perez, a. | bioRxiv Science

bioRxiv2025

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

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.

biophysics↗

bioRxiv2025

MDZip: Neural Compression of Molecular Dynamics Trajectories for Scalable Storage and Ensemble Reconstruction

The size of molecular dynamics (MD) trajectories remains a major obstacle for data sharing, long-term storage, and ensemble analysis at scale. Existing solutions often rely on frame subsampling or reduced atom representations, which limit the utility of shared datasets. Here, we present MDZip, a neural compression framework based on convolutional autoencoders trained per system to reconstruct atomic trajectories with high geometric fidelity from compact latent representations. MDZip achieves over 95% reduction in storage size across a diverse benchmark of proteins, protein-peptide complexes, and nucleic acids. Despite operating in a physics-agnostic manner, the reconstructed trajectories accurately preserve ensemble-level features, including RMSD fluctuations, pairwise distance distributions, radius of gyration, and projections onto principal and time-lagged independent components. A residual (skip-connected) autoencoder variant consistently improves reconstruction accuracy and reduces outliers. While local structural deviations can impair energetic fidelity, short energy minimization partially recovers physically reasonable conformations. This framework enables customizable compression-accuracy trade-offs and supports a modular workflow for sharing latent representations, decoder models, and reconstruction protocols. MDZip offers a scalable solution to current storage limitations, facilitating broader dissemination of MD data without sacrificing essential dynamical information.

biophysics↗