Database supported candidate search for metabolite identification.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Biochemistry

Background:

• Mass spectrometry (MS) is crucial for identifying small molecules and metabolites by their exact mass.
• Compounds with similar masses or molecular formulas necessitate further analysis using tandem mass spectrometry (MS/MS).
• In silico fragmentation for MS/MS analysis is computationally intensive for large compound libraries.

Purpose of the Study:

• To develop and evaluate an efficient computational approach for identifying candidate compounds from large databases using MS/MS spectral data.
• To improve the speed and accuracy of compound identification in metabolomics and small molecule analysis.

Main Methods:

• A relational database of 28 million compounds from PubChem was utilized.
• A training phase associated tandem-MS peaks with corresponding fragment structures.
• Query MS/MS spectra were translated into fragment structures for searching and ranking candidates based on matching fragments.

Main Results:

• The developed approach successfully retrieves and sorts candidate compounds based on matching fragment structures.
• Cross-validation demonstrated that larger training datasets improved the average relative ranking position (RRP) from 0.65 to 0.72.
• The method enables downstream algorithms to process candidate compounds in order of relevance.

Conclusions:

• The presented computational strategy offers an efficient method for candidate compound retrieval from extensive databases using MS/MS data.
• This approach enhances the identification of metabolites and small compounds by optimizing the use of spectral information.
• The findings support the application of this method in metabolomics and drug discovery workflows.