Functional and informatics analysis enables glycosyltransferase activity prediction View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-11-12

AUTHORS

Min Yang, Charlie Fehl, Karen V. Lees, Eng-Kiat Lim, Wendy A. Offen, Gideon J. Davies, Dianna J. Bowles, Matthew G. Davidson, Stephen J. Roberts, Benjamin G. Davis

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. More... »

PAGES

1109-1117

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41589-018-0154-9

DOI

http://dx.doi.org/10.1038/s41589-018-0154-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1108043125

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30420693


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