A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs

Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species

RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model's superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model's capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of "biological grammars" in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications.

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.

Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction

O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site's web server and source code are publicly available to the community.

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme

Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.

Inhibition of DNMT1 methyltransferase activity via glucose-regulated O-GlcNAcylation alters the epigenome

The DNA methyltransferase activity of DNMT1 is vital for genomic maintenance of DNA methylation. We report here that DNMT1 function is regulated by O-GlcNAcylation, a protein modification that is sensitive to glucose levels, and that elevated O-GlcNAcylation of DNMT1 from high glucose environment leads to alterations to the epigenome. Using mass spectrometry and complementary alanine mutation experiments, we identified S878 as the major residue that is O-GlcNAcylated on human DNMT1. Functional studies in human and mouse cells further revealed that O-GlcNAcylation of DNMT1-S878 results in an inhibition of methyltransferase activity, resulting in a general loss of DNA methylation that preferentially occurs at partially methylated domains (PMDs). This loss of methylation corresponds with an increase in DNA damage and apoptosis. These results establish O-GlcNAcylation of DNMT1 as a mechanism through which the epigenome is regulated by glucose metabolism and implicates a role for glycosylation of DNMT1 in metabolic diseases characterized by hyperglycemia.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

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.

Cancer-cell-secreted miR-122 suppresses O-GlcNAcylation to promote skeletal muscle proteolysis

A decline in skeletal muscle mass and low muscular strength are prognostic factors in advanced human cancers. Here we found that breast cancer suppressed O-linked N-acetylglucosamine (O-GlcNAc) protein modification in muscle through extracellular-vesicle-encapsulated miR-122, which targets O-GlcNAc transferase (OGT). Mechanistically, O-GlcNAcylation of ryanodine receptor 1 (RYR1) competed with NEK10-mediated phosphorylation and increased K48-linked ubiquitination and proteasomal degradation; the miR-122-mediated decrease in OGT resulted in increased RYR1 abundance. We further found that muscular protein O-GlcNAcylation was regulated by hypoxia and lactate through HIF1A-dependent OGT promoter activation and was elevated after exercise. Suppressed O-GlcNAcylation in the setting of cancer, through increasing RYR1, led to higher cytosolic Ca2+ and calpain protease activation, which triggered cleavage of desmin filaments and myofibrillar destruction. This was associated with reduced skeletal muscle mass and contractility in tumour-bearing mice. Our findings link O-GlcNAcylation to muscular protein homoeostasis and contractility and reveal a mechanism of cancer-associated muscle dysregulation.

Residue-Residue Contact Can Be a Potential Feature for the Prediction of Lysine Crotonylation Sites

Lysine crotonylation (Kcr) is involved in plenty of activities in the human body. Various technologies have been developed for Kcr prediction. Sequence-based features are typically adopted in existing methods, in which only linearly neighboring amino acid composition was considered. However, modified Kcr sites are neighbored by not only the linear-neighboring amino acid but also those spatially surrounding residues around the target site. In this paper, we have used residue-residue contact as a new feature for Kcr prediction, in which features encoded with not only linearly surrounding residues but also those spatially nearby the target site. Then, the spatial-surrounding residue was used as a new scheme for feature encoding for the first time, named residue-residue composition (RRC) and residue-residue pair composition (RRPC), which were used in supervised learning classification for Kcr prediction. As the result suggests, RRC and RRPC have achieved the best performance of RRC at an accuracy of 0.77 and an area under curve (AUC) value of 0.78, RRPC at an accuracy of 0.74, and an AUC value of 0.80. In order to show that the spatial feature is of a competitively high significance as other sequence-based features, feature selection was carried on those sequence-based features together with feature RRPC. In addition, different ranges of the surrounding amino acid compositions' radii were used for comparison of the performance. After result assessment, RRC and RRPC features have shown competitively outstanding performance as others or in some cases even around 0.20 higher in accuracy or 0.3 higher in AUC values compared with sequence-based features.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

On the Need to Tell Apart Fraternal Twins eEF1A1 and eEF1A2, and Their Respective Outfits

eEF1A1 and eEF1A2 are paralogous proteins whose presence in most normal eukaryotic cells is mutually exclusive and developmentally regulated. Often described in the scientific literature under the collective name eEF1A, which stands for eukaryotic elongation factor 1A, their best known activity (in a monomeric, GTP-bound conformation) is to bind aminoacyl-tRNAs and deliver them to the A-site of the 80S ribosome. However, both eEF1A1 and eEF1A2 are endowed with multitasking abilities (sometimes performed by homo- and heterodimers) and can be located in different subcellular compartments, from the plasma membrane to the nucleus. Given the high sequence identity of these two sister proteins and the large number of post-translational modifications they can undergo, we are often confronted with the dilemma of discerning which is the particular proteoform that is actually responsible for the ascribed biochemical or cellular effects. We argue in this review that acquiring this knowledge is essential to help clarify, in molecular and structural terms, the mechanistic involvement of these two ancestral and abundant G proteins in a variety of fundamental cellular processes other than translation elongation. Of particular importance for this special issue is the fact that several de novo heterozygous missense mutations in the human EEF1A2 gene are associated with a subset of rare but severe neurological syndromes and cardiomyopathies.

O-GlcNAcylation Prediction: An Unattained Objective

BACKGROUND

O-GlcNAcylation is an essential post-translational modification (PTM) in mammalian cells. It consists in the addition of a N-acetylglucosamine (GlcNAc) residue onto serines or threonines by an O-GlcNAc transferase (OGT). Inhibition of OGT is lethal, and misregulation of this PTM can lead to diverse pathologies including diabetes, Alzheimer's disease and cancers. Knowing the location of O-GlcNAcylation sites and the ability to accurately predict them is therefore of prime importance to a better understanding of this process and its related pathologies.

PURPOSE

Here, we present an evaluation of the current predictors of O-GlcNAcylation sites based on a newly built dataset and an investigation to improve predictions.

METHODS

Several datasets of experimentally proven O-GlcNAcylated sites were combined, and the resulting meta-dataset was used to evaluate three prediction tools. We further defined a set of new features following the analysis of the primary to tertiary structures of experimentally proven O-GlcNAcylated sites in order to improve predictions by the use of different types of machine learning techniques.

RESULTS

Our results show the failure of currently available algorithms to predict O-GlcNAcylated sites with a precision exceeding 9%. Our efforts to improve the precision with new features using machine learning techniques do succeed for equal proportions of O-GlcNAcylated and non-O-GlcNAcylated sites but fail like the other tools for real-life proportions where ~1.4% of S/T are O-GlcNAcylated.

CONCLUSION

Present-day algorithms for O-GlcNAcylation prediction narrowly outperform random prediction. The inclusion of additional features, in combination with machine learning algorithms, does not enhance these predictions, emphasizing a pressing need for further development. We hypothesize that the improvement of prediction algorithms requires characterization of OGT's partners.

O-GlcNAcylation promotes the migratory ability of hepatocellular carcinoma cells via regulating FOXA2 stability and transcriptional activity

O-GlcNAcylation is a posttranslational modification that regulates numerous nuclear and cytoplasmic proteins and is emerging as a key regulator of various biological processes, such as transcription, signal transduction, and cell motility. Although increasing evidence has shown that elevated levels of global O-GlcNAcylation are linked to the metastasis in hepatocellular carcinoma (HCC) cells, the underlying mechanism is still ambiguous. In this study, we demonstrated that forkhead box protein A2 (FOXA2), an essential transcription factor for liver homeostasis and HCC developing, was O-GlcNAcylated by O-GlcNAc transferase (OGT) and regulates HCC cells migration and invasion. Opposite FOXA2 and OGT expression tendency were observed in HCC tissues, and lower FOXA2 levels predicted a poor prognosis in HCC patients. The reduction of FOXA2 in HCC cells was found to be inversely correlated with the cellular O-GlcNAcylation and cell migratory ability. Notably, we found that FOXA2 was modified by O-GlcNAcylation and that O-GlcNAcylation activated the ubiquitination degradation of FOXA2 in highly metastatic HCC cells. Although this modification did not affect FOXA2 nuclear localization capability, O-GlcNAcylation on FOXA2 was key for attenuating FOXA2-mediated transcription. O-GlcNAcylation decreased the transcription of FOXA2 downstream target gene E-cadherin and it ultimately promoted O-GlcNAcylation-mediated HCC cell migration and invasion. The results provide insights into the role of O-GlcNAcylation in regulating FOXA2 activity and suggest its important implications in HCC metastasis.

OGP: A Repository of Experimentally Characterized O-Glycoproteins to Facilitate Studies on O-Glycosylation

Numerous studies on cancers, biopharmaceuticals, and clinical trials have necessitated comprehensive and precise analysis of protein O-glycosylation. However, the lack of updated and convenient databases deters the storage of and reference to emerging O-glycoprotein data. To resolve this issue, an O-glycoprotein repository named OGP was established in this work. It was constructed with a collection of O-glycoprotein data from different sources. OGP contains 9354 O-glycosylation sites and 11,633 site-specific O-glycans mapping to 2133 O-glycoproteins, and it is the largest O-glycoprotein repository thus far. Based on the recorded O-glycosylation sites, an O-glycosylation site prediction tool was developed. Moreover, an OGP-based website is already available (https://www.oglyp.org/). The website comprises four specially designed and user-friendly modules: statistical analysis, database search, site prediction, and data submission. The first version of OGP repository and the website allow users to obtain various O-glycoprotein-related information, such as protein accession Nos., O-glycosylation sites, O-glycopeptide sequences, site-specific O-glycan structures, experimental methods, and potential O-glycosylation sites. O-glycosylation data mining can be performed efficiently on this website, which will greatly facilitate related studies. In addition, the database is accessible from OGP website (https://www.oglyp.org/download.php).

The human O-GlcNAcome database and meta-analysis

Over the past 35 years, ~1700 articles have characterized protein O-GlcNAcylation. Found in almost all living organisms, this post-translational modification of serine and threonine residues is highly conserved and key to biological processes. With half of the primary research articles using human models, the O-GlcNAcome recently reached a milestone of 5000 human proteins identified. Herein, we provide an extensive inventory of human O-GlcNAcylated proteins, their O-GlcNAc sites, identification methods, and corresponding references ( www.oglcnac.mcw.edu ). In the absence of a comprehensive online resource for O-GlcNAcylated proteins, this list serves as the only database of O-GlcNAcylated proteins. Based on the thorough analysis of the amino acid sequence surrounding 7002 O-GlcNAc sites, we progress toward a more robust semi-consensus sequence for O-GlcNAcylation. Moreover, we offer a comprehensive meta-analysis of human O-GlcNAcylated proteins for protein domains, cellular and tissue distribution, and pathways in health and diseases, reinforcing that O-GlcNAcylation is a master regulator of cell signaling, equal to the widely studied phosphorylation.

Regulation of DELLA Proteins by Post-translational Modifications

DELLA proteins are the negative regulators of the gibberellin (GA) signaling pathway. GAs have a pervasive effect on plant physiology, influencing processes that span the entire life cycle of the plant. All the information encoded by GAs, either environmental or developmental in origin, is canalized through DELLAs, which modulate the activity of many transcription factors and transcriptional regulators. GAs unlock the signaling pathway by triggering DELLA polyubiquitination and degradation by the 26S proteasome. Recent reports indicate, however, that there are other pathways that trigger DELLA polyubiquitination and degradation independently of GAs. Moreover, results gathered during recent years indicate that other post-translational modifications (PTMs), namely phosphorylation, SUMOylation and glycosylation, modulate DELLA function. The convergence of several PTMs in DELLA therefore highlights the strict regulation to which these proteins are subject. In this review, we summarize these discoveries and discuss DELLA PTMs from an evolutionary perspective and examine the possibilities these and other post-translational regulations offer to improve DELLA-dependent agronomic traits.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

OGT-Mediated KEAP1 Glycosylation Accelerates NRF2 Degradation Leading to High Phosphate-Induced Vascular Calcification in Chronic Kidney Disease

Unraveling the complex regulatory pathways that mediate the effects of phosphate on vascular smooth muscle cells (VSMCs) may provide novel targets and therapies to limit the destructive effects of vascular calcification (VC) in patients with chronic kidney disease (CKD). Our previous studies have highlighted several signaling networks associated with VSMC autophagy, but the underlying mechanisms remain poorly understood. Thereafter, the current study was performed to characterize the functional relevance of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in high phosphate-induced VC in CKD settings. We generated VC models in 5/6 nephrectomized rats in vivo and VSMC calcification models in vitro. Artificial modulation of OGT (knockdown and overexpression) was performed to explore the role of OGT in VSMC autophagy and VC in thoracic aorta, and in vivo experiments were used to substantiate in vitro findings. Mechanistically, co-immunoprecipitation (Co-IP) assay was performed to examine interaction between OGT and kelch like ECH associated protein 1 (KEAP1), and in vivo ubiquitination assay was performed to examine ubiquitination extent of nuclear factor erythroid 2-related factor 2 (NRF2). OGT was highly expressed in high phosphate-induced 5/6 nephrectomized rats and VSMCs. OGT silencing was shown to suppress high phosphate-induced calcification of VSMCs. OGT enhances KEAP1 glycosylation and thereby results in degradation and ubiquitination of NRF2, concurrently inhibiting VSMC autophagy to promote VSMC calcification in 5/6 nephrectomized rats. OGT inhibits VSMC autophagy through the KEAP1/NRF2 axis and thus accelerates high phosphate-induced VC in CKD.

The Emerging Role of Galectins and O-GlcNAc Homeostasis in Processes of Cellular Differentiation

Galectins are a family of soluble β-galactoside-binding proteins with diverse glycan-dependent and glycan-independent functions outside and inside the cell. Human cells express twelve out of sixteen recognized mammalian galectin genes and their expression profiles are very different between cell types and tissues. In this review, we summarize the current knowledge on the changes in the expression of individual galectins at mRNA and protein levels in different types of differentiating cells and the effects of recombinant galectins on cellular differentiation. A new model of galectin regulation is proposed considering the change in O-GlcNAc homeostasis between progenitor/stem cells and mature differentiated cells. The recognition of galectins as regulatory factors controlling cell differentiation and self-renewal is essential for developmental and cancer biology to develop innovative strategies for prevention and targeted treatment of proliferative diseases, tissue regeneration, and stem-cell therapy.

OGT suppresses S6K1-mediated macrophage inflammation and metabolic disturbance

Enhanced inflammation is believed to contribute to overnutrition-induced metabolic disturbance. Nutrient flux has also been shown to be essential for immune cell activation. Here, we report an unexpected role of nutrient-sensing O-linked β-N-acetylglucosamine (O-GlcNAc) signaling in suppressing macrophage proinflammatory activation and preventing diet-induced metabolic dysfunction. Overnutrition stimulates an increase in O-GlcNAc signaling in macrophages. O-GlcNAc signaling is down-regulated during macrophage proinflammatory activation. Suppressing O-GlcNAc signaling by O-GlcNAc transferase (OGT) knockout enhances macrophage proinflammatory polarization, promotes adipose tissue inflammation and lipolysis, increases lipid accumulation in peripheral tissues, and exacerbates tissue-specific and whole-body insulin resistance in high-fat-diet-induced obese mice. OGT inhibits macrophage proinflammatory activation by catalyzing ribosomal protein S6 kinase beta-1 (S6K1) O-GlcNAcylation and suppressing S6K1 phosphorylation and mTORC1 signaling. These findings thus identify macrophage O-GlcNAc signaling as a homeostatic mechanism maintaining whole-body metabolism under overnutrition.

O-GlcNAc transferase inhibits visceral fat lipolysis and promotes diet-induced obesity

Excessive visceral fat accumulation is a primary risk factor for metabolically unhealthy obesity and related diseases. The visceral fat is highly susceptible to the availability of external nutrients. Nutrient flux into the hexosamine biosynthetic pathway leads to protein posttranslational modification by O-linked β-N-acetylglucosamine (O-GlcNAc) moieties. O-GlcNAc transferase (OGT) is responsible for the addition of GlcNAc moieties to target proteins. Here, we report that inducible deletion of adipose OGT causes a rapid visceral fat loss by specifically promoting lipolysis in visceral fat. Mechanistically, visceral fat maintains a high level of O-GlcNAcylation during fasting. Loss of OGT decreases O-GlcNAcylation of lipid droplet-associated perilipin 1 (PLIN1), which leads to elevated PLIN1 phosphorylation and enhanced lipolysis. Moreover, adipose OGT overexpression inhibits lipolysis and promotes diet-induced obesity. These findings establish an essential role for OGT in adipose tissue homeostasis and indicate a unique potential for targeting O-GlcNAc signaling in the treatment of obesity.

Computational Prediction of Protein O-GlcNAc Modification

Protein O-GlcNAcylation on serine and threonine residues is a significant posttranslational modification. Experimental techniques can uncover only a small portion of O-GlcNAcylation sites. Several computational algorithms have been proposed as necessary auxiliary tools to identify potential O-GlcNAcylation sites. This chapter discusses the metrics and procedures used to assess prediction tools and surveys six computational tools for the prediction of protein O-GlcNAcylation sites. Analyses of these tools using an independent test dataset indicated the advantages and disadvantages of the six existing prediction methods. We also discuss the challenges that may be faced while developing novel predictors in the future.

Research progress in protein posttranslational modification site prediction

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

Critical observations that shaped our understanding of the function(s) of intracellular glycosylation (O-GlcNAc)

Almost 100 years after the first descriptions of proteins conjugated to carbohydrates (mucins), several studies suggested that glycoproteins were not restricted to the serum, extracellular matrix, cell surface, or endomembrane system. In the 1980s, key data emerged demonstrating that intracellular proteins were modified by monosaccharides of O-linked β-N-acetylglucosamine (O-GlcNAc). Subsequently, this modification was identified on thousands of proteins that regulate cellular processes as diverse as protein aggregation, localization, post-translational modifications, activity, and interactions. In this Review, we will highlight critical discoveries that shaped our understanding of the molecular events underpinning the impact of O-GlcNAc on protein function, the role that O-GlcNAc plays in maintaining cellular homeostasis, and our understanding of the mechanisms that regulate O-GlcNAc-cycling.

OGT (O-GlcNAc Transferase) Selectively Modifies Multiple Residues Unique to Lamin A

The LMNA gene encodes lamins A and C with key roles in nuclear structure, signaling, gene regulation, and genome integrity. Mutations in LMNA cause over 12 diseases ('laminopathies'). Lamins A and C are identical for their first 566 residues. However, they form separate filaments in vivo, with apparently distinct roles. We report that lamin A is β-O-linked N-acetylglucosamine-(O-GlcNAc)-modified in human hepatoma (Huh7) cells and in mouse liver. In vitro assays with purified O-GlcNAc transferase (OGT) enzyme showed robust O-GlcNAcylation of recombinant mature lamin A tails (residues 385⁻646), with no detectable modification of lamin B1, lamin C, or 'progerin' (Δ50) tails. Using mass spectrometry, we identified 11 O-GlcNAc sites in a 'sweet spot' unique to lamin A, with up to seven sugars per peptide. Most sites were unpredicted by current algorithms. Double-mutant (S612A/T643A) lamin A tails were still robustly O-GlcNAc-modified at seven sites. By contrast, O-GlcNAcylation was undetectable on tails bearing deletion Δ50, which causes Hutchinson⁻Gilford progeria syndrome, and greatly reduced by deletion Δ35. We conclude that residues deleted in progeria are required for substrate recognition and/or modification by OGT in vitro. Interestingly, deletion Δ35, which does not remove the majority of identified O-GlcNAc sites, does remove potential OGT-association motifs (lamin A residues 622⁻625 and 639⁻645) homologous to that in mouse Tet1. These biochemical results are significant because they identify a novel molecular pathway that may profoundly influence lamin A function. The hypothesis that lamin A is selectively regulated by OGT warrants future testing in vivo, along with two predictions: genetic variants may contribute to disease by perturbing OGT-dependent regulation, and nutrient or other stresses might cause OGT to misregulate wildtype lamin A.

MDD-carb: a combinatorial model for the identification of protein carbonylation sites with substrate motifs

Background

Carbonylation, which takes place through oxidation of reactive oxygen species (ROS) on specific residues, is an irreversibly oxidative modification of proteins. It has been reported that the carbonylation is related to a number of metabolic or aging diseases including diabetes, chronic lung disease, Parkinson’s disease, and Alzheimer’s disease. Due to the lack of computational methods dedicated to exploring motif signatures of protein carbonylation sites, we were motivated to exploit an iterative statistical method to characterize and identify carbonylated sites with motif signatures.

Results

By manually curating experimental data from research articles, we obtained 332, 144, 135, and 140 verified substrate sites for K (lysine), R (arginine), T (threonine), and P (proline) residues, respectively, from 241 carbonylated proteins. In order to examine the informative attributes for classifying between carbonylated and non-carbonylated sites, multifarious features including composition of twenty amino acids (AAC), composition of amino acid pairs (AAPC), position-specific scoring matrix (PSSM), and positional weighted matrix (PWM) were investigated in this study. Additionally, in an attempt to explore the motif signatures of carbonylation sites, an iterative statistical method was adopted to detect statistically significant dependencies of amino acid compositions between specific positions around substrate sites. Profile hidden Markov model (HMM) was then utilized to train a predictive model from each motif signature. Moreover, based on the method of support vector machine (SVM), we adopted it to construct an integrative model by combining the values of bit scores obtained from profile HMMs. The combinatorial model could provide an enhanced performance with evenly predictive sensitivity and specificity in the evaluation of cross-validation and independent testing.

Conclusion

This study provides a new scheme for exploring potential motif signatures at substrate sites of protein carbonylation. The usefulness of the revealed motifs in the identification of carbonylated sites is demonstrated by their effective performance in cross-validation and independent testing. Finally, these substrate motifs were adopted to build an available online resource (MDD-Carb, http://csb.cse.yzu.edu.tw/MDDCarb/) and are also anticipated to facilitate the study of large-scale carbonylated proteomes.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0511-4) contains supplementary material, which is available to authorized users.

Very-Low-Density Lipoprotein of Metabolic Syndrome Modulates Gap Junctions and Slows Cardiac Conduction

Very-low-density lipoproteins (VLDL) is a hallmark of metabolic syndrome (MetS) and each manifestation of MetS is related to atrial fibrillation (AF) risks. Slowed atrial conduction is a mechanism of AF in MetS. We hypothesized that VLDL can modulate and reduce atrial gap junctions. VLDLs were separated from normal (Normal-VLDL) and MetS (MetS-VLDL) individuals. VLDLs (15 µg/g) and equivalent volumes of saline (CTL) were injected respectively to C57BL/6 mice for 6 weeks. Electrocardiograms demonstrated that MetS-VLDL induced prolongation of P wave (P = 0.041), PR intervals (P = 0.014), QRS duration and QTc interval (both P = 0.003), but Normal-VLDL did not. Optical mapping of perfused hearts confirmed slowed conduction on atria and ventricles of MetS-VLDL mice. Slowed cardiac conduction was associated with significant atrial and ventricular remodeling, along with systolic dysfunction and comparable intra-cardiac fibrosis. MetS-VLDL induced downregulation of Cx40 and Cx43 at transcriptional, translational and tissue levels, and it also enhanced O-GlcNAcylation of Cx40 and Cx43. Protein structure analyses predicted O-GlcNAcylation at serine 18 of Cx40 and Cx43 which may impair stability of gap junctions. In conclusion, MetS-VLDL modulates gap junctions and delays both atrial and ventricular conduction. VLDL may contribute to the pathophysiology of atrial fibrillation and ventricular arrhythmias in MetS.

A study of the structural properties of sites modified by the O-linked 6-N-acetylglucosamine transferase

Protein O-GlcNAcylation (O-GlcNAc) is an essential post-translational modification (PTM) in higher eukaryotes. The O-linked β-N-acetylglucosamine transferase (OGT), targets specific Serines and Threonines (S/T) in intracellular proteins. However, unlike phosphorylation, fewer than 25% of known O-GlcNAc sites match a clear sequence pattern. Accordingly, the three-dimensional structures of O-GlcNAc sites were characterised to investigate the role of structure in molecular recognition. From 1,584 O-GlcNAc sites in 620 proteins, 143 were mapped to protein structures determined by X-ray crystallography. The modified S/T were 1.7 times more likely to be annotated in the REM465 field which defines missing residues in a protein structure, while 7 O-GlcNAc sites were solvent inaccessible and unlikely to be targeted by OGT. 132 sites with complete backbone atoms clustered into 10 groups, but these were indistinguishable from clusters from unmodified S/T. This suggests there is no prevalent three-dimensional motif for OGT recognition. Predicted features from the 620 proteins were compared to unmodified S/T in O-GlcNAcylated proteins and globular proteins. The Jpred4 predicted secondary structure shows that modified S/T were more likely to be coils. 5/6 methods to predict intrinsic disorder indicated O-GlcNAcylated S/T to be significantly more disordered than unmodified S/T. Although the analysis did not find a pattern in the site three-dimensional structure, it revealed the residues around the modification site are likely to be disordered and suggests a potential role of secondary structure elements in OGT site recognition.

Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease

Protein modification by O-linked β-N-acetylglucosamine (O-GlcNAc) is emerging as an important factor in the pathogenesis of sporadic Alzheimer's disease (AD); however, detailed molecular characterization of this important protein post-translational modification at the proteome level has been highly challenging, owing to its low stoichiometry and labile nature. Herein, we report the most comprehensive, quantitative proteomics analysis for protein O-GlcNAcylation in postmortem human brain tissues with and without AD by the use of isobaric tandem mass tag labelling, chemoenzymatic photocleavage enrichment, and liquid chromatography coupled to mass spectrometry. A total of 1850 O-GlcNAc peptides covering 1094 O-GlcNAcylation sites were identified from 530 proteins in the human brain. One hundred and thirty-one O-GlcNAc peptides covering 81 proteins were altered in AD brains as compared with controls (q < 0.05). Moreover, alteration of O-GlcNAc peptide abundance could be attributed more to O-GlcNAcylation level than to protein level changes. The altered O-GlcNAcylated proteins belong to several structural and functional categories, including synaptic proteins, cytoskeleton proteins, and memory-associated proteins. These findings suggest that dysregulation of O-GlcNAcylation of multiple brain proteins may be involved in the development of sporadic AD. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features

BACKGROUND

Protein carbonylation, an irreversible and non-enzymatic post-translational modification (PTM), is often used as a marker of oxidative stress. When reactive oxygen species (ROS) oxidized the amino acid side chains, carbonyl (CO) groups are produced especially on Lysine (K), Arginine (R), Threonine (T), and Proline (P). Nevertheless, due to the lack of information about the carbonylated substrate specificity, we were encouraged to develop a systematic method for a comprehensive investigation of protein carbonylation sites.

RESULTS

After the removal of redundant data from multipe carbonylation-related articles, totally 226 carbonylated proteins in human are regarded as training dataset, which consisted of 307, 126, 128, and 129 carbonylation sites for K, R, T and P residues, respectively. To identify the useful features in predicting carbonylation sites, the linear amino acid sequence was adopted not only to build up the predictive model from training dataset, but also to compare the effectiveness of prediction with other types of features including amino acid composition (AAC), amino acid pair composition (AAPC), position-specific scoring matrix (PSSM), positional weighted matrix (PWM), solvent-accessible surface area (ASA), and physicochemical properties. The investigation of position-specific amino acid composition revealed that the positively charged amino acids (K and R) are remarkably enriched surrounding the carbonylated sites, which may play a functional role in discriminating between carbonylation and non-carbonylation sites. A variety of predictive models were built using various features and three different machine learning methods. Based on the evaluation by five-fold cross-validation, the models trained with PWM feature could provide better sensitivity in the positive training dataset, while the models trained with AAindex feature achieved higher specificity in the negative training dataset. Additionally, the model trained using hybrid features, including PWM, AAC and AAindex, obtained best MCC values of 0.432, 0.472, 0.443 and 0.467 on K, R, T and P residues, respectively.

CONCLUSION

When comparing to an existing prediction tool, the selected models trained with hybrid features provided a promising accuracy on an independent testing dataset. In short, this work not only characterized the carbonylated substrate preference, but also demonstrated that the proposed method could provide a feasible means for accelerating preliminary discovery of protein carbonylation.

Proteomic analysis reveals O-GlcNAc modification on proteins with key regulatory functions in Arabidopsis

Genetic studies have shown essential functions of O-linked N-acetylglucosamine (O-GlcNAc) modification in plants. However, the proteins and sites subject to this posttranslational modification are largely unknown. Here, we report a large-scale proteomic identification of O-GlcNAc-modified proteins and sites in the model plant Arabidopsis thaliana Using lectin weak affinity chromatography to enrich modified peptides, followed by mass spectrometry, we identified 971 O-GlcNAc-modified peptides belonging to 262 proteins. The modified proteins are involved in cellular regulatory processes, including transcription, translation, epigenetic gene regulation, and signal transduction. Many proteins have functions in developmental and physiological processes specific to plants, such as hormone responses and flower development. Mass spectrometric analysis of phosphopeptides from the same samples showed that a large number of peptides could be modified by either O-GlcNAcylation or phosphorylation, but cooccurrence of the two modifications in the same peptide molecule was rare. Our study generates a snapshot of the O-GlcNAc modification landscape in plants, indicating functions in many cellular regulation pathways and providing a powerful resource for further dissecting these functions at the molecular level.

GIW and InCoB are advancing bioinformatics in the Asia-Pacific

GIW/InCoB2015 the joint 26th International Conference on Genome Informatics (GIW) and 14th International Conference on Bioinformatics (InCoB) held in Tokyo, September 9-11, 2015 was attended by over 200 delegates. Fifty-one out of 89 oral presentations were based on research articles accepted for publication in four BMC journal supplements and three other journals. Sixteen articles in this supplement and six articles in the BMC Systems Biology GIW/InCoB2015 Supplement are covered by this introduction. The topics range from genome informatics, protein structure informatics, image analysis to biological networks and biomarker discovery.