Modeling Infrared Spectroscopy of Nucleic Acids: Integrating Vibrational Non-Condon Effects with Machine Learning Schemes

Vibrational non-Condon effects, which describe how molecular vibrational transitions are influenced by a system's rotational and translational degrees of freedom, are often overlooked in spectroscopy studies of biological macromolecules. In this work, we explore these effects in the modeling of infrared (IR) spectra for nucleic acids in the 1600-1800 cm^-1 region. Through electronic structure calculations, we reveal that the transition dipole moments of the C═O and C═C stretching modes in nucleobases are highly sensitive to solvation, hydrogen bonding, and base stacking conditions. To incorporate vibrational non-Condon effects into spectroscopy modeling, we use local electric fields on chromophore atoms as collective coordinates and leverage experimental IR spectra of oligonucleotides to develop deep neural network-based transition dipole strength (TDS) maps for the C═O and C═C chromophores. By integrating molecular dynamics simulations with a mixed quantum/classical treatment of the line shape theory, we apply the TDS maps to calculate the IR spectra of nucleoside 5'-monophosphates, DNA double helices and yeast phenylalanine tRNA. The resulting theoretical spectra show quantitative agreement with experimental measurements. While the predictions for nucleoside 5'-monophosphates are comparable to baseline performance, the TDS maps yield significantly improved IR peak intensities across all oligonucleotides. This theoretical framework effectively bridges atomistic simulations and IR spectroscopy experiments, offering molecular insights into how vibrational non-Condon effects impact the observed spectral features.