MetFrag

MetFrag performs in silico fragmentation and database searching to identify small molecules from tandem mass spectrometry (MS/MS) data.

Key Features:

• Compound Database Integration: Retrieves candidate structures from PubChem, ChemSpider, KEGG, or user-defined structure data files (SDF).
• In Silico Fragmentation and Scoring: Performs combinatorial in silico fragmentation to predict fragment ions and scores candidates based on fragment matches.
• Scoring Criteria: Filters and scores candidates using occurrence of specific elements or substructures, retention time information, reference data, and suspect lists.
• Algorithmic Refinements: Applies InChIKey filtering to reduce redundancy caused by stereoisomerism and improve candidate selection.
• Enhanced Information Retrieval: Retrieves additional reference, data source, and patent information via ChemSpider and PubChem web services.
• Retention Time Integration: Calculates retention time predictions using user-provided data or incorporates retention time as user-defined scores to refine ranking.

Scientific Applications:

• Metabolomics: Identification of endogenous and exogenous metabolites from MS/MS datasets for metabolomic profiling.
• Drug Discovery: Candidate structure elucidation and prioritization for small molecules relevant to drug discovery and chemical biology.
• HR-MS/MS Benchmarking: Benchmarking and evaluation of in silico fragmentation performance using high-resolution tandem mass spectrometry (HR-MS/MS) datasets.

Methodology:

Candidate retrieval from PubChem, ChemSpider, KEGG, or SDF files; combinatorial in silico fragmentation; filtering and scoring based on element/substructure occurrence, retention time, reference data, and suspect lists; InChIKey filtering to reduce stereoisomer redundancy; retrieval of reference and patent information via ChemSpider and PubChem web services; retention time predictions incorporated as user-defined scores; evaluated on HR-MS/MS datasets and compared against CFM-ID, with MetFrag2.2 showing higher Top 1 ranks on original and merged HR-MS/MS spectra datasets.