What does this strategy enable?
Bioactivity-Based Molecular Networking adds an orthogonal biological evidence layer to molecular networking. Fractionated samples are profiled by LC-MS/MS and tested in a quantitative bioassay. Feature abundances across fractions are correlated with bioactivity, producing a bioactivity score that is mapped onto the molecular network. This allows active molecular families and candidate analogues to be prioritized before isolation while preserving molecular-network annotation and dereplication context.
Transferable workflow
Workflow
- 1
Generate fractions and quantitative bioassay data
Fractionate the bioactive extract or sample set and measure a quantitative biological response for the same fractions used for metabolomics.
- 2
Acquire untargeted LC-MS/MS profiles
Profile the fractions by LC-MS/MS so that feature abundance across fractions and fragmentation spectra are available.
- 3
- 4
Calculate feature–bioactivity associations
Correlate feature abundance across fractions with the quantitative bioassay response to calculate a bioactivity score for each feature.
- 5
Build and annotate the molecular network
Generate a molecular network from MS/MS similarity and attach the bioactivity scores as node attributes so active molecular families can be recognized in chemical context.
- 6
Prioritize and validate active chemistry
Select high-scoring features or molecular families for isolation, orthogonal structural work, and direct activity confirmation.
Connected resources
Tools used in this strategy
Evidence of reuse
Published implementations and adaptations
These papers applied, adapted, extended, benchmarked or formalized the Strategy after its originating publication. They are examples of precedent, not a ranking of software choices.
Bioactivity-Based Molecular Networking-Guided Isolation of Epicolidines A-C from the Endophytic Fungus Epicoccum sp. 1-042
Used bioactivity-based molecular networking-guided fractionation in an endophytic fungal natural-product study to prioritize and isolate antibacterial polycyclic tetramic acids.
Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus
Applied bioactive molecular networking to fractionated Fucus vesiculosus extracts, combining antimicrobial activity against MRSA with UPLC-QToF-MS/MS feature abundances, MZmine preprocessing, GNPS feature-based molecular networking, Pearson correlation scoring and Cytoscape visualization.
Strategy profile
Related objectives
Phenotype association, Molecular-family discovery, Dereplication
Components
Sample preparation, Chromatography, Data acquisition, Preprocessing and feature extraction, Molecular networking, Statistical analysis, Biological validation, Data and workflow sharing
Biological context
Broadly applicable
Ion mobility
Ion mobility not required
Scope and considerations
Designed for
- Bioassay-guided natural-product discovery where the same fractions or samples have quantitative LC-MS feature data and biological activity measurements.
- Prioritizing active analogues within molecular families before purification.
Important requirements
- Reproducible quantitative bioassay values matched to metabolomics samples.
- Sufficient variation in both feature abundance and bioactivity across the sample or fraction series.
- The supplied implementation uses MZmine 2 or Optimus exports and an R script or R-kernel Jupyter notebook; check current export compatibility.
Considerations
- Correlation between a feature and bioactivity is prioritization evidence, not proof that the feature causes the phenotype.
- Coeluting or covarying metabolites can receive similar activity scores and require follow-up isolation or orthogonal validation.
Reusable resources
Publication
Nothias L-F, Nothias-Esposito M, da Silva R, et al. Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation. Journal of Natural Products 81, 758–767 (2018).