteric modulators with similar
                      affinities to the target but with inhibitory efficacy
                      between the 26.3 and $76\%.$ Using a set of experimentally
                      related local descriptors, ML enabled us to connect the
                      molecular dynamics (MD) accessible to ligand-bound
                      (perturbed) and unbound (unperturbed) systems to the degree
                      of ATPase allosteric inhibition. The ML analysis of the
                      comparative perturbed ensembles revealed a redistribution of
                      dynamic states in the inhibitor-bound versus inhibitor-free
                      systems following allosteric binding. Linear regression
                      models were built to quantify the percentage of experimental
                      variance explained by the predicted inhibitor-bound TRAP1
                      states. Our strategy provides a comparative MD–ML
                      framework to infer allosteric ligand functionality.
                      Alleviating the time scale issues which prevent the routine
                      use of MD, a combination of MD and ML represents a promising
                      strategy to support in silico mechanistic studies and drug
                      design.},
      cin          = {IAS-5 / INM-9},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {524 - Molecular and Cellular Information Processing
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-524},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33369425},
      UT           = {WOS:000661200000009},
      doi          = {10.1021/acs.jpcb.0c09742},
      url          = {https://juser.fz-juelich.de/record/890174},
}