Metabolic pathway predictions for metabolomics: a molecular structure matching approach

Area of Science:

• Biochemistry
• Cheminformatics
• Systems Biology

Background:

• Metabolic pathways consist of enzyme-catalyzed reactions converting substrates to products.
• Structural similarity between compounds can link new molecules to known metabolic pathways.
• Integrating multi-omics data (metabolomics, proteomics, genomics) requires accurate compound identification.

Purpose of the Study:

• To present TrackSM, a cheminformatics tool for associating chemical compounds with known metabolic pathways.
• To leverage molecular structure matching for pathway identification.
• To facilitate the integration of metabolomics, proteomics, and genomics data.

Main Methods:

• Developed TrackSM, a cheminformatics tool utilizing molecular structure matching algorithms.
• Applied TrackSM to associate chemical compounds with metabolic pathways.
• Validated TrackSM's performance using KEGG pathway classifications.

Main Results:

• TrackSM successfully associated 93% of tested structures to their correct KEGG pathway class.
• TrackSM achieved 88% accuracy in assigning structures to their correct individual KEGG pathway.
• The tool demonstrated high efficacy in mapping small molecules to biochemical pathways.

Conclusions:

• TrackSM is an effective tool for associating novel small molecules with known metabolic pathways.
• The tool aids in linking diverse biological datasets, including metabolomics, proteomics, and genomics.
• TrackSM offers a valuable approach to enhance biochemical pathway mapping and data integration.