Isolation of Glycoproteins and Identification of Their N-Linked Glycosylation Sites

2006 ◽  
pp. 177-186 ◽  
Author(s):  
Hui Zhang ◽  
Ruedi Aebersold
Keyword(s):  
Glycosylation Sites

Structural Aspects on the Glycosylation Sites in Human Fibrinogen

1979 ◽  
Vol 41 (04) ◽  
pp. 671-676 ◽  
Author(s):  
Edda Töpfer-Petersen ◽  
Friedrich Lottspeich ◽  
Agnes Henschen
Keyword(s):  
Human Fibrinogen ◽  
Glycosylation Sites ◽  
Structural Aspects

A Mucin-Specific Protease Enables Molecular and Functional Analysis of Human Cancer-Associated Mucins

2018 ◽  
Author(s):  
Stacy A. Malaker ◽  
Kayvon Pedram ◽  
Michael J. Ferracane ◽  
Elliot C. Woods ◽  
Jessica Kramer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Molecular Mechanisms ◽  
Cultured Cells ◽  
High Resolution Mass Spectrometry ◽  
Human Cancer ◽  
Glycosylation Sites ◽  
Bacterial Protease ◽  
Specific Protease ◽  
To Come ◽  
And Function

<div> <div> <div> <p>Mucins are a class of highly O-glycosylated proteins that are ubiquitously expressed on cellular surfaces and are important for human health, especially in the context of carcinomas. However, the molecular mechanisms by which aberrant mucin structures lead to tumor progression and immune evasion have been slow to come to light, in part because methods for selective mucin degradation are lacking. Here we employ high resolution mass spectrometry, polymer synthesis, and computational peptide docking to demonstrate that a bacterial protease, called StcE, cleaves mucin domains by recognizing a discrete peptide-, glycan-, and secondary structure- based motif. We exploited StcE’s unique properties to map glycosylation sites and structures of purified and recombinant human mucins by mass spectrometry. As well, we found that StcE will digest cancer-associated mucins from cultured cells and from ovarian cancer patient-derived ascites fluid. Finally, using StcE we discovered that Siglec-7, a glyco-immune checkpoint receptor, specifically binds sialomucins as biological ligands, whereas the related Siglec-9 receptor does not. Mucin-specific proteolysis, as exemplified by StcE, is therefore a powerful tool for the study of glycoprotein structure and function and for deorphanizing mucin-binding receptors. </p> </div> </div> </div>

Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence

2020 ◽  
Vol 27 (3) ◽  
pp. 178-186 ◽  
Author(s):  
Ganesan Pugalenthi ◽  
Varadharaju Nithya ◽  
Kuo-Chen Chou ◽  
Govindaraju Archunan
Keyword(s):  
Random Forest ◽  
Protein Sequence ◽  
Glycosylation Site ◽  
Computational Method ◽  
The Other ◽  
Eukaryotic Protein ◽  
Random Forest Method ◽  
Glycosylation Sites ◽  
Human And Mouse ◽  
Better Than

Background: N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. Objective: In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. Methods: In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. Results: Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. Conclusion: Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

scholarly journals Spontaneous Cleavages of a Heterologous Protein, the CenA Endoglucanase of Cellulomonas fimi, in Escherichia coli

2021 ◽  
Vol 14 ◽  
pp. 117863612110246
Author(s):  
Cheuk Yin Lai ◽  
Ka Lun Ng ◽  
Hao Wang ◽  
Chui Chi Lam ◽  
Wan Keung Raymond Wong
Keyword(s):  
Escherichia Coli ◽  
Cellulolytic Bacterium ◽  
Systematic Screening ◽  
Cellulomonas Fimi ◽  
E Coli ◽  
Substrate Complex ◽  
Enzyme Substrate ◽  
Glycosylation Sites ◽  
Endogenous Proteins ◽  
Cellulose Binding

CenA is an endoglucanase secreted by the Gram-positive cellulolytic bacterium, Cellulomonas fimi, to the environment as a glycosylated protein. The role of glycosylation in CenA is unclear. However, it seems not crucial for functional activity and secretion since the unglycosylated counterpart, recombinant CenA (rCenA), is both bioactive and secretable in Escherichia coli. Using a systematic screening approach, we have demonstrated that rCenA is subjected to spontaneous cleavages (SC) in both the cytoplasm and culture medium of E. coli, under the influence of different environmental factors. The cleavages were found to occur in both the cellulose-binding (CellBD) and catalytic domains, with a notably higher occurring rate detected in the former than the latter. In CellBD, the cleavages were shown to occur close to potential N-linked glycosylation sites, suggesting that these sites might serve as ‘attributive tags’ for differentiating rCenA from endogenous proteins and the points of initiation of SC. It is hypothesized that glycosylation plays a crucial role in protecting CenA from SC when interacting with cellulose in the environment. Subsequent to hydrolysis, SC would ensure the dissociation of CenA from the enzyme-substrate complex. Thus, our findings may help elucidate the mechanisms of protein turnover and enzymatic cellulolysis.

scholarly journals Tissue plasminogen activator is a ligand of cation-independent mannose 6-phosphate receptor and consists of glycoforms that contain mannose 6-phosphate

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
James J. Miller ◽  
Richard N. Bohnsack ◽  
Linda J. Olson ◽  
Mayumi Ishihara ◽  
Kazuhiro Aoki ◽  
...  
Keyword(s):  
Plasminogen Activator ◽  
Tissue Plasminogen Activator ◽  
Dependent Manner ◽  
Plasminogen Activation ◽  
Urokinase Plasminogen Activator Receptor ◽  
Plasminogen Activation System ◽  
Extracellular Region ◽  
Glycosylation Sites ◽  
Tissue Plasminogen ◽  
Mannose 6 Phosphate

AbstractPlasmin is the key enzyme in fibrinolysis. Upon interaction with plasminogen activators, the zymogen plasminogen is converted to active plasmin. Some studies indicate plasminogen activation is regulated by cation-independent mannose 6-phosphate receptor (CI-MPR), a protein that facilitates lysosomal enzyme trafficking and insulin-like growth factor 2 downregulation. Plasminogen regulation may be accomplished by CI-MPR binding to plasminogen or urokinase plasminogen activator receptor. We asked whether other members of the plasminogen activation system, such as tissue plasminogen activator (tPA), also interact with CI-MPR. Because tPA is a glycoprotein with three N-linked glycosylation sites, we hypothesized that tPA contains mannose 6-phosphate (M6P) and binds CI-MPR in a M6P-dependent manner. Using surface plasmon resonance, we found that two sources of tPA bound the extracellular region of human and bovine CI-MPR with low-mid nanomolar affinities. Binding was partially inhibited with phosphatase treatment or M6P. Subsequent studies revealed that the five N-terminal domains of CI-MPR were sufficient for tPA binding, and this interaction was also partially mediated by M6P. The three glycosylation sites of tPA were analyzed by mass spectrometry, and glycoforms containing M6P and M6P-N-acetylglucosamine were identified at position N448 of tPA. In summary, we found that tPA contains M6P and is a CI-MPR ligand.

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