A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

Epitope Mapping of an Anti-Chinese/Golden Hamster Podoplanin Monoclonal Antibody.

Chinese hamster (Cricetulus griseus) and golden hamster (Mesocricetus auratus) are important animal models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, which affect several organs, including respiratory tract, lung, and kidney. Podoplanin (PDPN) is a marker of lung type I alveolar cells, kidney podocytes, and lymphatic endothelial cells. The development of anti-PDPN monoclonal antibodies (mAbs) for these animals is essential to evaluate the pathogenesis by SARS-CoV-2 infections. Using the Cell-Based Immunization and Screening method, we previously developed an anti-Chinese hamster PDPN (ChamPDPN) mAb, PMab-281 (mouse IgG3, kappa), and further changed its subclass into IgG2a (281-mG2a-f), both of which can recognize not only ChamPDPN but also golden hamster PDPN (GhamPDPN) by flow cytometry and immunohistochemistry. In this study, we examined the critical epitope of 281-mG2a-f, using enzyme-linked immunosorbent assay (ELISA) with synthesized peptides. First, we performed ELISA with peptides derived from ChamPDPN and GhamPDPN extracellular domain, and found that 281-mG2a-f reacted with the peptides, which commonly possess the KIPFEELxT sequence. Next, we analyzed the reaction with the alanine-substituted mutants, and revealed that 281-mG2a-f did not recognize the alanine-substituted peptides of I75A, F77A, and E79A of ChamPDPN. Furthermore, these peptides could not inhibit the recognition of 281-mG2a-f to ChamPDPN-expressing cells by flow cytometry. The results indicate that the binding epitope of 281-mG2a-f includes Ile75, Phe77, and Glu79 of ChamPDPN, which are shared with GhamPDPN.

Development of a Monoclonal Antibody PMab-292 Against Ferret Podoplanin.

Ferrets (Mustela putorius furo) have been used as small animal models to investigate severe acute respiratory syndrome coronaviruses (SARS-CoV and SARS-CoV-2) infections. Pathological analyses of these tissue samples, including those of the lung, are, therefore, essential to understand the pathogenesis of SARS-CoVs and evaluate the action of therapeutic monoclonal antibodies (mAbs) against this disease. However, mAbs that recognize ferret-derived proteins and distinguish between specific cell types, such as lung epithelial cells, are limited. Podoplanin (PDPN) has been identified as an essential marker in lung type I alveolar epithelial cells, kidney podocytes, and lymphatic endothelial cells. In this study, an anti-ferret PDPN (ferPDPN) mAb PMab-292 (mouse IgG1, kappa) was established using the Cell-Based Immunization and Screening (CBIS) method. PMab-292 recognized ferPDPN-overexpressed Chinese hamster ovary-K1 (CHO/ferPDPN) cells by flow cytometry and Western blotting. The kinetic analysis using flow cytometry showed that the KD of PMab-292 for CHO/ferPDPN was 3.4 × 10-8 M. Furthermore, PMab-292 detected lung type I alveolar epithelial cells, lymphatic endothelial cells, and glomerular/Bowman's capsule in the kidney using immunohistochemistry. Hence, these results propose the usefulness of PMab-292 in analyzing ferret-derived tissues for SARS-CoV-2 research.

Cross-Recognition of SARS-CoV-2 B-Cell Epitopes with Other Betacoronavirus Nucleoproteins.

The B and T lymphocytes of the adaptive immune system are important for the control of most viral infections, including COVID-19. Identification of epitopes recognized by these cells is fundamental for understanding how the immune system detects and removes pathogens, and for antiviral vaccine design. Intriguingly, several cross-reactive T lymphocyte epitopes from SARS-CoV-2 with other betacoronaviruses responsible for the common cold have been identified. In addition, antibodies that cross-recognize the spike protein, but not the nucleoprotein (N protein), from different betacoronavirus have also been reported. Using a consensus of eight bioinformatic methods for predicting B-cell epitopes and the collection of experimentally detected epitopes for SARS-CoV and SARS-CoV-2, we identified four surface-exposed, conserved, and hypothetical antigenic regions that are exclusive of the N protein. These regions were analyzed using ELISA assays with two cohorts: SARS-CoV-2 infected patients and pre-COVID-19 samples. Here we describe four epitopes from SARS-CoV-2 N protein that are recognized by the humoral response from multiple individuals infected with COVID-19, and are conserved in other human coronaviruses. Three of these linear surface-exposed sequences and their peptide homologs in SARS-CoV-2 and HCoV-OC43 were also recognized by antibodies from pre-COVID-19 serum samples, indicating cross-reactivity of antibodies against coronavirus N proteins. Different conserved human coronaviruses (HCoVs) cross-reactive B epitopes against SARS-CoV-2 N protein are detected in a significant fraction of individuals not exposed to this pandemic virus. These results have potential clinical implications.

SARS-CoV-2 Antibody Binding and Neutralization in Dried Blood Spot Eluates and Paired Plasma.

Wide-scale assessment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibodies is critical to understanding population seroprevalence, correlates of protection, and the longevity of vaccine-elicited responses. Most SARS-CoV-2 studies characterize antibody responses in plasma/sera. While reliable and broadly used, these samples pose several logistical restrictions, such as requiring venipuncture for collection and a cold chain for transportation and storage. Dried blood spots (DBS) overcome these barriers as they can be self-collected by fingerstick and mailed and stored at ambient temperature. Here, we evaluate the suitability of DBS for SARS-CoV-2 antibody assays by comparing several antibody responses between paired plasma and DBS from SARS-CoV-2 convalescent and vaccinated individuals. We found that DBS not only reflected plasma antibody binding by enzyme-linked immunosorbent assay (ELISA) and epitope profiles using phage display, but also yielded SARS-CoV-2 neutralization titers that highly correlated with paired plasma. Neutralization measurement was further streamlined by adapting assays to a high-throughput 384-well format. This study supports the adoption of DBS for numerous SARS-CoV-2 binding and neutralization assays. IMPORTANCE Plasma and sera isolated from venous blood represent conventional sample types used for the evaluation of SARS-CoV-2 antibody responses after infection or vaccination. However, collection of these samples is invasive and requires trained personnel and equipment for immediate processing. Once collected, plasma and sera must be stored and shipped at cold temperatures. To define the risk of emerging SARS-CoV-2 variants and the longevity of immune responses to natural infection and vaccination, it will be necessary to measure various antibody features in populations around the world, including in resource-limited areas. A sampling method that is compatible with these settings and is suitable for a variety of SARS-CoV-2 antibody assays is therefore needed to continue to understand and curb the COVID-19 pandemic.

Potent neutralization of SARS-CoV-2 variants of concern by an antibody with an uncommon genetic signature and structural mode of spike recognition.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineages that are more transmissible and resistant to currently approved antibody therapies poses a considerable challenge to the clinical treatment of coronavirus disease (COVID-19). Therefore, the need for ongoing discovery efforts to identify broadly reactive monoclonal antibodies to SARS-CoV-2 is of utmost importance. Here, we report a panel of SARS-CoV-2 antibodies isolated using the linking B cell receptor to antigen specificity through sequencing (LIBRA-seq) technology from an individual who recovered from COVID-19. Of these antibodies, 54042-4 shows potent neutralization against authentic SARS-CoV-2 viruses, including variants of concern (VOCs). A cryoelectron microscopy (cryo-EM) structure of 54042-4 in complex with the SARS-CoV-2 spike reveals an epitope composed of residues that are highly conserved in currently circulating SARS-CoV-2 lineages. Further, 54042-4 possesses uncommon genetic and structural characteristics that distinguish it from other potently neutralizing SARS-CoV-2 antibodies. Together, these findings provide motivation for the development of 54042-4 as a lead candidate to counteract current and future SARS-CoV-2 VOCs.

Computational epitope map of SARS-CoV-2 spike protein.

The primary immunological target of COVID-19 vaccines is the SARS-CoV-2 spike (S) protein. S is exposed on the viral surface and mediates viral entry into the host cell. To identify possible antibody binding sites, we performed multi-microsecond molecular dynamics simulations of a 4.1 million atom system containing a patch of viral membrane with four full-length, fully glycosylated and palmitoylated S proteins. By mapping steric accessibility, structural rigidity, sequence conservation, and generic antibody binding signatures, we recover known epitopes on S and reveal promising epitope candidates for structure-based vaccine design. We find that the extensive and inherently flexible glycan coat shields a surface area larger than expected from static structures, highlighting the importance of structural dynamics. The protective glycan shield and the high flexibility of its hinges give the stalk overall low epitope scores. Our computational epitope-mapping procedure is general and should thus prove useful for other viral envelope proteins whose structures have been characterized.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Linear epitope landscape of the SARS-CoV-2 Spike protein constructed from 1,051 COVID-19 patients.

To fully decipher the immunogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike protein, it is essential to assess which part is highly immunogenic in a systematic way. We generate a linear epitope landscape of the Spike protein by analyzing the serum immunoglobulin G (IgG) response of 1,051 coronavirus disease 2019 (COVID-19) patients with a peptide microarray. We reveal two regions rich in linear epitopes, i.e., C-terminal domain (CTD) and a region close to the S2' cleavage site and fusion peptide. Unexpectedly, we find that the receptor binding domain (RBD) lacks linear epitope. We reveal that the number of responsive peptides is highly variable among patients and correlates with disease severity. Some peptides are moderately associated with severity and clinical outcome. By immunizing mice, we obtain linear-epitope-specific antibodies; however, no significant neutralizing activity against the authentic virus is observed for these antibodies. This landscape will facilitate our understanding of SARS-CoV-2-specific humoral responses and might be useful for vaccine refinement.

Antibody Binding Epitope Mapping (AbMap) of Hundred Antibodies in a Single Run.

Antibodies play essential roles in both diagnostics and therapeutics. Epitope mapping is essential to understand how an antibody works and to protect intellectual property. Given the millions of antibodies for which epitope information is lacking, there is a need for high-throughput epitope mapping. To address this, we developed a strategy, Antibody binding epitope Mapping (AbMap), by combining a phage displayed peptide library with next-generation sequencing. Using AbMap, profiles of the peptides bound by 202 antibodies were determined in a single test, and linear epitopes were identified for >50% of the antibodies. Using spike protein (S1 and S2)-enriched antibodies from the convalescent serum of one COVID-19 patient as the input, both linear and potentially conformational epitopes of spike protein specific antibodies were identified. We defined peptide-binding profile of an antibody as the binding capacity (BiC). Conceptually, the BiC could serve as a systematic and functional descriptor of any antibody. Requiring at least one order of magnitude less time and money to map linear epitopes than traditional technologies, AbMap allows for high-throughput epitope mapping and creates many possibilities.

Mapping Neutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain by Structure-Guided High-Resolution Serology.

Analysis of the specificity and kinetics of neutralizing antibodies (nAbs) elicited by SARS-CoV-2 infection is crucial for understanding immune protection and identifying targets for vaccine design. In a cohort of 647 SARS-CoV-2-infected subjects, we found that both the magnitude of Ab responses to SARS-CoV-2 spike (S) and nucleoprotein and nAb titers correlate with clinical scores. The receptor-binding domain (RBD) is immunodominant and the target of 90% of the neutralizing activity present in SARS-CoV-2 immune sera. Whereas overall RBD-specific serum IgG titers waned with a half-life of 49 days, nAb titers and avidity increased over time for some individuals, consistent with affinity maturation. We structurally defined an RBD antigenic map and serologically quantified serum Abs specific for distinct RBD epitopes leading to the identification of two major receptor-binding motif antigenic sites. Our results explain the immunodominance of the receptor-binding motif and will guide the design of COVID-19 vaccines and therapeutics.