LIPID MAPS

LIPID MAPS provides integrated databases and computational resources for curation, classification, structure search, and mass-spectrometry-based structure prediction of biologically relevant lipids.

Key Features:

• LIPID MAPS Structure Database (LMSD): A relational database of lipid structures and annotations integrating data from core laboratories, experimental results, computational predictions, and curated sources including LIPID BANK and LIPIDAT.
• Consistent structural representation: Ensures standardized depiction of lipid structures for retrieval and integration across datasets.
• Text- and structure-based search: Retrieval by LIPID MAPS ID, systematic or common names, mass, formula, category, main class, and subclass plus substructure searches and exact matches of user-drawn structures.
• LIPID MAPS Proteome Database (LMPD): An object-relational repository cataloging lipid-associated protein sequences and annotations initially for human and mouse lipid metabolism proteins.
• External annotation integration: LMPD leverages annotations from UniProt, EntrezGene, ENZYME, GO, and KEGG and links to external databases.
• Structured lipid vocabulary and identifiers: A controlled vocabulary classifies lipids into eight classes (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) and assigns a unique 12-digit identifier to each molecule.
• Structure generation: Generates chemical structures for six categories of lipids with specification of chain lengths, head groups, double bond positions, and stereochemistry.
• MS-based structure prediction: Prediction tools for mono/di/triacylglycerols, glycerophospholipids, and cardiolipins to aid interpretation of mass spectrometry data.

Scientific Applications:

• Systems biology and lipidomics classification: Provides a structured vocabulary and identifiers to support systematic lipid classification and data integration.
• Lipid identification from MS data: Supports interpretation of mass-spectrometry experiments through MS-based structure prediction for specific lipid classes.
• Proteome annotation for lipid metabolism: Catalogs lipid-associated proteins and consolidates annotations from UniProt, EntrezGene, ENZYME, GO, and KEGG.
• Data integration and communication: Enables consistent cross-database integration and international communication via standardized structure representation and 12-digit identifiers.

Methodology:

Relational implementation of the LMSD and object-relational implementation of the LMPD; integration and curation of data from core labs, experimental findings, computational predictions, LIPID BANK, and LIPIDAT; structure-based search supporting substructure and exact matching of drawn structures; MS-based structure prediction for mono/di/triacylglycerols, glycerophospholipids, and cardiolipins; assignment of a controlled vocabulary and unique 12-digit identifiers to lipid molecules.