Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

Immunoinformatics design of novel multi-epitope vaccine against Trueperella Pyogenes using collagen adhesion protein, fimbriae, and pyolysin.

Trueperella pyogenes (T. pyogenes) is an opportunistic pathogen that causes infertility, mastitis, and metritis in animals. T. pyogenes is also a zoonotic disease and is considered an economic loss agent in the livestock industry. Therefore, vaccine development is necessary. Using an immunoinformatics approach, this study aimed to construct a multi-epitope vaccine against T. pyogenes. The collagen adhesion protein, fimbriae, and pyolysin (PLO) sequences were initially retrieved. The HTL, CTL, and B cell epitopes were predicted. The vaccine was designed by binding these epitopes with linkers. To increase vaccine immunogenicity, profilin was added to the N-terminal of the vaccine construct. The antigenic features and safety of the vaccine model were investigated. Docking, molecular dynamics simulation of the vaccine with immune receptors, and immunological simulation were used to evaluate the vaccine's efficacy. The vaccine's sequence was then optimized for cloning. The vaccine construct was designed based on 18 epitopes of T. pyogenes. The computational tools validated the vaccine as non-allergenic, non-toxic, hydrophilic, and stable at different temperatures with acceptable antigenic features. The vaccine model had good affinity and stability to bovine TLR2, 4, and 5 as well as stimulation of IgM, IgG, IL-2, IFN-γ, and Th1 responses. This vaccine also increased long-lived memory cells, dendritic cells, and macrophage population. In addition, codon optimization was done and cloned in the E. coli K12 expression vector (pET-28a). For the first time, this study introduced a novel multi-epitope vaccine candidate based on collagen adhesion protein, fimbriae, and PLO of T. pyogenes. It is expected this vaccine stimulates an effective immune response to prevent T. pyogenes infection.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction.

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

Novel toxin-based mRNA vaccine against Clostridium perfringens using in silico approaches.

Clostridium perfringens is a bacterium that causes gastrointestinal diseases in humans and animals. The several powerful toxins such as alpha toxin (CPA), beta toxin (CPB), enterotoxin (CPE), Epsilon toxin (ETX), and theta toxin, play a major role in its pathogenesis. Traditional vaccine development methods are time-consuming and costly. In silico approaches offer an alternative strategy for designing vaccines by analyzing biological data and predicting immunogenic peptides. In this study, computational tools were utilized to design a RNA vaccine targeting C. perfringens toxins. Toxin protein sequences were retrieved and their linear B-cell, MHCI, and MHCII binding epitopes were predicted. Allergenicity, toxigenicity, and IFN-γ induction were assessed to select non-allergenic, non-toxic, and IFN-γ-inducing epitopes. Molecular docking was performed to identify epitopes that fit within the binding cleft of MHC alleles. A final peptide vaccine construct was designed with selected epitopes separated by a linker sequence. The antigenicity and physicochemical properties of the vaccine were evaluated. Immune response simulation showed enhanced secondary and tertiary immune responses, increased levels of immunoglobulins, cytotoxic T lymphocytes, helper T lymphocytes, macrophage activity, and elevated levels IFN-γ and interleukin-2. Docking analysis was done to assess interactions between the vaccine structure and Toll-like receptors. Codon optimization was performed, and a final RNA vaccine construct was designed. The secondary structure of the RNA vaccine was predicted and validated. Overall, this study demonstrates the potential of in silico approaches for designing an RNA vaccine against C. perfringens toxins, contributing to improved prevention and control of associated diseases.

Development of a potent recombinant scFv antibody against the SARS-CoV-2 by in-depth bioinformatics study: Paving the way for vaccine/diagnostics development.

BACKGROUND: The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches? METHOD: Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (VH/VL). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the VH-VL. Finally, this scFv was analyzed/optimized using various web servers. RESULTS: Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs). CONCLUSIONS: Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen.

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence.

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).

Screening and identification of B cell epitope within the major capsid protein L1 of HPV 52, using monoclonal antibodies.

The L1 protein of Human papillomavirus (HPV), the main capsid protein, induces the formation of neutralizing antibodies. In this study, HPV52 L1 protein was induced to be expressed. Monoclonal antibody (mAb) 6A7 against L1 protein were screened by cell fusion techniques. Western Blot and immunofluorescence assay (IFA) demonstrated the specificity of the mAb. The L1 protein was truncated for prokaryotic expression (N1∼N7) and Dot-ELISA showed that 6A7 recognized N3 (aa 200-350). The immunodominant regions were truncated again for expression, with 6A7 recognizing N6 (aa 251-305). The N6 proteins were further truncated and then were constructed an four-segment eukaryotic expression vector. IFA showed that 6A7 could recognize amino acid 262-279. Amino acid 262-279 was selected to be truncated into short peptides P1 and P2. Finally, Peptide-ELISA and Dot-ELISA showed that the epitope regions of mAb 6A7 were amino acid 262-273. The mAbs with defined epitopes can lay the foundation for the analysis of antigenic epitope characteristics and promote the development of epitope peptide vaccines.

In Silico Design of a New Epitope-Based Vaccine against Grass Group 1 Allergens.

Allergic diseases are a global public health problem that affects up to 30% of the population in industrialized societies. More than 40% of allergic patients suffer from grass pollen allergy. Grass pollen allergens of group 1 and group 5 are the major allergens, since they induce allergic reactions in patients at high rates. In this study, we used immunoinformatic approaches to design an effective epitope-based vaccine against the grass group 1 allergens. After the alignment of all known pollen T-cell and B-cell epitopes from pollen allergens available in the public databases, the epitope GTKSEVEDVIPEGWKADTSY was identified as the most suitable for further analyses. The target sequence was subjected to immunoinformatics analyses to predict antigenic T-cell and B-cell epitopes. Population coverage analysis was performed for CD8+ and CD4+ T-cell epitopes. The selected T-cell epitopes (VEDVIPEGW and TKSEVEDVIPEGWKA) covered 78.87% and 98.20% of the global population and 84.57% and 99.86% of the population of Europe. Selected CD8+, CD4+ T-cell and B-cell epitopes have been validated by molecular docking analysis. CD8+ and CD4+ T-cell epitopes showed a very strong binding affinity to major histocompatibility complex (MHC) class I (MHC I) molecules and MHC class II (MHC II) molecules with global energy scores of -72.1 kcal/mol and -89.59 kcal/mol, respectively. The human IgE-Fc (PDB ID 4J4P) showed a lower affinity with B-cell epitope (ΔG = -34.4 kcal/mol), while the Phl p 2-specific human IgE Fab (PDB ID 2VXQ) had the lowest binding with the B-cell epitope (ΔG = -29.9 kcal/mol). Our immunoinformatics results demonstrated that the peptide GTKSEVEDVIPEGWKADTSY could stimulate the immune system and we performed ex vivo tests showed that the investigated epitope activates T cells isolated from patients with grass pollen allergy, but it is not recognized by IgE antibodies specific for grass pollen allergens. This confirms the importance of such studies to establish universal epitopes to serve as a basis for developing an effective vaccine against a particular group of allergens. Further in vivo studies are needed to validate the effectiveness of such a vaccine against grass pollen allergens.

Multi-epitope vaccine candidates based on mycobacterial membrane protein large (MmpL) proteins against Mycobacterium ulcerans.

Buruli ulcer (BU) is a neglected tropical disease. It is caused by the bacterium Mycobacterium ulcerans and is characterized by skin lesions. Several studies were performed testing the Bacillus Calmette-Guérin (BCG) vaccine in human and animal models and M. ulcerans-specific vaccines in animal models. However, there are currently no clinically accepted vaccines to prevent M. ulcerans infection. The aim of this study was to identify T-cell and B-cell epitopes from the mycobacterial membrane protein large (MmpL) proteins of M. ulcerans. These epitopes were analysed for properties including antigenicity, immunogenicity, non-allergenicity, non-toxicity, population coverage and the potential to induce cytokines. The final 8 CD8+, 12 CD4+ T-cell and 5 B-cell epitopes were antigenic, non-allergenic and non-toxic. The estimated global population coverage of the CD8+ and CD4+ epitopes was 97.71%. These epitopes were used to construct five multi-epitope vaccine constructs with different adjuvants and linker combinations. The constructs underwent further structural analyses and refinement. The constructs were then docked with Toll-like receptors. Three of the successfully docked complexes were structurally analysed. Two of the docked complexes successfully underwent molecular dynamics simulations (MDS) and post-MDS analysis. The complexes generated were found to be stable. However, experimental validation of the complexes is required.

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/).

Vaccination with prefusion-stabilized respiratory syncytial virus fusion protein elicits antibodies targeting a membrane-proximal epitope.

IMPORTANCE: Respiratory syncytial virus (RSV) is the leading cause of bronchiolitis and pneumonia in infants, infecting all children by age 5. RSV also causes substantial morbidity and mortality in older adults, and a vaccine for older adults based on a prefusion-stabilized form of the viral F glycoprotein was recently approved by the FDA. Here, we investigate a set of antibodies that belong to the same public clonotype and were isolated from individuals vaccinated with a prefusion-stabilized RSV F protein. Our results reveal that these antibodies are highly potent and recognize a previously uncharacterized antigenic site on the prefusion F protein. Vaccination with prefusion RSV F proteins appears to boost the elicitation of these neutralizing antibodies, which are not commonly elicited by natural infection.

BCEDB: a linear B-cell epitopes database for SARS-CoV-2.

The 2019 Novel Coronavirus (SARS-CoV-2) has infected millions of people worldwide and caused millions of deaths. The virus has gone numerous mutations to replicate faster, which can overwhelm the immune system of the host. Linear B-cell epitopes are becoming promising in prevention of various deadly infectious diseases, breaking the general idea of their low immunogenicity and partial protection. However, there is still no public repository to host the linear B-cell epitopes for facilitating the development vaccines against SARS-CoV-2. Therefore, we developed BCEDB, a linear B-cell epitopes database specifically designed for hosting, exploring and visualizing linear B-cell epitopes and their features. The database provides a comprehensive repository of computationally predicted linear B-cell epitopes from Spike protein; a systematic annotation of epitopes including sequence, antigenicity score, genomic locations of epitopes, mutations in different virus lineages, mutation sites on the 3D structure of Spike protein and a genome browser to visualize them in an interactive manner. It represents a valuable resource for peptide-based vaccine development. Database URL: http://www.oncoimmunobank.cn/bcedbindex.

A reverse vaccinology approach to design an mRNA-based vaccine to provoke a robust immune response against HIV-1.

There have been substantial advances in HIV research over the past three decades, but we are still far from our goal of eliminating HIV-1 infection entirely. Numerous ever-evolving antigens are produced as a result of HIV-1's genetic variability. Developing an effective vaccination is challenging because of the structural properties of the viral envelope glycoprotein that obscure conserved receptor-binding sites and the presence of carbohydrate moieties that prevent antibodies from reaching potential epitopes. To work on an HIV-specific vaccine, this study identified 5 HIV-surface proteins, from the literature, to screen potential epitopes and construct an mRNA vaccine. A wide range of immunological-informatics techniques were utilized to develop a construct that efficiently stimulated cellular and humoral immune responses. The vaccine was produced with 31 epitopes, a TLR4 agonist termed RpfE that acts as an adjuvant, secretion boosters, subcellular trafficking structures, and linkers. It was determined that this suggested vaccine would cover 98.9 percent of the population, making it widely available. We, furthermore, carried out an immunological simulation of the vaccine illustrating the active and stable responses from innate and adaptive immune cells, the memory cells remained active for up to 350 days after vaccine injection, whereas the antigen was excreted from the body within 24 hours. Docking performed with TLR-4 and TLR-3 showed significant interaction with -11.9kcal/mol and -18.2kcal/mol-1 respectively. Molecular dynamics simulations further validated the vaccine's stability, with a dissociation constant of 1.7E-11 for the TLR3-vaccine complex and 5.8E-11 for the TLR4-vaccine complex. Lastly, codon optimization was carried out to guarantee that the designed mRNA construct would be translated into the host successfully. This vaccine adaptation, if tested in-vitro, would be efficacious and potent as predicted.

Development of a candidate multi-epitope vaccine against Sphingobacterium spiritivorum : Reverse vaccinology and immunoinformatics approach.

OBJECTIVES: To develop a candidate vaccine aginst the Sphingobacterium spiritivorum. METHODS: Since there is currently no vaccine against this pathogen, we employed in-silico methods to extensively explore the outer membrane toxin-producing proteins found specifically in S. spiritivorum to forecast a multi-epitope chimeric vaccine design. This computational study was conducted in Saudi Arabia in 2022 (study design: computational; ethical approval not applicable). RESULTS: TThe vaccine peptide comprises multiple linear and conformational B-cell epitopes, which have the potential to elicit humoral immunity. Projected B-cell- derived T-cell epitopes for outer membrane proteins are present in the produced protein. The docking and molecular dynamic simulation results indicating that the chimeric vaccine had adequate binding stability with TLR-4. Following the immunological simulation, significant levels of immune cell expression were observed as immunoglobulin (Ig) M and IgG, IgM, IgM1, and IgM2, and independently IgG1 and IgG2. CONCLUSION: The developed vaccine candidate is suitable for further testing and can assist experimental vaccinologists in developing an effective vaccine against S. spiritivorum.

[A multi-stage and multi-epitope vaccine against Mycobacterium tuberculosis based on an immunoinformatics approach].

Objectives To develop a multi-stage and multi-epitope vaccine, which consists of epitopes from the early secretory and latency-associated antigens of Mycobacterium tuberculosis (MTB). Methods The B-cell, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes of 12 proteins were predicted using an immunoinformatics. The epitopes with antigenicity, without cytotoxicity and sensitization, were further screened to construct the multi-epitope vaccine. Furthermore, the proposed vaccine underwent physicochemical properties analysis and secondary structure prediction as well as 3D structure modeling, refinement and validation. Then the refined model was docked with TLR4. Finally, an immune simulation of the vaccine was carried out. Results The proposed vaccine, which consists of 12 B-cell, 11 CTL and 12 HTL epitopes, had a flexible and stable globular conformation as well as a thermostable and hydrophilic structure. A stable interaction of the vaccine with TLR4 was confirmed by molecular docking. The efficiency of the candidate vaccine to trigger effective cellular and humoral immune responses was assessed by immune simulation. Conclusion A multi-stage multi-epitope MTB vaccine construction strategy based on immunoinformatics is proposed, which is expected to prevent both active and latent MTB infection.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections.

BACKGROUND: COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. METHODS AND RESULTS: In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. CONCLUSION: The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. RESULTS: Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. AVAILABILITY AND IMPLEMENTATION: The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.

Proteomics-based vaccine targets annotation and design of subunit and mRNA-based vaccines for Monkeypox virus (MPXV) against the recent outbreak.

Monkeypox Virus (MPXV) is a growing public health threat with increasing cases and fatalities globally. To date, no specific vaccine or small molecule therapeutic choices are available for the treatment of MPXV disease. In this work, we employed proteomics and structural vaccinology approaches to design mRNA and multi-epitopes-based vaccines (MVC) against MPXV. We first identified ten proteins from the whole proteome of MPXV as potential vaccine targets. We then employed structural vaccinology approaches to map potential epitopes of these proteins for B cell, cytotoxic T lymphocytes (CTL), and Helper T lymphocytes (HTL). Finally, 9 CTL, 6 B cell, and 5 HTL epitopes were joined together through suitable linkers to construct MVC (multi-epitope vaccine) and mRNA-based vaccines. Molecular docking, binding free energy calculation, and in silico cloning revealed robust interaction of the designed MVC with toll-like receptor 2 (TLR2) and efficient expression in E. Coli K12 strain. The immune simulation results revealed that the antigen titer after the injection reached to the maximum level on the 5th day and an abrupt decline in the antigen titer was observed upon the production of IgM, IgG and IgM + IgG, dendritic cells, IFN-gamma, and IL (interleukins), which suggested the potential of our designed vaccine candidate for inducing an immune response against MPXV.

An in silico approach for prediction of B cell and T cell epitope candidates against Chikungunya virus.

Several outbreaks of Chikungunya virus (CHIKV) had been reported since 1952 when mankind had his first encounter against the virus in Tanzania. Although these reports designate the CHIKV to be rarely fatal, cases of outbreaks in the last decade accompanied by severe complications and death poses a challenge to the development of effective treatment methods. Several attempts to vaccine development against CHIKV still remains unsuccessful. In this study, we aimed at the prediction of B-cell and T cell epitopes against CHIKV by using immunoinformatics. This, in turn, can contribute to development of an epitope based vaccine against CHIKV. Both linear and discontinuous B-cell epitopes, as well as Cytotoxic T-lymphocyte epitopes, were predicted for the CHIKV Envelope (E1 and E2) glycoproteins and (NS2). The antigenic CTL epitopes with highest binding affinities with type-1 MHC were selected and the peptides were docked to them. Docking followed by molecular dynamics simulations were performed to assess the stability of the docked complexes.

Immunoinformatics and Reverse Vaccinology Driven Predication of a Multi-epitope Vaccine against Borrelia burgdorferi and Validation through in silico Cloning and Immune Simulation.

BACKGROUND: Borrelia burgdorferi is regarded as an extremely dangerous bacteria causing infectious disease in humans, resulting in musculoskeletal pain, fatigue, fever and cardiac symptom. Because of all alarming concerns, no such prophylaxis setup has been available against Borrelia burgdorferi till now. In fact, vaccine construction using traditional methods is so expensive and time-consuming. Therefore, considering all concerns, we designed a multi-epitope-based vaccine design against Borrelia burgdorferi using in silico approaches. OBJECTIVE: To design an effective and safe vaccine that can activate cell-mediated and humoral immunity against Borrelia burgdorferi by using various bioinformatics tools. METHODS: The present study utilized different computational methodologies, covering different ideas and elements in bioinformatics tools. The protein sequence of Borrelia burgdorferi was retrieved from the NCBI database. Different B and T cell epitopes were predicated using the IEDB tool. Efficient B and T cell epitopes were further assessed for vaccine construction using linkers AAY, EAAAK and GPGPG, respectively. Furthermore, the tertiary structure of constructed vaccine was predicated, and its interaction was determined with TLR9 using ClusPro software. In addition, further atomic level detail of docked complex and their immune response were further determined by MD simulation and C-ImmSim tool, respectively. RESULTS: A protein with immunogenic potential and good vaccine properties (candidate) was identified based on high binding scores, low percentile rank, non-allergenicity and good immunological properties, which were further used to calculate epitopes. Additionally, molecular docking possesses strong interaction; seventeen H-bonds interactions were reported, such as THR101-GLU264, THR185-THR270, ARG 257-ASP210, ARG 257-ASP 210, ASP259-LYS 174, ASN263-GLU237, CYS 265-GLU 233, CYS 265-TYR 197, GLU267- THR202, GLN 270-THR202, TYR345-ASP 210, TYR345-THR 213, ARG 346-ASN209, SER350- GLU141, SER350-GLU141, ASP 424-ARG220 and ARG426-THR216 with TLR-9. Finally, high expression was determined in E. coli (CAI = (0.9045), and GC content = (72%)). Using the IMOD server, all-atom MD simulations of docked complex affirmed its significant stability. The outcomes of immune simulation indicate that both T and B cells represent a strong response to the vaccination component. CONCLUSION: This type of in-silico technique may precisely decrease valuable time and expenses in vaccine designing against Borrelia burgdorferi for experimental planning in laboratories. Currently, scientists frequently utilize bioinformatics approaches that speed up their vaccine-based lab work.