O-GlcNAcylation Prediction: An Unattained Objective

Full-length single-molecule protein fingerprinting

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis.

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning

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 http://oglcnac.org/pred_dl.

O-GlcNAcylation: the sweet side of epigenetics

Histones display a wide variety of post-translational modifications, including acetylation, methylation, and phosphorylation. These epigenetic modifications can influence chromatin structure and function without altering the DNA sequence. Histones can also undergo post-translational O-GlcNAcylation, a rather understudied modification that plays critical roles in almost all biological processes and is added and removed by O-linked N-acetylglucosamine transferase and O-GlcNAcase, respectively. This review provides a current overview of our knowledge of how O-GlcNAcylation impacts the histone code both directly and by regulating other chromatin modifying enzymes. This highlights the pivotal emerging role of O-GlcNAcylation as an essential epigenetic marker.

An overview of tools to decipher O-GlcNAcylation from historical approaches to new insights

O-GlcNAcylation is a post-translational modification which affects approximately 5000 human proteins. Its involvement has been shown in many if not all biological processes. Variations in O-GlcNAcylation levels can be associated with the development of diseases. Deciphering the role of O-GlcNAcylation is an important issue to (i) understand its involvement in pathophysiological development and (ii) develop new therapeutic strategies to modulate O-GlcNAc levels. Over the past 30 years, despite the development of several approaches, knowledge of its role and regulation have remained limited. This review proposes an overview of the currently available tools to study O-GlcNAcylation and identify O-GlcNAcylated proteins. Briefly, we discuss pharmacological modulators, methods to study O-GlcNAcylation levels and approaches for O-GlcNAcylomic profiling. This review aims to contribute to a better understanding of the methods used to study O-GlcNAcylation and to promote efforts in the development of new strategies to explore this promising modification.