Vibrational circular dichroism (VCD) is an effective technique to study the characteristic amide-I vibrational mode of peptides and thus determine their secondary structures. However, the structure-spectrum relationship for the VCD spectra of peptides is yet to be established. Theoretical modeling has been playing an important role in connecting the peptide structures to their infrared (IR) spectra, and in this work, we aim at achieving a similar goal for the amide-I VCD spectra of peptides by developing a mixed quantum/classical approach.<br/><br/>We devise an ab-initio based scheme to compute the transition magnetic dipole moment, a key quantity in simulating VCD, based on small model systems such as N-methylacetamide, and use machine learning (ML) to accelerate its evaluation onto larger and realistic peptides. We then validate the new method by comparing the computed VCD spectra of several representative peptides with well-defined secondary structures including alpha-helix, beta-hairpin, and 3-10-helix to their experimental spectra. The analyses of the molecular dynamics simulations and the simulated VCD spectra shed light on the molecular origins of some observed VCD spectral features.

Modeling amide-I vibrational circular dichroism of peptides

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