BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.

Area of Science:

• Metabolomics and Mass Spectrometry
• Computational Chemistry
• Bioinformatics

Background:

• A significant portion of metabolic features identified via mass spectrometry (MS) remain unannotated.
• Accurate molecular formula annotation is crucial for identifying unknown compounds in metabolomics.
• Current methods for formula annotation can be computationally intensive and may miss novel compounds.

Purpose of the Study:

• To develop a novel de novo molecular formula annotation method using bottom-up tandem mass spectrometry (MS/MS).
• To improve the efficiency and accuracy of identifying unknown metabolic features in complex samples.
• To enable the discovery of novel molecular formulae not present in existing chemical databases.

Main Methods:

• Implementation of bottom-up tandem MS (MS/MS) interrogation for de novo formula annotation.
• Prioritization of MS/MS-explainable formula candidates, incorporating machine-learned ranking.
• Integration of false discovery rate estimation for robust annotation and global optimization for peak interrelationships.

Main Results:

• The developed approach reduced the molecular formula candidate space by an average of 42.8% compared to exhaustive enumeration.
• Benchmarking on MS/MS libraries and real metabolomics datasets demonstrated high annotation accuracy.
• Over 5,000 novel molecular formulae were confidently annotated from 155,321 unidentified spectra, alongside 37 fatty acid amide molecules in human fecal data.

Conclusions:

• Bottom-up MS/MS interrogation provides an effective strategy for de novo molecular formula annotation in metabolomics.
• The method significantly enhances the identification of unknown metabolic features and expands chemical space discovery.
• The associated bioinformatics software, BUDDY, facilitates the application of this approach in research.