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Global probabilistic annotation of metabolic networks enables enzyme discovery

Nature Chemical Biology volume 8, pages 848–854 (2012)Cite this article

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Abstract

Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, owing to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for global biochemical reconstruction using sampling (GLOBUS) not only provides annotation probabilities for each functional assignment but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote sequence identity to known enzymes. We apply GLOBUS to genomes of Bacillus subtilis and Staphylococcus aureus and validate the method predictions by experimentally demonstrating the 6-phosphogluconolactonase activity of YkgB and the role of the Sps pathway for rhamnose biosynthesis in B. subtilis.

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Figure 1: Overview of the GLOBUS method.
Figure 2: GLOBUS precision-recall performance.
Figure 3: In vitro biochemical assays used to characterize activities of SpsI and SpsJ using high-precision MS.
Figure 4: In vitro biochemical assays used to characterize the 6-phosphogluconolactonase activity of YkgB.

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Acknowledgements

We thank R. Hüttenhain for technical assistance and measuring the peptide samples. We thank P. Eichenberger from the Biology Department at New York University for providing mutant strains. This work was supported in part by US National Institutes of Health grant GM079759 to D.V. and National Centers for Biomedical Computing grant U54CA121852 to Columbia University.

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Author notes

  1. Germán Plata, Tobias Fuhrer and Tzu-Lin Hsiao: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA

    Germán Plata, Tzu-Lin Hsiao & Dennis Vitkup

  2. Integrated Program in Cellular, Molecular, Structural and Genetic Studies, Columbia University, New York, New York, USA

    Germán Plata

  3. Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Tobias Fuhrer & Uwe Sauer

  4. Department of Biomedical Informatics, Columbia University, New York, New York, USA

    Tzu-Lin Hsiao & Dennis Vitkup

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Contributions

G.P., T.-L.H. and D.V. performed computational research and data analysis. T.F. performed experimental research and analysis. D.V. directed computational research. D.V. and U.S. directed experimental research. G.P., T.F., T.-L.H. and D.V. wrote the manuscript. All authors read and edited the manuscript.

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Correspondence to Dennis Vitkup.

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Supplementary information

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Supplementary Methods and Supplementary Results (PDF 1949 kb)

Supplementary Data Set 1

Overview of detected peptides validated by PeptideProphet with 1% error rate cutoff for YkgB band (XLSX 30 kb)

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Plata, G., Fuhrer, T., Hsiao, TL. et al. Global probabilistic annotation of metabolic networks enables enzyme discovery. Nat Chem Biol 8, 848–854 (2012). https://doi.org/10.1038/nchembio.1063

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