MPLID (Membrane Protein–Lipid Interaction Database): A Large-Scale Experimental Resource of Residue-Level Protein–Lipid Contacts
TL;DR
MPLID (Membrane Protein–Lipid Interaction Database), a curated residue-level dataset comprising 4,704 membrane proteins representing 813 sequence clusters at 30% identity, 8,055,325 residues, and 80,439 structurally observed lipid-contact annotations, provides the scale needed for training deep-learning models for lipid-contact prediction, with applications in structure-guided drug design and membrane-protein engineering.
Abstract
Abstract Membrane proteins constitute approximately 20–30% of all proteomes and represent over 60% of current drug targets. Although protein–lipid interactions play important structural and regulatory roles in membrane-associated proteins, most existing structural resources focus on identifying whether a residue lies within a membrane region, typically inferred from computational hydrophobicity-based positioning algorithms. This approach does not directly address a distinct biological question: which residues at the protein surface make direct physical contact with lipid molecules? Answering this question from experimental data is critical for understanding lipid-mediated allostery, designing lipid-mimetic therapeutics, and training accurate machine learning models for lipid-binding-site prediction. We present MPLID (Membrane Protein–Lipid Interaction Database), a curated residue-level dataset comprising 4,704 membrane proteins representing 813 sequence clusters at 30% identity, 8,055,325 residues, and 80,439 structurally observed lipid-contact annotations (1.00% observed positive rate). Labels are derived exclusively from experimentally resolved lipid molecules in experimentally determined Protein Data Bank structures using a 4.0 Å all-atom heavy-atom distance cutoff. Because many native lipid interactions are lost or remain unresolved during purification and structure determination, this observed rate represents a lower bound, and the non-contact class inevitably contains false negatives. The dataset uses a curated list of 117 candidate lipid identifiers across ten functional categories, including 90 PDB-derived ligand codes audited against the RCSB Chemical Component Dictionary and 27 CHARMM-style lipid identifiers encountered in cryo-EM depositions. These identifiers span phospholipids, cardiolipin, sphingolipids, sterols, fatty acids, glycerolipids, detergent mimetics, lipid A components, and CHARMM simulation nomenclature. To minimize data leakage, proteins are clustered at 30% sequence identity using MMseqs2, yielding 813 clusters partitioned into training (2,578), validation (1,051), and test (1,075) splits. Amino acid composition analysis reveals biologically consistent enrichment at lipid-contact sites: tryptophan (1.88×), arginine (1.44×), glycine (1.36×), lysine (1.33×), and phenylalanine (1.23×) are enriched, whereas proline (0.51×), isoleucine (0.57×), and aspartate (0.59×) are depleted. MPLID addresses a distinct biological question from existing resources such as OPM, MemBlob, and BioDolphin/PLIP by identifying residues that directly contact experimentally resolved lipid molecules rather than residues positioned within computationally defined membrane boundaries. With 4,704 proteins and more than 8 million annotated residues, MPLID provides the scale needed for training deep-learning models for lipid-contact prediction, with applications in structure-guided drug design and membrane-protein engineering. The dataset adheres to FAIR principles and is freely available under a CC0 public-domain dedication. Structurally resolved contacts represent only a subset of biological protein–lipid interactions, and MPLID is intended as an experimentally grounded resource rather than a complete catalogue of lipid-binding sites.