GT-Predict

Name: GT-Predict
Published: 2018-01-01T00:00:00Z
License: https://ora.ox.ac.uk/terms_of_use

Documentation:: This package contains the code and program (.exe files) for our "GT-Predict" enzyme functional annotation software. Included are all datasets and validation techniques. We utilized informatics and machine learning to build predictive software for enzyme functional annotation. The original application, GT-Predict, utilized the glycosyltransferase family 1 enzymes (GT1s) found in plants.

Language:: English
Keywords:: machine learning

glycosyltransferases

biocatalysis

functional prediction

enzymology
Subjects:: informatics

Enzymes

Chemistry

biochemistry
Pubs id:: 1480489
UUID:: uuid:1b174bc0-4058-4057-8db4-59872c2b6d99
Local pid:: pubs:1480489
Deposit date:: 2018-04-01

Related item:: Functional and informatics analysis enables glycosyltransferase activity prediction
Description:: 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.

Notes:: Free for academic users. For commercial use, please contact Ben Davis at [email protected]

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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Dataset