How Well Do Molecular Dynamics Force Fields Model Peptides? A Systematic Benchmark Across Diverse Folding Behaviors

Abstract

Linear peptides play essential roles in biology and drug discovery, frequently mediating protein-protein interactions through short, flexible motifs. However, their structural plasticity--ranging from disordered to context-dependent folding--makes them challenging targets for molecular simulations. In this work, we benchmark the performance of eleven popular and emerging fixed-charge force fields across a curated set of twelve peptides spanning structured miniproteins, context-sensitive epitopes, and disordered sequences. Each peptide was simulated from both folded (200 ns) and extended (10 {micro}s) states to assess stability, folding behavior, and force field biases. Our analysis reveals consistent trends: some force fields exhibit strong structural bias, others allow reversible fluctuations, and no single model performs optimally across all systems. The study highlights limitations in current force fields ability to balance disorder and secondary structure, particularly when modeling conformational selection. These results offer practical guidance for peptide modeling and establish a benchmark framework for future force field development and validation in peptide-relevant regimes.