Tripeptide dynamics from empirical and machine-learned energy functions

Molecular dynamics (MD) simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm^-1 that compares with 20 cm^-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly redshifted below 3,000 cm^-1. The amide-I vibrations are split by 23 cm^-1, which is consistent with vibrational circular dichroism spectra that were reported to have a splitting of 30 cm^-1. The present work demonstrates that using ML-PES's stable, meaningful, and quantitative MD simulations for hydrated tripeptides on the nanosecond timescale is feasible and provides insight into and an interpretive basis for experiments.