% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Kelly:1025182,
      author       = {Kelly, Maria S. and Capelli, Riccardo and Kahawatte,
                      Shehani and Wijethunga, Hesaree and Carloni, Paolo and Dima,
                      Ruxandra I.},
      title        = {{O}ptimizing biophysical predictor selection for katanin
                      allosteric transitions via metadynamics and machine
                      learning},
      issn         = {0006-3495},
      reportid     = {FZJ-2024-02759},
      year         = {2024},
      abstract     = {Katanin, a microtubule-severing enzyme, plays a pivotal
                      role in regulating cytoskeletal dynamics, cell division, and
                      intracellular transport. Katanin does this by forming a
                      hexamer and undergoing a conformational change from its open
                      “spiral” to closed “ring” state, which enacts
                      mechanical forces required for the removal of tubulin dimers
                      from the microtubule lattice. Allosteric regulation has
                      emerged as a critical aspect of katanin’s functionality,
                      where binding of ATP and tubulin carboxy-terminal tails
                      (CTTs) allows katanin to transition from spiral to ring.
                      Additionally, CTT sequence diversity and post-translational
                      modifications are known to modulate katanin activity. Thus,
                      there is a need to learn more about katanin’s allosteric
                      response to ligand binding to understand its full mechanism.
                      Here, we ran molecular dynamics simulations of katanin and
                      surveyed a wide range of biophysical descriptors that reduce
                      the dimensionality of the all-atomistic output while
                      allowing us to identify katanin’s allosteric responses to
                      ligand binding. We studied many physical and chemical
                      predictors within katanin’s monomeric and hexameric form,
                      such as solvent accessibility and salt bridge distances,
                      using machine learning classification algorithms to
                      attribute large descriptor differences to allosteric
                      responses from the binding of either ATP or the CTT.
                      Effective predictors were then utilized as collective
                      variables for metadynamics simulations, that introduce bias
                      potentials to aid in the exploration of the free energy
                      landscape to simulate katanin’s transition from its spiral
                      to ring configuration. We can then test how the binding of
                      relevant CTT sequences affects katanin’s free energy
                      landscape during this transition. In total, we can study the
                      complexity of allosteric regulation through various
                      applications using multiple biophysical features of
                      katanin.},
      month         = {Feb},
      date          = {2024-02-10},
      organization  = {Biophysical Society Meeting,
                       Philadelphia (USA), 10 Feb 2024 - 14
                       Feb 2024},
      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)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.1016/j.bpj.2023.11.1698},
      url          = {https://juser.fz-juelich.de/record/1025182},
}