NPOmix

NPOmix links mass spectrometry fragmentation data to biosynthetic gene clusters to facilitate discovery and prioritization of microbial specialized metabolites.

Key Features:

• Machine Learning Classifier: NPOmix employs a k-nearest neighbor classifier to connect fragmentation spectra with their corresponding biosynthetic gene clusters independent of chemical class.
• Pattern-Based Genome Mining Pipeline: Integrates a pipeline that links biosynthetic genes to the metabolites they encode using mass spectrometry data from bacterial cultures and environmental microbiomes.
• Integration with Omics Data: Leverages paired datasets such as the Paired Omics Data Platform and validated links from MIBiG to associate mass spectra with experimentally validated biosynthetic genes.
• Application in Drug Discovery: Links genomic predictions of biosynthetic potential to metabolomic data to prioritize specialized metabolites for drug discovery efforts.
• Siderophore Mining Example: Provides a computational example demonstrating application of the approach to siderophore mining.

Scientific Applications:

• Untargeted metabolomics (LC-MS/MS): Applies to untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry to link observed spectra to biosynthetic gene clusters.
• Natural product and bioactivity prediction: Enables prediction and prioritization of cryptic metabolites' structures or bioactivities by linking genetic predictions to observed metabolic profiles.
• Environmental and culture-based metabolomics: Facilitates analysis of environmental crude extracts, microbial cultures, and environmental microbiomes where traditional culturing is impractical.

Methodology:

NPOmix uses a k-nearest neighbor classifier and a pattern-based genome mining pipeline to associate mass spectrometry fragmentation spectra with biosynthetic gene clusters, leveraging paired omics datasets (Paired Omics Data Platform) and validated links from MIBiG, with pattern recognition performed independent of chemical class.