Monoclonal antibody targeting a novel linear epitope on nucleoprotein confers pan-reactivity to influenza A virus

Nucleoprotein (NP) functions crucially in the replicative cycle of influenza A virus (IAV) via forming the ribonucleoprotein complex together with PB2, PB1, and PA proteins. As its high conservation, NP ranks one of the hot targets for design of universal diagnostic reagents and antiviral drugs for IAV. Here, we report an anti-NP murine monoclonal antibody (mAb) 5F10 prepared from traditional lymphocyte hybridoma technique with the immunogen of a clade 2.3.4.4 H5N1 subtype avian influenza virus. The specificity of mAb 5F10 to NP protein was confirmed by immunofluorescence assay and western blotting, and the mAb 5F10 could be used in immunoprecipitation and immunohistochemistry assays. Importantly, mAb 5F10 possessed broad-spectrum reactivity against H1~H11 subtypes of avian influenza viruses, including various HA clades of H5Nx subtype. In addition, mAb 5F10 also showed good affinity with H1N1 and H3N2 subtype influenza viruses of swine and human origin. Furthermore, the recognized antigenic epitope of mAb 5F10 was identified to consist of the conserved amino acid motif ⁸¹EHPSA⁸⁵ in the second flexible loop region of NP protein through screening the phage display peptide library. Collectively, the mAb 5F10 which recognizes the novel universal NP linear B-cell epitope of IAV with diverse origins and subtypes will be a powerful tool for NP protein-based structural, functional, and mechanistic studies, as well as the development of detection methods and universal vaccines for IAV. KEY POINTS: • A broad-spectrum mAb against various subtypes and sources of IAV was developed • The mAb possessed good reactivity in IFA, western blot, IP, and IHC assays • The mAb targeted a novel conserved linear B-cell epitope involving ⁸¹EHPSA⁸⁵ on NP protein.

A novel linear epitope at the C-terminal region of the classical swine fever virus E2 protein elicits neutralizing activity

Classical swine fever virus (CSFV) is a member of the genus Pestivirus, which causes serious economic losses. The re-emergence of the disease in Japan in 2018 has increased awareness of CSFV. In this study, Balb/c mice were immunized with plant-derived E2 protein, and four monoclonal antibodies (mAbs) 4B11, 7B3, 11A5 and 6F3 were generated. Two of these mAbs, 4B11 and 7B3, effectively blocked CSFV infection of PK-15 cells. Both mAbs recognized a novel linear epitope, 256CLIGNTTVKVHASDER271. The neutralizing ability of anti-CSFV serum decreased 63%, when pre-incubated with the linear peptide at 200 μg/mL. Structural analysis showed that this linear epitope is present at the border of Domain C and Domain D on the surface of the E2 protein. Alignment of amino acid sequences showed that the epitope was conserved in different subgroups of CSFV but not in other members of the Pestivirus genus. Consistently with the analysis above, this epitope distinguished antibodies against CSFV from those against bovine viral diarrhea virus (BVDV). Our study provides an ideal candidate peptide for new vaccine design and differential diagnosis of CSFV. These findings will contribute to the control and eradication of classical swine fever.

iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features

Linear B-cell epitopes are critically important for immunological applications, such as vaccine design, immunodiagnostic test, and antibody production, as well as disease diagnosis and therapy. The accurate identification of linear B-cell epitopes remains challenging despite several decades of research. In this work, we have developed a novel predictor, Identification of Linear B-cell Epitope (iLBE), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon-rank sum test. Then the random forest (RF) algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset. iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests. A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.

Deep learning methods improve linear B-cell epitope prediction

Background

B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes.

Results

After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available.

Conclusions

We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

Conservation region finding for influenza A viruses by machine learning methods of N-linked glycosylation sites and B-cell epitopes

Influenza type A, a serious infectious disease of the human respiratory tract, poses an enormous threat to human health worldwide. It leads to high mortality rates in poultry, pigs, and humans. The primary target identity regions for the human immune system are hemagglutinin (HA) and neuraminidase (NA), two surface proteins of the influenza A virus. Research and development of vaccines is highly complex because the influenza A virus evolves rapidly. This study focused on three genetic features of viral surface proteins: ribonucleic acid (RNA) sequence conservation, linear B-cell epitopes, and N-linked glycosylation. On the basis of these three properties, we analyzed 12,832 HA and 9487 NA protein sequences, which we retrieved from the influenza virus database. We classified the viral surface protein sequences into the 18 HA and 11 NA subtypes that have been identified thus far. Using available analytic tools, we searched for the representative strain of each virus subtype. Furthermore, using machine learning methods, we looked for conservation regions with sequences showing linear B-cell epitopes and N-linked glycosylation. Compared to the prediction of the Immune Epitope Database (IEDB) antibody neutralization response (i.e., screening of antibody sequence regions), in this study, the virus sequence coverage was large and accurate and contained N-linked glycosylation sites. The results of this study proved that we can use the machine learning-based prediction method to solve the problem of vaccine invalidation that occurred during the rapid evolution of the influenza A virus and also as a prevaccine assessment. In addition, the screening fragments can be used as a universal influenza vaccine design reference in the future.

B-Cell Epitope Mapping Using a Library of Overlapping Synthetic Peptides in an Enzyme-Linked Immunosorbent Assay

This chapter describes a strategy for mapping linear B-cell epitopes of proteins using synthetic biotinylated peptides in an ELISA.A set of overlapping peptides were designed based upon a known amino acid sequence of the target protein, VapA (Virulence-associated Protein A) of the bacterium Rhodococcus equi, an important pulmonary pathogen in foals. The peptides synthesized as biotinylated peptides were coated directly onto micro titer plates which had been pre-coated with NeutrAvidin™ and used to screen sera from foals confirmed to have R. equi disease. A linear B-cell epitope was identified which corresponded to a 20 mer sequence of the VapA protein.

Immunogenicity of synthetic peptide constructs based on PvMSP9E795-A808, a linear B-cell epitope of the P. vivax Merozoite Surface Protein-9

Plasmodium vivax Merozoite Surface Protein-9 (PvMSP-9) is a malaria vaccine candidate naturally immunogenic in humans and able to induce high antibody titers in animals when delivered as a recombinant protein. Recently, we identified the sequence EAAPENAEPVHENA (PvMSP9_E795-A808) as the main linear B-cell epitope in naturally exposed individuals. However, the potential of PvMSP9_E795-A808 as an immunogen in experimental animal models remained unexplored. Here we assess the immunogenicity of PvMSP9_E795-A808 using synthetic peptides. The peptides tested in BALB/c mice include two repeats of the sequence EAAPENAEPVHENA tested alone (peptide RII), or linked to an autologous (PvMSP9 peptide pL; pLRII) or heterologous (p2 tetanus toxin universal T cell epitope; TTRII) T cell epitope. Immune responses were evaluated by ELISA, FLUOROSPOT, and indirect immunofluorescence. We show that all of the peptide constructs tested were immunogenic eliciting specific IgG antibodies at different levels, with a prevalence of IgG1 and IgG2. Animals immunized with synthetic peptides containing T cell epitopes (pLRII or TTRII) had more efficient antibody responses that resulted in higher antibody titers able to recognize the native protein by immunofluorescence. Relevantly, the frequency of IFN-γ secreting SFC elicited by immunization with TTRII synthetic peptide was comparable to that reported to the PvMSP9-Nt recombinant protein. Taken together, our study indicates that PvMSP9_E795-A808 is highly immunogenic in mice and further studies to evaluate its value as promising vaccine target are warranted. Moreover, our study supports the critical role of CD4 T cell epitopes to enhance humoral responses induced by subunit based vaccines.

Identification of a novel linear B-cell epitope in the p27 of Avian leukosis virus

Avian leukosis virus (ALV) is an avian oncogenic retrovirus that can induce virus-associated neoplasia and causes great economic loss in poultry industry. It is known that the capsid antigen p27 is the group-specific antigen that is highly conserved among all ALV subgroups, and is the most abundant immunogenic viral protein. In the present study, five overlapping fragments (GST- p27-F1/2, GST- p27-F2-1/2/3) of ALV-p27 were subjected to Western blotting analysis using a monoclonal antibody (5D3) against ALV-p27 to identify the epitope. The result showed that the epitope recognized by 5D3 is located within 173-240 amino acid of the ALV-p27 protein. For precise mapping of this epitope, a set of overlapping peptides were synthesized. Indirect enzyme linked immunosorbent assay (ELISA) revealed that ¹⁹³CFRQKSQPDI²⁰² motif was the minimal fragment recognized by 5D3, so this motif represented a linear B-cell epitope of ALV-p27. Homology analysis indicated that 5D3 defined epitope is highly conserved among ALV strains. The identified epitope might be useful in clinical applications and as a tool for further study of the structure and function of ALV-p27.

Comprehensive mapping of a novel NS1 epitope conserved in flaviviruses within the Japanese encephalitis virus serocomplex

Nonstructural protein-1 (NS1) of the Japanese encephalitis virus (JEV) is an immunogenic protein that is a potential candidate for the development of vaccines and diagnostic reagents. NS1 is known to be more specific than the E protein in serological testing of flavivirus infections. However, NS1 exhibits cross-reactivity among flaviviruses even within the same genus and more so within a serocomplex. However, the cross-reactive epitopes on JEV NS1 are poorly characterized. The present study describes the full mapping of a linear B-cell epitope that is common and specific to the JEV serocomplex of Flaviviridae. We generated an NS1-specific monoclonal antibody that cross-reacts with the West Nile virus (WNV) NS1 protein by immunizing mice with recombinant JEV NS1. For epitope mapping, 51 partially overlapping peptides spanning the entire NS1 protein were expressed with a glutathione S-transferase (GST) tag and screened using monoclonal antibodies. Two linear epitope-containing peptides were identified using enzyme-linked immunosorbent assay (ELISA). By sequentially removing amino acid residues from the carboxy and amino terminal of peptides, we successfully identified the smallest unit of the linear epitope required to react with the monoclonal antibody. The linear epitope was located in amino acids residues ²²⁷ETHTLW²³². Furthermore, results of the sequence alignment revealed that the epitope was highly conserved among JEV strains. Notably, the epitope is highly conserved among viruses of the JEV serocomplex. Furthermore, the homologous regions on NS1 proteins from dengue viruses showed no cross-reactivity with the monoclonal antibodies. The epitope was recognized by antisera against the WNV but not against the dengue virus. This novel JEV serocomplex-specific linear B-cell epitope of NS1 would be helpful in the development of new vaccines and diagnostic assays.

Identification of a linear B-Cell epitope on avian reovirus protein sigmaC

SigmaC (σC) protein, which mediates virus attachment to target cells, is the most variable proteins of avian reovirus (ARV). It is responsible for inducing protective antibody immune responses in animals. To understand the antigenic determinants of σC protein, a set of partially overlapping and consecutive peptides spanning σC were expressed and then screened with the monoclonal antibody (mAb) 2B5 directed against σC. The mAb 2B5 recognized peptides with the σC motif (45)ELLHRSISDISTTV(58). Further identification of the displayed B-cell epitope was conducted with a set of truncated peptides expressed as GST fusion proteins. The Western blot and ELISA results indicated that (45)ELLHRSISDI(54) was the minimal determinant of the linear B-cell epitope. Using sequences analysis, we found that this epitope was not a common motif shared among the other members of the ARV and DRV groups. Furthermore, cross reactivity analysis showed that the associated coding motif of other ARV and DRV groups was not recognized by 2B5. These data suggested that (45)ELLHRSISDI(54) was a type-specific linear B-cell epitope of avian reovirus. The results in this study may have potential applications in the development of diagnostic techniques and epitope-based marker vaccines against ARV, which is prevalent in China.