An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Deep learning of antibody epitopes using positional permutation vectors

Background

The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.

Results

Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training.

Conclusions

With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Leukolectins support lifelong innate immunity in lower vertebrates, and reveal dichotomies of several leukocytic lineages

Innate immunity is vital for animal homeostasis and survival. First-line immuno-defense for fish larvae involves mucus enriched with leukolectin (LL) secreted by dermal lectocytes. Later during the critical transition from yolk-nutrition to feeding, additional larval immuno-protection in zebrafish (zF) is provided by macrophages containing LL (lectophages). This work investigated new LL-expression in embryos and in blood, structures of fish leukocytic LL and LL-genes, and LL-presence in chicken leukocytes. In zF-embryos, lectophages appear ∼10 hpf, while later, cells co-expressing myeloperoxidase- and LL-mRNA were detected (∼19 hpf). Furthermore, protein-extracts of Atlantic salmon (Ssal) leukocytes contained LL-proteins, compartmentalized in the cytosol. Cloning and sequencing revealed 94 % nt-sequence identity between variants of Ssal-leukolectins. Highly conserved LLs allowed production of epitope-specific anti-LL IgGs. Immuno-fluorescence-analysis demonstrated that most Ssal-bloodcells were LL-negative, but both some large cells with protrusions and some small, rounded cells did express LL. Immunoperoxidase-staining method confirmed LL-expression in some Ssal-leukocytes, identified as macrophages, PMN-leukocytes, thrombocytes and dendritic cells. However, closer examination revealed a dichotomy of these cell-categories into either LL-positive, or LL-negative variants. In situ hybridization demonstrated profuse LL-expression in Ssal head kidney interstitial tissue, while LL-transcripts were absent in large kidney tubules. Both hematopoietic (non-pigmented) marrow cells and melano-macrophages expressed LL-mRNA, implying that leukolectins provide lifelong innate immuno-protection. PCR-amplification using Ssal-leukocytic DNA as template, and direct sequencing yielded a leukocytic ll-gene. Some cells in salmon, cod, halibut, oikopleura and zebrafish embryos express LL-proteins and/or LL-mRNA, and LL-mRNA is detected in salmon, cod and chicken leukocytes. However, current genomes for these species lack recognizable LL-loci except the Ssal_v3.1 Genome-assembly. The data demonstrate an unexpected dichotomy of some leukocyte lineages into LL-positive or LL-negative cell-variants. Such dichotomies suggest exploring differential impacts from the duplicated leukocyte-lineages in health and disease.

SARS-CoV-2 Vaccines: The Advantage of Mucosal Vaccine Delivery and Local Immunity

Immunity against respiratory pathogens is often short-term, and, consequently, there is an unmet need for the effective prevention of such infections. One such infectious disease is coronavirus disease 19 (COVID-19), which is caused by the novel Beta coronavirus SARS-CoV-2 that emerged around the end of 2019. The World Health Organization declared the illness a pandemic on 11 March 2020, and since then it has killed or sickened millions of people globally. The development of COVID-19 systemic vaccines, which impressively led to a significant reduction in disease severity, hospitalization, and mortality, contained the pandemic's expansion. However, these vaccines have not been able to stop the virus from spreading because of the restricted development of mucosal immunity. As a result, breakthrough infections have frequently occurred, and new strains of the virus have been emerging. Furthermore, SARS-CoV-2 will likely continue to circulate and, like the influenza virus, co-exist with humans. The upper respiratory tract and nasal cavity are the primary sites of SARS-CoV-2 infection and, thus, a mucosal/nasal vaccination to induce a mucosal response and stop the virus' transmission is warranted. In this review, we present the status of the systemic vaccines, both the approved mucosal vaccines and those under evaluation in clinical trials. Furthermore, we present our approach of a B-cell peptide-based vaccination applied by a prime-boost schedule to elicit both systemic and mucosal immunity.

Allergenicity and Linear Epitope Analysis of Scy p 8, an Allergen from Mud Crab

Scy p 8 (triosephosphate isomerase) as a crab allergen in inducing distinct T-helper (Th) cell differentiation and a linear epitope associated with allergenicity remain elusive. In this study, mice sensitized with Scy p 8 exhibited significantly upregulated levels of IgE, IgG1, and IL-4 release, inducing a Th2 immune response. Moreover, the release of IFN-γ (Th1) and the levels of Treg cells were downregulated, while IL-17A (Th17) was upregulated, indicating that Scy p 8 disrupted the Th1/Th2 balance and Th17/Treg balance in mice. Furthermore, bioinformatics prediction and serum samples from crab-allergic patients and mice enabled the discovery of 8 linear epitopes of Scy p 8. Meanwhile, the analysis of peptide similarity and tertiary superposition revealed that 8 epitopes of Scy p 8 exhibited conservation across various species, potentially resulting in cross-reactivity. These findings possess the potential to enhance the comprehension of crab allergens, thereby establishing a foundation for investigating cross-reactivity.

Universal characterization of epitope immunodominance from a multiscale model of clonal competition in germinal centers

We introduce a multiscale model for affinity maturation, which aims to capture the intraclonal, interclonal, and epitope-specific organization of the B-cell population in a germinal center. We describe the evolution of the B-cell population via a quasispecies dynamics, with species corresponding to unique B-cell receptors (BCRs), where the desired multiscale structure is reflected on the mutational connectivity of the accessible BCR space, and on the statistical properties of its fitness landscape. Within this mathematical framework, we study the competition among classes of BCRs targeting different antigen epitopes, and we construct an effective immunogenic space where epitope immunodominance relations can be universally characterized. We finally study how varying the relative composition of a mixture of antigens with variable and conserved domains allows for a parametric exploration of this space, and we identify general principles for the rational design of two-antigen cocktails.

Plasmodium vivax serological exposure markers: PvMSP1-42-induced humoral and memory B-cell response generates long-lived antibodies

Plasmodium vivax serological exposure markers (SEMs) have emerged as promising tools for the actionable surveillance and implementation of targeted interventions to accelerate malaria elimination. To determine the dynamic profiles of SEMs in current and past P. vivax infections, we screened and selected 11 P. vivax proteins from 210 putative proteins using protein arrays, with a set of serum samples obtained from patients with acute P. vivax and documented past P. vivax infections. Then we used a murine protein immune model to initially investigate the humoral and memory B cell response involved in the generation of long-lived antibodies. We show that of the 11 proteins, especially C-terminal 42-kDa region of P. vivax merozoite surface protein 1 (PvMSP1-42) induced longer-lasting long-lived antibodies, as these antibodies were detected in individuals infected with P. vivax in the 1960-1970s who were not re-infected until 2012. In addition, we provide a potential mechanism for the maintenance of long-lived antibodies after the induction of PvMSP1-42. The results indicate that PvMSP1-42 induces more CD73+CD80+ memory B cells (MBCs) compared to P. vivax GPI-anchored micronemal antigen (PvGAMA), allowing IgG anti-PvMSP1-42 antibodies to be maintained for a long time.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1

Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.

Animal-derived food allergen: A review on the available crystal structure and new insights into structural epitope

Immunoglobulin E (IgE)-mediated food allergy is a rapidly growing public health problem. The interaction between allergens and IgE is at the core of the allergic response. One of the best ways to understand this interaction is through structural characterization. This review focuses on animal-derived food allergens, overviews allergen structures determined by X-ray crystallography, presents an update on IgE conformational epitopes, and explores the structural features of these epitopes. The structural determinants of allergenicity and cross-reactivity are also discussed. Animal-derived food allergens are classified into limited protein families according to structural features, with the calcium-binding protein and actin-binding protein families dominating. Progress in epitope characterization has provided useful information on the structural properties of the IgE recognition region. The data reveals that epitopes are located in relatively protruding areas with negative surface electrostatic potential. Ligand binding and disulfide bonds are two intrinsic characteristics that influence protein structure and impact allergenicity. Shared structures, local motifs, and shared epitopes are factors that lead to cross-reactivity. The structural properties of epitope regions and structural determinants of allergenicity and cross-reactivity may provide directions for the prevention, diagnosis, and treatment of food allergies. Experimentally determined structure, especially that of antigen-antibody complexes, remains limited, and the identification of epitopes continues to be a bottleneck in the study of animal-derived food allergens. A combination of traditional immunological techniques and emerging bioinformatics technology will revolutionize how protein interactions are characterized.

Immunoinformatics approach for designing a multiepitope subunit vaccine against porcine epidemic diarrhea virus genotype IIA spike protein

Background

Porcine epidemic diarrhea (PED), caused by the porcine epidemic diarrhea virus (PEDV), is associated with high mortality and morbidity rates, especially in neonatal pigs. This has resulted in significant economic losses for the pig industry. PEDV genotype II-based vaccines were found to confer better immunity against both heterologous and homologous challenges; specifically, spike (S) proteins, which are known to play a significant role during infection, are ideal for vaccine development.

Aim

This study aims to design a multi-epitope subunit vaccine targeting the S protein of the PEDV GIIa strain using an immunoinformatics approach.

Methods

Various bioinformatics tools were used to predict HTL, CTL, and B-cell epitopes. The epitopes were connected using appropriate linkers and conjugated with the CTB adjuvant and M-ligand. The final multiepitope vaccine construct (fMEVc) was then docked to toll-like receptor 4 (TLR4). The stability of the fMEVc-TLR4 complex was then simulated using GROMACS. C-immsim was then used to predict the in vitro immune response of the fMEVc.

Results

Six epitopes were predicted to induce antibody production, ten epitopes were predicted to induce CTL responses, and four epitopes were predicted to induce HTL responses. The assembled epitopes conjugated with the CTB adjuvant and M-ligand, fMEVc, is antigenic, non-allergenic, stable, and soluble. The construct showed a favorable binding affinity for TLR4, and the protein complex was shown to be stable through molecular dynamics simulations. A robust immune response was induced after immunization, as demonstrated through immune stimulation.

Conclusion

In conclusion, the multi-epitope subunit vaccine construct for PEDV designed in this study exhibits promising antigenicity, stability, and immunogenicity, eliciting robust immune responses and suggesting its potential as a candidate for further vaccine development.

A Multi-epitope Subunit Vaccine Identification and Development Against Scrub Typhus (Orientia tsutsugamushi) Using Immunoinformatics Approaches

Background The pathogen Orientia tsutsugamushi, which causes scrub typhus, is rapidly spreading throughout the tropics. As a measure to improve public health, the development of a vaccine for human use is essential. Scrub typhus is listed as one of the underdiagnosed and underreported febrile infections. This vector-borne zoonotic infection appears as eschar on the patient's skin. Methods Immunoinformatics was employed to predict the multi-epitope subunit vaccine that will activate both B and T cells. The final vaccine includes lipoprotein LprA as an adjuvant at the N-terminus along with B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL)-binding epitopes to boost immunogenicity. Assessing the vaccine's physiochemistry demonstrates that it is both antigenic and non-allergic. The vaccine structure was developed, enhanced, confirmed, and disulfide-engineered to provide the best possible model. Using molecular docking, the interaction of the produced vaccine with toll-like receptor 2 (TLR2) was analyzed, and the vaccine-receptor complex was stabilized by molecular dynamics (MD) simulation. According to in silico cloning, Escherichia coli can efficiently produce the recommended vaccine. Additionally, the efficacy of the in silico-developed vaccine must be evaluated in an in vitro and in vivo experiment. Results The developed vaccine successfully stimulates cellular and humoral immune responses. The vaccine, which has three B-cell epitopes, three HCL epitopes, and nine CTL epitopes, can bind firmly to immunological receptors. Dynamic investigations of the vaccine-receptor complex show a strong interaction and stable conformation. Conclusion In this study, the vaccine candidate demonstrated strong antigenicity, stability, and solubility while also being non-allergenic to host cells. The vaccine candidate's stability with the TLR2 immune receptor is established by binding studies, and in silico cloning verifies efficient and stable expression in the bacterial system.

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

Computational identification of antibody-binding epitopes from mimotope datasets

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Molecular characterization of Anaplasma ovis Msp4 protein in strains isolated from ticks in Turkey: A multi-epitope synthetic vaccine antigen design against Anaplasma ovis using immunoinformatic tools

Tick-borne pathogens increasingly threaten animal and human health as well as cause great economic loss in the livestock industry. Among these pathogens, Anaplasma ovis causing a decrease in meat and milk yield is frequently detected in sheep in many countries including Turkey. This study aimed to reveal potential vaccine candidate epitopes in Msp4 protein using sequence data from Anaplasma ovis isolates and then to design a multi-epitope protein to be used in vaccine formulations against Anaplasma ovis. For this purpose, Msp4 gene was sequenced from Anaplasma ovis isolates (n:6) detected in ticks collected from sheep in Turkey and the sequence data was compared with previous sequences from different countries in order to detect the variations of Msp4 gene/protein. Potential vaccine candidate and diagnostic epitopes were predicted using various immunoinformatics tools. Among the discovered vaccine candidate epitopes, antigenic and conserved were selected, and then a multi-epitope protein was designed. The designed vaccine protein was tested for the assessment of TLR-2, IgG, and IFN-g responses by molecular docking and immune simulation analyses. Among the discovered epitopes, EVASEGSGVM and YQFTPEISLV epitopes with properties of high antigenicity, non-allergenicity, and non-toxicity were proposed to be used for Anaplasma ovis in further serodiagnostic and vaccine studies.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

In silico design of a broad-spectrum multiepitope vaccine against influenza virus

Influenza remains a global health concern due to its potential to cause pandemics as a result of rapidly mutating influenza virus strains. Existing vaccines often struggle to keep up with these rapidly mutating flu viruses. Therefore, the development of a broad-spectrum peptide vaccine that can stimulate an optimal antibody response has emerged as an innovative approach to addressing the influenza threat. In this study, an immunoinformatic approach was employed to rapidly predict immunodominant epitopes from different antigens, aiming to develop an effective multiepitope influenza vaccine (MEV). The immunodominant B-cell linear epitopes of seasonal influenza strains hemagglutinin (HA) and neuraminidase (NA) were predicted using an antibody-peptide microarray, involving a human cohort including vaccinees and infected patients. On the other hand, bioinformatics tools were used to predict immunodominant cytotoxic T-cell (CTL) and helper T-cell (HTL) epitopes. Subsequently, these epitopes were evaluated by various immunoinformatic tools. Epitopes with high antigenicity, high immunogenicity, non-allergenicity, non-toxicity, as well as exemplary conservation were then connected in series with appropriate linkers and adjuvants to construct a broad-spectrum MEV. Moreover, the structural analysis revealed that the MEV candidates exhibited good stability, and the docking results demonstrated their strong affinity to Toll-like receptors 4 (TLR4). In addition, molecular dynamics simulation confirmed the stable interaction between TLR4 and MEVs. Three injections with MEVs showed a high level of B-cell and T-cell immune responses according to the immunological simulations in silico. Furthermore, in-silico cloning was performed, and the results indicated that the MEVs could be produced in considerable quantities in Escherichia coli (E. coli). Based on these findings, it is reasonable to create a broad-spectrum MEV against different subtypes of influenza A and B viruses in silico.

T and B cell epitope analysis for the immunogenicity evaluation and mitigation of antibody-based therapeutics

The surge in the clinical use of therapeutic antibodies has reshaped the landscape of pharmaceutical therapy for many diseases, including rare and challenging conditions. However, the administration of exogenous biologics could potentially trigger unwanted immune responses such as generation of anti-drug antibodies (ADAs). Real-world experiences have illuminated the clear correlation between the ADA occurrence and unsatisfactory therapeutic outcomes as well as immune-related adverse events. By retrospectively examining research involving immunogenicity analysis, we noticed the growing emphasis on elucidating the immunogenic epitope profiles of antibody-based therapeutics aiming for mechanistic understanding the immunogenicity generation and, ideally, mitigating the risks. As such, we have comprehensively summarized here the progress in both experimental and computational methodologies for the characterization of T and B cell epitopes of therapeutics. Furthermore, the successful practice of epitope-driven deimmunization of biotherapeutics is exceptionally highlighted in this article.

In silico epitope prediction of Borrelia burgdorferi sensu lato antigens for the detection of specific antibodies

Despite many years of research, serodiagnosis of Lyme disease still faces many obstacles. Difficulties arise mainly due to the low degree of amino acid sequence conservation of the most immunogenic antigens among B. burgdorferi s.l. genospecies, as well as differences in protein production depending on the environment in which the spirochete is located. Mapping B-cell epitopes located on antigens allows for a better understanding of antibody-pathogen interactions which is essential for the development of new and more effective diagnostic tools. In this study, in silico B-cell epitope mapping was performed to determine the theoretical diagnostic potential of selected B. burgdorferi s.l. proteins (BB0108, BB0126, BB0298, BB0689, BB0323, FliL, PstS, SecD, EF-Tu). Bioinformatics software predicted 35 conserved linear and 31 conformational epitopes with the degree of identity among B. burgdorferi s.l. of at least 85%, which may prove to be useful in the development of a new tool for the diagnosis of Lyme disease.

Bioinformatic Approach of B and T Cell Epitopes of PLD and CP40 Proteins of Corynebacterium pseudotuberculosis ovis Mexican Isolate 2J-L towards a Peptide-Based Vaccine

Mapping B and T cell epitopes constitutes an important action for peptide vaccine design. PLD and CP40 virulence factors of Corynebacterium pseudotuberculosis biovar ovis, a causal agent of Caseous Lymphadenitis, have been evaluated in a murine model as good candidates for vaccine development. Therefore, the goal of this work was to in silico analyze B and T cell epitopes of the PLD and CP40 proteins of a Mexican isolate of Corynebacterium pseudotuberculosis ovis. The Immune Epitope Data Base and Resource website was employed to predict the linear and conformational B-cell, T CD4+, and T CD8+ epitopes of PLD and CP40 proteins of Corynebacterium pseudotuberculosis ovis Mexican strain 2J-L. Fifty B cell epitopes for PLD 2J-L and forty-seven for CP40 2J-L were estimated. In addition, T CD4+ and CD8+ cell epitopes were predicted for PLD 2J-L (MHC I:16 epitopes, MHC II:10 epitopes) and CP40 2J-L (MHC I: 15 epitopes, MHC II: 13 epitopes). This study provides epitopes, paying particular attention to sequences selected by different predictor programs and overlap sequences as B and T cell epitopes. PLD 2J-L and CP40 2J-L protein epitopes may aid in the design of a promising peptide-based vaccine against Caseous Lymphadenitis in Mexico.

Potential and limitations of epitope mapping and molecular targeting in Hymenoptera venom allergy

Hymenoptera venom (HV) allergy can lead to life threatening conditions by specific IgE (sIgE)-mediated anaphylactic reactions. The knowledge about major allergens from venom of different clinically relevant species increased in the last decades, allowing the development of component-resolved diagnostics in which sIgE to single allergens is analysed. Despite these advances, the precise regions of the allergens that bind to IgE are only known for few HV allergens. The detailed characterization of IgE epitopes may provide valuable information to improve immunodiagnostic tests and to develop new therapeutic strategies using allergen-derived peptides or other targeted approaches. Epitope-resolved analysis is challenging, since the identification of conformational epitopes present in many allergens demands complex technologies for molecular analyses. Furthermore, functional analysis of the epitopeś interaction with their respective ligands is needed to distinguish epitopes that can activate the allergic immune response, from those that are recognized by irrelevant antibodies or T cell receptors from non-effector cells. In this review, we focus on the use of mapping and molecular targeting approaches for characterization of the epitopes of the major venom allergens of clinically relevant Hymenoptera species. The screening of the most relevant allergen peptides by epitope mapping could be helpful for the development of molecules that target major and immunodominant epitopes blocking the allergen induced cellular reactions as novel approach for the treatment of HV allergy.

Epitope Mapping of BmpA and BBK32 Borrelia burgdorferi Sensu Stricto Antigens for the Design of Chimeric Proteins with Potential Diagnostic Value

Lyme disease is a tick-borne zoonosis caused by Gram-negative bacteria belonging to the Borrelia burgdorferi sensu lato (s.l.) group. In this study, IgM- and IgG-specific linear epitopes of two B. burgdorferi sensu stricto (s.s.) antigens BmpA and BBK32 were mapped using a polypeptide array. Subsequently, two chimeric proteins BmpA-BBK32-M and BmpA-BBK32-G were designed to validate the construction of chimeras using the identified epitopes for the detection of IgM and IgG, respectively, by ELISA. IgG-ELISA based on the BmpA-BBK32-G antigen showed 71% sensitivity and 95% specificity, whereas a slightly lower diagnostic utility was obtained for IgM-ELISA based on BmpA-BBK32-M, where the sensitivity was also 71% but the specificity decreased to 89%. The reactivity of chimeric proteins with nondedicated antibodies was much lower. These results suggest that the identified epitopes may be useful in the design of new forms of antigens to increase the effectiveness of Lyme disease serodiagnosis. It has also been proven that appropriate selection of epitopes enables the construction of chimeric proteins exhibiting reactivity with a specific antibody isotype.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Hiding in plain sight: an epitope-based strategy for a subunit malaria vaccine

Recent data suggest that approaches to developing a subunit blood-stage malaria vaccine may be misdirected. While antigenic polymorphism is recognized as a challenge, efforts to counter this have primarily involved enhancing the quantity and quality of antibody with potent adjuvants, identifying conserved target proteins, or combining multiple antigens to broaden the immune response. However, paradoxically, evidence has emerged that narrowing, rather than broadening, the immune response may be required to obtain an immune response protective against multiple Plasmodium strains. Non-immunodominant, conserved epitopes are crucial. The evidence comes from studying the immune response to red cell surface-expressed antigens but should also be applicable to merozoite surface antigens. Strategies to define the targets of these highly focused immune responses are provided.

Antibody Cross-Reactivity in Serodiagnosis of Lyme Disease

Lyme disease is a tick-borne disease caused by spirochetes belonging to the Borrelia burgdorferi sensu lato complex. The disease is characterized by a varied course; therefore, the basis for diagnosis is laboratory methods. Currently, a two-tiered serological test is recommended, using an ELISA as a screening test and a Western blot as a confirmatory test. This approach was introduced due to the relatively high number of false-positive results obtained when using an ELISA alone. However, even this approach has not entirely solved the problem of false-positive results caused by cross-reactive antibodies. Many highly immunogenic B. burgdorferi s.l. proteins are recognized nonspecifically by antibodies directed against other pathogens. This also applies to antigens, such as OspC, BmpA, VlsE, and FlaB, i.e., those commonly used in serodiagnostic assays. Cross-reactions can be caused by both bacterial (relapsing fever Borrelia, Treponema pallidum) and viral (Epstein-Baar virus, Cytomegalovirus) infections. Additionally, a rheumatoid factor has also been shown to nonspecifically recognize B. burgdorferi s.l. proteins, resulting in false-positive results. Therefore, it is necessary to carefully interpret the results of serodiagnostic tests so as to avoid overdiagnosis of Lyme disease, which causes unnecessary implementations of strong antibiotic therapies and delays in the correct diagnosis.

Validation of an antigenic site targeted by monoclonal antibodies against Puumala virus

Identification of B-cell epitopes facilitates the development of vaccines, therapeutic antibodies and diagnostic tools. Previously, the binding site of the bank vole monoclonal antibody (mAb) 4G2 against Puumala virus (PUUV, an orthohantavirus in the Hantaviridae family of the Bunyavirales order) was predicted using a combination of methods, including pepscan, phage-display, and site-directed mutagenesis of vesicular stomatitis virus (VSV) particles pseudotyped with Gn and Gc glycoproteins from PUUV. These techniques led to the identification of the neutralization escape mutation F915A. To our surprise, a recent crystal structure of PUUV Gc in complex with Fab 4G2 revealed that residue F915 is distal from epitope of mAb 4G2. To clarify this issue and explore potential explanations for the inconsistency, we designed a mutagenesis experiment to probe the 4G2 epitope, with three PUUV pseudoviruses carrying amino acid changes E725A, S944F, and S946F, located within the structure-based 4G2 epitope on the Gc. These amino acid changes were able to convey neutralization escape from 4G2, and S944F and S946F also conveyed escape from neutralization by human mAb 1C9. Furthermore, our mapping of all the known neutralization evasion sites from hantaviral Gcs onto PUUV Gc revealed that over 60 % of these sites reside within or close to the epitope of mAb 4G2, indicating that this region may represent a crucial area targeted by neutralizing antibodies against PUUV, and to a lesser extent, other hantaviruses. The identification of this site of vulnerability could guide the creation of subunit vaccines against PUUV and other hantaviruses in the future.

Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach

The merger of COVID-19 and seasonal influenza infections is considered a potentially serious threat to public health. These two viral agents can cause extensive and severe lower and upper respiratory tract infections with lung damage with host factors. Today, the development of vaccination has been shown to reduce the risk of hospitalization and mortality from the COVID-19 virus and influenza epidemics. Therefore, this study contributes to an immunoinformatics approach to producing a vaccine that can elicit strong and specific immune responses against COVID-19 and influenza A and B viruses. The NCBI, GISAID, and Uniprot databases were used to retrieve sequences. Linear B cell, Cytotoxic T lymphocyte, and Helper T lymphocyte epitopes were predicted using the online servers. Population coverage of MHC I epitopes worldwide for SARS-CoV-2, Influenza virus H3N2, H3N2, and Yamagata/Victoria were 99.93%, 68.67%, 68.38%, and 85.45%, respectively. Candidate epitopes were linked by GGGGS, GPGPG, and KK linkers. Different epitopes were permutated several times to form different peptides and then screened for antigenicity, allergenicity, and toxicity. The vaccine construct was analyzed for physicochemical properties, conformational B-cell epitopes, interaction with Toll-like receptors, and IFN-gamma-induced. Immune stimulation response of final construct was evaluated using C-IMMSIM. Eventually, the final construct sequence was codon-optimized for Escherichia coli K12 and Homo sapiens to design a multi-epitope vaccine and mRNA vaccine. In conclusion, due to the variable nature of SARS-CoV-2 and influenza proteins, the design of a multi-epitope vaccine can protect against all their standard variants, but laboratory validation is required.Communicated by Ramaswamy H. Sarma.

An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies

Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and inaccessibility of datasets for model training. In this study, we curated a dataset of >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM captured key sequence motifs of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of antibody response to influenza virus, but also provides an invaluable resource for applying deep learning to antibody research.

Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries

Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.

Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.

Identification of Potential Novel B-Cell Epitopes of Capsid Protein VP2 in Senecavirus A

Senecavirus A (SVA) is a type of nonenveloped single-stranded, positive-sense RNA virus. The VP2 protein is a structural protein that plays an important role in inducing early and late immune responses of the host. However, its antigenic epitopes have not been fully elucidated. Therefore, defining the B epitopes of the VP2 protein is of great importance to revealing its antigenic characterization. In this study, we analyzed B-cell immunodominant epitopes (IDEs) of the VP2 protein from the SVA strain CH/FJ/2017 using the Pepscan approach and a bioinformatics-based computational prediction method. The following four novel IDEs of VP2 were identified: IDE1, 41TKSDPPSSSTDQPTTT56; IDE2, 145PDGKAKSLQELNEEQW160; IDE3, 161VEMSDDYRTGKNMPF175; and IDE4, 267PYFNGLRNRFTTGT280. Most of the IDEs were highly conserved among the different strains. To our knowledge, the VP2 protein is a major protective antigen of SVA that can induce neutralizing antibodies in animals. Here, we analyzed the immunogenicity and neutralization activity of four IDEs of VP2. Consequently, all four IDEs showed good immunogenicity that could elicit specific antibodies in guinea pigs. A neutralization test in vitro showed that the peptide-specific guinea pig antisera of IDE2 could neutralize SVA strain CH/FJ/2017, and IDE2 was identified as a novel potential neutralizing linear epitope. This is the first time VP2 IDEs have been identified by using the Pepscan method and a bioinformatics-based computational prediction method. These results will help elucidate the antigenic epitopes of VP2 and clarify the basis for immune responses against SVA. IMPORTANCE The clinical symptoms and lesions caused by SVA are indistinguishable from those of other vesicular diseases in pigs. SVA has been associated with recent outbreaks of vesicular disease and epidemic transient neonatal losses in several swine-producing countries. Due to the continuing spread of SVA and the lack of commercial vaccines, the development of improved control strategies is urgently needed. The VP2 protein is a crucial antigen on the capsids of SVA particles. Furthermore, the latest research showed that VP2 could be a promising candidate for the development of novel vaccines and diagnostic tools. Hence, a detailed exploration of epitopes in the VP2 protein is necessary. In this study, four novel B-cell IDEs were identified using two different antisera with two different methods. IDE2 was identified as a new neutralizing linear epitope. Our findings will help in the rational design of epitope vaccines and further understanding of the antigenic structure of VP2.

Dynamic structural analysis-based epitope prediction of Exendin-4 in aqueous solution

The study of epitopes has a broad range of applications in drug discovery, vaccine design, and immunotherapy. In this study, an epitope prediction method was developed based on the dynamic structure of protein antigens. Solvent accessible surface area, charge, and root mean square fluctuation were introduced as the key residue property parameters. The epitope prediction algorithm was established by constructing a three-parameter complex metrics of seven-peptide groups. The method was applied to predict the epitopes of Exendin-4, an effective antidiabetic drug. The epitopes of both the natural and C-terminal amidated forms of Exendin-4 were predicted and compared in their folded and intermediate states. In the folded state, the epitopes of natural Exendin-4 (His1-Phe6 and Asp9-Val19) were found to be nearly identical to the epitopes of C-terminal aminated Exendin-4 (His1-Thr7 and Asp9-Val19). In the intermediate state, however, the epitopes of natural Exendin-4 (His1-Gly4, Phe6 and Lys12-Arg20) covered fewer amino acids than the epitopes of C-terminal aminated Exendin-4 (His1-Gly4, Phe6, Asp9-Val19 and Trp25-Lys27). The comparison with the results from other prediction tools demonstrates the reliability of our predicted epitopes of Exendin-4.

Bioinformatics-based prediction and screening of immunogenic epitopes of Toxoplasma gondii rhoptry proteins 7, 21 and 22 as candidate vaccine target

Introduction

Toxoplasmosis is a well-known zoonotic disease caused by Toxoplasma gondii. The main causes of the disease range from eating undercooked or contaminated meat and shellfish to cleaning litter trays into which cats that excreted toxoplasma via faeces. This pathogen can live for a very long time, possibly a lifetime, within the bodies of humans and other animals.

Aims and objectives

This study aimed to predict and analyse candidate immunogenic epitopes for vaccine development by evaluating the physio-chemical properties, multiple sequence alignment, secondary and tertiary structures, phosphorylation sites, transmembrane domains, and signal peptides, of T. gondii rhoptry proteins ROP7, ROP21, and ROP22 using bioinformatics tools.

Methods

To find immunogenic epitopes of rhoptry proteins, numerous bioinformatics web servers were used containing multiple sequence alignment, physiochemical properties, antigenicity and allergenicity, post-translational modification sites (PTMs), signal peptides, transmembrane domains, secondary and tertiary structures, and screening of predicted epitopes. We evaluated immunogenic linear B-cell epitopes as candidate proteins for vaccine development.

Results

Nine epitopes were identified for each protein, and analysis of immunogenicity, revealed three candidate epitopes for ROP7, one for ROP21, and four for ROP22. Among all candidate epitopes, ROP22 contained the most immunogenic epitopes with immunogenicity score of 0.50575.

Conclusion

We acquired detailed information on predicted immunogenic epitopes using in-silico methods. The results provide a foundation for further experimental analysis of toxoplasmosis, and potential vaccine development.

The ClusPro AbEMap web server for the prediction of antibody epitopes

Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.

Screening epitope peptides based on a phage-displayed random peptide and peptide microarrays to contribute to improving the diagnostic efficiency of systemic lupus erythematosus

BACKGROUND

Systemic lupus erythematosus (SLE) is one of the most common autoimmune diseases in China. At present, there are hundreds of autoantibodies in SLE patients; however, only a dozen of the autoantibodies can be routinely detected, and the available diagnostic antibodies are not sufficient for diagnosis or differential diagnosis of SLE patients with atypical clinical manifestations or other autoimmune diseases. Therefore, it is necessary to find new diagnostic markers to improve the diagnostic effect of SLE.

METHODS

The displayed random peptide library and peptide microarray were combined to identify SLE-related epitope peptides. A case-control design was used. The IgG antibodies in the sera from SLE patients, healthy controls, and other autoimmune disease controls underwent a reaction with the phage-display random peptide library, respectively. Selected epitope peptides were used to construct a peptide chip. A total of 644 serum samples (including 296 SLE patients, 168 disease controls, and 180 healthy controls) were used for further screening and verification. Peptides with an area under the curve (AUC) > 0.650 were further verified by ELISA. Finally, 500 serum samples (including 200 SLE patients, 150 disease controls, and 150 healthy controls) were used to verify and evaluate the diagnostic and differential diagnostic efficacy of the selected peptides.

RESULTS

After the previous screening, five epitope peptides (SLE_P19, SLE_P20, SLE_P27, SLE_P28, and SLE_P29) may have potential as SLE diagnostic markers. Additionally, SLE_P27 was superior to the other four peptides in the diagnosis and differential diagnosis of SLE and rheumatoid arthritis (RA). The AUC of SLE_P27 was 0.938, the sensitivity was 76.00%, the specificity was 92.70%, the positive likelihood ratio was 10.411, the negative likelihood ratio was 0.259, and the accuracy was 84.40%. The diagnostic efficacy of SLE can be increased by combining the five selected peptides with the anti-double stranded DNA antibody (anti-dsDNA)and anti-Smith antibodies (anti-Sm).

CONCLUSIONS

In this study, we identified five peptides that may serve as potential biomarkers for SLE diagnosis using the strategy of combining the displayed random peptide library with the peptide microarray. The combination of selected peptides and existing autoantibodies can significantly improve the diagnostic efficiency. These specific peptides are expected to be new diagnostic markers for SLE.

Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs

Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.

Production of monoclonal antibody of heat-labile toxin A subunit to identify enterotoxigenic Escherichia coli by epitope mapping using synthetic peptides

Background

Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea through two enterotoxins, a heat-labile toxin and a heat-stable toxin. These toxins alter the cellular signaling pathways, ultimately triggering an increase in chloride secretion and watery diarrhea.

Objective

For the development of an ETEC vaccine, we attempted to construct a peptide-specific monoclonal antibody library against heat-labile enterotoxin A subunit (LT-A) by epitope mapping using synthetic peptides.

Methods

Sera produced by five mice immunized with recombinant LT-A protein were examined for specific recognition with synthetic 15-mer and 34-mer peptides of LT-A proteins using enzyme-linked immunosorbent assay. The analysis revealed that the synthetic peptides number 8, 16, 24, 33, 36, 38, and 39 reacted with an anti-LT-A polyclonal antibody. For the possible prediction of LT-A epitopes, each full-length protein sequence was subjected to BCPreds analysis and three-dimensional protein structure analysis. The data showed that three peptides (synthetic peptide numbers: 33, 36, and 38-39) have identical antigenic specificities with LT-A protein, suggesting the usefulness of these linear peptide epitopes.

Results

Based on these peptides, we produced monoclonal antibodies to improve the specificity of LT-A detection. Monoclonal antibodies produced from two peptides (numbers 33 and 36) showed affinity for an LT-A recombinant antigen. Moreover, peptide epitope prediction analysis showed that the sites of the three peptides were identical to those exhibiting actual antigenicity. Also, it was confirmed that the amino acid sequence that actually showed antigenicity was included in the peptide predicted only by ETEC-LT-A-33. Also, the specificity of the antibody for ETEC-LT-A-33 was validated using bacterial cells, and the neutralizing effect of the antibody was determined by assessing cytokine release in infected HCT-8 cells.

Conclusion

The monoclonal antibodies produced in this study are useful toolsfor vaccine production against ETEC and can be used to identify peptide antigencandidates.

Identification of a linear B-cell epitope on the African swine fever virus CD2v protein

African swine fever virus (ASFV) poses a serious threat to domestic pigs and wild boars, which is responsible for substantial production and economic losses. A dominant ASFV specific linear B cell epitope that reacted with the convalescent serum was explored and identified with the help of immune informatics techniques. It is essential in understanding the host immunity and in developing diagnostic technical guidelines and vaccine design. The confirmation of dominant epitopes with a positive serological matrix is feasible. To improve the immunogenicity of the epitope, we designed the dominant epitope of CD2v in the form of 2 branch Multiple-Antigen peptide (MAPs-2), CD2v-MAPs-2. Notably, CD2v peptide can be taken up by dendritic cells (DCs) to activate T lymphocytes and induce highly effective valence antibodies in BALB/c mice. The specific CD8+ T cell response were observed. The dominant epitope peptide identified in this study was able to effectively activate humoral and cellular immunity in mice model.

Three-dimensional models of antigens with serodiagnostic potential for leprosy: An in silico study

BACKGROUND

Leprosy is a disease caused by Mycobacterium leprae (M. leprae), an intracellular pathogen that has tropism and affects skin and nervous system cells. The disease has two forms of presentation: Paucibacillary and multibacillary, with different clinical and immunological manifestations. Unlike what occurs in the multibacillary form , the diagnostic tests for the paucibacillary form are nonspecific and not very sensitive, allowing the existence of infected individuals without treatment, which contributes to the spread of the pathogen in the population. To mitigate this contamination, more sensitive diagnostic tests capable of detecting paucibacillary patients are needed.

AIM

To predict the three-dimensional structure models of M. leprae antigens with serodiagnostic potential for leprosy.

METHODS

In this in silico study, satisfactory templates were selected in the Protein Data Bank (PDB) using Basic Local Alignment Search Tool to predict the structural templates of ML2038, ML0286, ML0050, and 85B antigens by comparative modeling. The templates were selected according to general criteria such as sequence identity, coverage, X-ray resolution, Global Model Quality Estimate value and phylogenetic relationship; Clustal X 2.1 software was used in this analysis. Molecular modeling was completed using the software Modeller 9v13. Visualization of the models was made using ViewerLite 4.2 and PyMol software, and analysis of the quality of the predicted models was performed using the QMEAN score and Z-score. Finally, the three-dimensional moels were validated using the MolProbity and Verify 3D platforms.

RESULTS

The three-dimensional structure models of ML2038, ML0286, ML0050, and 85B antigens of M. leprae were predicted using the templates PDB: 3UOI (90.51% identity), PDB: 3EKL (87.46% identity), PDB: 3FAV (40.00% identity), and PDB: 1F0N (85.21% identity), respectively. The QMEAN and Z-score values indicated the good quality of the structure models. These data refer to the monomeric units of antigens, since some of these antigens have quaternary structure. The validation of the models was performed with the final three-dimensional structure - monomer (ML0050 and 85B antigens) and quaternary structures (ML2038 and ML0286). The majority of amino acid residues were observed in favorable and allowed regions in the Ramachandran plot, indicating correct positioning of the side chain and absence of steric impediment. The MolProbity score value and Verify 3D results of all models indicated a satisfactory prediction.

CONCLUSION

The polarized immune response against M. leprae creates a problem in leprosy detection. The selection of immunodominant epitopes is essential for the development of more sensitive serodiagnostic tests, for this it is important to know the three-dimensional structure of the antigens, which can be predicted with bioinformatics tools.

Seafood allergy: Allergen, epitope mapping and immunotherapy strategy

Seafoods are fashionable delicacies with high nutritional values and culinary properties, while seafood belongs to worldwide common food allergens. In recent years, many seafood allergens have been identified, while the diversity of various seafood species give a great challenge in identifying and characterizing seafood allergens, mapping IgE-binding epitopes and allergen immunotherapy development, which are critical for allergy diagnostics and immunotherapy treatments. This paper reviewed the recent progress on seafood (fish, crustacean, and mollusk) allergens, IgE-binding epitopes and allergen immunotherapy for seafood allergy. In recent years, many newly identified seafood allergens were reported, this work concluded the current situation of seafood allergen identification and designation by the World Health Organization (WHO)/International Union of Immunological Societies (IUIS) Allergen Nomenclature Sub-Committee. Moreover, this review represented the recent advances in identifying the IgE-binding epitopes of seafood allergens, which were helpful to the diagnosis, prevention and treatment for seafood allergy. Furthermore, the allergen immunotherapy could alleviate seafood allergy and provide promising approaches for seafood allergy treatment. This review represents the recent advances and future outlook on seafood allergen identification, IgE-binding epitope mapping and allergen immunotherapy strategies for seafood allergy prevention and treatment.

Design of multi-epitope based vaccine against Mycobacterium tuberculosis: a subtractive proteomics and reverse vaccinology based immunoinformatics approach

Tuberculosis is an airborne transmissible disease caused by Mycobacterium tuberculosis that infects millions of lives worldwide. There is still no single comprehensive therapy or preventative available for the lethal illness. Currently, the available vaccine, BCG is ineffectual in preventing the prophylactic adult pulmonary TB and reactivation of latent tuberculosis. Therefore, this investigation was intended to design a new multi-epitope vaccine that can address the existing problems. The subtractive proteomics approach was implemented to prioritize essential, virulence, druggable, and antigenic proteins as suitable vaccine candidates. Furthermore, a reverse vaccinology-based immunoinformatics technique was employed to identify potential B-cell, helper T lymphocytes (HTL), and cytotoxic T lymphocytes (CTL) epitopes from the target proteins. Immune-stimulating adjuvant, linkers, and PADRE (Pan HLA-DR epitopes) amino acid sequences along with the selected epitopes were used to construct a chimeric multi-epitope vaccine. The molecular docking and normal mode analysis (NMA) were carried out to evaluate the binding mode of the designed vaccine with different immunogenic receptors (MHC-I, MHC-II, and Tlr4). In addition, the MD simulation, followed by essential dynamics study and MMPBSA analysis, was carried out to understand the dynamics and stability of the complexes. In-silico cloning was accomplished using E.coli as an expression system to express the designed vaccine successfully. Finally, the immune simulation study has foreseen that our designed vaccine could induce a significant immune response by elevation of different immunoglobulins in the host. However, there is an imperative need for the experimental validation of the designed vaccine in animal models to confer effectiveness and safety.HIGHLIGHTSMulti-epitope based vaccine was designed against Mycobacterium tuberculosis using subtractive proteomics and Immunoinformatics approach.The vaccine was found to be antigenic, non-allergenic, immunogenic, and stable based on in-silico prediction.Population coverage analysis of the proposed vaccine predicts an effective response in the world population.The molecular docking, MD simulation, and MM-PBSA study confirm the stable interaction of the vaccine with immunogenic receptors.In silico cloning and immune simulation of the vaccine demonstrated its successful expression in E.coli and induction of immune response in the host. Communicated by Ramaswamy H. Sarma.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Mapping of antibody epitopes based on docking and homology modeling

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

Evaluation of a mimotope of the Rickettsia outer membrane protein A (OmpA) as an antigen in enzyme-linked immunosorbent assay to detect rickettsiosis in capybaras (Hydrochoerus hydrochaeris), horses (Equus caballus), and opossums (Didelphis sp.)

Rickettsia rickettsii is the etiological agent of Rocky Mountain spotted fever, which is an important tick-borne zoonosis and, in Brazil, it causes Brazilian spotted fever, which has high lethality rate. This study aimed to evaluate a synthetic peptide corresponding to a segment of the outer membrane protein A (OmpA) as an antigen in a serological test for the diagnosis of rickettsial infections. The amino acid sequence of the peptide was selected by predicting B cell epitopes using B Cell Epitope Prediction (Immune Epitope Database and Analysis Resource) and Epitopia and OmpA sequences of Rickettsia rickettsii strain 'Brazil' and Rickettsia parkeri strains 'Maculatum 20' and 'Portsmouth'. A peptide with amino acid sequence common to both Rickettsia species was synthesized and arbitrarily named OmpA-pLMC. To evaluate this peptide in enzyme-linked immunosorbent assay (ELISA), serum samples of capybara (Hydrochoerus hydrochaeris), horse (Equus caballus), and opossum (Didelphis albiventris) that had been previously tested by indirect immunofluorescence assay (IFA) for rickettsial infection were separated into IFA-positive and IFA-negative groups and used in the assay. There were no significant differences in ELISA optical density (OD) values between IFA-positive and IFA-negative groups with horse samples. The mean OD values were significantly higher in the IFA-positive capybara serum samples (IFA-pos vs. IFA-neg = 2.389 ± 0.761 vs. 1.760 ± 0.840). However, receiver operating characteristic (ROC) curve analysis did not show significant diagnostic parameters. On the other hand, 12 out of 14 (85.7%) opossum samples of the IFA-positive group showed reactivity in ELISA, and this was significantly higher than of the IFA-negative group (0.7196 ± 0.440 vs. 0.2318 ± 0.098, respectively; 85.7% sensitivity, 100% specificity). Therefore, our results show that OmpA-pLMC has a potential to be used in immunodiagnostic assays to detect spotted fever group rickettsial infections.

Novel insights into the somatic proteome of Strongyloides stercoralis infective third-stage larvae

BACKGROUND

Strongyloidiasis is a neglected tropical disease affecting an estimated 600 million people, particularly in resource-limited settings. The infection can persist lifelong due to unusual auto-infective cycle of Strongyloides stercoralis. The lack of a diagnostic gold standard and limited knowledge of the mechanisms underpinning this chronic infection are key issues in disease management. To date, only a few proteomics studies have been conducted to elucidate the molecular mechanisms associated with Strongyloides parasitism or to highlight novel immunological markers, with the result that our knowledge of S. stercoralis proteome remains limited. This study aims at expanding the characterization of S. stercoralis infective larvae (iL3) in order to further explore the mechanisms of parasitism and to highlight possible novel targets for serodiagnosis.

METHODS

iL3 obtained from an infected subject were analysed by high-throughput tandem mass spectrometry. To achieve a more comprehensive characterization of the iL3 proteome we analysed the experimental dataset using an automatic search strategy combined with manual annotation, which included gene ontology (GO) analysis, InterPro annotation, assessment of the homology with Homo sapiens and other pathogens of clinical importance and B-cell epitope prediction.

RESULTS

Our pipeline identified 430 S. stercoralis proteins, 187 (43%) of which were uncharacterized. Oxidoreductases and peptidases were amongst the most represented protein categories, as highlighted by molecular function GO analyses, while membrane and mitochondrial proteins were the most represented cellular component GO categories. A high proportion of proteins bearing the CAP, SCP or thioredoxin domain or belonging to cysteine-rich secretory, transthyretin-like or peptidase protein families were also identified. Additionally, we highlighted nine proteins displaying low homology with H. sapiens or other related pathogens and bearing amino acid sequences with immunogenic properties.

CONCLUSIONS

Our comprehensive description and annotation of the S. stercoralis iL3 proteome contribute to expanding the 'omics characterization of this parasite and provide experimental evidence on the most represented proteins associated with S. stercoralis parasitism, as inferred from genomic and transcriptomic data. Moreover, novel candidate immunogenic proteins to be evaluated as novel serological diagnostic markers are highlighted.

First Impressions Matter: Immune Imprinting and Antibody Cross-Reactivity in Influenza and SARS-CoV-2

Many rigorous studies have shown that early childhood infections leave a lasting imprint on the immune system. The understanding of this phenomenon has expanded significantly since 1960, when Dr. Thomas Francis Jr first coined the term "original antigenic sin", to account for all previous pathogen exposures, rather than only the first. Now more commonly referred to as "immune imprinting", this effect most often focuses on how memory B-cell responses are shaped by prior antigen exposure, and the resultant antibodies produced after subsequent exposure to antigenically similar pathogens. Although imprinting was originally observed within the context of influenza viral infection, it has since been applied to the pandemic coronavirus SARS-CoV-2. To fully comprehend how imprinting affects the evolution of antibody responses, it is necessary to compare responses elicited by pathogenic strains that are both antigenically similar and dissimilar to strains encountered previously. To accomplish this, we must be able to measure the antigenic distance between strains, which can be easily accomplished using data from multidimensional immunological assays. The knowledge of imprinting, combined with antigenic distance measures, may allow for improvements in vaccine design and development for both influenza and SARS-CoV-2 viruses.

LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings

Accurately detecting linear B-cell epitopes (BCEs) makes great sense in vaccine design, immunodiagnostic test, antibody production, disease prevention and treatment. Wet-lab experiments for determining linear BCEs are both expensive and laborious, which are not able to meet the recognition needs of modern massive protein sequence data. Instead, computational methods can efficiently identify linear BCEs with low cost. Although several computational methods are available, the performance is still not satisfactory. Thus, we propose a new method, LBCE-XGB, to forecast linear BCEs based on XGBoost algorithm. To represent the biological information concealed in peptide sequences, the embeddings of the residues were obtained from a pre-trained domain-specific BERT model. In addition, the other five types of attributes comprising amino acid composition, amino acid antigenicity scale were also extracted. The best feature combination was determined according to the cross-validation results. Against the models developed by other deep learning and machine learning algorithms, LBCE-XGB achieves the top performance with an AUROC of 0.845 for fivefold cross-validation. The results on the independent test set show that our model attains an AUROC of 0.838 which is substantially higher than other state-of-the-art methods. The outcomes indicate that the representations of BERT could be an effective feature in predicting linear BCEs and we believe that LBCE-XGB could be a useful medium for detecting linear B cell epitopes with high accuracy and low cost.

Design of a multi-epitope-based vaccine consisted of immunodominant epitopes of structural proteins of SARS-CoV-2 using immunoinformatics approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown rapid global spread and has resulted in a significant death toll worldwide. In this study, we aimed to design a multi-epitope vaccine against SARS-CoV-2 based on structural proteins S, M, N, and E. We identified B- and T-cell epitopes and then the antigenicity, toxicity, allergenicity, and similarity of predicted epitopes were analyzed. T-cell epitopes were docked with corresponding HLA alleles. Consequently, the selected T- and B-cell epitopes were included in the final construct. All selected epitopes were connected with different linkers and flagellin and pan-HLA DR binding epitopes (PADRE) as an adjuvant were used in the vaccine construct. Furthermore, molecular docking was used to evaluate the complex between the final vaccine construct and two alleles, HLA-A*02:01 and HLA-DRB1*01:01. Finally, codons were optimized for in silico cloning into pET28a(+) vector using SnapGene. The final vaccine construct comprised 11 CTL, HTL, and B-cell epitopes corresponding to 394 amino acid residues. In silico evaluation showed that the designed vaccine might potentially promote an immune response. Further in vivo preclinical and clinical testing is required to determine the safety and efficacy of the designed vaccine.

Recent advances in immunoassay technologies for the detection of human coronavirus infections

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the seventh coronavirus (CoV) that has spread in humans and has become a global pandemic since late 2019. Efficient and accurate laboratory diagnostic methods are one of the crucial means to control the development of the current pandemic and to prevent potential future outbreaks. Although real-time reverse transcription-polymerase chain reaction (rRT-PCR) is the preferred laboratory method recommended by the World Health Organization (WHO) for diagnosing and screening SARS-CoV-2 infection, the versatile immunoassays still play an important role for pandemic control. They can be used not only as supplemental tools to identify cases missed by rRT-PCR, but also for first-line screening tests in areas with limited medical resources. Moreover, they are also indispensable tools for retrospective epidemiological surveys and the evaluation of the effectiveness of vaccination. In this review, we summarize the mainstream immunoassay methods for human coronaviruses (HCoVs) and address their benefits, limitations, and applications. Then, technical strategies based on bioinformatics and advanced biosensors were proposed to improve the performance of these methods. Finally, future suggestions and possibilities that can lead to higher sensitivity and specificity are provided for further research.