De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4

Area of Science:

• Metabolomics
• Computational Chemistry
• Analytical Chemistry

Background:

• Metabolite identification is crucial for understanding biological systems.
• High-resolution tandem mass spectrometry (HR-MS/MS) generates complex data.
• Existing tools often struggle with accurate de novo annotation and identification of novel compounds.

Purpose of the Study:

• To present best practices for utilizing SIRIUS 4, a computational tool for metabolite identification.
• To demonstrate how to integrate SIRIUS 4 into existing mass spectrometry workflows.
• To highlight the value of SIRIUS 4 beyond traditional spectral library searching.

Main Methods:

• Utilizing SIRIUS 4 for de novo molecular formula annotation of HR-MS/MS data.
• Employing predicted molecular fingerprints to infer compound properties.
• Searching fragmentation spectra against structure databases.
• Integrating SIRIUS 4 into data analysis pipelines.

Main Results:

• SIRIUS 4 achieves outstanding accuracy in de novo molecular formula annotation.
• Over 70% correct identifications are obtained when searching fragmentation spectra in a structure database.
• Predicted fingerprints provide valuable information for compounds absent from databases.
• SIRIUS 4 offers capabilities beyond standard spectral library matching.

Conclusions:

• SIRIUS 4 is a powerful computational tool for accurate metabolite identification.
• Leveraging SIRIUS 4's features enhances the analysis of mass spectrometry data.
• The tool provides significant value in identifying known and unknown compounds in complex biological samples.