Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

TL;DR

This paper demonstrates the use of empirical and machine-learned energy functions for molecular dynamics simulations of tripeptides, achieving quantitative agreement with experimental spectroscopy and exploring conformational space.

Contribution

It introduces machine-learned potential energy surfaces trained on high-level data for accurate MD simulations of tripeptides in various states.

Findings

ML-PES yields quantitative spectral agreement with experiments

Simulations reveal conformational dynamics and spectral shifts in tripeptides

Feasibility of nanosecond-scale MD simulations with ML potentials

Abstract

Molecular dynamics 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 which 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 red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments.