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Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Nature Methods volume 18, pages 1524–1531 (2021)Cite this article

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Abstract

Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

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Fig. 1: Spectral entropy as a parameter to characterize MS/MS spectra.
Fig. 2: Schema for calculating similarity between MS/MS spectra of small molecules.
Fig. 3: ROC curves for 43 different spectral similarity algorithms for 434,287 accurate mass MS/MS spectra of the small-molecule NIST20 database.
Fig. 4: Robustness of MS/MS similarity algorithms against random noise ions.
Fig. 5: Relationship between FDR and spectral entropy levels for NIST20 accurate mass MS/MS spectra.
Fig. 6: Relationship between FDRs and spectral similarity in experimental spectra.

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Data availability

NIST Tandem Mass Spectral Library, 2020 release (NIST20) spectra are commercial available and can be purchased from multiple vendors. MassBank of North America database (Massbank.us) spectra can be freely downloaded from Massbank.us (https://massbank.us/). The metabolome dataset of the human upper gut intestinal tract can be freely downloaded from MetabolomicsWorkbench (https://www.metabolomicsworkbench.org/) with accession code ST001794. Source data are provided with this paper.

Code availability

The code for calculating spectral entropy and spectral similarity can be found at GitHub https://github.com/YuanyueLi/SpectralEntropy and at Zenodo https://doi.org/10.5281/zenodo.5591020.

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Acknowledgements

This work was supported by the funding ‘West Coast Metabolomics Center for Compound Identification’, which was provided by the National Institutes of Health under award number NIH U2C ES030158 to O.F. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA

    Yuanyue Li, Tobias Kind, Jacob Folz, Arpana Vaniya, Sajjan Singh Mehta & Oliver Fiehn

  2. Olobion, Parc Científic de Barcelona, Barcelona, Spain

    Sajjan Singh Mehta

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  1. Yuanyue Li
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Contributions

O.F. supervised and directed this project. Y.L. developed and performed the analysis, T.K. assisted with the concept of the study, J.F. and A.V. acquired and curated experimental data. S.S.M. tested and improved the source code. Y.L. and O.F. wrote the manuscript with comments from all the other authors.

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Correspondence to Oliver Fiehn.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Warwick Dunn, Junmin Peng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Example spectra with different spectral entropy.

Examples of experimental MS/MS spectra downloaded from MassBank.us that are characteristic for spectra with entropy levels 0-4. The precursor ion is shown in grey.

Extended Data Fig. 2 Impact of collision energy on spectral entropy.

(a) Spectral entropy violin density plots for low energy (<15 eV), mid-energy (15-45 eV) and high- energy (>45 eV) high resolution NIST20 MS/MS spectra. (b) Relationship between the increase of collision energy and the increase of spectral entropy in NIST20 MS/MS spectra for positively and negatively charged molecules.

Extended Data Fig. 3 Distribution of spectral entropy within compound classes in the NIST20 MS/MS library consisting of at least 50 molecules with collision energies between 20–40 eV.

The number of spectra is shown on the right of each compound class. For each boxplot, the center is the median. Left and right hinges depict the first and third quartiles. The whiskers stretch to 1.5-times the interquartile range of the corresponding hinge. (a) Positive mode electrospray MS/MS spectra (b) Negative mode electrospray MS/MS spectra.

Extended Data Fig. 4 NIST20 Orbitrap MS/MS spectra for ATP and ADP at collision energy 30 eV.

This comparison shows that similar structures may give highly similar mass spectra. Dot-product similarity yields much higher values, while differences between the mass spectra are most apparent for lower abundant ions that are emphasized by dynamic weighted similarity scores (for example for m/z 410).

Extended Data Fig. 5 Intensity weight for entropy similarity.

(a) The relationship between spectral entropy and weights to be used in the spectral entropy similarity algorithm. (b) The relationship between normalized AUC and entropy cutoff.

Extended Data Fig. 6 Receiver-operator characteristic curves for using MS/MS spectra from the Massbank.us database search against Massbank.us database.

(a) Spectral similarity was compared without precursor ion removal. (b) Spectral similarity was compared with precursor ion removal.

Source data

Extended Data Fig. 7 Calculate the bond difference between molecules in chemical libraries.

Isomer molecules that differed only by one bond were considered highly similar and therefore used as ‘true positives’ in FDR calculations in similarity calculations.

Extended Data Fig. 8 Frequency distribution of identified and unknown metabolites detected in a new experimental dataset from samples of the upper human gut intestinal tract.

At (normalized entropy)4 > 0.8, identified metabolites are almost absent but spectra of unknown compounds show a rapid increase in frequency due to poor spectral quality.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Supplementary Figs. 1–6

Reporting Summary

Supplementary Table 1

The AUC for the ROC curve in Figs. 3 and 4 and Supplementary Fig. 4.

Supplementary Table 2

Newly annotated compounds from the previously unannotated spectra of the human gut metabolome dataset.

Source data

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 6

Statistical Source Data.

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Li, Y., Kind, T., Folz, J. et al. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat Methods 18, 1524–1531 (2021). https://doi.org/10.1038/s41592-021-01331-z

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