Glycosylation Sites In Protein Research Articles

2′-FL glycosylation of bovine milk protein matrices alters protein structure and reduces antibody affinity

Three glycosylated proteins were prepared from 2′-fucosyllactose (2′-FL) with α-lactalbumins, β-lactoglobulin and whey protein isolate, respectively, and their protein structures, glycosylation sites, digestion and transport capacities, and antibody binding capacities were determined. The findings indicate that following heating at 68 °C for 90 min, 2′-FL can form stable bonds with amino acid residues in milk proteins, leading to enhanced antioxidant properties, reduced hydrophobicity, and modified secondary structure content of the proteins. The glycosylation sites were mainly located in the amino acid sequence of β-lactoglobulin (K47, K91 and K135) and significantly reduce the affinity of its specific antibodies. 2′-FL glycosylation did not have a significant effect on the digestibility of cow's milk proteins, but it could significantly reduce the affinity of their specific antibody, indicating that it could be used in producing hypoallergenic dairy product.

Serpentoviruses Exhibit Diverse Organization and ORF Composition with Evidence of Recombination.

Serpentoviruses are a subfamily of positive sense RNA viruses in the order Nidovirales, family Tobaniviridae, associated with respiratory disease in multiple clades of reptiles. While the broadest viral diversity is reported from captive pythons, other reptiles, including colubrid snakes, turtles, and lizards of captive and free-ranging origin are also known hosts. To better define serpentoviral diversity, eleven novel serpentovirus genomes were sequenced with an Illumina MiSeq and, when necessary, completed with other Sanger sequencing methods. The novel serpentoviral genomes, along with 57 other previously published serpentovirus genomes, were analyzed alongside four outgroup genomes. Genomic analyses included identifying unique genome templates for each serpentovirus clade, as well as analysis of coded protein composition, potential protein function, protein glycosylation sites, differences in phylogenetic history between open-reading frames, and recombination. Serpentoviral genomes contained diverse protein compositions. In addition to the fundamental structural spike, matrix, and nucleoprotein proteins required for virion formation, serpentovirus genomes also included 20 previously uncharacterized proteins. The uncharacterized proteins were homologous to a number of previously characterized proteins, including enzymes, transcription factors, scaffolding, viral resistance, and apoptosis-related proteins. Evidence for recombination was detected in multiple instances in genomes from both captive and free-ranging snakes. These results show serpentovirus as a diverse clade of viruses with genomes that code for a wide diversity of proteins potentially enhanced by recombination events.

Standard Multi-Layer Perceptron on Positive - Unlabeled Glycosylation Site Dataset

Abstract—The implementation of computational approaches for protein glycosylation site prediction is becoming popular since the experimental-validated glycosylation data became more abundant. Some of the data were found to be wrong after the experiment was again carried out with more sophisticated technology. To solve this issue, the latest state-of-the-art model trained the model based on a positive-unlabelled algorithm. The aim of this research is to explore the possibility of an approach applying a simple neural network algorithm and still achieve high classification performance. The model proposed in this research gave competitive results with fewer preprocessing steps. Increasing the accuracy of glycosylation site prediction can complement laboratory-based methods and is very useful for understanding the role of glycosylation.

Identification genetic variations in some heat shock protein genes of Tali goat breed and study their structural and functional effects on relevant proteins.

Animals of different regions have adapted to adverse environmental conditions by modifying their phenotypic and genotypic characteristics in the long run. In this study, the effect of genetic variations of 10 heat shock protein (HSP) genes (HSP70A4, HSP70A9, HSP40C17, HSP40C27, HSP90AA1, HSP90AB1, HSPB7, HSPB11, HSPD1 and HSPE1) on the three-dimensional protein structure and function of proteins in Tali goat (a tropical breed) were studied and were compared with Saanen goat (as a sensitive breed). A pooled DNA of 15 samples from blood was sequenced and mapped to the goat reference sequence. The bioinformatics analysis was used to identify nsSNPs in the Tali breed and was compared with the Saanen goat. Four online bioinformatics tools (Sorting Intolerant from Tolerant, Protein Variation Effect Analyzer, Polymorphism Phenotyping version2 and Single Nucleotide Polymorphism Database and Gene Ontology) showed three deleterious missense nsSNPs and seven natural missense SNPs in these HSPs genes of Tali goat. Out of 10 reported nsSNPs, 5 nsSNPs in HSP70A4, 1 nsSNP inHSP70A9, 2 nsSNPs in HSP40C17, 1 nsSNP in HSP40C27 and 1 nsSNP in HSPD1 were detected. ConSurf tools showed that the majority of the predicted nsSNPs occur in conserved sites. Moreover, several post-translational modification (PTM) predictors computed the probability of post-translation change of nsSNPs. The putative phosphorylation and glycosylation sites in HSPs proteins were substitutions rs669769139 and rs666336692 of the Tali goat breed. These results on the effect of type of genetic variants on the function of HSP proteins will assist to predict the resistance to hard conditions in goat breeds. Considering that the identified SNPid rs669769139 (S248) which is located on the N-terminal ATPase domain of HSP70A4 is a PTM site with a highly conserved score and a natural substitution on changing the stability and benign protein that can affect the functional and structural characterization of HSPs protein for adaptation to the local climate.

Unraveling the glycosylated immunopeptidome with HLA-Glyco

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

Analysis of GSH1-GSH3 Genes by Comprehensive Use of Bioinformatics Method in Tomato

GSH is a ubiquitous gene family in animals and plants, which plays a key regulatory role in plant growth, development and stress response. In this study, three GSH genes are identified in the Phytozome database, and bioinformatics analysis of physicochemical properties, structural domains, promoter cis-acting elements and phosphorylation sites is performed. The results show that the numbers of introns and exons of tomato GSH1-GSH3 are quite different. Promoters cis-acting elements are analyzed in GSH gene promoters, which account for a large proportion of light-responsive, hormone-responsive and protein synthesis elements. Transcription factors based on different tissues group data analysis reveal that the expression of GSH genes is different in different tissue development processes. The highest expression sites of GSH1-GSH3 are in 1-cm fruit, GSH2 currant tomato fruit and roots five days after veraison. According to physicochemical properties, transmembrane structure and signal peptide, the study finds that GSH proteins are all hydrophilic proteins and do not have a transmembrane structure and signal peptides. Based on the data analysis of protein phosphorylation sites and glycosylation sites, it is found that the number of the two sites differ significantly between GSH proteins. By means of prediction of secondary and tertiary structures in proteins, it finds that the three GSH proteins have four secondary structures: α-helix, extended chain, β-turn and random coil, but the proportions are different, and the tertiary structures of the three are quite different. Based on the analysis of protein phylogenetic tree, it is believed that tomato and potato are closely related. According to the protein interaction network, it points out that there is a strong link between GSH1 and GSH3. Through the analysis of tomato GSH family system, this study is expected to provide a theoretical basis for further research on the biological function of GSH gene.

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. Hence, there is a demand to build fast and efficient computational methods to address this problem. Here, we present the SPRINT-Gly framework containing the largest dataset and a prediction model of glycosylation sites for a given protein sequence. In this framework, we construct a large dataset containing N- and O-linked glycosylation sites of human and mouse proteins, collected from different sources. We then introduce the SPRINT-Gly method to predict putative N- and O-linked sites. SPRINT-Gly is a machine learning-based approach consisting of a number of trained predictive models for glycosylation sites in both human and mouse proteins, separately. The method is built by incorporating sequence-based, predicted structural, and physicochemical information of the neighboring residues of each N- and O-linked glycosylation site and by training deep learning neural network and support vector machine as classifiers. SPRINT-Gly outperformed other existing methods by achieving 18% and 50% higher Matthew's correlation coefficient for N- and O-linked glycosylation site prediction, respectively. SPRINT-Gly is publicly available as an online and stand-alone predictor at https://sparks-lab.org/server/sprint-gly/ .

System-Wide Quantitative N-Glycoproteomic Analysis from K562 Cells and Mouse Liver Tissues.

As a key regulator of many biological processes, glycosylation is an essential post-translational modification (PTM) in the living system. Over 50% of human proteins are known to be glycosylated. Alterations in glycoproteins are directly linked to many diseases, making it crucial to understand system-wide glycosylation changes. The majority of known glycoproteins are from plasma membrane; however, glycosylation is a dynamic process that occurs throughout multiple subcellular organelles and involves sets of enzymes, chaperones, transporters, and sugar donor molecules. Many glycoproteins are expressed not only in plasma membranes but also in subcellular organelles. Here, we developed a mass-spectrometry-based quantitative workflow for the system-wide N-glycoproteomic analysis of membrane and cytosolic proteins extracted using a MEM-PER kit. The kit facilitates the extraction and solubilization of both membrane and cytosolic proteins in a simple, efficient, and reproducible manner. We analyzed the K562 cell line and mouse liver tissue to evaluate this approach. A total of 934 glycosites, 5154 glycopeptides, and 536 glycoproteins from the K562 cell line and a total of 1449 glycosites, 7549 glycopeptides, and 660 glycoproteins from mouse liver tissue were identified. This simple and reproducible approach provides a unique way to understand system-wide glycosylation in biological processes and enables the identification and quantitation of glycan profiles at glycosylation sites in proteins.

Mutation Profiles, Glycosylation Site Distribution and Codon Usage Bias of Human Papillomavirus Type 16.

Human papillomavirus type 16 (HPV16) is the most prevalent HPV type causing cervical cancers. Herein, using 1597 full genomes, we systemically investigated the mutation profiles, surface protein glycosylation sites and the codon usage bias (CUB) of HPV16 from different lineages and sublineages. Multiple lineage- or sublineage-conserved mutation sites were identified. Glycosylation analysis showed that HPV16 lineage D contained the highest number of different glycosylation sites from lineage A in both L1 and L2 capsid proteins, which might lead to their antigenic distances between the two lineages. CUB analysis showed that the HPV16 open reading frames (ORFs) preferred codons ending with A/T. The CUB of HPV16 ORFs was mainly affected by natural selection except for E1, E5 and L2. HPV16 only shared some of the preferred codons with humans, which might help reduce competition in translational resources. These findings increase our understanding of the heterogeneity between HPV16 lineages and sublineages, and the adaptation mechanism of HPV in human cells. In summary, this study might facilitate HPV classification and improve vaccine development and application.

Modernized uniform representation of carbohydrate molecules in the Protein Data Bank.

Since 1971, the Protein Data Bank (PDB) has served as the single global archive for experimentally determined 3D structures of biological macromolecules made freely available to the global community according to the FAIR principles of Findability-Accessibility-Interoperability-Reusability. During the first 50years of continuous PDB operations, standards for data representation have evolved to better represent rich and complex biological phenomena. Carbohydrate molecules present in more than 14,000 PDB structures have recently been reviewed and remediated to conform to a new standardized format. This machine-readable data representation for carbohydrates occurring in the PDB structures and the corresponding reference data improves the findability, accessibility, interoperability and reusability of structural information pertaining to these molecules. The PDB Exchange MacroMolecular Crystallographic Information File data dictionary now supports (i) standardized atom nomenclature that conforms to International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) recommendations for carbohydrates, (ii) uniform representation of branched entities for oligosaccharides, (iii) commonly used linear descriptors of carbohydrates developed by the glycoscience community and (iv) annotation of glycosylation sites in proteins. For the first time, carbohydrates in PDB structures are consistently represented as collections of standardized monosaccharides, which precisely describe oligosaccharide structures and enable improved carbohydrate visualization, structure validation, robust quantitative and qualitative analyses, search for dendritic structures and classification. The uniform representation of carbohydrate molecules in the PDB described herein will facilitate broader usage of the resource by the glycoscience community and researchers studying glycoproteins.

Multilayered glycoproteomic analysis reveals the hepatotoxic mechanism in perfluorooctane sulfonate (PFOS) exposure mice.

Perfluorooctane sulfonate (PFOS) is one of the most widely used and distributed perfluorinated compounds proven to cause adverse health outcomes. Datasets of ecotoxico-genomics and proteomics have given greater insights for PFOS toxicological effect. However, the molecular mechanisms of hepatotoxicity of PFOS on post-translational modifications (PTMs) regulation, which is most relevant for regulating the activity of proteins, are not well elucidated. Protein glycosylation is one of the most ubiquitous PTMs associated with diverse cellular functions, which are critical towards the understanding of the multiple biological processes and toxic mechanisms exposed to PFOS. Here, we exploit the multilayered glycoproteomics to quantify the global protein expression levels, glycosylation sites, and glycoproteins in PFOS exposure and wild-type mouse livers. The identified 2439 proteins, 1292 glycosites, and 799 glycoproteins were displayed complex heterogeneity in PFOS exposure mouse livers. Quantification results reveal that 241 dysregulated proteins (fold change≥2, p<0.05) in PFOS exposure mouse livers were involved in the lipid and xenobiotic metabolism. While, 16 overexpressed glycoproteins were exclusively related to neutrophil degranulation, cellular responses to stress, protein processing in endoplasmic reticulum (ER). Moreover, the interactome and functional network analysis identified HP and HSP90AA1 as the potential glycoprotein biomarkers. These results provide unique insights into a deep understanding of the mechanisms of PFOS induced hepatotoxicity and liver disease. Our platform of multilayered glycoproteomics can be adapted to diverse ecotoxicological research.

2020 FASEB Science Research Conference on Microbial Glycobiology, July 13-14, 2020.

2020 FASEB Science Research Conference on Microbial Glycobiology, July 13-14, 2020.

Variations in SARS-CoV-2 Spike Protein Cell Epitopes and Glycosylation Profiles During Global Transmission Course of COVID-19.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to an outbreak of a pandemic worldwide. For better understanding the viral spike (S) protein variations and its potential effects on the interaction with the host immune system and also in vaccine development, the cell epitopes, glycosylation profile and their changes during the global transmission course were characterized and compared with SARS-CoV for their glycosylation profile. We analyzed totally 7,813 sequences screened from 8,897 whole genome sequences on GISAID database up to April 26, and 18 S protein amino acid variations with relatively high frequency (≥10−3) were identified. A total of 228 sequences of variants had multiple variations, of note, most of them harboring the D614G mutation. Among the predicted 69 linear B cell epitopes, 175 discontinuous B cell epitopes and 41 cytotoxic T lymphocyte epitopes in the viral S protein, we found that the protein structure and its potential function of some sites changed, such as the linear epitope length shortened and discontinuous epitope disappeared of G476S. In addition, we detected 9 predicted N-glycosylation sites and 3 O-glycosylation sites unique to SARS-CoV-2, but no evidently observed variation of the glycan sites so far. Our findings provided an important snapshot of temporal and geographical distributions on SARS-CoV-2 S protein cell epitopes and glycosylation sites, which would be an essential basis for the selection of vaccine candidates.

Computationally Designed Peptides for Zika Virus Detection: An Incremental Construction Approach.

Herein, and in contrast to current production of anti-Zika virus antibodies, we propose a semi-combinatorial virtual strategy to select short peptides as biomimetic antibodies/binding agents for the detection of intact Zika virus (ZIKV) particles. The virtual approach was based on generating different docking cycles of tetra, penta, hexa, and heptapeptide libraries by maximizing the discrimination between the amino acid motif in the ZIKV and dengue virus (DENV) envelope protein glycosylation site. Eight peptides, two for each length (tetra, penta, hexa, and heptapeptide) were then synthesized and tested vs. intact ZIKV particles by using a direct enzyme linked immunosorbent assay (ELISA). As a reference, we employed a well-established anti-ZIKV antibody, the antibody 4G2. Three peptide-based assays had good detection limits with dynamic range starting from 105 copies/mL of intact ZIKV particles; this was one order magnitude lower than the other peptides or antibodies. These three peptides showed slight cross-reactivity against the three serotypes of DENV (DENV-1, -2, and -3) at a concentration of 106 copies/mL of intact virus particles, but the discrimination between the DENV and ZIKV was lost when the coating concentration was increased to 107 copies/mL of the virus. The sensitivity of the peptides was tested in the presence of two biological matrices, serum and urine diluted 1:10 and 1:1, respectively. The detection limits decreased about one order of magnitude for ZIKV detection in serum or urine, albeit still having for two of the three peptides tested a distinct analytical signal starting from 106 copies/mL, the concentration of ZIKV in acute infection.

Glycosylation of the hemagglutinin protein of H9N2 subtype avian influenza virus influences its replication and virulence in mice

N-Linked glycosylation of hemagglutinin (HA) has been demonstrated to regulate the virulence and receptor-binding specificity of avian influenza virus (AIV). In this study, we characterized the variation trend of naturally isolated H9N2 viruses for the potential N-linked glycosylation sites in HA proteins, and explored any important role of some glycosylation sites. HA genes of 19 H9N2 subtype AIV strains since 2001 were sequenced and analyzed for the potential glycosylation sites. The results showed that the viruses varied by losing one potential glycosylation site at residues 200 to 202, and having an additional one at residues 295 to 297 over the past few years. Further molecular and single mutation analysis revealed that the N200Q mutation lost an N-linked glycosylation at positions 200 to 202 of the HA protein and affected the human-derived receptor affinity. We further found that this N-linked glycosylation increased viral productivity in the lung of the infected mice. These findings provide a novel insight on understanding the determinants of host adaption and virulence of H9N2 viruses in mammals.

SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively. The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features. http://sparks-lab.org/server/SPRINT-Gly/. Supplementary data are available at Bioinformatics online.

Positive-unlabelled learning of glycosylation sites in the human proteome

BackgroundAs an important type of post-translational modification (PTM), protein glycosylation plays a crucial role in protein stability and protein function. The abundance and ubiquity of protein glycosylation across three domains of life involving Eukarya, Bacteria and Archaea demonstrate its roles in regulating a variety of signalling and metabolic pathways. Mutations on and in the proximity of glycosylation sites are highly associated with human diseases. Accordingly, accurate prediction of glycosylation can complement laboratory-based methods and greatly benefit experimental efforts for characterization and understanding of functional roles of glycosylation. For this purpose, a number of supervised-learning approaches have been proposed to identify glycosylation sites, demonstrating a promising predictive performance. To train a conventional supervised-learning model, both reliable positive and negative samples are required. However, in practice, a large portion of negative samples (i.e. non-glycosylation sites) are mislabelled due to the limitation of current experimental technologies. Moreover, supervised algorithms often fail to take advantage of large volumes of unlabelled data, which can aid in model learning in conjunction with positive samples (i.e. experimentally verified glycosylation sites).ResultsIn this study, we propose a positive unlabelled (PU) learning-based method, PA2DE (V2.0), based on the AlphaMax algorithm for protein glycosylation site prediction. The predictive performance of this proposed method was evaluated by a range of glycosylation data collected over a ten-year period based on an interval of three years. Experiments using both benchmarking and independent tests show that our method outperformed the representative supervised-learning algorithms (including support vector machines and random forests) and one-class learners, as well as currently available prediction methods in terms of F1 score, accuracy and AUC measures. In addition, we developed an online web server as an implementation of the optimized model (available at http://glycomine.erc.monash.edu/Lab/GlycoMine_PU/) to facilitate community-wide efforts for accurate prediction of protein glycosylation sites.ConclusionThe proposed PU learning approach achieved a competitive predictive performance compared with currently available methods. This PU learning schema may also be effectively employed and applied to address the prediction problems of other important types of protein PTM site and functional sites.

A Method for Mapping Glycosylation Sites in Proteins.

The analysis of protein glycosylation by mass spectrometry (MS) has been a challenging technical problem. Quantification by HPLC of N-linked glycans can be executed by the use of peptide-N-glycosidase F to release them from the protein, followed by attachment of a fluorescent label and subsequent fluorescence detection. Similar quantification of O-linked glycans is not possible, as a result of the lack of a universal deglycosylation enzyme. Site-specific analyses by MS, such as the use of proteases to digest the glycoprotein, are difficult to use for quantification of glycans, as a result of the presence of miscleavages. Here, we present a new application of a digestion method for native proteins using resin-bound, thermally stabilized proteases. The use of this enzymatic treatment eliminates miscleavages around the site of glycosylation, thereby allowing site-specific relative quantification of glycans on glycoproteins. A native, intact human mAb was digested using a thermally stable, resin-bound trypsin to produce glycopeptides from the Fc region using a single-step protocol. A 1 mg sample was treated with 60 µg trypsin for 3 h at 70°C. After digestion, acetonitrile was added, and the mixture was centrifuged to remove the resin before analysis. Liquid chromatography (LC)/MS with hydrophilic interaction chromatography was used to analyze the glycopeptides produced. All of the glycopeptides found resulted from a single peptide (EEQYNSTYR). The LC/MS analysis of the glycopeptides is compared with that of fluorescently labeled glycans. Quantitative analysis produced a correlation coefficient of 0.87 for the linear fit between the glycopeptide and released glycan methods.

Unmasking Stem-Specific Neutralizing Epitopes by Abolishing N-Linked Glycosylation Sites of Influenza Virus Hemagglutinin Proteins for Vaccine Design.

Influenza virus hemagglutinin (HA) protein consists of two components, i.e., a globular head region and a stem region that are folded within six disulfide bonds, plus several N-linked glycans that produce a homotrimeric complex structure. While N-linked glycosylation sites on the globular head are variable among different strains and different subtypes, N-linked glycosylation sites in the stem region are mostly well conserved among various influenza virus strains. Targeting highly conserved HA stem regions has been proposed as a useful strategy for designing universal influenza vaccines. Since the HA stem region is constituted by an HA1 N-terminal part and a full HA2 part, we expressed a series of recombinant HA mutant proteins with deleted N-linked glycosylation sites in the HA1 stem and HA2 stem regions of H5N1 and pH1N1 viruses. Unmasking N-glycans in the HA2 stem region (H5 N484A and H1 N503A) was found to elicit more potent neutralizing antibody titers against homologous, heterologous, and heterosubtypic viruses. Unmasking the HA2 stem N-glycans of H5HA but not H1HA resulted in more CR6261-like and FI6v3-like antibodies and also correlated with the increase of cell fusion inhibition activity in antisera. Only H5 N484A HA2 stem mutant protein immunization increased the numbers of antibody-secreting cells, germinal center B cells, and memory B cells targeting the stem helix A epitopes in splenocytes. Unmasking the HA2 stem N-glycans of H5HA mutant proteins showed a significantly improvement in the protection against homologous virus challenges but did so to a less degree for the protection against heterosubtypic pH1N1 virus challenges. These results may provide useful information for designing more effective influenza vaccines. N-linked glycosylation sites in the stem regions of influenza virus hemagglutinin (HA) proteins are mostly well conserved among various influenza virus strains. Targeting highly conserved HA stem regions has been proposed as a useful strategy for designing universal influenza vaccines. Our studies indicate that unmasking the HA2 stem N-glycans of recombinant HA proteins from H5N1 and pH1N1 viruses induced more potent neutralizing antibody titers against homologous and heterosubtypic viruses. However, only immunization with the H5N1 HA2 stem mutant protein can refocus B antibody responses to the helix A epitope for inducing more CR6261-like/FI6v3-like and fusion inhibition antibodies in antisera, resulting in a significant improvement for the protection against lethal H5N1 virus challenges. These results may provide useful information for designing more effective influenza vaccines.

Hepatitis C virus: The role of N-glycosylation sites of viral genotype 1b proteins for formation of viral particles in insect and mammalian cells

Hepatitis C virus (HCV) is characterized by considerable genetic variability and, as a consequence, it has 6 genotypes and multitude of subtypes. HCV envelope glycoproteins are involved in the virion formation; the correct folding of these proteins plays the key role in virus infectivity. Glycosylation at certain sites of different genotypes HCV glycoproteins shows substantial differences in functions of the individual glycans (Goffard et al., 2005; Helle et al., 2010) [1,2]. In this study, differential glycosylation sites of HCV genotype 1b envelope proteins in insect and mammalian cells was demonstrated. We showed that part of glycosylation sites was important for folding of the proteins involved in the formation of viral particles. Point mutations were introduced in the protein N-glycosylation sites of HCV (genotype 1b) and the mutant proteins were analyzed using baculovirus expression system in mammalian and insect cells. Our data showed that, in contrast to HCV 1a and 2a, the folding of HCV 1b envelope proteins E2 (sites N1, N2, N10) and E1 (sites N1, N5) was disrupted, however that did not prevent the formation of virus-like particles (VLP) with misfolded glycoproteins having densities typical for HCV particles containing RNA fragments. Experimental data are supported by mathematical modeling of the structure of E1 mutant variants.