IR Spectroscopy: From Experimental Spectra to High-Resolution Structural Analysis by Integrating Simulations and Machine Learning

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 <i>N</i>-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.