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

Area of Science:

• Biophysics
• Computational Chemistry
• Spectroscopy

Background:

• Understanding biomolecular function requires atomic-scale structural insights into dynamic processes.
• Vibrational infrared (IR) spectroscopy, combined with simulations and quantum-chemical calculations, can reveal subtle structural changes.
• Accurate prediction of vibrational spectra from simulations is essential for the inverse problem of structure inference.

Purpose of the Study:

• To address the forward problem in IR spectroscopy: predicting vibrational spectra from known molecular structures.
• To evaluate computational approaches (normal-mode analysis, Fourier-transformed dipole autocorrelation) for spectrum prediction.
• To assess different simulation levels (QM/MM, ML, classical MM) for their accuracy in spectral prediction.

Main Methods:

• Normal-mode analysis and Fourier-transformed dipole autocorrelation were used to predict IR spectra.
• Simulations were performed using hybrid quantum mechanics/molecular mechanics (QM/MM), machine-learned (ML), and classical molecular mechanics (MM) models.
• Predicted spectra were compared against experimental IR spectra of N-methylacetamide, a peptide bond model.

Main Results:

• The study evaluated the capabilities and limitations of current theoretical biophysical methods for IR spectrum prediction.
• Different simulation levels showed varying degrees of accuracy in reproducing experimental spectra.
• The findings highlight the challenges in accurately decoding structural information from vibrational spectroscopy data using current computational approaches.

Conclusions:

• Accurate prediction of vibrational spectra from biomolecular simulations is a key step towards inferring molecular structures.
• Current computational methods have limitations in precisely decoding structural information from IR spectroscopy.
• Future artificial intelligence (AI)-enhanced models hold significant potential for direct IR-based structure determination, aiding in understanding diseases like neurodegeneration.