LipidCRED: Lipid Computational Reaction and Enzyme Database

Abstract

Mass spectrometry-based lipidomic approaches generate vast datasets of individual lipid species, but translating these compendiums of abundances into meaningful biological insights, particularly regarding enzymatic pathways, remains a significant challenge. Existing bioinformatics tools often struggle with nomenclature heterogeneity, limited reaction coverage, the need for extensive post-hoc validation of enzyme predictions, and/or cumbersome user workflows. To address these limitations, I developed Lipid Computational Reaction and Enzyme Database (LipidCRED), a novel bioinformatics platform designed for comprehensive and validated exploration of lipid metabolism. LipidCRED integrates a robust relational database, amalgamating curated information from SwissLipids, Rhea, UniProtKB, and the HUGO Gene Nomenclature Committee (HGNC), with a sophisticated software engine. Key innovations include advanced nomenclature parsing, a "LipidMatcher" module that intelligently infers specific molecular products from class-level reactions using carbon-chain matching rules, and an emphasis on pre-validated enzyme data. A user-friendly R/Shiny web interface ensures broad accessibility. Benchmarking against leading tools (LipidOne, LINEX, BioPAN) demonstrated LipidCRED's competitive performance in validated lipid-enzyme associations, uniquely providing highly accurate enzyme lists requiring minimal filtering. By streamlining the path from complex lipid lists to reliable enzymatic insights, LipidCRED empowers researchers to more effectively unravel the roles of lipid metabolism in health and disease.