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Functional and informatics analysis enables glycosyltransferase activity prediction

Nature Chemical Biology volume 14, pages 1109–1117 (2018)Cite this article

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Abstract

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical–bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions.

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Fig. 1: Challenges and solutions for the rational prediction of multisubstrate enzyme reactions.
Fig. 2: Strategy for function-based chemical–bioinformatic modeling of GT1 transformations.
Fig. 3: Overall donor- and acceptor-utilization patterns for the active GT1 library.
Fig. 4: Comparison of clustering techniques for the acceptor dataset.
Fig. 5: GT-Predict development, validation, and utilization.
Fig. 6: GT-Predict extends functional annotation to other species, kingdoms, and GT families.

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Data availability

Activity datasets, mass spectrograms, and the protein FASTA sequences used herein are included in a package available through the Oxford University Research Archive at https://doi.org/10.5287/bodleian:zg5195kaE.

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Acknowledgements

We gratefully acknowledge A. Osbourne (JIC) for contribution of A. strigosa GT1 genes As08 (UGT74H5) and As09 (UGT88C4); R. Edwards and M. Brazier-Hicks for sharing activity data; and I. Mear for assistance with coding. This work was funded by the BBSRC (EGA16205, EGA16206, and EGA17763) and the EPSRC (the UK Catalysis Hub, EP/K014668/1 and EP/M013219/1).

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Author notes

  1. Min Yang

    Present address: UCL School of Pharmacy, London, UK

  2. These authors contributed equally: Min Yang, Charlie Fehl.

Authors and Affiliations

  1. Chemistry Research Laboratory, Oxford University, Oxford, UK

    Min Yang, Charlie Fehl & Benjamin G. Davis

  2. Department of Engineering Science, University of Oxford, Oxford, UK

    Karen V. Lees & Stephen J. Roberts

  3. Center for Novel Agricultural Products, Department of Biology, University of York, York, UK

    Eng-Kiat Lim & Dianna J. Bowles

  4. York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK

    Wendy A. Offen & Gideon J. Davies

  5. Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Bath, UK

    Matthew G. Davidson

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Contributions

G.J.D., D.J.B., M.G.D., S.J.R., and B.G.D. designed the research; M.Y., C.F., and K.V.L. performed the research; M.Y., C.F., K.V.L., E.-K.L. W.A.O., G.J.D., S.J.R., and B.G.D. analyzed the data; G.J.D., D.J.B., M.G.D., S.J.R., and B.G.D. wrote the paper; all authors read and commented on the paper. M.Y. and C.F. contributed equally to this work.

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Correspondence to Benjamin G. Davis.

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Supplementary Tables 1–9, Supplementary Figures 1–16, and Supplementary Note

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Supplementary Video 1

Installation and use of GT-Predict on Microsoft Windows 10

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Yang, M., Fehl, C., Lees, K.V. et al. Functional and informatics analysis enables glycosyltransferase activity prediction. Nat Chem Biol 14, 1109–1117 (2018). https://doi.org/10.1038/s41589-018-0154-9

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