DOI: 10.5061/dryad.ttdz08m92
title: Data from: Elucidating the impact of red blood cell membrane components on melittin-induced pore formation with molecular dynamics simulations

description: Understanding the membrane-disrupting mode of action of antimicrobial
 peptides (AMPs) in complex biological membranes is critical for the design
 of therapeutically viable AMPs that are both active against microbial
 pathogens and nontoxic to human cells. To assess human toxicity in AMP
 design studies, melittin (MEL) – a highly charged 26-amino acid AMP
 sourced from bee venom – is often used as the positive control in
 experimental human red blood cell (RBC) hemolysis assays. Molecular
 dynamics (MD) simulations have proved invaluable in elucidating the
 pore-formation mechanism of MEL in single-lipid zwitterionic membranes.
 However, modeling pore formation in lipid bilayers containing multiple
 lipid species, like RBC membranes, has been limited due to the challenges
 of using atomistic MD simulations to capture long-timescale membrane
 restructuring events that depend on lipid heterogeneity, leaflet
 asymmetry, and cholesterol content. To address these challenges and access
 larger time scales, in this work, we utilize the coarse-grained MARTINI
 force field to model four lipid membranes of increasing complexity,
 ranging from single-lipid POPC membranes to asymmetric RBC-mimetic
 membranes containing cholesterol. Through the application of a nucleation
 collective variable (ξ) to create transmembrane pores and a
 coarse-grained-to-atomistic backmapping strategy, we studied MEL
 pore-lining affinity and pore nucleation free energies to assess the
 effect of lipid complexity and cholesterol on MEL pore formation. We find
 that although cholesterol strongly inhibits MEL-induced pore formation
 regardless of lipid content, pore nucleation is more favorable in RBC
 versus single-lipid POPC membranes when cholesterol is absent due to the
 enrichment of anionic POPS lipids near the pore that permits increased
 conformational flexibility for MEL. These results provide new physical
 insight into factors that affect pore formation in compositionally complex
 membranes and are a step toward understanding how AMPs can be designed to
 selectively induce pores in membranes with different compositions.
description: # Data from: Elucidating the impact of red blood cell membrane components
 on melittin-induced pore formation with molecular dynamics simulations
 Dataset DOI: [10.5061/dryad.ttdz08m92](10.5061/dryad.ttdz08m92) ##
 Description of the data and file structure ### Overview This work
 implements the nucleation collective variable ξ proposed by Hub et. al
 (10.1021/acs.jctc.7b00106) to expand on our recently developed
 coarse-grained to atomistic backmapping and umbrella sampling
 methodology (10.1021/acs.jpcb.4c03276) to rapidly resolve free energy
 profiles for melittin-lined pores in red blood cell (RBC) lipid membranes
 with molecular dynamics (MD) simulations. All simulations were run with
 [Gromacs
 2021.5](https://manual.gromacs.org/2021.5/user-guide/getting-started.html)
 patched with PLUMED 2.8. ### Folder Structure The provided tarball
 (Supporting_Data.tar.gz) contains the following folders to aid with the
 reproducibility of the results of this work: * **analysis_output** --&gt;
 Stores all trajectory analysis results from MD simulation trajectories *
 **backmapping** --&gt; Contains all necessary .map files for backmapping
 CG to AA configurations, including the 20 amino acids, 4 lipids (POPC,
 POPE, POPS, POSM), and cholesterol (CHOL)  * **ff** --&gt; All
 coarse-grained (martini2.2) and atomistic (charmm36) force field
 parameters used for simulations in this work * **gro_start** --&gt;
 Initial configurations for all umbrella sampling windows * **idx** --&gt;
 Index files (.ndx) for all systems for use with Gromacs * **long_equil**
 --&gt; All long timescale MARTINI simulations used to initialize MEL
 pore-lining configurations * **mdp** --&gt; Molecular dynamics parameter
 files (.mdp) for atomistic umbrella sampling simulations * **topology**
 --&gt; Gromacs topology files (.top) for all systems * **umb_samp** --&gt;
 Umbrella sampling trajectories for 23 windows for each system ###
 Long-Timescale CG Equilibration Simulations All long-timescale CG
 equilibration simulations (following the methodology from the
 'Long-Timescale Pore Equilibration' section in the main text)
 are provided in the '**long_equil**' folder. The following files
 are provided for each system: * '**groups.dat**' file that
 stores the atom indices of the membrane (Mem), lipid head phosphates
 (POxygens), and water W beads (Waters) that are biased as part of the
 implementation of ξ. * **Nucleation_CV.cpp** for implementing ξ in PLUMED
 * Files for each simulation replica (T1, T2, T3) for the selected ξ~long~
 for each replica. * Includes a .dat file for each replica, which is used
 to implement a moving restraint that increases ξ over either 5.65 µs (0.8
 ≤ ξ~long~ ≤ 0.875, Figure S7) or 6.2 µs (0.7 ≤ ξ~long~ ≤ 0.775, Figure 2a)
 to prepare MEL pore-lining configurations ### Backmapping CG to AA
 Configurations As described in the manuscript, MEL pore-lined
 configurations are first achieved with a long timescale pore equilibration
 step (either 5.65 or 6.2 µs, trajectory results are stored in the
 **long_equil** folder), followed by 500 ns of sampling for each window
 across 23 windows ranging from a flat membrane state (ξ = 0.2) to
 nucleated pore state (ξ = 1.0). Final CG configurations from each window
 are then backmapped to the CHARMM36 force field using .map files provided
 in the **backmapping/Mapping** directory. Backmapping from the MARTINI to
 CHARMM36 force field can be achieved with the provided **CG_to_AA.sh**
 script, which has the following user-provided inputs at the top of the
 script: * **memb** --&gt; Lipid membrane type (either **POPC** or **RBC**)
 * **chol** --&gt; % cholesterol in membrane (**0CHOL** or **50CHOL**) *
 **pep** --&gt; Whether the membrane contains melittin (**8 MEL**) or is a
 bare membrane (**Pure_Memb**) * **trial** --&gt; Which of the 3
 independent replicas (**Trial_1**, **Trial_2**, **Trial_3**) * **kink**
 --&gt; If the MARTINI model used for MEL is fully α-helical (**no**) or
 contains a kink at T11 (**yes**) ### Running AA Umbrella Sampling
 Simulations The **run_umbrella_sample.sh** script provided can be utilized
 to set up and run atomistic (AA) umbrella sampling simulations from
 backmapped CG to AA initial configurations across the 23 windows of
 increasing pore size. ### Trajectory Analysis The following shell (.sh)
 and Python (.py) scripts are also provided to aid with the reproducibility
 of the following figures in the main text: * **Figure 2b** --&gt;
 Calculating number of pore-lining MEL during long-timescale CG
 equilibration simulations (Figures 2a, S7) * First, run
 **mel_pore_line.sh** * Centers membrane in *z* direction, calculates MEL
 density in *z* direction, and then integrates density profile from *z* =
 -0.5 nm to *z* = 0.5 nm. * Second, run **mel_pore_line.py** * Converts MEL
 pore density to the number of pore-lining MEL by normalizing the density
 by the density attributed to 1 pore-lining MEL * Visualizes Number of
 Pore-Lining MEL vs. time (**Figure S13**), which is used to calculate the
 'Pore-Lining MEL' metric in **Figure 2b**. * **Figure 3** --&gt;
 Calculating POPS flip-flop and lateral enrichment metrics
 from long-timescale CG equilibration simulations * **Figure 3a** --&gt;
 run **lipid_flip_flop.py** * Extracts .gro file every 50 ns from
 simulation trajectory * Approximates the membrane center in the *z*
 direction by calculating the average *z* coordinate of all lipid C4A and
 C4B beads * Counts the number of PO4 beads in the upper and lower leaflets
 for each lipid type (POPC, POPE, POPS, POSM) * Visualizes POPS in the
 upper leaflet vs. time as **POPS_flip_flop.svg** * **Figure 3b** --&gt;
 run **lipid_enrich.py** * Extracts .gro file every 50 ns from simulation
 trajectory * Counts the number of PO4 beads within a 2 nm radius of the
 pore center (*x* = 5 nm, *y* = 5 nm) for each lipid type (POPC, POPE,
 POPS, POSM) * Visualizes POPS within 2 nm of pore center vs. time
 as **POPS_enrich.svg** * **Figures 4a and 5** --&gt; Average PMF profiles
 vs ξ across 3 replicas * All PMF free energy files are provided as
 '**freefile.txt**' in the **umb_samp** folder * 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 6** --&gt; Average area per lipid (APL) and bilayer thickness for atomistic umbrella sampling trajectories * **Figure 6a** --&gt; run **APL.py** * Determines the number of lipids + CHOL in the upper leaflet * Calculates average membrane area during simulation with *gmx energy* and normalizes by the number of upper leaflet lipids + CHOL to get APL * **Figure 6b** --&gt; run **Thickness.py** * Creates a density profile of all membrane lipid head phosphorus atoms in the *z* direction * Calculates the *z* value of the phosphorus peak in both the upper and lower leaflet and subtracts these quantities to get the membrane thickness * **Figure 7a** --&gt; Average MEL tilt angle * Run **pep_helix_angle.py** * Creates an index file (tilt.ndx) that stores the N-terminal nitrogen and C-terminal carbon for each MEL * Calculates the tilt angle for each MEL vs. time * Visualizes average tilt angle for each MEL as '**Tilt_Angles.svg**' * **Figure 8** --&gt; Calculate Lipid Fraction Near Pore * Run **lipid_enrich_AA.py** * 'radius' variable (in nm) in file set to target an average of 30 lipids considered for the fully nucleated pore state (ξ=1) for each RBC system considered: * **radius = 1.6** for chol = 0CHOL and pep = Pure_Memb * **radius = 2.0** for chol = 0CHOL and pep = 8MEL * **radius = 2.0** for chol = 50CHOL and pep = Pure_Memb * **radius = 2.2** for chol = 50CHOL and pep = 8MEL * Script calculates the number of lipid head phosphorus within '**radius**' nm of the pore center (*x* = 5 nm, *y* = 5 nm) for each lipid and normalizes by the total number of lipid head phosphorus within '**radius**' * Number of lipids within pore center '**radius**' vs time is stored as '**inrange_P.csv**'