Molecular Dynamics Simulation Dataset: POPC Bilayers at Varying Sizes (128, 256, 512 and 1024 lipids) and Hydrations (40, 80 and 160 water/lipid) Analyzed with CPPTRAJ, GROMACS, MDAnalysis, and LiPyphilic

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Authors: Carvalho, Filipe André da Silva, Maximiano, Pedro, Simões, Pedro Nuno NL, Hashemi, Mohtadin
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Abstract

This dataset contains 500 ns all-atom Molecular Dynamics (MD) trajectories and parameter analysis scripts for POPC bilayers, covering system sizes of 128, 256, 512, and 1024 lipids at hydration levels of 40, 80, and 160 waters per lipid. The data supports a two-fold study: Biophysical Convergence: Assessing how structural properties (APL, bilayer thickness, order parameters, headgroup tilt) and the dynamic property of lateral diffusion converge as a function of membrane size and hydration. Software Benchmarking: Comparing the accuracy and computational performance of four major analysis packages: CPPTRAJ, GROMACS, MDAnalysis, and LiPyphilic. Repository Contents To reproduce the trajectory analysis: Water-stripped unwrapped (for diffusion analysis) and pre-processed (centered/imaged, for structural analysis) trajectory files sampled at 0.2 ns/frame, distributed across four per-system-size archives: trajectories_128L.zip, trajectories_256L.zip, trajectories_512L.zip, and trajectories_1024L.zip. The corresponding topology and index files are provided separately in topology_indices.zip. A complete set of Bash and Python scripts used to calculate Area Per Lipid (APL), bilayer thickness, order parameters, headgroup orientation, and lateral diffusion (MSD); scripts for intra-replicate convergence diagnostics and inter-replicate statistical analysis; and the software performance benchmarking suite (inside scripts.zip). Refer to the readme.md file for further details. To reproduce the simulations: Simulation input files, topologies, and index files required to reproduce the MD simulations (MD_simulations_input&topology_files.zip). Refer to the readme.md file for guidance.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
23 April 2026
Document type
Dataset