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@ARTICLE{Scherlo:1047547,
      author       = {Scherlo, Marvin and Phillips, Dominic and Künne, Ricarda
                      and Ippoliti, Emiliano and Gerwert, Klaus and Kötting,
                      Carsten and Carloni, Paolo and Mey, Antonia S. J. S. and
                      Rudack, Till},
      title        = {{IR} {S}pectroscopy: {F}rom {E}xperimental {S}pectra to
                      {H}igh-{R}esolution {S}tructural {A}nalysis by {I}ntegrating
                      {S}imulations and {M}achine {L}earning},
      journal      = {The journal of physical chemistry / B},
      volume       = {129},
      number       = {45},
      issn         = {1520-6106},
      address      = {Washington, DC},
      publisher    = {Americal Chemical Society},
      reportid     = {FZJ-2025-04373},
      pages        = {11652–11665},
      year         = {2025},
      abstract     = {Understanding biomolecular function at the atomic scale
                      requires detailed insight into the structural changes
                      underlying dynamic processes. Vibrational infrared (IR)
                      spectroscopy─when paired with biomolecular simulations and
                      quantum-chemical calculations─determines bond length
                      variations on the order of 0.01 Å, providing insights into
                      these structural changes. Here, we address the forward
                      problem in IR spectroscopy: predicting high-accuracy
                      vibrational spectra from known molecular structures
                      identified by biomolecular simulations. Solving this problem
                      lays the groundwork for the inverse problem: inferring
                      structural ensembles directly from experimental IR spectra.
                      We evaluate two computational approaches, normal-mode
                      analysis and Fourier-transformed dipole autocorrelation,
                      against experimental IR spectra of N-methylacetamide, a
                      prototypical model for peptide bond vibrations. Spectra are
                      derived from simulation models at multiple levels of theory,
                      including hybrid quantum mechanics/molecular mechanics,
                      machine-learned, and classical molecular mechanics
                      approaches. Our results highlight the capabilities and
                      limitations of current theoretical biophysical approaches to
                      decode structural information from experimental vibrational
                      spectroscopy data. These insights underscore the potential
                      of future artificial intelligence (AI)-enhanced models to
                      enable direct IR-based structure determination. For example,
                      resolving the so-far experimentally inaccessible structures
                      of toxic oligomers involved in neurodegenerative diseases,
                      enabling improved disease diagnostics and targeted
                      therapies.},
      cin          = {INM-9},
      ddc          = {530},
      cid          = {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)16},
      doi          = {10.1021/acs.jpcb.5c04866},
      url          = {https://juser.fz-juelich.de/record/1047547},
}