MPLID: Membrane Protein-Lipid Interface Dataset

A large-scale dataset of experimentally validated lipid contact residues derived from experimentally determined structures in the Protein Data Bank. 100% experimental labels with no computational database dependencies.

Overview

MPLID provides residue-level annotations of lipid contacts for 4,704 proteins with experimentally resolved lipid molecules, representing the largest experimentally validated dataset of its kind. All labels are derived exclusively from lipid molecules resolved in PDB structures (X-ray crystallography, cryo-EM) using a 4.0 Å all-atom heavy-atom distance cutoff.

Key Statistics

Dataset Splits

Splits are performed at the cluster level using 30% sequence identity to prevent data leakage.

What Makes MPLID Different

100% Experimental Labels

MPLID derives labels directly from experimentally determined PDB coordinates (X-ray crystallography, cryo-EM). A residue is labeled positive if any of its heavy atoms (non-hydrogen) is within 4.0 Å of any heavy atom of an experimentally resolved lipid molecule. No computational predictions, no membrane plane algorithms, no literature curation required.

Comparison with Other Datasets

MPLID and other protein-lipid datasets answer different biological questions:

Important: DREAMM identifies residues that functionally interact with membranes through biophysical experiments; MPLID identifies residues structurally proximate to crystallized lipids. These are complementary approaches, not directly comparable benchmarks. Models trained on one may not transfer to the other.

Installation

git clone https://github.com/omagebright/MPLID.git
cd MPLID
pip install -r requirements.txt

Dependencies

• Python >= 3.8
• pandas >= 1.3.0
• numpy >= 1.20.0
• biopython >= 1.79
• scikit-learn >= 1.0.0
• requests >= 2.25.0

Quick Start

Loading the Dataset

import pandas as pd

# Load training data (compressed format)
train = pd.read_csv('data/processed/train_residues.csv.gz', compression='gzip')

print(f"Training samples: {len(train):,}")
print(f"Positive samples: {train['is_contact'].sum():,}")
print(f"Positive rate: {train['is_contact'].mean():.2%}")

# Filter for contact residues
contacts = train[train['is_contact'] == True]
print(f"\nContact residues: {len(contacts):,}")
print(f"Unique proteins with contacts: {contacts['pdb_id'].nunique():,}")

Data Format

Each CSV file contains the following columns:

Repository Structure

MPLID/
├── data/
│   ├── processed/           # Ready-to-use dataset files
│   │   ├── train_residues.csv.gz
│   │   ├── val_residues.csv.gz
│   │   ├── test_residues.csv.gz
│   │   └── protein_metadata.csv
│   └── statistics/          # Dataset statistics
│       └── pipeline_stats.json
├── scripts/                 # Data processing pipeline
│   └── run_rcsb_only_pipeline.py
├── notebooks/               # Analysis notebooks
│   └── dataset_exploration.ipynb
└── docs/                    # Documentation
    ├── METHODOLOGY.md
    └── DATA_DICTIONARY.md

Methodology

Data Source

MPLID uses a single data source: the RCSB Protein Data Bank. We query RCSB for all structures containing recognized lipid chemical components (117 codes), download the original PDB files preserving HETATM records, and compute contacts directly from atomic coordinates.

No OPM database, no membrane plane calculations, no external computational dependencies.

Contact Definition

A residue is labeled as a lipid contact if:

d_min(R, L) = min_{a ∈ R_heavy, b ∈ L_heavy} ||r_a - r_b|| ≤ 4.0 Å

Where R_heavy is the set of all heavy atoms (non-hydrogen) of residue R and L_heavy is the set of all heavy atoms of lipid molecule L.

Supported Lipid Types (117 codes)

• Phospholipids (24): PC1, PCW, POV, PEE, PGV, LHG, PIP, etc.
• Cardiolipin (4): CDL, C9V, 18W, LCL
• Sphingolipids (7): SPH, S1P, HXJ, CRT, GCR, GSP, GLF
• Sterols (9): CLR, CHD, Y01, ERG, SIT, STI, etc.
• Fatty acids (14): PLM, MYR, OLA, STE, ARA, DHA, etc.
• Glycerolipids (6): TGL, DAG, MAG, GMO, etc.
• Detergents (22): LDA, LMT, BOG, OLC, DPC, SDS, etc.
• Lipid A (3): LPA, KDO, 6LP
• CHARMM simulation (27): POPC, POPE, POPG, etc.
• Generic (1): LIP

See docs/DATA_DICTIONARY.md for the complete list.

Quality Control

1. Sequence clustering: 30% identity threshold using MMseqs2
2. Cluster-level splitting: Entire clusters assigned to same split
3. No data leakage: No protein in test shares >30% identity with training

Reproducing the Dataset

# Single command to regenerate the entire dataset from RCSB
python scripts/run_rcsb_only_pipeline.py

# With options
python scripts/run_rcsb_only_pipeline.py --skip-rcsb-query  # Use cached RCSB results
python scripts/run_rcsb_only_pipeline.py --max-proteins 100  # Test with subset

Limitations

1. Crystallization artifacts: Some lipid contacts may reflect crystallization conditions rather than native interactions
2. Detergents included: Dataset includes detergent molecules used in crystallization
3. Static structures: Does not capture dynamic or transient lipid interactions
4. Class imbalance: 1.00% positive rate (100:1 ratio) requires appropriate ML techniques

Citation

If you use this dataset, please cite:

@article{omage2026mplid,
  author = {Omage, Folorunsho Bright and Neshich, Goran},
  title = {{MPLID} ({Membrane Protein-Lipid Interaction Database}): A Large-Scale Experimental Resource of Residue-Level Protein-Lipid Contacts},
  journal = {GigaScience},
  year = {2026},
  doi = {10.5281/zenodo.18487584}
}

License

• Code: MIT License (see LICENSE)
• Data: CC0 1.0 Universal (Public Domain)

Acknowledgments

This research was funded by the São Paulo Research Foundation (FAPESP) (grant nos. 2023/02691-2, 2025/23708-6).

Contact

Folorunsho Bright Omage

• Email: [email protected]
• ORCID: 0000-0002-9750-5034

For questions or issues, please open a GitHub issue or contact the author directly.