Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides

TL;DR

This paper develops and benchmarks machine learning potentials, especially MACE, for accurately modeling hydrogen atom transfer reactions in peptides, enabling quantum-level simulations of complex biological processes.

Contribution

It introduces a systematic approach to generate datasets and compares neural network architectures, demonstrating MACE's superior accuracy and transferability for HAT PES modeling in peptides.

Findings

MACE achieves a mean absolute error of 1.13 kcal/mol on DFT barrier predictions.

The ML potential generalizes beyond training data to model HAT barriers in collagen I.

The approach enables large-scale, quantum-accurate simulations of peptide reactivity.

Abstract

Hydrogen atom transfer (HAT) reactions are essential in many biological processes, such as radical migration in damaged proteins, but their mechanistic pathways remain incompletely understood. Simulating HAT is challenging due to the need for quantum chemical accuracy at biologically relevant scales; thus, neither classical force fields nor DFT-based molecular dynamics are applicable. Machine-learned potentials offer an alternative, able to learn potential energy surfaces (PESs) with near-quantum accuracy. However, training these models to generalize across diverse HAT configurations, especially at radical positions in proteins, requires tailored data generation and careful model selection. Here, we systematically generate HAT configurations in peptides to build large datasets using semiempirical methods and DFT. We benchmark three graph neural network architectures (SchNet, Allegro, and MACE) on their ability to learn HAT PESs and indirectly predict reaction barriers from energy predictions. MACE consistently outperforms the others in energy, force, and barrier prediction, achieving a mean absolute error of 1.13 kcal/mol on out-of-distribution DFT barrier predictions. Using molecular dynamics, we show our MACE potential is stable, reactive, and generalizes beyond training data to model HAT barriers in collagen I. This accuracy enables integration of ML potentials into large-scale collagen simulations to compute reaction rates from predicted barriers, advancing mechanistic understanding of HAT and radical migration in peptides. We analyze scaling laws, model transferability, and cost-performance trade-offs, and outline strategies for improvement by combining ML potentials with transition state search algorithms and active learning. Our approach is generalizable to other biomolecular systems, enabling quantum-accurate simulations of chemical reactivity in complex environments.