B-cell epitope mapping for the design of vaccines and effective diagnostics

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.

Immunoinformatic approach for multi-epitope vaccine design against Staphylococcus aureus based on hemolysin proteins

Staphylococcus aureus is a common bacterium that causes a variety of infections in humans. This microorganism produces several virulence factors, including hemolysins, which contribute to its disease-causing ability. The treatment of S. aureus infections typically involves the use of antibiotics. However, the emergence of antibiotic-resistant strains has become a major concern. Therefore, vaccination against S. aureus has gained attention as an alternative approach. Vaccination has the advantage of stimulating the immune system to produce specific antibodies that can neutralize bacteria and prevent infection. However, developing an effective vaccine against S. aureus has proven to be challenging. This study aimed to use in silico methods to design a multi-epitope vaccine against S. aureus infection based on hemolysin proteins. The designed vaccine contained four B-cell epitopes, four CTL epitopes, and four HTL epitopes, as well as the ribosomal protein L7/L12 and pan-HLA DR-binding epitope, included as adjuvants. Furthermore, the vaccine was non-allergenic and non-toxic with the potential to stimulate the TLR2-, TLR-4, and TLR-6 receptors. The predicted vaccine exhibited a high degree of antigenicity and stability, suggesting potential for further development as a viable vaccine candidate. The population coverage of the vaccine was 94.4 %, indicating potential widespread protection against S. aureus. Overall, these findings provide valuable insights into the design of an effective multi-epitope vaccine against S. aureus infection and pave the way for future experimental validations.

Identification and characterization of linear B-cell epitopes on African swine fever virus H171R protein

African swine fever virus (ASFV) is a highly contagious and often fatal pathogen that poses a significant threat to the swine industry worldwide. The H171R protein, a structural component of ASFV, plays crucial roles in viral assembly, host cell entry, and modulation of the host immune response. This study aimed to comprehensively characterize the linear B-cell epitopes on the H171R protein to facilitate the development of diagnostic tools and subunit vaccines against ASFV. A combined approach involving bioinformatics analysis and experimental techniques was employed. The recombinant H171R protein was expressed and purified, and specific monoclonal antibodies were generated through immunization and hybridoma technology. Systematic epitope mapping using overlapping peptide fragments and alanine-scanning mutagenesis revealed four minimal linear epitopes: 84HPLLPYQQSSDEQP97, 93SDEQPMMPYQQPPG106, 111PYEQIYHKKHASQQ124, and 129LNDYYQHILALGDED143. The identified epitopes exhibited strong immunogenicity, as demonstrated by their reactivity with ASFV-positive swine sera. Critical amino acid residues within each epitope were identified through mutational analysis. Structural modeling and visualization of the H171R protein provided insights into the spatial distribution and accessibility of the epitope regions. These findings contribute to a better understanding of the H171R protein's antigenic properties and lay the foundation for developing effective diagnostic assays and subunit vaccines against African swine fever.

IMPORTANCE

African swine fever virus (ASFV) poses a severe threat to the global swine industry. This study characterizes linear B-cell epitopes on the crucial ASFV H171R protein, facilitating the development of improved diagnostics and subunit vaccines. Four immunogenic epitopes were identified, offering valuable information for designing sensitive diagnostic assays and potential subunit vaccine candidates. By advancing the understanding of H171R's antigenic landscape, this research contributes to controlling ASFV's devastating impacts, safeguarding the swine industry, and ensuring food security.

Targeting Polyprotein to Design Potential Multiepitope Vaccine against Omsk Hemorrhagic Fever Virus (OHFV) by Evaluating Allergenicity, Antigenicity, and Toxicity Using Immunoinformatic Approaches

Omsk Hemorrhagic Fever Virus (OHFV) is an RNA virus with a single-stranded, positive-sense genome. It is classified under the Flaviviridae family. The genome of this virus is 98% similar to the Alkhurma hemorrhagic fever virus (AHFV), which belongs to the same family. Cases of the virus have been reported in various regions of Saudi Arabia. Both OHFV and AHFV have similarities in pathogenic polyprotein targets. No effective and licensed vaccines are available to manage OHFV infections. Therefore, an effective and safe vaccine is required that can activate protective immunity against OHFV. The current study aimed to design a multiepitope subunit vaccine against the OHFV utilizing several immunoinformatic tools. The polyprotein of OHFV was selected and potent antigenic, non-allergenic, and nontoxic cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes were chosen. After screening, eight (8) CTL, five (5) HTL, and six (6) B cell epitopes were joined with each other using different linkers. Adjuvant human beta defensin-2 was also linked to the epitopes to increase vaccine antigenic and immunogenic efficiency. The designed vaccine was docked with Toll-like receptor 4 (TLR4) as it activates and induces primary and secondary immune responses against OHFV. Codon optimization was carried out, which resulted in a CAI value of 0.99 and 53.4% GC contents. In addition, the construct was blindly docked to the TLR4 immune receptor and subjected to conformational dynamics simulation analysis to interpret the intricate affinity and comprehend the time-dependent behavior. Moreover, it was predicted that immune responses to the developed vaccine construct reported formation of strong humoral and cellular immune cells. Therefore, the proposed vaccine may be considered in experimental assays to combat OHFV infections. Laboratory experiments for the above predictions are essential in order to evaluate the effectiveness, safety, and protective properties of the subject in question.

Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping

Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Immunoinformatic based designing of potential immunogenic novel mRNA and peptide-based prophylactic vaccines against H5N1 and H7N9 avian influenza viruses

Influenza viruses are the most common cause of serious respiratory illnesses worldwide and are responsible for a significant number of annual fatalities. Therefore, it is crucial to look for new immunogenic sites that might trigger an effective immune response. In the present study, bioinformatics tools were used to design mRNA and multiepitope-based vaccines against H5N1 and H7N9 subtypes of avian influenza viruses. Several Immunoinformatic tools were employed to extrapolate T and B lymphocyte epitopes of HA and NA proteins of both subtypes. The molecular docking approach was used to dock the selected HTL and CTL epitopes with the corresponding MHC molecules. Eight (8) CTL, four (4) HTL, and Six (6) linear B cell epitopes were chosen for the structural arrangement of mRNA and of peptide-based prophylactic vaccine designs. Different physicochemical characteristics of the selected epitopes fitted with suitable linkers were analyzed. High antigenic, non-toxic, and non-allergenic features of the designed vaccines were noted at a neutral physiological pH. Codon optimization tool was used to check the GC content and CAI value of constructed MEVC-Flu vaccine, which were recorded to be 50.42% and 0.97 respectively. the GC content and CAI value verify the stable expression of vaccine in pET28a + vector. In-silico immunological simulation the MEVC-Flu vaccine construct revealed a high level of immune responses. The molecular dynamics simulation and docking results confirmed the stable interaction of TLR-8 and MEVC-Flu vaccine. Based on these parameters, vaccine constructs can be regarded as an optimistic choice against H5N1 and H7N9 strains of the influenza virus. Further experimental testing of these prophylactic vaccine designs against pathogenic avian influenza strains may clarify their safety and efficacy.Communicated by Ramaswamy H. Sarma.

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.

New Formulation of a Subunit Vaccine Candidate against Lawsonia intracellularis Increases Humoral and Cellular Immune Responses

Previously, we designed a subunit vaccine candidate based on three L. intracellularis antigens with promising results in pigs. In this study, antigens were produced individually to achieve an even antigen ratio in the formulation. The emulsion characterization included the drop size and the mechanical and thermal stability. Immune response was evaluated by indirect and sandwich ELISAs, qPCR, and flow cytometry. The vaccine candidate's safety was assessed by histopathology and monitoring the clinical behavior of animals. The average production yielded for the chimeric antigen as inclusion bodies was around 75 mg/L. The formulation showed mechanical and thermal stability, with a ratio Hu/Ho > 0.85 and a drop size under 0.15 nm. Antigens formulated at a ratio of 1:1:1 induced a significant immune response in inoculated pigs that persisted until the end of the experiment (week 14). The dose of 200 μg significantly activated cellular response measured by transcriptional and translational levels of cytokines. The cell proliferation assay revealed an increment of lymphocytes T CD4+ at the same dose. Animals gained weight constantly and showed proper clinical behavior during immunization assays. This research demonstrated the immunological robustness of the new subunit vaccine candidate against Porcine Proliferative Enteropathy evenly formulated with three chimeric antigens of L. intracellularis.

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/).

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.

Immunoinformatics studies of heat shock proteins 27 and 70: Development of potent therapeutic vaccine constructs against human papillomavirus-related cancers

Heat shock proteins (HSPs) improve cross-presentation of linked tumor antigens, thus they can be exploited in therapeutic vaccine design. Herein, in silico analyses of different vaccine constructs were performed based on human papillomavirus (HPV)-16 E7 protein linked to Homo sapiens/Mus musculus Hsp27 or Hsp70 in multiepitope and whole sequence forms. Then, computational comparison between different orientations of Hsp/E7 was carried out in both forms. Finally, molecular docking was performed between the designed constructs and signaling (TLRs) or endocytic (CD14, LOX-1 and SREC-1) receptors. Our data represented the high-ranked T-cell epitopes and the potential B-cell epitopes of Homo sapiens/Mus musculus Hsp27 and Hsp70. Moreover, molecular docking showed that whole sequence of Hsp27 had better interaction with all receptors than whole sequence of Hsp70 suggesting likely stronger stimulation of innate and adaptive immunity. All designed Homo sapiens/Mus musculus Hsp27/E7 constructs had better docking scores with the endocytic receptors especially SREC-1 than all designed Homo sapiens/Mus musculus Hsp70/E7 constructs in both orientations. Generally, the multiepitope-/whole sequence-based Homo sapiens/Mus musculus Hsp27-E7 fusion constructs showed more conservancy and immunogenicity than other designed constructs. These fusion constructs were non-allergenic, non-toxic and stable suggesting them as promising vaccine candidates against HPV-related cancers.

Molecular engineering tools for the development of vaccines against infectious diseases: current status and future directions

INTRODUCTION

The escalating global changes have fostered conditions for the expansion and transmission of diverse biological factors, leading to the rise of emerging and reemerging infectious diseases. Complex viral infections, such as COVID-19, influenza, HIV, and Ebola, continue to surface, necessitating the development of effective vaccine technologies.

AREAS COVERED

This review article highlights recent advancements in molecular biology, virology, and genomics that have propelled the design and development of innovative molecular tools. These tools have promoted new vaccine research platforms and directly improved vaccine efficacy. The review summarizes the cutting-edge molecular engineering tools used in creating novel vaccines and explores the rapidly expanding molecular tools landscape and potential directions for future vaccine development.

EXPERT OPINION

The strategic application of advanced molecular engineering tools can address conventional vaccine limitations, enhance the overall efficacy of vaccine products, promote diversification in vaccine platforms, and form the foundation for future vaccine development. Prioritizing safety considerations of these novel molecular tools during vaccine development is crucial.

In silico design and evaluation of a novel mRNA vaccine against BK virus: a reverse vaccinology approach

Human polyomavirus type 1, or BK virus (BKV), is a ubiquitous pathogen belonging to the polyomaviridae family mostly known for causing BKV-associated nephropathy (BKVN) and allograft rejection in kidney transplant recipients (KTRs) following the immunosuppression regimens recommended in these patients. Reduction of the immunosuppression level and anti-viral agents are the usual approaches for BKV clearance, which have not met a desired outcome yet. There are also debating matters such as the effect of this pathogen on emerging various comorbidities and the related malignancies in the human population. In this study, a reverse vaccinology approach was implemented to design a mRNA vaccine against BKV by identifying the most antigenic proteins of this pathogen. Potential immunogenic T and B lymphocyte epitopes were predicted through various immunoinformatic tools. The final epitopes were selected according to antigenicity, toxicity, allergenicity, and cytokine inducibility scores. According to the obtained results, the designed vaccine was antigenic, neutral at the physiological pH, non-toxic, and non-allergenic with a world population coverage of 93.77%. Since the mRNA codon optimization ensures the efficient expression of the vaccine in a host cell, evaluation of different parameters showed our designed mRNA vaccine has a stable structure. Moreover, it had strong interactions with toll-like receptor 4 (TLR4) according to the molecular dynamic simulation studies. The in silico immune simulation analyses revealed an overall increase in the immune responses following repeated exposure to the designed vaccine. Based on our findings, the vaccine candidate is ready to be tested as a promising novel mRNA therapeutic vaccine against BKV.

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.

Allergenicity of peanut allergens and its dependence on the structure

Food allergies are a global food safety problem. Peanut allergies are common due, in part, to their popular utilization in the food industry. Peanut allergy is typically an immunoglobulin E-mediated reaction, and peanuts contain 17 allergens belonging to different families in peanut. In this review, we first introduce the mechanisms and management of peanut allergy, followed by the basic structures of associated allergens. Subsequently, we summarize methods of epitope localization for peanut allergens. These methods can be instrumental in speeding up the discovery of allergenicity-dependent structures. Many attempts have been made to decrease the allergenicity of peanuts. The structures of hypoallergens, which are manufactured during processing, were analyzed to strengthen the desensitization process and allergen immunotherapy. The identification of conformational epitopes is the bottleneck in both peanut and food allergies. Further, the identification and modification of such epitopes will lead to improved strategies for managing and preventing peanut allergy. Combining traditional wet chemistry research with structure simulation studies will help in the epitopes' localization.

Protein Nanoparticle-Mediated Delivery of Recombinant Influenza Hemagglutinin Enhances Immunogenicity and Breadth of the Antibody Response

The vast majority of seasonal influenza vaccines administered each year are derived from virus propagated in eggs using technology that has changed little since the 1930s. The immunogenicity, durability, and breadth of response would likely benefit from a recombinant nanoparticle-based approach. Although the E2 protein nanoparticle (NP) platform has been previously shown to promote effective cell-mediated responses to peptide epitopes, it has not yet been reported to deliver whole protein antigens. In this study, we synthesized a novel maleimido tris-nitrilotriacetic acid (NTA) linker to couple protein hemagglutinin (HA) from H1N1 influenza virus to the E2 NP, and we evaluated the HA-specific antibody responses using protein microarrays. We found that recombinant H1 protein alone is immunogenic in mice but requires two boosts for IgG to be detected and is strongly IgG1 (Th2) polarized. When conjugated to E2 NPs, IgG2c is produced leading to a more balanced Th1/Th2 response. Inclusion of the Toll-like receptor 4 agonist monophosphoryl lipid A (MPLA) significantly enhances the immunogenicity of H1-E2 NPs while retaining the Th1/Th2 balance. Interestingly, broader homo- and heterosubtypic cross-reactivity is also observed for conjugated H1-E2 with MPLA, compared to unconjugated H1 with or without MPLA. These results highlight the potential of an NP-based delivery of HA for tuning the immunogenicity, breadth, and Th1/Th2 balance generated by recombinant HA-based vaccination. Furthermore, the modularity of this protein-protein conjugation strategy may have utility for future vaccine development against other human pathogens.

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.

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.

Antigenic Determinant of Helicobacter pylori FlaA for Developing Serological Diagnostic Methods in Children

The early diagnosis of Helicobacter pylori infection is important for gastric cancer prevention and treatment. Although endoscopic biopsy is widely used for H. pylori diagnosis, an accurate biopsy cannot be performed until a lesion becomes clear, especially in pediatric patients. Therefore, it is necessary to develop convenient and accurate methods for early diagnosis. FlaA, an essential factor for H. pylori survival, shows high antigenicity and can be used as a diagnostic marker. We attempted to identify effective antigens containing epitopes of high diagnostic value in FlaA. Full-sized FlaA was divided into several fragments and cloned, and its antigenicity was investigated using Western blotting. The FlaA fragment of 1345-1395 bp had strong immunogenicity. ELISA was performed with serum samples from children by using the 1345-1395 bp recombinant antigen fragment. IgG reactivity showed 90.0% sensitivity and 90.5% specificity, and IgM reactivity showed 100% sensitivity and specificity. The FlaA fragment of 1345-1395 bp discovered in the present study has antigenicity and is of high value as a candidate antigen for serological diagnosis. The FlaA 1345-1395 bp epitope can be used as a diagnostic marker for H. pylori infection, thereby controlling various gastric diseases such as gastric cancer and peptic ulcers caused by H. pylori.

First identification of Nocardia seriolae GapA adhesion function and its three B-cell epitopes with cell-binding activity

Fish nocardiosis mainly caused by Nocardia seriolae (N. seriolae) is a serious threat to aquaculture. Bacterial adhesion to host cells mediated by adhesin is an initial step of pathogenesis. But it is not clear whether glyceraldehyde-3-phosphate dehydrogenase (GapA) is an adhesin of N. seriolae. Here, recombinant GapA protein (rGapA) was prokaryotic expressed, and its role in the bacterial adhesion to Ctenopharyngodon idella kidney cells was investigated by indirect immunofluorescence, protein-binding assay and adhesion inhibition assay. The results showed that an obvious green fluorescence was observed on the surface of the cells co-incubated with rGapA protein; the cytomembrane proteins of the cells pretreated with rGapA could react with anti-rGapA antibody; and the antibody significantly inhibited the adhesion ability of the bacteria. Subsequently, B-cell linear epitopes of GapA protein were identified by using a immunoinformatics approach combined with peptide ELISA and Western blot for the first time. It was found that four predicted epitopes (Ep_58-69 , Ep_139-150 , Ep_186-197 , Ep_318-329 ) could all react with anti-rGapA antibody and obviously inhibit the immunoreactivity between rGapA and anti-rGapA antibody, and they were confirmed as indeed B-cell linear epitopes of the protein. Furthermore, flow cytometry analysis found the percentage of positive cells co-incubated with FITC-labelled epitope peptides (Ep_139-150 , Ep_186-197 , Ep_318-329 ) was significantly higher than those in the FITC-labelled Ep_58-69 , unrelated control peptide and cell control. Collectively, GapA is an adhesin of N. seriolae, and epitope peptides (Ep_139-150 , Ep_186-197 , Ep_318-329 ) possess cell-binding activity, which are potential candidates for developing a multiple epitopes-based adhesin vaccine against fish nocardiosis.

An Update of Bovine Hemoplasmas Based on Phylogenetic and Genomics Analysis

Mycoplasma wenyonii and 'Candidatus Mycoplasma haemobos' are bacteria that have been described as significant hemoplasmas that infect cattle worldwide. Currently, three bovine hemoplasma genomes are known. This work aimed to describe the main genomic characteristics and the evolutionary relationships between hemoplasmas, and provide a list of epitopes predicted by immunoinformatics as diagnostic candidates for bovine hemoplasmosis. Thus far, there is no vaccine to prevent this disease that economically impacts cattle production worldwide. Additionally, there is a lack of vaccines against bovine hemoplasmosis. In this work, we performed a genomic characterization of hemoplasmas, including two Mexican strains reported in bovines in the last few years. The generated information is a new scenario about the phylogeny of hemoplasmas. Also, we show genomic features among hemoplasmas that strengthen their characteristic genome plasticity of intracellular lifestyles. Finally, the elucidation of antigenic proteins in Mexican strains represents an opportunity to develop molecular detection methods and diagnoses.

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

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

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

A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach

Malaria is a complex disease caused by parasites of the genus Plasmodium and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. Thus, a combination of different approaches is needed to control malaria. Resistance to first-line drugs and insecticides, on the other hand, makes the need for an effective vaccination more urgent than ever. Because erythrocyte parasites cause the most clinical symptoms, developing a vaccination for this stage of infection might be highly beneficial. In this research, we employed various bioinformatics methods to create an efficient multi-epitope vaccine that induces antibodies against the blood stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The B, HTL epitopes, and epitope conservation were predicted. The predicted epitopes (including 5 B and 5 HTL epitopes) were connected using suitable linkers, and the flagellin molecule was used as an adjuvant to improve its immunogenicity. The final construct vaccine with 414 amino acids long was designed. The vaccine's allergenicity, antigenicity, solubility, physicochemical characteristics, 2D and 3D structure modeling, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation were tested. In silico immune simulation results showed significantly elevated IgG1 and IgM and T helper cells, INF γ, IL 2, and B-cell populations after the injection of the designed vaccine. These significant computational analyses indicated that our proposed vaccine candidate might activate suitable immune responses against malaria. However, in vitro and in vivo studies are essential for further validation.

Immunodominant B-Cell Linear Epitope on the VP1 P Domain of a Feline Norovirus Cat Model

Norovirus (NoV) infection remains a major public health concern worldwide. Appropriate animal models are essential for the development of effective NoV vaccines. We previously established the feline NoV (FNoV)-cat model as a surrogate animal model for human NoV infection. In the present study, we analyzed the B-cell linear epitope in the P domain of FNoV to confirm the basic immunological features of the FNoV-cat model. B-cell linear epitopes were present in the P2 subdomain. We compared antibody levels to peptides containing the B-cell linear epitope (P-10) in three FNoV-infected cats with time-course changes in viral load and symptom scoring. After FNoV infection, viral shedding and clinical symptoms were shown to improve by elevated levels of antibodies against P-10 in the plasma. This report provides important information for understanding NoV infections in humans and cats.

Multi-stage transcriptome profiling of the neglected food-borne echinostome Artyfechinostomum sufrartyfex reveal potential diagnostic and drug targets

Lack of effective surveillance and control methods for neglected helminth diseases particularly in context of rural areas in India is a serious concern in terms of public health. With regard to the emerging food-borne echinostomid Artyfechinostomum sufrartyfex infection in the country, the current study is an in silico attempt to screen for plausible diagnostic and drug targets against the trematode. Transcriptome of adult, encysted and excysted metacercaria stages of the parasite was generated using Illumina sequencing platform. A de-novo assembly strategy utilizing transcriptome data generated from the three lifecycle stages was followed to generate the representative transcripts. Longest open reading frames identified for the transcripts were further conceptually translated into their respective protein sequences. Detailed analysis of this dataset through various bioinformatics pipelines and tools eventually identified 14 credible diagnostic and 10 drug targets along with their FDA-approved and ZINC molecules. Some of the important diagnostic candidates include thioredoxin peroxidase, haemoglobinase, cathepsin L, cathepsin L-like and B-like cysteine proteases. Among the drug targets, uncharacterized sodium dependent transporter and bifunctional protein Aas were identified as top targets exhibiting significant interaction with Rifamycin and ZINC02820058 molecule, respectively. Further, B-cell epitope analysis of the diagnostic targets revealed unique epitopes for 10 of them thus indicating their potential role in specific diagnosis of the parasite. The diagnostic candidates along with a number of lesser known drug targets and their ligand molecules identified in this study provides a reasonable basis for evaluation and development of future intervention strategies against A. sufrartyfex.

Engineered microbial systems for advanced drug delivery

The human body is a natural habitat for a multitude of microorganisms, with bacteria being the major constituent of the microbiota. These bacteria colonize discrete anatomical locations that provide suitable conditions for their survival. Many bacterial species, both symbiotic and pathogenic, interact with the host via biochemical signaling. Based on these attributes, commensal and attenuated pathogenic bacteria have been engineered to deliver therapeutic molecules to target specific diseases. Recent advances in synthetic biology have enabled us to perform complex genetic modifications in live bacteria and bacteria-derived particles, which simulate micron or submicron lipid-based vectors, for the targeted delivery of therapeutic agents. In this review, we highlight various examples of engineered bacteria or bacteria-derived particles that encapsulate, secrete, or surface-display therapeutic molecules for the treatment or prevention of various diseases. The review highlights recent studies on (i) the production of therapeutics by microbial cell factories, (ii) disease-triggered release of therapeutics by sense and respond systems, (iii) bacteria targeting tumor hypoxia, and (iv) bacteria-derived particles as chassis for drug delivery. In addition, we discuss the potential of such drug delivery systems to be translated into clinical therapies.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

ELISA-Based Analysis Reveals an Anti-SARS-CoV-2 Protein Immune Response Profile Associated with Disease Severity

Since the start of the COVID-19 pandemic, many studies have investigated the humoral response to SARS-CoV-2 during infection. Studies with native viral proteins constitute a first-line approach to assessing the overall immune response, but small peptides are an accurate and valuable tool for the fine characterization of B-cell epitopes, despite the restriction of this approach to the determination of linear epitopes. In this study, we used ELISA and peptides covering a selection of structural and non-structural SARS-CoV-2 proteins to identify key epitopes eliciting a strong immune response that could serve as a biological signature of disease characteristics, such as severity, in particular. We used 213 plasma samples from a cohort of patients well-characterized clinically and biologically and followed for COVID-19 infection. We found that patients developing severe disease had higher titers of antibodies mapping to multiple specific epitopes than patients with mild to moderate disease. These data are potentially important as they could be used for immunological profiling to improve our knowledge of the quantitative and qualitative characteristics of the humoral response in relation to patient outcome.

Antibodies targeting enzyme inhibition as potential tools for research and drug development

Antibodies have transformed biomedical research and are now being used for different experimental applications. Generally, the interaction of enzymes with their specific antibodies can lead to a reduction in their enzymatic activity. The effect of the antibody is dependent on its narrow i.e. the regions of the enzyme to which it is directed. The mechanism of this inhibition is rarely a direct combination of the antibodies with the catalytic site, but is rather due to steric hindrance, barring the substrate access to the active site. In several systems, however, the interaction with the antibody induces conformational changes on the enzyme that can either inhibit or enhance its catalytic activity. The extent of enzyme inhibition or enhancement is, therefore, a reflection of the nature and distribution of the various antigenic determinants on the enzyme molecule. Currently, the mode of action of many enzymes has been elucidated at the molecular level. We here review the molecular mechanisms and recent trends by which antibodies inhibit the catalytic activity of enzymes and provide examples of how specific antibodies can be useful for the neutralization of biologically active molecules.

Mapping antibody binding using multiplexed epitope substitution analysis

A more complete understanding of antibody epitopes would aid the development of diagnostics, therapeutic antibodies, and vaccines. However, current methods for mapping antibody binding to epitopes require a targeted experimental approach, which limits throughput. To address these limitations, we developed Multiplexed Epitope Substitution Analysis (MESA) which can rapidly characterize various distinct epitopes using millions of antibody-binding peptides. We screened peptides from a random 12-mer library that bound to human serum antibody repertoires and determined their sequences using next-generation sequencing (NGS). Computationally, we divided target epitope sequences into overlapping k-mer subsequences and substituted the positions in each k-mer with all 20 amino acids, mimicking a saturation mutagenesis. We then determined enrichments of the substituted k-mers in the screened peptide dataset and used these enrichments to identify substitutions favored for binding at each position in the target epitope, ultimately revealing the precise binding motif. To validate MESA, we determined binding motifs for monoclonal antibodies spiked into serum, recovering the expected binding positions and amino acid preferences. To characterize epitopes bound by a population, we analyzed 50 serum specimens to determine the binding motifs within various target epitopes from common pathogens. Additionally, by analyzing various HSV-1 glycoprotein epitopes, MESA revealed unique binding signatures for HSV-1 seropositive specimens and demonstrated the variability of binding signatures within a population. These results demonstrate that MESA can rapidly identify and characterize binding motifs for an unlimited number of epitopes from a single experiment, accelerating discoveries and enhancing our understanding of antibody-epitope interactions.

Emerging targets for anticancer vaccination: PD-1

Among the mechanisms by which tumor cells escape the immune surveillance, one is the interaction between programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1). Inhibition of the PD-1/PD-L1 pathway with monoclonal antibodies as immune checkpoint inhibitors targeting PD-1 or its ligand, PD-L1, represents a milestone in cancer therapy. The application of these antibodies, however, suffers from drawbacks including failure to show a response or benefit in a majority of patients following monotherapy or combination therapy, their frequent administration, and cost intensiveness. Small peptides capable of interfering with PD-1/PD-L1 interaction represent interesting alternatives to antibody-based immune checkpoint inhibitors. Moreover, peptides representing PD-1 or PD-L1 sequences can be used in active immunization approaches to induce antibodies that enhance antitumor immunity by effectively preventing PD-1-mediated inhibition in the host. Importantly, such peptides can readily be combined with peptides derived from cancer antigens to effectively induce an antitumor immune response. In this review, we have summarized the recent developments in the use of small molecules and peptides either to directly block PD-1/PD-L1 interaction, or in vaccination approaches to induce antibody responses stimulating anticancer immunity by blocking PD-1-mediated T-cell inhibition.

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Blockade of the PD-1/PD-L1 interaction by antibodies as immune checkpoint inhibitors (ICIs) is a milestone in immunotherapy.

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Treatment by ICIs has disadvantages, like frequent administration, low response in some patients, and cost intensiveness.

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Direct blockade by small compounds or vaccination by peptides are two promising alternatives to the treatments with ICIs.

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Such alternatives may pave the way to therapeutics which could be used as monotherapy, or in combination with ICIs.

Designing and development of epitope-based vaccines against Helicobacter pylori

Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize ‘peptidome’ representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Insights into the biochemical features and immunogenic epitopes of common bradyzoite markers of the ubiquitous Toxoplasma gondii

The widespread distribution of Toxoplasma gondii (T. gondii) infection and its harsh outcomes in pregnant women and immunocompromised patients lead researchers towards vaccination strategies. The present in silico investigation was done to reveal biophysical properties and immunogenic epitopes of six bradyzoite markers for rational vaccine design in future. For this purpose, different web servers were used to predict antigenicity, allergenicity, solubility, physicochemical properties, post-translational modification sites (PTMs), the presence of signal peptide and transmembrane domains. Moreover, the secondary and tertiary structures of the proteins were revealed followed by refinement and validation. Finally, NetCTL server was used to predict cytotoxic T-lymphocyte (CTL) epitopes, with subsequent immunogenicity analysis. Also, IEDB server was utilized to predict helper T-lymphocyte (HTL) epitopes, followed by IFN-γ and IL-4 induction, antigenicity and population coverage analysis. As well, several linear antigenic B-cell epitopes were found, with good water solubility and without allergenicity. Totally, these proteins showed appropriate antigenicity, abundant PTMs as well as many CTL, HTL and B-cell epitopes, which could be directed for future vaccination studies in the context of multi-epitope vaccine design.

Conformational epitope matching and prediction based on protein surface spiral features

Background

A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.

Results

We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.

Conclusions

The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Production and Characterization of Monoclonal Antibodies Against N Protein of Rift Valley Fever Virus

The DNA fragment encoding predicted main antigenic region, aa 14-245 on N protein of Rift Valley virus (RVFV) was cloned into the vector pET-28a (+) and p3xFLAG-CMV-10. The recombinant pET-28a-N1 protein was expressed in Escherichia coli BL21 (DE3) with 1 mM isopropyl-b-thio-galactopyranoside at 37°C for 5 hours, and purified by protein purifier. Three monoclonal antibodies (mAbs) named 3A5, 3A6, and 3A7 against N protein were obtained by fusing mouse myeloma cell line SP2/0 with spleen lymphocytes from pET-28a-N1 protein-immunized mice. Finally, the mAbs were characterized by enzyme-linked immunosorbent assays, indirect immunofluorescent assays, and Western blot. The results show that all the mAbs possess high specificity and react with both prokaryotic and eukaryotic N protein, which could provide important materials for the research on the function of N protein and the diagnostic methods of RVFV.

In silico prediction of T-cell and B-cell epitopes of human papillomavirus type 16 L1 protein

Human papillomavirus type 16 (HPV-16) is one of the most important cause of developing cervical cancer. Therefore, effective epitope-based vaccine design for HPV-16 would be of major medical benefit. The aim of our study was to identify B- and T-cell epitopes of HPV-16 L1 protein. In this study, the HPV-16 L1 gene was isolated from HPV recovered from five vaginal swab samples using specific primers and finally sequenced. The ExPASy translate tool (http://web.expasy.org/translate/) was used to convert nucleotide sequence into amino acid sequence. Bioinformatic analysis was employed to predict suitable B- and T-cell epitopes and immunogenicity, allergenicity, and toxicity of predicted epitopes were then evaluated. Afterward, the selected T-cell epitopes were docked using Molegro Virtual Docker software. The two epitopes ₂₀₇ AMDFTTLQA₂₁₅ and ₂₀₀ MVDTGFGAM₂₀₈ have showed a very strong binding affinity to HLA-A0201 and HLA-B3501 molecules, respectively. Outcome of B-cell epitope prediction showed that epitope ₄₇₅ KAKPKFTLGKRK ATPTTSSTSTTAKRKK₅₀₂ contained overlapped epitope, which might be the epitope associated with the production of neutralizing antibody response. Based on this finding, the predicted B- and T-cell epitopes are promising targets for epitope-based vaccine development against HPV-16. Further in vivo and in vitro experiments are needed to confirm our findings.

Genome-wide analysis of Indian SARS-CoV-2 genomes to identify T-cell and B-cell epitopes from conserved regions based on immunogenicity and antigenicity

SARS-CoV-2 has a high transmission rate and shows frequent mutations, thus making vaccine development an arduous task. However, researchers around the globe are working hard to find a solution e.g. synthetic vaccine. Here, we have performed genome-wide analysis of 566 Indian SARS-CoV-2 genomes to extract the potential conserved regions for identifying peptide based synthetic vaccines, viz. epitopes with high immunogenicity and antigenicity. In this regard, different multiple sequence alignment techniques are used to align the SARS-CoV-2 genomes separately. Subsequently, consensus conserved regions are identified after finding the conserved regions from each aligned result of alignment techniques. Further, the consensus conserved regions are refined considering that their lengths are greater than or equal to 60nt and their corresponding proteins are devoid of any stop codons. Subsequently, their specificity as query coverage are verified using Nucleotide BLAST. Finally, with these consensus conserved regions, T-cell and B-cell epitopes are identified based on their immunogenic and antigenic scores which are then used to rank the conserved regions. As a result, we have ranked 23 consensus conserved regions that are associated with different proteins. This ranking also resulted in 34 MHC-I and 37 MHC-II restricted T-cell epitopes with 16 and 19 unique HLA alleles and 29 B-cell epitopes. After ranking, the consensus conserved region from NSP3 gene is obtained that is highly immunogenic and antigenic. In order to judge the relevance of the identified epitopes, the physico-chemical properties and binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles.

In Silico Prediction of T and B Cell Epitopes of SAG1-Related Sequence 3 (SRS3) Gene for Developing Toxoplasma gondii Vaccine

: Toxoplasmosis is a worldwide infection that can lead to serious problems in immune-compromised individuals and fetuses. A DNA vaccine strategy would be an ideal tool against Toxoplasma gondii. One of the necessary measures to provide an effective vaccine is the selection of proteins with high antigenicity. The SAG1-related sequence 3 (SRS3) protein is a major surface antigen in T. gondii that can be used as a vaccine candidate. In the present study, bioinformatics and computational methods were utilized to predict protein characteristics, as well as secondary and tertiary structures. The in silico approach is highly suited to analyze, design, and evaluate DNA vaccine strategies. Hence, in silico prediction was used to identify B and T cell epitopes and compare the antigenicity of SRS3 and other candidate genes of Toxoplasma previously applied in the production of vaccines. The results of the analysis theoretically showed that SRS3 has multiple epitopes with high antigenicity, proposing that SRS3 is a promising immunogenic candidate for the development of DNA vaccines against toxoplasmosis.

Design of Epitope-Based Peptide Vaccine against Pseudomonas aeruginosa Fructose Bisphosphate Aldolase Protein Using Immunoinformatics

Pseudomonas aeruginosa is a common pathogen that is responsible for serious hospital-acquired infections, ventilator-associated pneumonia, and various sepsis syndromes. Also, it is a multidrug-resistant pathogen recognized for its ubiquity and its intrinsically advanced antibiotic-resistant mechanisms. It usually affects immunocompromised individuals but can also infect immunocompetent individuals. There is no vaccine against it available till now. This study predicts an effective epitope-based vaccine against fructose bisphosphate aldolase (FBA) of Pseudomonas aeruginosa using immunoinformatics tools. The protein sequences were obtained from NCBI, and prediction tests were undertaken to analyze possible epitopes for B and T cells. Three B cell epitopes passed the antigenicity, accessibility, and hydrophilicity tests. Six MHC I epitopes were found to be promising, while four MHC II epitopes were found promising from the result set. Nineteen epitopes were shared between MHC I and II results. For the population coverage, the epitopes covered 95.62% worldwide excluding certain MHC II alleles. We recommend in vivo and in vitro studies to prove its effectiveness.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

Identification of immunodominant linear epitopes from SARS-CoV-2 patient plasma

A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) is the source of a current pandemic (COVID-19) with devastating consequences in public health and economic stability. Using a peptide array to map the antibody response of plasma from healing patients (12) and heathy patients (6), we identified three immunodominant linear epitopes, two of which correspond to key proteolytic sites on the spike protein (S1/S2 and S2') known to be critical for cellular entry. We show biochemical evidence that plasma positive for the epitope adjacent to the S1/S2 cleavage site inhibits furin-mediated proteolysis of spike.