Potential B-cell Research Articles

Identification and Characterization of Novel Mutants of Nsp13 Protein among Indian SARS-CoV-2 Isolates

Background: SARS-CoV-2, the causative agent of COVID-19, has mutated rapidly, enabling it to adapt and evade the immune system of the host. Emerging SARS-CoV-2 variants with crucial mutations pose a global challenge in the context of therapeutic drugs and vaccines developing globally. There are currently no specific therapeutics or vaccines available to combat SARS-CoV-2 devastation. Concerning this, the current study aimed to identify and characterize the mutations found in the Nsp13 of SARS-CoV-2 in Indian isolates. Methods: In the present study, the Clustal omega tool was used for mutational analysis. The impact of mutations on protein stability, flexibility, and function was predicted using the DynaMut and PROVEAN tools. Furthermore, B-cell epitopes contributed by Nsp13 were identified using various predictive immunoinformatic tools. Results: Non-structural protein Nsp13 sequences from Indian isolates were analyzed by comparing them with the firstly reported Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) protein sequence in Wuhan, China. Out of 825 Nsp13 protein sequences, a total of 38 mutations were observed among Indian isolates. Our data showed that mutations in Nsp13 at various positions (H164Y, A237T, T214I, C309Y, S236I, P419S, V305E, G54S, H290Y, P53S, A308Y, and A308Y) have a significant impact on the protein's stability and flexibility. Moreover, the impact of Nsp13 mutations on protein function was predicted based on the PROVEAN score that indicated 15 mutants as neutral and 23 mutants as deleterious effects. Immunological parameters of Nsp13, such as antigenicity, allergenicity, and toxicity, were evaluated to predict the potential B-cell epitopes. The predicted peptide sequences were correlated with the observed mutants. Our predicted data showed that there are seven high-rank linear epitopes as well as 18 discontinuous B-cell epitopes based on immunoinformatic tools. Moreover, it was observed that out of the total 38 identified mutations among Indian SARS-CoV-2 Nsp13 protein, four mutant residues at positions 142 (E142), 245 (H245), 247 (V247), and 419 (P419) were localised in the predicted B cell epitopic region. Conclusion: Altogether, the results of the present in silico study might help to understand the impact of the identified mutations in Nsp13 protein on its stability, flexibility, and function.

Identification of B-Cell Epitopes for Eliciting Neutralizing Antibodies against the SARS-CoV-2 Spike Protein through Bioinformatics and Monoclonal Antibody Targeting.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global public health crisis. Effective COVID-19 vaccines developed by Pfizer-BioNTech, Moderna, and Astra Zeneca have made significant impacts in controlling the COVID-19 burden, especially in reducing the transmission of SARS-CoV-2 and hospitalization incidences. In view of the emergence of new SARS-CoV-2 variants, vaccines developed against the Wuhan strain were less effective against the variants. Neutralizing antibodies produced by B cells are a critical component of adaptive immunity, particularly in neutralizing viruses by blocking virus attachment and entry into cells. Therefore, the identification of protective linear B-cell epitopes can guide epitope-based peptide designs. This study reviews the identification of SARS-CoV-2 B-cell epitopes within the spike, membrane and nucleocapsid proteins that can be incorporated as potent B-cell epitopes into peptide vaccine constructs. The bioinformatic approach offers a new in silico strategy for the mapping and identification of potential B-cell epitopes and, upon in vivo validation, would be useful for the rapid development of effective multi-epitope-based vaccines. Potent B-cell epitopes were identified from the analysis of three-dimensional structures of monoclonal antibodies in a complex with SARS-CoV-2 from literature mining. This review provides significant insights into the elicitation of potential neutralizing antibodies by potent B-cell epitopes, which could advance the development of multi-epitope peptide vaccines against SARS-CoV-2.

Neospora caninum SRS2 Protein: Essential Vaccination Targets and Biochemical Features for Next-Generation Vaccine Design.

Vaccination is a standout preventive measure to combat neosporosis among cattle herds. The present in silico study was done to evaluate the physicochemical properties and potent immunogenic epitopes of N. caninum SRS2 protein as a possible vaccine candidate. Web-based tools were used to predict physicochemical properties, antigenicity, allergenicity, solubility, posttranslational modification (PTM) sites, transmembrane domains and signal peptide, and secondary and tertiary structures as well as intrinsically disordered regions, followed by identification and screening of potential linear and conformational B-cell epitopes and those peptides having affinity to bind mouse major histocompatibility complex (MHC) and cytotoxic T lymphocyte (CTL). The protein had 401 residues with a molecular weight of 42 kDa, representing aliphatic index of 69.35 (thermotolerant) and GRAVY score of -0.294 (hydrophilic). There were 53 PTM sites without a signal peptide in the sequence. Secondary structure comprised mostly by extended strand, followed by helices and coils. The Ramachandran plot of the refined model showed 90.2%, 8.8%, 0.5%, and 0.5% residues in the favored, additional allowed, generously allowed, and disallowed regions, correspondingly. Additionally, various potential B-cell (linear and conformational), CTL, and MHC-binding epitopes were predicted for N. caninum SRS2. These epitopes could be further utilized in the multiepitope vaccine constructs directed against neosporosis.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4911245 proteins from 196352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

In silico and in vivo approaches to recombinant multi-epitope immunogen of GroEL provides efficient cross protection against S. Typhimurium, S. flexneri, and S. dysenteriae

ObjectivesStress or Heat Shock Proteins (HSPs) have been included in various operations like protein folding, autophagy, and apoptosis. HSP families recognize as protective antigens in a wide range of bacteria because they have been conserved through evolution. Due to their homology as well as antigenicity they are competent for applying in cross-protection against bacterial diseases. MethodsIn the present study, bioinformatics approaches utilized to design epitope-based construction of Hsp60 (or GroEL) protein. In this regard, potential B-cell and T-cell epitopes except for allergenic sequences were selected by immunoinformatic tools. The structural and functional aspects of the DNA, RNA, and protein levels were assessed by bioinformatics software. Following in silico investigations, recombinant GroEL multi-epitope of Salmonella typhi was expressed, purified, and validated. Mouse groups were immunized with recombinant protein and humoral immune response was measured by enzyme linked immunosorbent assay (ELISA). Animal challenge against Salmonella Typhimurium, Shigella flexneri, and Shigella dysenteriae was evaluated. Resultsrecombinant protein expression and purification with 14.3 kilodaltons (kDa) was confirmed by SDS-PAGE and western blotting. After animal administration, the immunoglobulins evaluated increase after each immunization. Immunized antisera exhibited 80%, 40%, and 40% protection against the lethal dose infection by S. Typhimurium, S. flexneri, and S. dysenteriae respectively. Passive immunization conferred 50%, 30%, and 30% protection in mice against S. Typhimurium, S. flexneri and S. dysentery respectively. In addition, bacterial organ load had exhibited a significant decrease in colony forming unit (CFU) in the liver and spleen of the immunized mice compared to the control. ConclusionOur study demonstrates the efficacy of S. Typhi recombinant GroEL multi-epitope to consider as a universal immunogen candidate versus multiple bacterial pathogens.

Designing Multi‐Epitope Subunit Vaccine Candidate for Zika Virus Utilizing In silico Tools

Background: The arboviruses Zika virus (ZIKV) is a pathogen that threatens human health. Scientists have warned that a single mutation in the mosquito-borne ZIKV could spark another major outbreak of the disease in humans. Therefore, designing a suitable vaccine for this virus seems necessary. This study aimed to predict the protective epitopes of envelope protein from the Zika virus with bioinformatics methods for multi-epitope vaccine development. Materials and Methods: Computational studies including the identification of potential B-cell and T-cell epitopes were used. For generating a multi-epitopic vaccine construct (MEVC), selected epitopes are connected by suitable linkers. To enhance protein immunogenicity, Maltosebound protein was added to the MEVC after the prediction and refinement of the 3D structure of the designed vaccine. The binding mode of the MEVC with toll-like receptor was investigated by molecular docking technique. Finally, molecular dynamics and in silico cloning were performed for the designed vaccine. Results: This study showed that this recombinant vaccine is nontoxic, nonallergenic, and thermostable and elicits immune responses against the Zika virus. Conclusion: The computational data suggest that the MEVC has appropriate characteristics and a high-quality structure.

An Integrated In Silico Approach to Develop Epitope-Based Peptide Vaccine Against SARS-CoV-2

Background: SARS-CoV-2 has been the talk of the town ever since the beginning of 2020. Every country is trying all possible steps to combat the disease ranging from shutting the complete economy of the country to the repurposing of drugs and vaccine development. The rapid data analysis and widespread tools have made bioinformatics capable of giving new insights to deal with the current scenario more efficiently through an emerging field, Vaccinomics. Objective: The present in-silico study was attempted to identify peptide fragments from spike surface glycoprotein of SARS-CoV-2 that can be efficiently used for the development of an epitope-based vaccine designing approach. Methodology: The epitopes of B and T-cell are predicted using integrated computational tools. VaxiJen server, NetCTL, and IEDB tools were used to study, analyze, and predict potent T-cell epitopes, its subsequent MHC-I interactions, and B-cell epitopes. The 3D structure prediction of peptides and MHC-I alleles (HLA-C*03:03) was further done using AutoDock4.0. Result: Based on result interpretation, the peptide sequence from 1138-1145 amino acid and sequence WTAGAAAYY and YDPLQPEL were obtained as potential B-cell and T-cell epitopes respectively. Conclusion: The peptide sequence WTAGAAAYY and the amino acid sequence from 1138-1145 of the spike protein of SARS-CoV-2 can be used as a probable B-cell epitope candidate. Also, the amino acid sequence YDPLQPEL can be used as a potent T-cell epitope. This in-silico study will help us to identify novel epitope-based peptide vaccine targets in the spike protein of SARS-CoV-2. Further, the in-vitro and in-vivo study needed to validate the findings.

Ensemble-based, glycan-dependent epitope analysis of SARS-CoV-2 spike protein

The global pandemic caused by the SARS-CoV-2 virus has impacted all aspects of human life. The spike protein, which is key to targeting and entering host cells by the virus, provides a major target for protective antibodies against the virus. Various neutralizing antibodies against the spike have been identified. Furthermore, the spike protein is the main therapeutic objective in development of vaccines. To explore potential B-cell epitopes on the spike protein, we have constructed a full-length, membrane-embedded, palmitoylated and fully glycosylated spike model and performed a 5-μs equilibrium simulation.

Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach.

Marburg virus disease (MVD) caused by the Marburg virus (MARV) generally appears with flu-like symptoms and leads to severe hemorrhagic fever. It spreads via direct contact with infected individuals or animals. Despite being considered to be less threatening in terms of appearances and the number of infected patients, the high fatality rate of this pathogenic virus is a major concern. Until now, no vaccine has been developed to combat this deadly virus. Therefore, vaccination for this virus is necessary to reduce its mortality. Our current investigation focuses on the design and formulation of a multi-epitope vaccine based on the structural proteins of MARV employing immunoinformatics approaches. The screening of potential T-cell and B-cell epitopes from the seven structural proteins of MARV was carried out through specific selection parameters. Afterward, we compiled the shortlisted epitopes by attaching them to an appropriate adjuvant and linkers. Population coverage analysis, conservancy analysis, and MHC cluster analysis of the shortlisted epitopes were satisfactory. Importantly, physicochemical characteristics, human homology assessment, and structure validation of the vaccine construct delineated convenient outcomes. We implemented disulfide bond engineering to stabilize the tertiary or quaternary interactions. Furthermore, stability and physical movements of the vaccine protein were explored using normal-mode analysis. The immune simulation study of the vaccine complexes also exhibited significant results. Additionally, the protein–protein docking and molecular dynamics simulation of the final construct exhibited a higher affinity toward toll-like receptor-4 (TLR4). From simulation trajectories, multiple descriptors, namely, root mean square deviations (rmsd), radius of gyration (Rg), root mean square fluctuations (RMSF), solvent-accessible surface area (SASA), and hydrogen bonds, have been taken into account to demonstrate the inflexible and rigid nature of receptor molecules and the constructed vaccine. Inclusively, our findings suggested the vaccine constructs’ ability to regulate promising immune responses against MARV pathogenesis.

"Immunoinformatic Identification of T-Cell and B-Cell Epitopes From Giardia lamblia Immunogenic Proteins as Candidates to Develop Peptide-Based Vaccines Against Giardiasis".

Giardiasis is one of the most common gastrointestinal infections worldwide, mainly in developing countries. The etiological agent is the Giardia lamblia parasite. Giardiasis mainly affects children and immunocompromised people, causing symptoms such as diarrhea, dehydration, abdominal cramps, nausea, and malnutrition. In order to develop an effective vaccine against giardiasis, it is necessary to understand the host-Giardia interactions, the immunological mechanisms involved in protection against infection, and to characterize the parasite antigens that activate the host immune system. In this study, we identify and characterize potential T-cell and B-cell epitopes of Giardia immunogenic proteins by immunoinformatic approaches, and we discuss the potential role of those epitopes to stimulate the host´s immune system. We selected the main immunogenic and protective proteins of Giardia experimentally investigated. We predicted T-cell and B-cell epitopes using immunoinformatic tools (NetMHCII and BCPREDS). Variable surface proteins (VSPs), structural (giardins), metabolic, and cyst wall proteins were identified as the more relevant immunogens of G. lamblia. We described the protein sequences with the highest affinity to bind MHC class II molecules from mouse (I-Ak and I-Ad) and human (DRB1*03:01 and DRB1*13:01) alleles, as well as we selected promiscuous epitopes, which bind to the most common range of MHC class II molecules in human population. In addition, we identified the presence of conserved epitopes within the main protein families (giardins, VSP, CWP) of Giardia. To our knowledge, this is the first in silico study that analyze immunogenic proteins of G. lamblia by combining bioinformatics strategies to identify potential T-cell and B-cell epitopes, which can be potential candidates in the development of peptide-based vaccines. The bioinformatics analysis demonstrated in this study provides a deeper understanding of the Giardia immunogens that bind to critical molecules of the host immune system, such as MHC class II and antibodies, as well as strategies to rational design of peptide-based vaccine against giardiasis.

Computational Design and Analysis of a Multi-epitope Against Influenza A virus.

Influenza A viruses are among the most studied viruses, however no effective prevention against influenza infection has been developed. So, designing an effective vaccine against Influenza A virus is a critical issue in the field of medical biotechnology. For this reason, to combat this disease, we have designed a novel multi-epitope vaccine candidate based on the several conserved and potential linear B-cell and T-cell binding epitopes by using the in silico approach. This vaccine consists of an ER signal conserved sequence, the PADRE conserved epitope and two conserved epitopes of Influenza matrix protein 2. T-cell binding epitopes from Matrix protein 2 were predicted by in silico tools of epitope prediction. The selected epitopes were joined by flexible linkers and physicochemical properties, toxicity, and allergenecity were investigated. The designed vaccine was antigenic, immunogenic, and non-allergenic with suitable physicochemical properties and has higher solubility. The final multi-epitope construct was modeled, confirmed by different programs and the molecular interactions with immune receptors were considered. The molecular docking assay indicated the interactions with immune-stimulatory toll-like receptor 3 (TLR3) and major histocompatibility complex class I (MHCI). The HADDOCK and H DOCK servers were used to make docking analysis, respectively. The docking analysis indicated a strong and stable binding interaction between the vaccine construct with major histocompatibility complex (MHC) class I and toll-like receptor 3. Overall, the findings suggest that the current vaccine may be a promising vaccine to prevent Influenza infection.

Exploration of antigenic determinants in spike glycoprotein of SARS-CoV2 and identification of five salient potential epitopes.

Emerging pathogens have been an eternal threat to mankind. In a series of pandemics caused by notorious coronaviruses, a newly emerged SARS-CoV2 virus is creating panic among the world population. The unavailability of reliable theranostics insists the exploration of antigenic determinants in spike glycoprotein of SARS-CoV2. The four novel inserts (‘70VSGTNGT76’, ‘150KSWM153’, 247SYLTPG252 and 674QTQTNSPRR682) in SARS-CoV2 spike protein were unraveled via multiple sequence alignment of spike proteins of SARS-CoV2, SARS-CoV, and MERS-CoV. The three-dimension (3D) modeling of the spike protein of the SARS-CoV2 and their interaction with the ACE2 receptor was delineated with the help of SWISS-MODEL and 3DLigandSite web servers. The predicted 3D model of SARS-CoV2 was further verified by SAVES, RAMPAGE, and ProSA-web tools. The potential B-cell immunogenic epitopes of SARS-CoV2 were predicted out by using various software viz. IEDB B-cell epitopes prediction tool, BepiPred linear epitope prediction tool, Emini Surface Accessibility Prediction tool, and Kolaskar-Tongaonkar antigenicity web tool. The five epitopes (i.e. ‘71SGTNGTKRFDN81, 247SYLTPG252, 634RVYST638, 675QTQTNSPRRARSV687, and 1054QSAPH1058) were selected as potent antigenic determinants. The quantum of information generated by this study will prove beneficial for the development of effective therapeutics, diagnostics, and multi-epitopic vaccines to combat this ongoing menace.

Epitope mapping from Mycobacterium leprae proteins: Convergent data from in silico and in vitro approaches for serodiagnosis of leprosy

Knowledge of immunodominant B-cell epitopes is essential to design powerful diagnostic strategies aiming for antibody detection. Outstanding progress in computational prediction has achieved a significant contribution to the biomedical fields, including immunodiagnosis. In silico analysis may have an even more important role when information concerning antigens from etiologic agents of neglected diseases, such as leprosy, is scarce. The aim of this study was to provide mapping of B-cell epitopes from two Mycobacterium leprae-derived antigens (Ag85B and ML2055), confirm their antigenicity, and to assess the ability of in silico immunoinformatics tools to accurately predict them. Linear B-cell epitopes predicted by ABCpred and SVMTrip servers were compared to antigenic regions of synthetic overlapping peptides that exhibited reactivity to antibodies from patients with leprosy. Our in vitro results identified several immunodominant regions that had also been indicated by in silico prediction, providing agreement between experimental and simulated data. After chemical synthesis, we used enzyme-linked immunosorbent assays to determine the effectiveness of the first identified sequence (GTNVPAEFLENFVHG) which had 72 % sensitivity and 78 % specificity (AUC = 0.79) while the second one (PVSSEAQPGDPNAPS) had 72 % sensitivity and 93.8 % specificity (AUC = 0.85). Using dot blotting, an easy-to-read visual test, both peptides could distinguish sera from patients with leprosy from those with tuberculosis and from sera of healthy volunteers. Our findings suggest that these synthetic peptides, with some refinement, may be useful as serological diagnostic antigens for leprosy. In addition, it was displayed that immunoinformatics provides reliable information for mapping potential B-cell epitopes for development of peptide-based diagnostic assays for neglected diseases.

APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes.

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.

Antibodies Targeting Two Epitopes in SARS-CoV-2 Neutralize Pseudoviruses with the Spike Proteins from Different Variants.

The COVID-19 pandemic was caused by SARS-CoV-2 infection. To prevent the spread of SARS-CoV-2, an effective vaccine is required. Two linear peptides from potential B-cell epitopes in the spike protein of SARS-CoV-2 (a.a. 440–460; a.a. 494–506) were synthesized and used to immunize rabbits. High-titer antibodies of IgG were produced, purified, and verified by Western blot analysis. Antibodies against these two epitopes could effectively neutralize SARS-CoV-2 pseudoviral particles with the spike proteins from not only the original strain (basal; wild-type), but also a strain with a single point mutation (D614G), and two other emerging variants (the Alpha and Beta variants) prevalent around the world, but not from SARS-CoV. In conclusion, antibodies against these two epitopes are protective. This information is important for the development of vaccines against SARS-CoV-2.

Differential Phosphoproteomics Provides New Insights into Role of DYRK1A in Lymphopoiesis

Dual Specificity Tyrosine-Regulated Kinase 1A (DYRK1A) is a serine/threonine (S/T) kinase that is encoded within the Down syndrome Critical Region of chromosome 21(Abbassi, Johns et al. 2015), underlying its potential role in neurologic disorders associated with Down syndrome (DS). Given that children with DS are at a significantly increased risk for developing leukemia, we are studying the role of DYRK1A in normal and malignant hematopoiesis. We previously reported a critical role for DYRK1A in lymphopoiesis through its phosphorylation of Cyclin D3, thereby mediating cell cycle exit and lymphocyte maturation(Thompson, Bhansali et al. 2015). In the absence of Dyrk1a, both B and T cell development is stalled at the transition from highly proliferative progenitors to maturing quiescent cells. Although Dyrk1a -deficient lymphocytes fail to exit the cell cycle, they also cease proliferating.In order to further delineate the role of DYRK1A in lymphopoiesis, we employed a phosphoproteomics-based approach to identify specific pathways affected by its activity. In brief, we treated primary murine pre-B cells with a novel DYRK1A inhibitor or DMSO for 2 and 9 hours. Treated cells were then collected, lysed and isolated proteins were digested with trypsin. The resultant peptides were phospho-enriched using TiO2in order to yield S/T phospho-peptides, which were then labeled with tandem mass tags (TMT). These labeled peptides were analyzed using mass spectroscopy. We included both unfractionated and fractionated lysates in our analysis in order to account for low abundance peptides. A cutoff of 1.5-fold change in phosphorylation with a false discovery rate of ≤1% upon inhibition of DYRK1A was implemented to devise a list of phospho-peptides that may be affected by DYRK1A activity.At 2 hours of DYRK1A inhibitor treatment, 101 proteins were significantly dephosphorylated and 37 proteins were significantly hyperphosphorylated. By contrast, at 9 hours of DYRK1A inhibitor treatment, 41 proteins were significantly dephosphorylated, while 784 proteins were significantly hyperphosphorylated, indicating that global phosphorylation trends downstream of DYRK1A are a time-dependent process. We also analyzed the GO Biological Process enrichment of the 2 hour timepoint and observed clear differences in the pathways associated within the dephosphorylated and hyperphosphorylated groups. The dephosphorylated proteins were enriched in processes such as cell cycle, mitosis, JAK-STAT signaling and metabolism, while the hyperphosphorylated proteins were enriched in chromatin assembly/disassembly and DNA packaging. Next, using the STRING database(Szklarczyk, Morris et al. 2017), we found that the dephosphorylated set of proteins map into 5 clusters based on their roles in cell biology. For example, Cyclin D3 clustered with Geminin, SKA3 and PLK1, which are involved in the regulation of mitosis. Another potential DYRK1A target is STAT3, which clustered with BCR and TdT, both of which are known to play a role in leukemia. In the hyperphosphorylated set of proteins, the only major cluster included several histone subunits. Notably, DYRK1A has been implicated in maintaining open chromatin for transcription of cytokine genes in DS-leukemia through phosphorylation of H3 at T45 and S57(Jang, Azebi et al. 2014). Upon DYRK1A inhibition, we observed hyperphosphorylation of alternate S/T residues of H1, H3 and H4 suggesting that chromatin regulation by DYRK1A may play a role in lymphopoiesis as well.We selected several proteins from the dephosphorylated cohort as potential DYRK1A substrates based on criteria including documented interactions, the presence of the DYRK1A consensus sequence and roles in cell biology. We generated phospho-deficient alleles of these putative targets and performed in vitro kinase assays to confirm they are direct substrates. This study includes analysis of several potential DYRK1A B-cell substrates such as STAT3, geminin, BCL7C, SKA3 and several members of the RNA Polymerase II complex, including MED15 and GRINL1A. Data on their contributions to B cell development will be presented. Our study provides a global view of the contribution of DYRK1A to phosphorylation events related to lymphopoiesis. The identification of novel substrates and their associated pathways may explain the paradoxical phenotype we observe in lymphocytes upon loss of Dyrk1a. DisclosuresNo relevant conflicts of interest to declare.

Immunoinformatics and molecular dynamics approaches: Next generation vaccine design against West Nile virus.

West Nile Virus (WNV) is a life threatening flavivirus that causes significant morbidity and mortality worldwide. No preventive therapeutics including vaccines against WNV are available for human use. In this study, immunoinformatics approach was performed to design a multi epitope-based subunit vaccine against this deadly pathogen. Human (HLA) and Mice (H-2) allele specific potential T-cell and B-cell epitopes were shortlisted through a stringent procedure. Molecular docking showed selected epitopes that have stronger binding affinity with human TLR-4. Molecular dynamics simulation confirmed the stable nature of the docked complex. Furthermore, in silico cloning analysis ensures efficient expression of desired gene in the microbial system. Interestingly, previous studies showed that two of our selected epitopes have strong immune response against WNV. Therefore, selected epitopes could be strong vaccine candidates to prevent WNV infections in human. However, further in vitro and in vivo investigations could be strengthening the validation of the vaccine candidate against WNV.

Epitope Peptide-Based Predication and Other Functional Regions of Antigenic F and HN Proteins of Waterfowl and Poultry Avian Avulavirus Serotype-1 Isolates From Uganda.

Uganda is a Newcastle disease (ND) endemic country where the disease is controlled by vaccination using live LaSota (genotype II) and I2 (genotype I) vaccine strains. Resurgent outbreak episodes call for an urgent need to understand the antigenic diversity of circulating wild Avian Avulavirus serotype-1 (AAvV-1) strains. High mutation rates and the continuous emergence of genetic and antigenic variants that evade immunity make non-segmented RNA viruses difficult to control. Antigenic and functional analysis of the key viral surface proteins is a crucial step in understanding the antigen diversity between vaccine lineages and the endemic wild ND viruses in Uganda and designing ND peptide vaccines. In this study, we used computational analysis, phylogenetic characterization, and structural modeling to detect evolutionary forces affecting the predicted immune-dominant fusion (F) and hemagglutinin-neuraminidase (HN) proteins of AAvV-1 isolates from waterfowl and poultry in Uganda compared with that in LaSota vaccine strain. Our findings indicate that mutational amino acid variations at the F protein in LaSota strain, 25 poultry wild-type and 30 waterfowl wild-type isolates were distributed at regions including the functional domains of B-cell epitopes or N-glycosylation sites, cleavage site, fusion site that account for strain variations. Similarly, conserved regions of HN protein in 25 Ugandan domestic fowl isolates and the representative vaccine strain varied at the flanking regions and potential linear B-cell epitope. The fusion sites, signal peptides, cleavage sites, transmembrane domains, potential B-cell epitopes, and other specific regions of the two protein types in vaccine and wild viruses varied considerably at structure by effective online epitope prediction programs. Cleavage site of the waterfowl isolates had a typical avirulent motif of 111GGRQGR'L117 with the exception of one isolate which showed a virulent motif of 111GGRQKR'F117. All the poultry isolates showed the 111GRRQKR'F117 motif corresponding to virulent strains. Amino acid sequence variations in both HN and F proteins of AAvV-1 isolates from poultry, waterfowl, and vaccine strain were distributed over the length of the proteins with no detectable pattern, but using the experimentally derived 3D structure data revealed key-mapped mutations on the surfaces of the predicted conformational epitopes encompassing the experimental major neutralizing epitopes. The phylogenic tree constructed using the full F gene and partial F gene sequences of the isolates from poultry and waterfowl respectively, showed that Ugandan ND aquatic bird and poultry isolates share some functional amino acids in F sequences yet do remain unique at structure and the B-cell epitopes. Recombination analyses showed that the C-terminus and the rest of the F gene in poultry isolates originated from prevalent velogenic strains. Altogether, these could provide rationale for antigenic diversity in wild ND isolates of Uganda compared with the current ND vaccine strains.

Identification of COVID-19 B-cell epitopes with phage-displayed peptide library

BackgroundCoronavirus disease 19 (COVID-19) first appeared in the city of Wuhan, in the Hubei province of China. Since its emergence, the COVID-19-causing virus, SARS-CoV-2, has been rapidly transmitted around the globe, overwhelming the medical care systems in many countries and leading to more than 3.3 million deaths. Identification of immunological epitopes on the virus would be highly useful for the development of diagnostic tools and vaccines that will be critical to limiting further spread of COVID-19.MethodsTo find disease-specific B-cell epitopes that correspond to or mimic natural epitopes, we used phage display technology to determine the targets of specific antibodies present in the sera of immune-responsive COVID-19 patients. Enzyme-linked immunosorbent assays were further applied to assess competitive antibody binding and serological detection. VaxiJen, BepiPred-2.0 and DiscoTope 2.0 were utilized for B-cell epitope prediction. PyMOL was used for protein structural analysis.Results36 enriched peptides were identified by biopanning with antibodies from two COVID-19 patients; the peptides 4 motifs with consensus residues corresponding to two potential B-cell epitopes on SARS-CoV-2 viral proteins. The putative epitopes and hit peptides were then synthesized for validation by competitive antibody binding and serological detection.ConclusionsThe identified B-cell epitopes on SARS-CoV-2 may aid investigations into COVID-19 pathogenesis and facilitate the development of epitope-based serological diagnostics and vaccines.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein–Barr virus

BackgroundThe ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity. MethodsPredictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes. ResultsComputational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability. ConclusionThis study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.