DOI: 10.5061/dryad.1rn8pk175
title: Data and code from: Decoding protein–membrane binding interfaces from surface-fingerprint-based geometric deep learning and molecular dynamics simulations

description: Predicting protein–membrane interactions is a formidable challenge due to
 the subtle physicochemical features that distinguish membrane-binding
 regions of a protein surface, as well as the scarcity of experimentally
 resolved membrane-bound protein conformations. Here, we present MaSIF-PMP,
 a geometric deep learning model that leverages molecular surface
 fingerprints to predict interfacial binding sites (IBSs) of peripheral
 membrane proteins (PMPs). MaSIF-PMP integrates geometric and chemical
 surface features to produce spatially resolved IBS predictions. Compared
 to existing models, MaSIF-PMP achieves superior performance for IBS
 classification, while feature ablation studies and transfer learning
 analyses reveal distinct determinants governing protein–membrane versus
 protein–protein interactions. We further show that molecular dynamics (MD)
 simulations can validate model predictions, refine IBS labels, and capture
 composition-dependent membrane binding patterns. These results establish
 MaSIF-PMP as an effective framework for IBS prediction and highlight the
 potential of incorporating conformational dynamics from MD to improve both
 model accuracy and biological interpretability.
description: # README Author: ByungUk Park\ README generated on: 10/21/2025 This
 archive (`data.tar.gz`) contains all data relevant to molecular dynamics
 (MD) simulations of peripheral membrane proteins (PMPs) with HMMM (Highly
 Mobile Membrane Mimetic) model systems, as described in the following
 work: B. Park and R. C. Van Lehn. (2025). Decoding protein-membrane
 binding interfaces from surface-fingerprint-based geometric deep learning
 and molecular dynamics simulations. *bioRxiv*.
 [https://doi.org/10.1101/2025.10.14.682447](https://doi.org/10.1101/2025.10.14.682447). The archive also includes scripts for running the simulations and generating MD-based membrane-binding interface labels. All simulations were performed using GROMACS 2021.5. ## File Types & Software Requirements Among the simulation files, some (`.gro`, `.ndx`, `.top`, `.itp`, `.mdp` and `.xvg`) are plain text files that can be opened with standard text editors, while others (`.tpr`, `.xtc`) are binary files that require GROMACS software to read or execute. * `.tpr`: Simulation input (coordinates, parameters, etc.) * `.ndx`: List of indices of atom/residue groups * `.top`: System topology and parameters * `.itp`: Modular components of a full topology (parameters, atom types, bonds, angles,dihedrals, etc.) * `.mdp`: Simulation control parameters (simulation time, temperature, pressure, etc.) * `.gro`: System structure (coordinates, atom type, box size) * `.xtc`: Compressed trajectory Software & package (tested version in parentheses): * GROMACS (2021.5) * Python (3.10) * MDTraj (1.10.1) * NumPy (1.24.4) * pandas (1.5.3) ## Directory Structure ### `1.alpha-tocopherol_transfer_protein/` Contains all input and output files for simulations of α-tocopherol transfer protein (α-TTP) with HMMM membranes.\ Includes `.gro`, `.itp`, `.mdp`, `.xtc` files, and the interface labels (`.csv`) derived from production-phase trajectories. Structure: * `input_files/` * Input files for replica simluations of each initial orientation (`orientation_#/` subdirectories) * Each `orientation_#/` dicrectory includes: * Initial structure (`.gro`), index (`.ndx`), topology (`.top`), and simulation parameter (`.mdp`) files generated by CHARMM-GUI * `toppar/` — force field and topology parameter files * `output_files/` * Output files for replica simulations of each orientation (`orientation_#/replica#` subdirectories) * Contains energy minimization, equilibration, and production outputs * `traj_based_iface/` * Consensus binding interface labels determined from production-phase trajectories, generated via `traj_based_pmp_iface_consensus_label.ipynb` * Subdirectories: * `orientation_#/` — consensus labels per orientation (`3W67_A_consensus_iface.csv`) in MaSIF-PMP-compatible format * `union/` — union of consensus labels across all orientations (`3W67_A_union_iface.csv`) * `3W67_A__iface_0.5nm.csv` * Ground-truth membrane-binding interface derived from the final snapshot of MD simulation (via `pmp_iface_from_coord.ipynb`) * Formatted for MaSIF-PMP compatibility * `3w67_mem_bound.pdb` * Membrane-bound conformation of α-TTP (orientation 1, replica 1) ### `2.phospholipase_A2/` Contains all input and output files for phospholipase A2 simulations with anionic and zwitterionic HMMM membranes. Subdirectories: * `anionic_memb/` * `zwitterionic_memb/` Each subdirectory follows the same organization as described in [1.alpha-tocopherol_transfer_protein](#1.alpha-tocopherol_transfer_protein).\ (Only single-replica simulations were run for each orientation; thus directories are named `orientation_#/` instead of `orientation_#/replica#`.) ### `3.glycosyl_hydrolase/` Contains input and output files for simulations of glycosyl hydrolase with HMMM membranes, following the same organizational structure described above. ### `4.oxysterol-binding_protein_homologue/TGN/` Contains input and output files for simulations of oxysterol-binding protein homologue (Osh4) with TGN-like HMMM membranes. Same structure as above, with additional files: * `1ZHZ_A_TGN_iface_0.5nm.csv` * Ground-truth interface from the final MD snapshot (`pmp_iface_from_coord.ipynb`) * MaSIF-PMP compatible format * `1ZHZ_A_TGN_iface_paper.csv` * Ground-truth interface from previous MD studies of Osh4–TGN membrane interactions * `1zhz_tgn_hmmm.pdb` * Membrane-bound conformation sampled from the simulation CSV files contain the following variables: * cathpdb * pdb * residue_name * IBS * chain_id * residue_number ## Scripts Scripts for running simulations and generating MD-based membrane-binding interface labels are provided below. * `pmp_iface_from_coord.ipynb` * Generates interface labels from simulation coordinate files (`.gro`) * Update variables before use: `root`, `sys_name`, `PDB_ID`, `hmmm_type`, etc. * `traj_based_pmp_iface_consensus_label.ipynb` * Generates production-phase trajectory-based interface labels (`.xtc`) * Update variables before use: `root`, `sys_name`, `PDB_ID`, `hmmm_type`, etc. * `submit_runs_charmmgui.sh` * Bash script for running energy minimization, equilibration, and production using CHARMM-GUI–generated inputs * Update parameters before running: * `root_dir`: path to the target system * `OMP_NUM_THREADS`: number of threads to match GPU/CPU configuration * Run command: ```sh $ bash submit_runs_charmmgui.sh ```