Kavli Affiliate: Nathan Lewis
| First 5 Authors: Thiru Siddharth, Nathan Lewis, , ,
| Summary:
The diverse metabolic pathways are fundamental to all living organisms, as
they harvest energy, synthesize biomass components, produce molecules to
interact with the microenvironment, and neutralize toxins. While discovery of
new metabolites and pathways continues, the prediction of pathways for new
metabolites can be challenging. It can take vast amounts of time to elucidate
pathways for new metabolites; thus, according to HMDB only 60% of metabolites
get assigned to pathways. Here, we present an approach to identify pathways
based on metabolite structure. We extracted 201 features from SMILES
annotations, and identified new metabolites from PubMed abstracts and HMDB.
After applying clustering algorithms to both groups of features, we quantified
correlations between metabolites, and found the clusters accurately linked 92%
of known metabolites to their respective pathways. Thus, this approach could be
valuable for predicting metabolic pathways for new metabolites.
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