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Unknown Metabolites Identification

Unknown Metabolite Identification

Defined by the Metabolomics Standards Initiative, there are four levels metabolite identification confidence: confidently identified compounds (level 1), putatively annotated compounds (level 2), putatively annotated compound classes (level 3) and unknown compounds (level 4). Definitive (level 1) identification requires comparing the two or more orthogonal properties such as retention time, m/z, and fragmentation mass spectrum for the metabolite of interest to the same properties of an authentic chemical standard observed under identical analytical conditions. Putative (level 2 or 3) annotation basically relies on only one or two properties and often based on comparison to data collected in different laboratories and acquired with different analytical methods, rather than a direct comparison with an authentic chemical standard under same analytical conditions.

An unknown metabolite is a small molecule that can repeatedly be detected but whose chemical identity has not been identified yet. Though unidentified and unclassified, these compounds can still be differentiated and quantified in a metabolomics experiment based upon spectral data. In LC-MS experiment, an unknown compound would be defined by a unique retention time, one or multiple masses, or a specific fragmentation pattern of the primary ions. In NMR experiment, an unknown compound would be defined by a pattern in the chemical shifts. Unknowns may represent previously undocumented small molecules, such as secondary products of metabolism or rare xenobiotics, or they may constitute molecules from established pathways but could not be assigned with current libraries of NMR reference spectra or MS fragmentation patterns.

Identification of unknown compounds is labor consuming and cost-intensive, often requiring preparative scale isolation for NMR studies or extensive chemical synthesis enabling structural comparisons using MS/MS. Therefore, unsurprisingly, most of reports focus on detection of metabolites with available authentic commercial standards or at least existing in metabolite databases. Because of the inherent instability of many metabolites and lack of demand, only a few thousand commercially analytical standards are available. However, these standards and the metabolites existed in databases accounts for only a portion of endogenous metabolites. Therefore, effective methods for prioritizing, studying, and ultimately identifying uncharacterized metabolites is of great importance for metabolomics study. Successful identification of unknown metabolites will exert a great impact on biomarker discovery and omics-research.

To identify the unknown compounds in metabolomic samples, different groups of metabolites should be discriminated. Metabolites of different nominal mass, metabolites with the same nominal mass but of different molecular formula and monoisotopic mass and metabolites of the same nominal and monoisotopic masses but with different chemical structures should be discriminated. For example, leucine and isoleucine are isomers different structure, however with the identical nominal and monoisotopic masses. What’s more, since single metabolites can be detected in a mass spectrometer as various derived species, it is important to assign the various derived species to parent metabolite correctly. For example, in amino acid analysis, the reaction of chemical derivatization by trimethylsilylation (TMS) and amino acids will result in generation of amino acids containing 1, 2 or 3 TMS groups, which all should be belong to the same parent amino acids.

By combining NMR, LC-MS/MS and rule- and algorithm-based methodologies, Creative Proteomics develops a new strategy to identify unknown metabolites allowing for accurate identification of metabolites in complex biological samples. Creative Proteomics is still on its way to develop the tools and databases and provide integration of these different tools and databases for true and complete identification of all metabolites in an automated manner in the future. 

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