ISOGlyP: de novo prediction of isoform-specific mucin-type O-glycosylation.
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ABSTRACT:
SUBMITTER: Mohl JE
PROVIDER: S-EPMC8022967 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:O-Glycosylation is one of the most important posttranslational modifications of proteins. It takes part in protein conformation, protein sorting, developmental processes and the modulation of enzymatic activities. In vertebrates, the basics of the biosynthetic pathway of O-glycans are already well understood. However, the regulation of the processes and the molecular aspects of defects, especially in correlation with cancer or developmental abnormalities, are still under investigation. The knowledge of the correlating invertebrate systems and evolutionary aspects of these highly conserved biosynthetic events may help improve the understanding of the regulatory factors of this pathway. Invertebrates display a broad spectrum of glycosylation varieties, providing an enormous potential for glycan modifications which may be used for the design of new pharmaceutically active substances. Here, overviews of the present knowledge of invertebrate mucin-type O-glycan structures and the currently identified enzymes responsible for the biosynthesis of these oligosaccharides are presented, and the few data dealing with functional aspects of O-glycans are summarised.
| S-EPMC6272458 | biostudies-literature
Project description:Cellular signaling by fibroblast growth factor receptors (FGFRs) is a highly regulated process mediated by specific interactions between distinct subsets of fibroblast growth factor (FGF) ligands and two FGFR isoforms generated by alternative splicing: an epithelial b- and mesenchymal c-isoforms. Here, we investigate the properties of a mini-protein, mb7, developed by an in silico design strategy to bind to the ligand-binding region of FGFR2. We describe structural, biophysical, and cellular analyses demonstrating that mb7 binds with high affinity to the c-isoforms of FGFR, resulting in inhibition of cellular signaling induced by a subset of FGFs that preferentially activate c-isoforms of FGFR. Notably, as mb7 blocks interaction between FGFR with Klotho proteins, it functions as an antagonist of the metabolic hormones FGF19 and FGF21, providing mechanistic insights and strategies for the development of therapeutics for diseases driven by aberrantly activated FGFRs.
| S-EPMC9636537 | biostudies-literature
Project description:Mucin-type O-glycosylation is an important post-translational modification that confers a variety of biological properties and functions to proteins. This post-translational modification has a particularly complex and differentially regulated biosynthesis rendering prediction and control of where O-glycans are attached to proteins, and which structures are formed, difficult. Because plants are devoid of GalNAc-type O-glycosylation, we have assessed requirements for establishing human GalNAc O-glycosylation de novo in plants with the aim of developing cell systems with custom-designed O-glycosylation capacity. Transient expression of a Pseudomonas aeruginosa Glc(NAc) C4-epimerase and a human polypeptide GalNAc-transferase in leaves of Nicotiana benthamiana resulted in GalNAc O-glycosylation of co-expressed human O-glycoprotein substrates. A chimeric YFP construct containing a 3.5 tandem repeat sequence of MUC1 was glycosylated with up to three and five GalNAc residues when co-expressed with GalNAc-T2 and a combination of GalNAc-T2 and GalNAc-T4, respectively, as determined by mass spectrometry. O-Glycosylation was furthermore demonstrated on a tandem repeat of MUC16 and interferon ?2b. In plants, prolines in certain classes of proteins are hydroxylated and further substituted with plant-specific O-glycosylation; unsubstituted hydroxyprolines were identified in our MUC1 construct. In summary, this study demonstrates that mammalian type O-glycosylation can be established in plants and that plants may serve as a host cell for production of recombinant O-glycoproteins with custom-designed O-glycosylation. The observed hydroxyproline modifications, however, call for additional future engineering efforts.
| S-EPMC3320939 | biostudies-literature
Project description:Changes in O-linked protein glycosylation are known to correlate with disease states but are difficult to monitor in a physiological setting because of a lack of experimental tools. Here, we report a technique for rapid profiling of O-linked glycoproteins in living animals by metabolic labeling with N-azidoacetylgalactosamine (GalNAz) followed by Staudinger ligation with phosphine probes. After injection of mice with a peracetylated form of GalNAz, azide-labeled glycoproteins were observed in a variety of tissues, including liver, kidney, and heart, in serum, and on isolated splenocytes. B cell glycoproteins were robustly labeled with GalNAz but T cell glycoproteins were not, suggesting fundamental differences in glycosylation machinery or metabolism. Furthermore, GalNAz-labeled B cells could be selectively targeted with a phosphine probe by Staudinger ligation within the living animal. Metabolic labeling with GalNAz followed by Staudinger ligation provides a means for proteomic analysis of this posttranslational modification and for identifying O-linked glycoprotein fingerprints associated with disease.
| S-EPMC1405625 | biostudies-literature
Project description:Acetylgalactosamine (GalNAc)-type O-glycosylation is an essential posttranslational modification (PTM) that plays fundamental roles in biology. Malfunction of this PTM is exemplified by the presence of truncated O-glycans in cancer. For instance, the glycoprotein MUC1 is overexpressed in many tumor tissues and tends to carry simple oligosaccharides that allow for the presentation of different tumor-associated antigens, such as the Tn or sTn antigens (GalNAc-α-1-O-Thr/Ser and Neu5Acα2-6GalNAcα1-O-Ser/Thr, respectively). In other cases, such as tumoral calcinosis associated with O-glycosylation of the fibroblast growth factor 23, O-glycans are absent or less abundant. Significant progress has been made in determining the three-dimensional structures of biomolecules that recognize GalNAc, such as antibodies, lectins, mucinases, GalNAc-transferases, and other glycosyltransferases. Analysis of the complexes between these entities and GalNAc-containing glycopeptides, in most cases derived from crystallographic or NMR analysis, provides an understanding of the key structural elements that control molecular recognition of these glycopeptides. Here, we describe and compare the binding sites of these proteins in detail, focusing on how the GalNAc moieties interact selectively with them. We also summarize the differences and similarities in GalNAc recognition. In general, the recognition of GalNAc-containing glycopeptides is determined by hydrogen bonds between hydroxyl groups and the N-acetyl group of GalNAc with proteins, as well as CH-π contacts in which the hydrophobic α-face of the sugar and the methyl group of NHAc can be involved. The latter interaction usually provides the basis for selectivity. It is worth noting that binding of these glycopeptides depends primarily on recognition of the sugar moiety, with some exceptions such as a few anti-MUC1 antibodies that primarily recognize the peptide backbone and use the sugar to facilitate shape complementarity or to establish a limited number of interactions with the protein. Focusing specifically on the GalNAc moiety, we can observe that there is some degeneracy of interactions within the same protein families, likely due to substrate flexibility. However, when all studied proteins are considered together, despite the commonalities within each protein family, no pattern can be discerned between the different families, apart from the presence of common residues such as Tyr, His, or Asp, which are responsible for hydrogen bonds. The lack of a pattern can be anticipated, given the diverse functions of mucinases, glycosyltransferases, antibodies, and lectins. Finally, it is important to point out that the conformational differences observed in solution in glycopeptides bearing GalNAc-α-1-O-Ser or GalNAc-α-1-O-Thr also can be found in the bound state. This unique characteristic is exploited, for instance, by the enzyme C1GalT1 to broadly glycosylate both acceptor substrates. The findings summarized in this review may contribute to the rational structure-guided development of therapeutic vaccines, novel diagnostic tools for early cancer detection, and new cancer treatments for cancer with tailored anti-Tn or anti-STn antibodies or new drugs to inhibit GalNAc-T isoenzymes.
| S-EPMC9996832 | biostudies-literature
Project description:BackgroundIncreasing efforts have been made to assess the potential of Escherichia coli strains for the production of complex recombinant proteins. Since a considerable part of therapeutic proteins are glycoproteins, the lack of the post-translational attachment of sugar moieties in standard E. coli expression strains represents a major caveat, thus limiting the use of E. coli based cell factories. The establishment of an E. coli expression system capable of protein glycosylation could potentially facilitate the production of therapeutics with a putative concomitant reduction of production costs.ResultsThe previously established E. coli strain expressing the soluble form of the functional human-derived glycosyltransferase polypeptide N-acetylgalactosaminyltransferase 2 (GalNAc-T2) was further modified by co-expressing the UDP-GlcNAc 4-epimerase WbgU derived from Plesiomonas shigelloides. This enables the conversion of uridine 5'-diphospho-N-acetylglucosamine (UDP-GlcNAc) to the sugar donor uridine 5'-diphospho-N-acetylgalactosamine (UDP-GalNAc) in the bacterial cytoplasm. Initially, the codon-optimised gene wbgU was inserted into a pET-derived vector and a Tobacco Etch Virus (TEV) protease cleavable polyhistidine-tag was translationally fused to the C- terminus of the amino acid sequence. The 4-epimerase was subsequently expressed and purified. Following the removal of the polyhistidine-tag, WbgU was analysed by circular dichroism spectroscopy to determine folding state and thermal transitions of the protein. The in vitro activity of WbgU was validated by employing a modified glycosyltransferase assay. The conversion of UDP-GlcNAc to UDP-GalNAc was shown by capillary electrophoresis analysis. Using a previously established chaperone pre-/co- expression platform, the in vivo activity of both glycosyltransferase GalNAc-T2 and 4-epimerase WbgU was assessed in E. coli, in combination with a mucin 10-derived target protein. Monitoring glycosylation by liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS), the results clearly indicated the in vivo glycosylation of the mucin-derived acceptor peptide.ConclusionIn the present work, the previously established E. coli- based expression system was further optimized and the potential for in vivo O-glycosylation was shown by demonstrating the transfer of sugar moieties to a mucin-derived acceptor protein. The results offer the possibility to assess the practical use of the described expression platform for in vivo glycosylations of important biopharmaceutical compounds in E. coli.
| S-EPMC6202839 | biostudies-literature
Project description:BackgroundAs one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.ResultsA new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O-glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O-glycosylation dataset with the assistance of Support Vector Machine (SVM). When the ratio of O-glycosylation to non-glycosylation sites in training datasets was set as 1:1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O-glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1:5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i.e. S+T predictor). Either in 1:1 or 1:5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O-glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors.ConclusionBecause of CKSAAP encoding's ability of reflecting characteristics of the sequences surrounding mucin-type O-glycosylation sites, CKSAAP_ OGlySite has been proved more powerful than the conventional binary encoding based method. This suggests that it can be used as a competitive mucin-type O-glycosylation site predictor to the biological community. CKSAAP_OGlySite is now available at http://bioinformatics.cau.edu.cn/zzd_lab/CKSAAP_OGlySite/.
| S-EPMC2335299 | biostudies-literature
Project description:Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.
| S-EPMC6607449 | biostudies-literature
Project description:Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowledge-based and contain different terms added together with arbitrary weights. Here, we test different search methods with one of the most accurate and predictive energy functions, namely Rosetta the knowledge-based force-field from Baker's group [Simons K, Kooperberg C, Huang E, Baker D (1997) J Mol Biol 268:209-225]. We use an implementation of a generalized ensemble search method to scale relevant parts of the energy function. This method, known as Hamiltonian Replica Exchange Monte Carlo, outperforms the original Monte Carlo Simulated Annealing used in the Rosetta package in terms of sampling low-energy states. It also outperforms another widely used generalized ensemble search method known as Temperature Replica Exchange Monte Carlo. Our results reveal clear deficiencies in the low-resolution Rosetta energy function in that the lowest energy structures are not necessarily the most native-like. By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.
| S-EPMC2631076 | biostudies-literature
Project description:The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than nonnative structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction method, but for larger and more complex proteins, the native state is virtually never sampled, and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper, we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical "linchpin" features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and, when constrained, dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality.
| S-EPMC2760740 | biostudies-literature