DOI: 10.5061/dryad.vq83bk42v
title: Data from: Free energy analysis of peptide-induced pore formation in lipid membranes by bridging atomistic and coarse-grained simulations

description: Antimicrobial peptides (AMPs) are attractive materials for combating the
 antimicrobial resistance crisis because they can kill target microbes by
 directly disrupting cell membranes. Although thousands of AMPs have been
 discovered, their molecular mechanisms of action are still poorly
 understood. One broad mechanism for membrane disruption is the formation
 of membrane-spanning hydrophilic pores which can be stabilized by AMPs. In
 this study, we use molecular dynamics (MD) simulations to investigate the
 thermodynamics of pore formation in model single-component lipid membranes
 in the presence of one of three AMPs: aurein 1.2, melittin, and magainin
 2. To overcome the general challenge of modeling long-timescale
 membrane-related behaviors, including AMP binding, clustering, and pore
 formation, we develop a generalizable methodology for sampling AMP-induced
 pore formation. This approach involves the long equilibration of peptides
 around a pore created with a nucleation collective variable by performing
 coarse-grained simulations, and then backmapping equilibrated AMP-membrane
 configurations to all-atom resolution. We then perform all-atom
 simulations to resolve free energy profiles for pore formation while
 accurately modeling the interplay of lipid-peptide-solvent interactions
 that dictate pore formation free energies. Using this approach, we
 quantify free energy barriers for pore formation without direct biases on
 peptides or whole lipids, allowing us to investigate mechanisms of pore
 formation for these 3 AMPs that are a consequence of unbiased peptide
 diffusion and clustering. Further analysis of simulation trajectories then
 relates variations in pore lining by AMPs, AMP-induced lipid disruptions,
 and salt bridges between AMPs to the observed pore formation free energies
 and corresponding mechanisms. This methodology and mechanistic analysis
 have the potential to generalize beyond the AMPs in this study to improve
 our understanding of pore formation by AMPs and related antimicrobial
 materials.
description: # Data from: Free energy analysis of peptide-induced pore formation in
 lipid membranes by bridging atomistic and coarse-grained simulations
 [https://doi.org/10.5061/dryad.vq83bk42v](https://doi.org/10.5061/dryad.vq83bk42v) ## Description of the data and file structure This document outlines the simulation and computational methods utilized in the paper “Free Energy Analysis of Peptide-Induced Pore Formation in Lipid Membranes by Bridging Atomistic and Coarse-Grained Simulations” to aid with reproducibility of results discussed. More detailed information on the workflow, including running umbrella sampling simulations and post-trajectory analysis, are included in the README file in the home directory of the attached tarball. All simulations were run with Gromacs 2021.5 patched with PLUMED 2.8. **1.**     **The PLUMED nucleation collective variable (ξ) to create aqueous transmembrane pores** *  Included as the file **Nucleation_CV.cpp** * Utilized for all atomistic (AA) and coarse-grained (CG) umbrella sampling simulations * AA = CHARMM36 force field * CG = MARTINI 2.2 force field * The following parameters are set for the calculation of ξ based on Hub et. al in J. Chem. Theory Comput. 2017, 13, 5, 2352–2366: * Groups: * **Mem** = all membrane atoms (AA) or beads (CG) to calculate the center of the membrane in the z direction * **Waters** = all water oxygens (AA) or water W beads (CG) * **POxygens** = all DMPC lipid head phosphate oxygens (AA) or PO4 beads (CG) * **NS** = defines the number of slices of the transmembrane cylinder * **DS** = slice thickness --&gt; AA = 0.1 nm, CG = 0.2 nm * **RCYL** = radius of transmembrane cylinder --&gt; AA & CG = 0.8 nm * **ZETA** = coefficient that defines slice occupancy after addition of 1 polar atom --&gt; AA & CG = 0.75 * **XCYL and YCYL** = defines lateral center of transmembrane cylinder **2.**     **Umbrella Sampling Simulations** *  All initial CG system files (gro, ndx, top) are provided for the 4 systems considered in this study: Pure DMPC, 8 AUR, 8 MEL, 8 MAG * The script **'run_cg_setup.sh'** is provided which conducts all equilibration and steered MD steps to prepare initial configurations for umbrella sampling * 23 windows are considered from ξ = 0.2 (flat membrane) to ξ = 1.0 (fully nucleated pore) * The script **'run_parallel_us.sh'** runs all 23 windows in parallel utilizing the MPI parallelization capabilities of Gromacs. **3.**     **Backmapping CG to AA configurations** * All mapping (.map) files required to backmap CG systems to their AA representations are provided in the 'backmapping/Mapping' folder * DMPC lipids, each peptide amino acid * The script '**CG_to_AA.sh'** is provided to streamline the backmapping process to generate initial configurations for AA umbrella sampling: * **NOTE: The files backward.py and initram.sh should be downloaded from the Software category provided and placed in the 'backmapping' folder before running CG_to_AA.sh** * Steps: * Backmap CG to AA with **backward.py** based on initial guesses of atom positions and enforced geometry considerations (e.g., chirality) * 2 energy minimization steps (*without* then *with* peptide-peptide and membrane-membrane nonbonded interactions turned on) * 4 restrained NVT equilibration steps with increasing timestep (∆t = 0.0002, 0.0005, 0.001, 0.002 ps) * 2 restrained NPT equilibration steps (∆t = 0.001, 0.002 ps) * 1 unrestrained NPT equilibration step (∆t = 0.002 ps) **4.**     **Trajectory analysis scripts and data** * The following are provided to aid with the reproducibility of Figures 2-7 in the main text: * **Figure 2** --&gt; **Peptide_Density_Convergence.sh** and **Peptide_Density_Convergence_analysis.py** * Centers membrane in box, calculates density of peptide in middle 1 nm of pore, converts density to estimated number of pore-lining peptides. * **Figure 3** * All PMF vs. ξ files are provided (freefile.txt) * Free energies are resolved with Grossfield WHAM --&gt; [http://membrane.urmc.rochester.edu/?page_id=126](http://membrane.urmc.rochester.edu/?page_id=126) * **Figure 4** --&gt; **num_dens**_**heatmap.py** * Calculates average number densities of groups of interest during AA umbrella sampling * 0.1 nm radial and 0.1 nm z bins starting from lateral pore center (defined by **XCYL** and **YCYL** above) * Following groups considered: * Lipid head phosphate * Water oxygen atoms * Peptides * Counterions * **Figure 5a** --&gt; **pep_helix_angle.py** * Creates index files defining N-terminal and C-terminal ends of each peptide * Calculates average tilt angle of each peptide relative to membrane surface per umbrella sampling window * **Figure 6a** --&gt; **SCD.sh** * Calculates average deuterium order parameters (SCD) of each i-1 to i+1 lipid tail carbon based on *gmx order* * **Figure 6c** --&gt; **SCD_radial.py** * Similar to SCD.sh, but only calculates the deuterium order parameter of tails with their head phosphate within a 2 nm radius of the pore center (defined by **XCYL** and **YCYL** above). * **Figure 7a** * All raw salt bridge .dat files are provided, which are the raw data (Figure S25) that were used in the calculation of the 'Normalized Salt Bridge' metric. * Salt bridges were calculated with the 'salt bridges' plugin in vmd setting a cutoff distance of 4 Angstroms (0.4 nm)  ### Version changes **Oct-2024:** Standardized all instances of 'gmx' to 'gmx_mpi' for calling Gromacs for simulation and analysis. Removed some extraneous files (e.g., centered trajectory .xtc) that are generated with provided scripts to reduce .tar download size.