O-linked β-N-acetylglucosamine
(O-GlcNAc)
is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine
residues of proteins, regulating a plethora of physiological and pathological
events. As a dynamic process, O-GlcNAc functions in a site-specific
manner. However, the experimental identification of the O-GlcNAc sites
remains challenging in many scenarios. Herein, by leveraging the recent
progress in cataloguing experimentally identified O-GlcNAc sites and
advanced deep learning approaches, we establish an ensemble model,
O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of
O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we
extracted the information on O-GlcNAc from the recently constructed
database O-GlcNAcAtlas, which contains thousands of experimentally
identified and curated O-GlcNAc sites on proteins from multiple species.
To overcome the imbalance between positive and negative data sets,
we selected five groups of negative data sets in humans and mice to
construct an ensemble predictor based on connection of a convolutional
neural network and bidirectional long short-term memory. By taking
into account three types of sequence information, we constructed four
network frameworks, with the systematically optimized parameters used
for the models. The thorough comparison analysis on two independent
data sets of humans and mice and six independent data sets from other
species demonstrated remarkably increased sensitivity and accuracy
of the O-GlcNAcPRED-DL models, outperforming other existing tools.
Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been
constructed, which is freely available at .