ProGlycProt: a repository of experimentally characterized prokaryotic glycoproteins

ProGlycProt V3.0: updated insights into prokaryotic glycoproteins and their glycosyltransferases

ProGlycProt is a comprehensive database of experimentally validated information about protein glycosylation in prokaryotes, including the glycoproteins, glycosyltransferases, and their accessory enzymes. The first release of ProGlycProt featured experimentally validated information on glycoproteins only. For the second release in 2019, the size and scope of the database were expanded twofold, and experimental data on cognate glycosyltransferases and their accessory proteins was incorporated. The growing research and technology interest in microbial glycoproteins and their enzymes is evident from the steady rise in academic publications and patents in this area. Accordingly, the third update comprises a new section on patents related to glycosylation methods, novel glycosyltransferases, and technologies developed therefrom. The structure gallery is reorganized, wherein the number and quality of the models are upgraded with the help of AlphaFold2. Over the years, the influx of experimental proteomics data into public repositories like PRIDE has surged. Harnessing this legacy data for in-silico glycoprotein identification is a smart approach. Version 3.0 adds 45 N-glycoprotein entries annotated from MS datasets available on PRIDE and reviewed by independent research groups. With a 67% rise in entries corresponding to 119 genera of prokaryotes, the ProGlycProt continues to be the exclusive database of experimentally validated comprehensive information about protein glycosylation in prokaryotes.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Biochemical characterization of an inverting S/O-HexNAc-transferase and evidence of S-linked glycosylation in Actinobacteria

Antimicrobial peptides harboring S- and or O-linked glycans are known as glycocins. Glycocins were first discovered and best characterized in Firmicutes. S-glycosylation is an enzymatic process catalyzed by S-glycosyltransferases of the GT2 family. Using a heterologous expression system, here we describe an inverting S/O-HexNAc-transferase (SvGT), encoded by ORF AQF52_3101 of S. venezuelae ATCC 15439, along with its acceptor substrate (SvC), encoded by ORF AQF52_3099. Using in vitro and in vivo assays, we define the distinct donor specificity, acceptor specificity, regioselectivity, chemoselectivity, and Y(G/A/K/Q/E ≠ ΔG)(C/S/T ≠ Y/N)(G/A ≠ P/Q)G as the minimum acceptor sequon of SvGT. Although UDP-GlcNAc served as the donor in the cellular milieu, SvGT could also utilize UDP-Glc and UDP-GalNAc as donors in vitro. Using mass spectrometry and western blotting, we provide evidence that an anti-O-GlcNAc antibody (CTD110.6) cross-reacts with S-GlcNAc and may be used to detect S-GlcNAcylated glycoconjugates directly. With an understanding of enzyme specificities, we finally employed SvGT to generate two proof-of-concept neoglycocins against L. monocytogenes. In conclusion, this study provides the first experimental evidence for S-glycosylation in Actinobacteria and the application of its S/O-HexNAc-transferase in glycocin engineering.

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).

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Prioritization of Mur family drug targets against A. baumannii and identification of their homologous proteins through molecular phylogeny, primary sequence, and structural analysis

Background

The World Health Organization (WHO) report stated that Acinetobacter baumannii had been classified as one of the most important pathogenic bacteria causing nosocomial infection in hospital patients due to multi-drug resistance (MDR). It is vital to find out new bacterial drug targets and annotated their structure and function for the exploration of new anti-bacterial agents. The present study utilized a systematic route to prioritize the potential drug targets that belong to Mur family of Acinetobacter baumannii and identify their homologous proteins using a computational approach such as sequence similarity search, multiple sequence alignment, phylogenetic analysis, protein sequence, and protein structure analysis.

Results

From the results of protein sequence analysis of eight Mur family proteins, they divided into three main enzymatic classes namely transferases (MurG, MurA and MraY), ligases (MurC, MurD, MurE, and MurF), and oxidoreductase (MurB). Based on the results of intra-comparative protein sequence analysis and enzymatic classification, we have chosen MurB, MurE, and MurG as the prioritized drug targets from A. baumannii and subjected them for further detailed studies of inter-species comparison. This inter-species comparison help us to explore the sequential and structural properties of homologous proteins in other species and hence, opens a gateway for new target identification and using common inhibitor for different bacterial species caused by various diseases. The pairwise sequence alignment results between A. baumannii’s MurB with A. calcoaceticus’s MurB, A. baumannii’s MurE with A. seifertii’s MurE, and A. baumannii’s MurG with A. pittii’s MurG showed that every group of the proteins are highly similar with each other and they showed sequence identity of 95.7% and sequence similarity of 97.2%.

Conclusion

Together with the results of secondary and three-dimensional structure predictions explained that three selected proteins (MurB, MurE, and MurG) from A. baumannii and their related proteins (AcMurB, AsMurE, and ApMurG) belong to mixed αβ class and they are very similar.

ProGlycProt V2.0, a repository of experimentally validated glycoproteins and protein glycosyltransferases of prokaryotes

Knowledge of glycosylation status and glycan-pattern of proteins are of considerable medical, academic and application interest. ProGlycProt V2.0 (www.proglycprot.org) therefore, is conceived and maintained as an exclusive web-resource providing comprehensive information on experimentally validated glycoproteins and protein glycosyltransferases (GTs) of prokaryotic origin. The second release of ProGlycProt features a major update with a 191% increase in the total number of entries, manually collected and curated from 607 peer-reviewed publications, on the subject. Protein GTs from prokaryotes that catalyze a varied range of glycan linkages are amenable glycoengineering tools. Therefore, the second release presents content that is greatly expanded and reorganized in two sub-databases: ProGPdb and ProGTdb. While ProGPdb provides information about validated glycoproteins (222 entries), ProGTdb catalogs enzymes/proteins that are instrumental in protein glycosylation, directly (122) or as accessory proteins (182). ProGlycProt V2.0 remains highly cross-referenced yet exclusive and complementary in content to other related databases. The second release further features enhanced search capability, a "compare" entries option and an innovative geoanalytical tool (MapView) facilitating location-assisted search-cum filtering of the entries using geo-positioning information of researchers/groups cited in the ProGlycProt V2.0 databases. Thus, ProGlycProt V2.0 continues to serve as a useful one-point web-resource on various evidence-based information on protein glycosylation in prokaryotes.

Protein glycosylation: Sweet or bitter for bacterial pathogens?

Protein glycosylation systems in many bacteria are often associated with crucial biological processes like pathogenicity, immune evasion and host-pathogen interactions, implying the significance of protein-glycan linkage. Similarly, host protein glycosylation has been implicated in antimicrobial activity as well as in promoting growth of beneficial strains. In fact, few pathogens notably modulate host glycosylation machineries to facilitate their survival. To date, diverse chemical and biological strategies have been developed for conjugate vaccine production for disease control. Bioconjugate vaccines, largely being produced by glycoengineering using PglB (the N-oligosaccharyltransferase from Campylobacter jejuni) in suitable bacterial hosts, have been highly promising with respect to their effectiveness in providing protective immunity and ease of production. Recently, a novel method of glycoconjugate vaccine production involving an O-oligosaccharyltransferase, PglL from Neisseria meningitidis, has been optimized. Nevertheless, many questions on defining antigenic determinants, glycosylation markers, species-specific differences in glycosylation machineries, etc. still remain unanswered, necessitating further exploration of the glycosylation systems of important pathogens. Hence, in this review, we will discuss the impact of bacterial protein glycosylation on its pathogenesis and the interaction of pathogens with host protein glycosylation, followed by a discussion on strategies used for bioconjugate vaccine development.

Asn-linked oligosaccharide chain of a crenarchaeon, Pyrobaculum calidifontis, is reminiscent of the eukaryotic high-mannose-type glycan

Pyrobaculum calidifontis is a hyperthermophilic archaeon that belongs to the phylum Crenarchaeota. In contrast to the phylum Euryarchaeota, only the N-glycan structure of the genus Sulfolobus is known in Crenarchaeota. Here, we enriched glycoproteins from cultured P. calidifontis cells, by ConA lectin chromatography. The MASCOT search identified proteins with at least one potential N-glycosylation site. The tandem mass spectrometry (MS/MS) analysis of 12 small tryptic glycopeptides confirmed the canonical N-glycosylation consensus in P. calidifontis. We determined the N-linked oligosaccharide structure produced by an in vitro enzymatic oligosaccharyl transfer reaction. Pyrobaculum calidifontis cells were cultured in rich medium supplemented with 13C-glucose, for the metabolic labeling of N-oligosaccharide donors. An incubation with a synthetic peptide substrate produced glycopeptides with isotopically labeled oligosaccharide moieties. The MS and nuclear magnetic resonance analyses revealed that the P. calidifontisN-glycan has a biantennary, high-mannose-type structure consisting of up to 11 monosaccharide residues. The base portion of the P. calidifontisN-glycan strongly resembles the eukaryotic core structure, α-Man-(1-3)-(α-Man-(1-6)-)β-Man-(1-4)-β-GlcNAc-(1-4)-β-GlcNAc-Asn. Structural differences exist in the anomeric configuration between Man and GlcNAc, and the chitobiose structure is chemically modified: one GlcNAc residue is oxidized to glucoronate, and the GlcNAc residues are both modified with an additional acetamido group at the C-3 position. As a result, the core structure of the P. calidifontisN-glycan is α-Man-(1-3)-(α-Man-(1-6)-)α-Man-(1-4)-β-GlcANAc3NAc-(1-4)-β-GlcNAc3NAc-Asn, in which the unique features of the P. calidifontisN-glycan are underlined. In spite of these differences, the structure of the P. calidifontisN-glycan is the most similar to the eukaryotic counterparts, among all archaeal N-glycans reported to date.

An iterative glycosyltransferase EntS catalyzes transfer and extension of O- and S-linked monosaccharide in enterocin 96

Glycosyltransferases are essential tools for in vitro glycoengineering. Bacteria harbor an unexplored variety of protein glycosyltransferases. Here, we describe a peptide glycosyltransferase (EntS) encoded by ORF0417 of Enterococcus faecalis TX0104. EntS di-glycosylates linear peptide of enterocin 96 - a known antibacterial, in vitro. It is capable of transferring as well as extending the glycan onto the peptide in an iterative sequential dissociative manner. It can catalyze multiple linkages: Glc/Gal(-O)Ser/Thr, Glc/Gal(-S)Cys and Glc/Gal(β)Glc/Gal(-O/S)Ser/Thr/Cys, in one pot. Using EntS generated glycovariants of enterocin 96 peptide, size and identity of the glycan are found to influence bioactivity of the peptide. The study identifies EntS as an enzyme worth pursuing, for in vitro peptide glycoengineering.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2011-2012

This review is the seventh update of the original article published in 1999 on the application of MALDI mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2012. General aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, and fragmentation are covered in the first part of the review and applications to various structural types constitute the remainder. The main groups of compound are oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. Also discussed are medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:255-422, 2017.

Sweet New Roles for Protein Glycosylation in Prokaryotes

Long-held to be a post-translational modification unique to Eukarya, it is now clear that both Bacteria and Archaea also perform protein glycosylation, namely the covalent attachment of mono- to polysaccharides to specific protein targets. At the same time, many of the roles assigned to this protein-processing event in eukaryotes, such as guiding protein folding/quality control, intracellular trafficking, dictating cellular recognition events and others, do not apply or are even irrelevant to prokaryotes. As such, protein glycosylation must serve novel functions in Bacteria and Archaea. Recent efforts have begun to elucidate some of these prokaryote-specific roles, which are addressed in this review.

Databases for Microbiologists

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. The purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.

Bio/chemoinformatics in India: an outlook

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.

Nuclear MEK1 sequesters PPARγ and bisects MEK1/ERK signaling: a non-canonical pathway of retinoic acid inhibition of adipocyte differentiation

Uncontrolled adipogenesis and adipocyte proliferation have been connected to human comorbidities. Retinoic acid (RA) is known to inhibit adipocyte differentiation, however the underlying mechanisms have not been adequately understood. This study reports that RA acting as a ligand to RA receptors (RARs and RXRs) is not a sine qua non to the inhibition of adipogenesis. Our intriguing observation of a negative correlation between increased retinoylation and adipogenesis led us to explore retinoylated proteins in adipocytes. Exportin (CRM1) was found to be retinoylated, which in turn can affect the spatio-temporal regulation of the important signaling molecule mitogen-activated protein kinase kinase 1 (MEK1), likely by disrupting its export from the nucleus. Nuclear enrichment of MEK1 physically sequesters peroxisome proliferator-activated receptor gamma (PPARγ), the master regulator of adipogenesis, from its target genes and thus inhibits adipogenesis while also disrupting the MEK1-extracellular-signal regulated kinase (ERK) signaling cascade. This study is first to report the inhibition of adipocyte differentiation by retinoylation.

N-linked glycosylation in Archaea: a structural, functional, and genetic analysis

N-glycosylation of proteins is one of the most prevalent posttranslational modifications in nature. Accordingly, a pathway with shared commonalities is found in all three domains of life. While excellent model systems have been developed for studying N-glycosylation in both Eukarya and Bacteria, an understanding of this process in Archaea was hampered until recently by a lack of effective molecular tools. However, within the last decade, impressive advances in the study of the archaeal version of this important pathway have been made for halophiles, methanogens, and thermoacidophiles, combining glycan structural information obtained by mass spectrometry with bioinformatic, genetic, biochemical, and enzymatic data. These studies reveal both features shared with the eukaryal and bacterial domains and novel archaeon-specific aspects. Unique features of N-glycosylation in Archaea include the presence of unusual dolichol lipid carriers, the use of a variety of linking sugars that connect the glycan to proteins, the presence of novel sugars as glycan constituents, the presence of two very different N-linked glycans attached to the same protein, and the ability to vary the N-glycan composition under different growth conditions. These advances are the focus of this review, with an emphasis on N-glycosylation pathways in Haloferax, Methanococcus, and Sulfolobus.

PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins

Posttranslational modifications (PTMs) define covalent and chemical modifications of protein residues. They play important roles in modulating various biological functions. Current PTM databases contain important sequence annotations but do not provide informative 3D structural resource about these modifications. Posttranslational modification structural database (PTM-SD) provides access to structurally solved modified residues, which are experimentally annotated as PTMs. It combines different PTM information and annotation gathered from other databases, e.g. Protein DataBank for the protein structures and dbPTM and PTMCuration for fine sequence annotation. PTM-SD gives an accurate detection of PTMs in structural data. PTM-SD can be browsed by PDB id, UniProt accession number, organism and classic PTM annotation. Advanced queries can also be performed, i.e. detailed PTM annotations, amino acid type, secondary structure, SCOP class classification, PDB chain length and number of PTMs by chain. Statistics and analyses can be computed on a selected dataset of PTMs. Each PTM entry is detailed in a dedicated page with information on the protein sequence, local conformation with secondary structure and Protein Blocks. PTM-SD gives valuable information on observed PTMs in protein 3D structure, which is of great interest for studying sequence-structure- function relationships at the light of PTMs, and could provide insights for comparative modeling and PTM predictions protocols. Database URL: PTM-SD can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/.

In silico platform for prediction of N-, O- and C-glycosites in eukaryotic protein sequences

Glycosylation is one of the most abundant and an important post-translational modification of proteins. Glycosylated proteins (glycoproteins) are involved in various cellular biological functions like protein folding, cell-cell interactions, cell recognition and host-pathogen interactions. A large number of eukaryotic glycoproteins also have therapeutic and potential technology applications. Therefore, characterization and analysis of glycosites (glycosylated residues) in these proteins is of great interest to biologists. In order to cater these needs a number of in silico tools have been developed over the years, however, a need to get even better prediction tools remains. Therefore, in this study we have developed a new webserver GlycoEP for more accurate prediction of N-linked, O-linked and C-linked glycosites in eukaryotic glycoproteins using two larger datasets, namely, standard and advanced datasets. In case of standard datasets no two glycosylated proteins are more similar than 40%; advanced datasets are highly non-redundant where no two glycosites’ patterns (as defined in methods) have more than 60% similarity. Further, based on our results with several algorihtms developed using different machine-learning techniques, we found Support Vector Machine (SVM) as optimum tool to develop glycosite prediction models. Accordingly, using our more stringent and non-redundant advanced datasets, the SVM based models developed in this study achieved a prediction accuracy of 84.26%, 86.87% and 91.43% with corresponding MCC of 0.54, 0.20 and 0.78, for N-, O- and C-linked glycosites, respectively. The best performing models trained on advanced datasets were then implemented as a user-friendly web server GlycoEP (http://www.imtech.res.in/raghava/glycoep/). Additionally, this server provides prediction models developed on standard datasets and allows users to scan sequons in input protein sequences.

Cell signaling, post-translational protein modifications and NMR spectroscopy

Post-translationally modified proteins make up the majority of the proteome and establish, to a large part, the impressive level of functional diversity in higher, multi-cellular organisms. Most eukaryotic post-translational protein modifications (PTMs) denote reversible, covalent additions of small chemical entities such as phosphate-, acyl-, alkyl- and glycosyl-groups onto selected subsets of modifiable amino acids. In turn, these modifications induce highly specific changes in the chemical environments of individual protein residues, which are readily detected by high-resolution NMR spectroscopy. In the following, we provide a concise compendium of NMR characteristics of the main types of eukaryotic PTMs: serine, threonine, tyrosine and histidine phosphorylation, lysine acetylation, lysine and arginine methylation, and serine, threonine O-glycosylation. We further delineate the previously uncharacterized NMR properties of lysine propionylation, butyrylation, succinylation, malonylation and crotonylation, which, altogether, define an initial reference frame for comprehensive PTM studies by high-resolution NMR spectroscopy.

GlycoPP: a webserver for prediction of N- and O-glycosites in prokaryotic protein sequences

Glycosylation is one of the most abundant post-translational modifications (PTMs) required for various structure/function modulations of proteins in a living cell. Although elucidated recently in prokaryotes, this type of PTM is present across all three domains of life. In prokaryotes, two types of protein glycan linkages are more widespread namely, N- linked, where a glycan moiety is attached to the amide group of Asn, and O- linked, where a glycan moiety is attached to the hydroxyl group of Ser/Thr/Tyr. For their biologically ubiquitous nature, significance, and technology applications, the study of prokaryotic glycoproteins is a fast emerging area of research. Here we describe new Support Vector Machine (SVM) based algorithms (models) developed for predicting glycosylated-residues (glycosites) with high accuracy in prokaryotic protein sequences. The models are based on binary profile of patterns, composition profile of patterns, and position-specific scoring matrix profile of patterns as training features. The study employ an extensive dataset of 107 N-linked and 116 O-linked glycosites extracted from 59 experimentally characterized glycoproteins of prokaryotes. This dataset includes validated N-glycosites from phyla Crenarchaeota, Euryarchaeota (domain Archaea), Proteobacteria (domain Bacteria) and validated O-glycosites from phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (domain Bacteria). In view of the current understanding that glycosylation occurs on folded proteins in bacteria, hybrid models have been developed using information on predicted secondary structures and accessible surface area in various combinations with training features. Using these models, N-glycosites and O-glycosites could be predicted with an accuracy of 82.71% (MCC 0.65) and 73.71% (MCC 0.48), respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence. A web server GlycoPP, implementing these models is available freely at http:/www.imtech.res.in/raghava/glycopp/.