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Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

Nature Biotechnology volume 39, pages 462–471 (2021)Cite this article

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Abstract

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

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Fig. 1: CANOPUS workflow.
Fig. 2: Method evaluation: number of ClassyFire compound classes predicted with a particular performance measure.
Fig. 3: Comparing the digestive system of GF and SPF mice.
Fig. 4: Molecular network of daidzein.
Fig. 5: Compound class distribution in Euphorbia species.
Fig. 6: Structural analysis of rivulariapeptolide 1155 using CANOPUS.
Fig. 7: Heterogeneous training for compound class prediction.

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Data availability

Input mzML/mzXML files are available at MassIVE (https://massive.ucsd.edu/) with the accession nos. MSV000079949 (mice data) and MSV000081082 (Euphorbia data). The mass spectrometry data for Rivularia sp. cyanobacteria were deposited at MassIVE (accession no. MSV000085578). The spectra for rivulariapeptolide 1155 were annotated in the GNPS spectral library (accession nos. CCMSLIB00005723986 and CCMSLIB00005723388). The structure database with ClassyFire annotations, the publicly available part of the evaluation data and the Cytoscape files for network visualization can be downloaded from https://bio.informatik.uni-jena.de/data/ and https://doi.org/10.6084/m9.figshare.13073051. Source data are provided with this paper.

Code availability

CANOPUS is part of SIRIUS software and can be downloaded from https://bio.informatik.uni-jena.de/software/canopus/. The source code of CANOPUS is available at https://github.com/boecker-lab/sirius-libs. The scripts for analysis and visualization of CANOPUS results are available at https://github.com/kaibioinfo/canopus_treemap.

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Acknowledgements

We thank Deutsche Forschungsgemeinschaft for providing financial support (no. BO 1910/20 to S.B., K.D. and M.L. and no. PE 2600/1 to D.P.), and the Academy of Finland (no. 310107/MACOME to J.R.). P.C.D., R.R. and W.H.G. were supported by the Gordon and Betty Moore Foundation (no. GBMF7622) and by the US National Institutes of Health (NIH; no. R01 GM107550). P.C.D. was supported by NIH grants nos. P41 GM103484 and R03 CA211211. L.-F.N. was supported by NIH grant no. R01 GM107550 and by the European Union’s Horizon 2020 program (MSCA-GF, no. 704786). We thank F. Kuhlmann and Agilent Technologies, Inc. for providing data used in the evaluation of CANOPUS. We thank Y. Djoumbou Feunang, D. Arndt and D. Wishart for providing ClassyFire annotations for a database of molecular structures. We thank K. Alexander, E. Caro-Diaz and B. Naman for assistance with the collection of Rivularia sp. Further, we thank S. Whitner and K. Joosten for 16S recombinant DNA analysis. We thank M. Ernst for valuable discussions on the Euphorbia plant study, and J. van der Hooft and S. Rogers for feedback on the manuscript.

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Authors and Affiliations

  1. Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany

    Kai Dührkop, Markus Fleischauer, Marcus Ludwig, Martin A. Hoffmann & Sebastian Böcker

  2. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    Louis-Félix Nothias, Daniel Petras & Pieter C. Dorrestein

  3. Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

    Raphael Reher & William H. Gerwick

  4. International Max Planck Research School ‘Exploration of Ecological Interactions with Molecular and Chemical Techniques’, Max Planck Institute for Chemical Ecology, Jena, Germany

    Martin A. Hoffmann

  5. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

    Daniel Petras

  6. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    William H. Gerwick

  7. Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Espoo, Finland

    Juho Rousu

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Contributions

K.D., J.R. and S.B. designed the research. K.D. and S.B. developed the computational method. K.D. implemented the computational method with contributions from M.L., M.F. and M.A.H. M.F. integrated CANOPUS into SIRIUS v.4.4. K.D., L.-F.N. and P.C.D. applied and evaluated the method in the mouse and Euphorbia studies. R.R. isolated rivulariapeptolide 1155 and applied CANOPUS (on mass spectrometry data collected and analyzed by D.P. and R.R. and supervised by W.H.G.) and one-/two-dimensional NMR analysis for its structural elucidation. K.D., S.B., L.-F.N. and R.R. wrote the manuscript, in concert with all authors.

Corresponding author

Correspondence to Sebastian Böcker.

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Competing interests

S.B., K.D., M.L., M.F. and M.A.H. are cofounders of Bright Giant GmbH. P.C.D. is scientific advisor for Sirenas LLC.

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Extended data

Extended Data Fig. 1 CANOPUS performance sunburst plot. Matthews correlation coefficient (MCC) for the 782 of 2,497 compound classes with at least 50 positive examples.

SVM training dataset. A darker green coloring corresponds to better prediction performance for the class. The size of each slice is chosen such that all classes fit into the figure and has no further meaning. Inner slices represent parent classes of outer slices.

Extended Data Fig. 2 Effect of removing a subclass from the MS/MS training data.

a–c, Regular evaluation setup: classes and subclasses are distributed into cross-validation folds, ensuring that methods are never evaluated on the same MS/MS data or structures they were trained on. d-f, We remove all flavonoid glycosides (the subclass) from the MS/MS training data (d), and then evaluate the predictor for glycosides (the class) on these removed MS/MS spectra (e). A perfect method would still classify all flavonoid glycoside MS/MS spectra as glycosides (f). CANOPUS exhibits only a small drop (68% to 97%) in correct classifications (c,f). In contrast, direct prediction performed mostly on par with CANOPUS before removing flavonoid glycosides from the MS/MS training data (c), but misses almost all of them (8%) afterwards (f). We were able to attribute this to the presence of isoflavonoid glycosides in the training data; these do not belong to the flavonoid class, but have highly similar structures and MS/MS spectra, except for the presence of a sugar residue. We observed that direct prediction in (d-f) uses the presence of a sugar residue to infer that a MS/MS spectrum is not a glycoside. In contrast, CANOPUS does not fall for this ‘bait’; heterogeneous training allows us to integrate the substantially more comprehensive structure data in its predictions.

Extended Data Fig. 3 Relative number of compounds annotated at varying ClassyFire class levels in the mice study (a) and the Euphorbia plant study (b).

The ClassyFire ChemOnt ontology is organized as a tree, where the Kingdom is either Organic compounds or Inorganic compounds. Superclasses like Lipids and lipid like molecules, Benzenoids are children of Kingdom class. Flavonoids and Steroids and steroid derivatives are examples for the Class level, while Flavonoid glycosides and Bile acids, alcohols, and derivatives are examples for subclasses. There can be up to 11 levels in the ontology. c, ClassyFire classes of compounds in the biological databases. We observe a similar distribution of class levels as for the two biological datasets, indicating that CANOPUS is comprehensively classifying compounds at all possible compound class levels.

Extended Data Fig. 4 Molecular network and compound class annotations (single class annotations) for the mice digestive system.

Node colors indicate the compound class annotated by CANOPUS; displayed compound classes were manually selected. When a compound is annotated with multiple classes, the class with the larger structural pattern is selected. Nodes are connected by an edge if the spectral similarity is 0.7 or higher.

Extended Data Fig. 5 Molecular network and compound class annotations (muliple class annotations) for the mice digestive system.

Node colors indicate the compound class annotated by CANOPUS; compound classes are the same as in Supplementary Fig. 4 1. Compounds belonging to multiple classes displayed as multicolored nodes. Nodes are connected by an edge if the spectral similarity is 0.7 or higher.

Extended Data Fig. 6 Number of compounds detected for each Euphorbia subgenus.

Orange bars indicate the number of compounds detected here, black ticks indicate the number of compounds reported in the original study. Higher numbers of detected features are not a measure of quality for the two methods, but depend mainly on the preprocessing executed before compound classification.

Extended Data Fig. 7 Number of compounds annotated as diterpenoids in different species of Euphorbia.

Left: absolute number of compounds. Right: relative number of compounds, that is, number of diterpenoids divided by total number of compounds in each species. Black ticks in the left figure mark the reported number of diterpenoids in the original study by Ernst et al.

Extended Data Fig. 8 Number of compounds annotated as triterpenoids in different species of Euphorbia.

Left: absolute number of compounds. Right: relative number of compounds, that is, number of triterpenoids divided by total number of compounds in each species. Black ticks in the left figure mark the reported number of triterpenoids in the original study by Ernst et al.

Extended Data Fig. 9 Number of diterpenoids in different species of Euphorbia.

Black bars show the amount of diterpenoids that have a benzoic acid ester (a), fatty acid ester (b) or two carboxylic acids (c).

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Supplementary information

Supplementary Information

Supplementary Tables 4, 5 and 7, Figs. 1–10 and Notes 1 and 2.

Reporting Summary

41587_2020_740_MOESM3_ESM.html

Supplementary Data. Interactive comparison of Euphorbia plants. The user can select any two plant species to be compared; two sunburst plots then show the number of compounds annotated by CANOPUS for each compound class. Mouse-over allows display details of a compound class, including the number and percentage of compounds belonging to this class and the ClassyFire ontology and description of the class.

Supplementary Table 1

Compound classes from ChemOnt ontology not predicted by CANOPUS.

Supplementary Table 2

Evaluation results for all query MS/MS from the SVM training dataset.

Supplementary Table 3

Performance of all evaluated methods for individual compound classes; evaluation on the independent dataset.

Supplementary Table 6

Standardized SMILES of all compound structures from MassBank and GNPS used as the SVM training dataset.

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Dührkop, K., Nothias, LF., Fleischauer, M. et al. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 39, 462–471 (2021). https://doi.org/10.1038/s41587-020-0740-8

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  • DOI: https://doi.org/10.1038/s41587-020-0740-8

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