Identification, Mapping, and Genetic Diversity of Novel Conserved Cross-Species Epitopes of RhopH2 in Plasmodium knowlesi With Plasmodium vivax

Malaria is a major public health concern, and any tangible intervention during the pre-elimination phase can result in a significant reduction in infection rates. Recent studies have reported that antigens producing cross-protective immunity can play an important role as vaccines and halt malaria transmission in different endemic regions. In this study, we studied the genetic diversity, natural selection, and discovered novel conserved epitopes of a high molecular weight rhoptry protein 2 (RhopH2) in clinical samples of Plasmodium knowlesi and Plasmodium vivax cross-protective domains, which has been proven to produce cross-protective immunity in both species. We found low levels of nucleotide diversity (P. knowlesi; π ~ 0.0093, SNPs = 49 and P. vivax π ~ 0.0014, SNPs = 23) in P. knowlesi (n = 40) and P. vivax (n = 65) samples in the PkRhopH2 cross-protective domain. Strong purifying selection was observed for both species (P. knowlesi; dS - dN = 2.41, p < 0.009, P. vivax; dS - dN = 1.58, p < 0.050). In silico epitope prediction in P. knowlesi identified 10 potential epitopes, of which 7 epitopes were 100% conserved within clinical samples. Of these epitopes, an epitope with 10 amino acids (QNSKHFKKEK) was found to be fully conserved within all P. knowlesi and P. vivax clinical samples and 80%–90% conservation within simian malaria ortholog species, i.e., P. coatneyi and P. cynomolgi. Phylogenetic analysis of the PkRhopH2 cross-protective domain showed geographical clustering, and three subpopulations of P. knowlesi were identified of which two subpopulations originated from Sarawak, Malaysian Borneo, and one comprised only the laboratory lines from Peninsular Malaysia. This study suggests that RhopH2 could be an excellent target for cross-protective vaccine development with potential for outwitting strain as well as species-specific immunity. However, more detailed studies on genetic diversity using more clinical samples from both species as well as the functional role of antibodies specific to the novel conserved epitope identified in this study can be explored for protection against infection.

Designing multi-epitope based peptide vaccine targeting spike protein SARS-CoV-2 B1.1.529 (Omicron) variant using computational approaches.

Since the SARS-CoV-2 outbreak in 2019, millions of people have been infected with the virus, and due to its high human-to-human transmission rate, there is a need for a vaccine to protect people. Although some vaccines are in use, due to the high mutation rate in the SARS-CoV-2 multiple variants, the current vaccines may not be sufficient to immunize people against new variant threats. One of the emerging variants of concern is B1.1.529 (Omicron), which carries ~30 mutations in the Spike protein of SARS-CoV-2 is predicted to evade antibodies recognition even from vaccinated people. We used a structure-based approach along with an epitope prediction server to develop a Multi-Epitope based Subunit Vaccine (MESV) involving SARS-CoV-2 B1.1.529 variant spike glycoprotein. The predicted epitope with better antigenicity and non-toxicity were used for designing and predicting vaccine construct features and structure models. The MESV construct In-silico cloning in pET28a expression vector predicted the construct to be highly translational. The proposed MESV vaccine construct was also subjected to immune simulation prediction and was found to be highly antigenic and elicit a cell-mediated immune response. The proposed MESV in the present study has the potential to be evaluated further for vaccine production against the newly identified B1.1.529 (Omicron) variant of concern.

Predicted CTL responses from pressured epitopes in SARS-CoV-2 correlate with COVID-19 severity

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Protective cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we focus on the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients and dictating mechanisms of immune evasion adopted by SARS-CoV-2 due to the imposed immune pressure at global and cohort levels. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that asymptomatic individuals recognize a larger number of pressured epitopes which could facilitate emergence of mutations at these epitopic regions to overcome the protectivity they offer to the host. Based on the severity of COVID-19, we also identify HLA-alleles and epitopes that offer higher protectivity against severe disease in infected individuals. Finally, we shortlist a set of pressured and protective epitopes that represent regions in the viral proteome that are under higher immune pressure across SARS-CoV-2 variants due to the protectivity they offer. Identification of such epitopes could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens, defined by the distribution of HLA-genotypes among members of a population.

Computational antigenic epitope prediction of clinical Indonesian Dengue virus NS1 protein

The identification of human Non-Structural-1 (NS1) protein epitopes will help us better understand Dengue virus (DENV) immunopathogenesis. In this study, several online and offline bioinformatic prediction tools were exploited to predict and analyze T-cell and B-cell epitopes of DENV NS1 consensus sequences originated from Indonesian clinical isolates. We identified a potential peptide at NS1155--163 (VEDYGFGIF) which interact with MHC-I allele HLA-B*40:01 and showed high binding affinity (IC50) scores ranging between 63.8 nM to 183.9 nM for all Indonesian DENV serotypes. Furthermore, we have succeeded identified a region at the C-terminal of Indonesian DENV NS1 protein between 325--344 as part of discontinuous antigenic epitope which conserved for all serotypes. Our analyses showed this region could induce strong and persistent antibody against all DENV serotypes by interacting with MHC-I molecule and also recognized by B-cell receptor. The identification of DENV NS1 T-cell and B-cell epitopes may help in the development of a new vaccine, drug discovery, and diagnostic system to help eradicate dengue infection.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Mapping Sequential IgE-Binding Epitopes on Major and Minor Egg Allergens

<b><i>Introduction:</i></b> Molecular studies of hen’s egg allergens help define allergic phenotypes, with IgE to sequential (linear) epitopes on the ovomucoid (OVM) protein associated with a persistent disease. Epitope profiles of other egg allergens are largely unknown. The objective of this study was to construct an epitope library spanning across 7 allergens and further evaluate sequential epitope-specific (<i>ses-</i>)IgE and <i>ses-</i>IgG₄ among baked-egg reactive or tolerant children. <b><i>Methods:</i></b> A Bead-Based Epitope Assay was used to identify informative IgE epitopes from 15-mer overlapping peptides covering the entire OVM and ovalbumin (OVA) proteins in 38 egg allergic children. An amalgamation of 12 B-cell epitope prediction tools was developed using experimentally identified epitopes. This ensemble was used to predict epitopes from ovotransferrin, lysozyme, serum albumin, vitellogenin-II fragment, and vitellogenin-1 precursor. <i>Ses-</i>IgE and <i>ses-</i>IgG₄ repertoires of 135 egg allergic children (82 reactive to baked-egg, the remaining 52 tolerant), 46 atopic controls, and 11 healthy subjects were compared. <b><i>Results:</i></b> 183 peptides from OVM and OVA were screened and used to create an aggregate algorithm, improving predictions of 12 individual tools. A final library of 65 sequential epitopes from 7 proteins was constructed. Egg allergic children had higher <i>ses-</i>IgE and lower <i>ses-</i>IgG₄ to predominantly OVM epitopes than both atopic and healthy controls. Baked-egg reactive children had similar <i>ses-</i>IgG₄ but greater <i>ses-</i>IgE than tolerant group. A combination of OVA-sIgE with <i>ses-</i>IgEs to OVM-023 and OVA-028 was the best predictor of reactive phenotype. <b><i>Conclusion:</i></b> We have created a comprehensive epitope library and showed that <i>ses-</i>IgE is a potential biomarker of baked-egg reactivity.