Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches

A novel multi-epitope-based peptide recombinant influenza A vaccine prototype utilizing neuraminidase and hemagglutinin surface proteins: From in silico to preliminary study

Neuraminidase and hemagglutinin serve as the crucial surface proteins of influenza viruses. Hemagglutinin, as a variable surface protein, is indispensable for vaccine development. Therefore, Neuraminidase must not be overlooked in the formulation of the recombinant vaccine prototype, which may serve as a candidate for designing a multi-epitope recombinant vaccine using immunoinformatics. Our study involves immunoinformatic screening and analysis to develop a recombinant multi-epitope vaccine prototype comprising immunodominant and conserved epitopes from influenza hemagglutinin and neuraminidase. Predicted B-cell and T-cell epitopes target a wide allele population. A 199-amino acid construct integrates MHCI1 and MHCII for both mouse and human hosts, connected by rigid and flexible linkers. Molecular docking findings suggest that this multi-epitope structure could activate TLR3,2 TLR7, and TLR8, thereby prompting protective immune responses. B-cell epitopes mediate adaptive immune responses by facilitating antigen recognition and memory formation Furthermore, the designed construct underwent in silico cloning of the vaccine prototype candidate in pET21a as a prokaryotic expression vector, followed by evaluation and exploration. It underwent characterization for physicochemical attributes, allergenicity, toxicity, and antigenicity. Validation through dynamic simulation confirms the stability of the construct. This pioneering immunoinformatic study proposes the potential of a recombinant protein vaccine prototype centered around neuraminidase and hemagglutinin immunodominant epitopes to elicit immune responses against a broad spectrum of viruses. Additionally, this vaccine prototype has been evaluated through both in silico and in vitro studies.

CD8+ T cell-based immunotherapy: Promising frontier in human diseases

The abundant cell components of the adaptive immune system called T lymphocytes (T cells) play important roles in mediating immune responses to eliminate the invaders and create the memory of the germs to form a new immunity for the next encounter. Among them, cytotoxic T cells expressing cell-surface CD8 are the most critical effector cells that directly eradicate the target infected cells by recognizing antigens presented by major histocompatibility complex class I molecules to protect our body from pathological threats. In the continuous evolution of immunotherapy, various CD8+ T cell-based therapeutic strategies have been developed based on the role and molecular concept of CD8+ T cells. The emergence of such remarkable therapies provides promising hope for multiple human disease treatments such as autoimmunity, infectious disease, cancer, and other non-infectious diseases. In this review, we aim to discuss the current knowledge on the utilization of CD8+ T cell-based immunotherapy for the treatment of various diseases, the molecular basis involved, and its limitations. Additionally, we summarize the recent advances in the use of CD8+ T cell-based immunotherapy and provide a comprehensive overview of CD8+ T cells, including their structure, underlying mechanism of function, and markers associated with CD8+ T cell exhaustion. Building upon these foundations, we delineate the advancement of CD8+ T cell-based immunotherapies with fundamental operating principles followed by research studies, and challenges, as well as illustrate human diseases involved in this development.

Screening helper T lymphocyte epitopes based on IFN-γ/IL-10 ratio for developing a novel multi-epitope vaccine candidate using Wolbachia surface protein as an adjuvant against visceral leishmaniasis

BACKGROUND

Visceral leishmaniasis (VL) is the most lethal form of leishmaniasis. In terms of anti-leishmanial vaccines, favorable immune responses are Th1 responses that primarily produce interferon gamma (IFN-γ) and activate macrophages for leishmanicidal effects. The selection of IFN-γ-inducing epitopes in silico may reduce the steps of pre-clinical evaluation and increase the certainty of the better-designed vaccine. Wolbachia surface protein (WSP) derived from Wolbachia bacteria that have been reported to reside in sandflies can trigger TLR2 and TLR4 activation to favor Th1 immune responses, which may serve as a potential adjuvant candidate for the Leishmania vaccine. Therefore, in this study, helper T lymphocyte epitopes that may induce favorable immune responses were identified, and WSP was served as an adjuvant to design a novel multi-epitope vaccine candidate.

METHODS

Leishmania hemoglobin receptor (HbR), kinetoplastid membrane protein-11 (KMP-11), glycoprotein of 63 kDa (Gp63), thiol-specific antioxidant antigen (TSA), and sterol 24-c-methyltransferase (SMT) were analyzed by immunoinformatics to screen helper T lymphocyte and cytotoxic T lymphocyte epitopes. The antigenicity, toxicity, allergenicity, and IFN-γ-inducing epitope potential of T epitopes were predicted. The immune simulation was performed to calculate IFN-γ/interleukin (IL)-10 ratios to predict the immune responses induced by the helper T lymphocyte epitopes. Molecular docking and molecular dynamics simulations were carried out to analyze the interactions and stability of the docked complexes. The immune simulation of a multi-epitope vaccine candidate was carried out to predict its immunogenicity.

RESULTS

Some helper T lymphocyte epitopes that were predicted with the potential of inducing Th1 responses and cytotoxic T lymphocyte epitopes were selected to develop a novel multi-epitope vaccine candidate with WSP as an adjuvant. It was found in molecular docking and interaction analysis that TLR2 and TLR4 can recognize WSP, supporting the potential of adjuvant for the Leishmania vaccine. The results from immune simulation demonstrated that the multi-epitope vaccine candidate induced obvious cytokine (IFN-γ, IL-12, and IL-2) secretion and Th1 as well as memory T cell production, similar to that of Leish-111f.

CONCLUSIONS

Our vaccine candidate may interact with TLR2 and TLR4 and exhibit good immunogenicity, favoring Leishmania clearance. Our strategy provides a novel multi-epitope vaccine candidate and references for other vaccine developments.

Computationally designed multi-epitope vaccine construct targeting the SARS-CoV-2 spike protein elicits robust immune responses in silico

Our research is driven by the need to design an advanced multi-epitope vaccine construct (MEVC) using the S-protein of SARS-CoV-2 to combat the emergence of new variants. Through rigorous computational screening, we have identified linear and discontinuous B-cell epitopes, CD8 + and CD4 + T-cell epitopes, ensuring extensive MEVC coverage across 90.03% of the global population. The MEVC, featuring four CD4 + and four CD8 + T-cell epitopes connected linearly with two adjuvant proteins on both ends, has been carefully designed to elicit robust immune response. Our in-silico analysis has confirmed the construct's antigenicity, non-allergenicity, and non-toxicity with optimized codon sequences for enhanced expression in E. coli K12. Furthermore, molecular docking and dynamics analyses have demonstrated its strong binding affinity with TLR-3 and TLR 4, and in-silico immune simulation yielded promising results on heightened B-cell and T-cell-mediated immunity. However, wet lab experiments are essential to validate computational findings to revolutionize the development of vaccines against SARS-CoV-2.

Design and Immune Profile of Multi-Epitope Synthetic Antigen Vaccine Against SARS-CoV-2: An In Silico and In Vivo Approach

BACKGROUND

The rapid advancement of the pandemic caused by SARS-CoV-2 and its variants reinforced the importance of developing easy-to-edit vaccines with fast production, such as multi-epitope DNA vaccines. The present study aimed to construct a synthetic antigen multi-epitope SARS-CoV-2 to produce a DNA vaccine.

METHODS

A database of previously predicted Spike and Nucleocapsid protein epitopes was created, and these epitopes were analyzed for immunogenicity, conservation, population coverage, and molecular docking.

RESULTS

A synthetic antigen with 15 epitopes considered immunogenic, conserved even in the face of variants and that were able to anchor themselves in the appropriate HLA site, together had more than 90% worldwide coverage. A multi-epitope construct was developed with the sequences of these peptides separated from each other by linkers, cloned into the pVAX1 vector. This construct was evaluated in vivo as a DNA vaccine and elicited T CD4+ and T CD8+ cell expansion in the blood and spleen. In hematological analyses, there was an increase in lymphocytes, monocytes, and neutrophils between the two doses. Furthermore, based on histopathological analysis, the vaccines did not cause any damage to the organs analyzed.

CONCLUSIONS

The present study generated a multi-epitope synthetic vaccine antigen capable of generating antibody-mediated and cellular immune responses.

A Bibliometric Analysis on Multi-epitope Vaccine Development Against SARS-CoV-2: Current Status, Development, and Future Directions

The etiological agent for the coronavirus disease 2019 (COVID-19), the SARS-CoV-2, caused a global pandemic. Although mRNA, viral-vectored, DNA, and recombinant protein vaccine candidates were effective against the SARS-CoV-2 Wuhan strain, the emergence of SARS-CoV-2 variants of concern (VOCs) reduced the protective efficacies of these vaccines. This necessitates the need for effective and accelerated vaccine development against mutated VOCs. The development of multi-epitope vaccines against SARS-CoV-2 based on in silico identification of highly conserved and immunogenic epitopes is a promising strategy for future SARS-CoV-2 vaccine development. Considering the evolving landscape of the COVID-19 pandemic, we have conducted a bibliometric analysis to consolidate current findings and research trends in multi-epitope vaccine development to provide insights for future vaccine development strategies. Analysis of 102 publications on multi-epitope vaccine development against SARS-CoV-2 revealed significant growth and global collaboration, with India leading in the number of publications, along with an identification of the most prolific authors. Key journals included the Journal of Biomolecular Structure and Dynamics, while top collaborations involved Pakistan-China and India-USA. Keyword analysis showed a prominent focus on immunoinformatics, epitope prediction, and spike glycoprotein. Advances in immunoinformatics, including AI-driven epitope prediction, offer promising avenues for the development of safe and effective multi-epitope vaccines. Immunogenicity may be further improved through nanoparticle-based systems or the use of adjuvants along with real-time genomic surveillance to tailor vaccines against emerging variants.

Computational studies on metabolic pathways of Coxiella burnetii to combat Q fever: A roadmap to vaccine development

Coxiella burnetii (Cbu) is the gram-negative intracellular pathogen responsible for deadly zoonotic infection, Q fever. The pathogen is environmentally stable and distributed throughout the world which is sustained in nature by chronic infection of ruminants. The epidemiological studies on Q fever indicates it as emerging public health problem in various countries and it is imperative to promptly identify an appropriate therapeutic solution for this pathogen. In the current study, metabolic pathways of Cbu were analysed by the combination of multiple computational tools for the prediction of suitable therapeutic candidates. We have identified 25 metabolic pathways which were specific to Cbu containing 287 unique proteins. A total of 141 proteins which were either virulent, essential or resistant were shortlisted that do not show homology with the host proteins and considered as potential targets for drug and vaccine development. The potential therapeutic targets were classified in to seven functional classes, i.e., metabolism, transport, gene expression and regulation, signal transduction, antimicrobial resistance, stress response regulator and unknown. The majority of the proteins were found to be present in metabolism and transport class. The functional annotation showed the predominant presence of proteins containing HATPase_c, Beta-lactamase, GerE, ACR_tran, PP-binding, CsrA domains. We have identified Type I secretion outer membrane protein for the design of multi-epitope subunit vaccine using reverse vacciniology approach. Four B cell epitopes, six MHC-I epitopes and four MHC-II epitopes were identified which are non-toxic, non-allergen and highly antigenic. The multi-epitope subunit vaccine construct was 327 amino acid residues long which include adjuvant, B cell epitopes, MHC-I epitopes and MHC-II epitopes. The Cholera enterotoxin subunit B is included as an adjuvant in the N terminal of vaccine construct which will help to produce a strong immune response to the vaccine. The multi-epitope vaccine construct was non-toxic, non-allergen and probable antigen having molecular weight 35.13954 kDa, aliphatic index 85.50, theoretical PI 9.65, GRAVY -0.001, and instability index of 28.37. The tertiary structure of the vaccine construct was modeled and physiochemical properties were predicted. After validation and refinement of tertiary structure the molecular docking of vaccine exhibited strong binding with TLR2, TLR3, TLR4, TLR5 and TLR8. The TLRs and vaccine construct formed hydrogen bonds, salt bridges and non-bonded contacts with all TLR receptors. The in-silico immune simulations showed the ability to trigger primary immune response as shown by increment in B-cell and T-cell population. The research paves the way for more effective control of zoonotic disease Q fever.

Enhanced Immune Response Against Echinococcus Granulosus Through a CTLA-4/B7 Affinity-Based Vaccine

Background: Echinococcosis is a zoonotic infectious disease that poses a significant threat to the health of individuals living in rural regions. While vaccination represents a potential strategy for disease prevention, there is currently no effective vaccine available for humans to prevent cystic echinococcosis (CE). This study aimed to design a novel multi-epitope vaccine (MEV) against Echinococcus granulosus for human use, employing immunoinformatics methods. Methods: We identified core epitopes from two key antigens, EgA31 and EgG1Y162, and integrated them into the immunoglobulin variable region of CTLA-4 (CTLA-4lgV) to create the CVE31-162 vaccine construct. The secondary and tertiary structures of the CVE31-162 were established using bioinformatics methods. The interaction between the CVE31-162 and B7 molecules was assessed through molecular dynamics simulations. Finally, both in vitro and in vivo experiments were conducted to validate the effectiveness of the CVE31-162 against the immunological effects of Echinococcus granulosus. Results: Bioinformatics analysis indicated that CVE31-162 exhibits favorable antigenicity, stability, and non-allergenicity. Furthermore, CVE31-162 demonstrated a stable three-dimensional structural model. Molecular docking (MD) and molecular dynamics simulations (MDS) revealed a strong binding affinity between CVE31-162 and B7 molecules. Immune simulation results suggested that the vaccine elicits robust humoral and cell-mediated immune responses. Both in vitro and in vivo experiments demonstrated that immunized mice exhibited significantly elevated levels of antigen-specific antibodies and enhanced lymphocyte proliferation compared to the control group. Conclusions:CVE31-162, which is based on the interaction between CTLA-4 and B7, represents a promising multi-epitope vaccine for Echinococcus granulosus.

In Silico design of a multi-epitope vaccine for Human Parechovirus: Integrating immunoinformatics and computational techniques

Human parechovirus (HPeV) is widely recognized as a severe viral infection affecting infants and neonates. Belonging to the Picornaviridae family, HPeV is categorized into 19 distinct genotypes. Among them, HPeV-1 is the most prevalent genotype, primarily associated with respiratory and digestive symptoms. Considering HPeV's role as a leading cause of life-threatening viral infections in infants and the lack of effective antiviral therapies, our focus centered on developing two multi-epitope vaccines, namely HPeV-Vax-1 and HPeV-Vax-2, using advanced immunoinformatic techniques. Multi-epitope vaccines have the advantage of protecting against various virus strains and may be preferable to live attenuated vaccines. Using the NCBI database, three viral protein sequences (VP0, VP1, and VP3) from six HPeV strains were collected to construct consensus protein sequences. Then the antigenicity, toxicity, allergenicity, and stability were analyzed after discovering T-cell and linear B-cell epitopes from the protein sequences. The fundamental structures of the vaccines were produced by fusing the selected epitopes with appropriate linkers and adjuvants. Comprehensive physicochemical, antigenic, allergic assays, and disulfide engineering demonstrated the effectiveness of the vaccines. Further refinement of secondary and tertiary models for both vaccines revealed promising interactions with toll-like receptor 4 (TLR4) in molecular docking, further confirmed by molecular dynamics simulation. In silico immunological modeling was employed to assess the vaccine's capacity to stimulate an immune reaction. In silico immunological simulations were employed to evaluate the vaccines' ability to trigger an immune response. Codon optimization and in silico cloning analyses showed that Escherichia coli (E. coli) was most likely the host for the candidate vaccines. Our findings suggest that these multi-epitope vaccines could be the potential HPeV vaccines and are recommended for further wet-lab investigation.

Designing a multi-epitope subunit vaccine against Orf virus using molecular docking and molecular dynamics

Orf virus (ORFV) is an acute contact, epitheliotropic, zoonotic, and double-stranded DNA virus that causes significant economic losses in the livestock industry. The objective of this study is to design an immunoinformatics-based multi-epitope subunit vaccine against ORFV. Various immunodominant cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL), and B-cell epitopes from the B2L, F1L, and 080 protein of ORFV were selected and linked by short connectors to construct a multi-epitope subunit vaccine. Immunogenicity was enhanced by adding an adjuvant β-defensin to the N-terminal of the vaccine using the EAAAK linker. The vaccine exhibited a significant degree of antigenicity and solubility, without allergenicity or toxicity. The 3D formation of the vaccine was subsequently anticipated, improved, and verified. The optimized model exhibited a lower Z-score of -4.33, indicating higher quality. Molecular docking results demonstrated that the vaccine strongly binds to TLR2 and TLR4. Molecular dynamics results indicated that the docked vaccine-TLR complexes were stable. Immune simulation analyses further confirmed that the vaccine can induce a marked increase in IgG and IgM antibody titers, and elevated levels of IFN-γ and IL-2. Finally, the optimized DNA sequence of the vaccine was cloned into the vector pET28a (+) for high expression in the E.coli expression system. Overall, the designed multi-epitope subunit vaccine is highly stable and can induce robust humoral and cellular immunity, making it a promising vaccine candidate against ORFV.

Construction of a Hybrid Vaccine Based on Der f 35-Derived Peptides with Reduced Allergenicity

INTRODUCTION

House dust mite is the primary trigger of allergic respiratory diseases worldwide, and allergen-specific immunotherapy (AIT) is the only disease-modifying treatment in the clinic. The use of allergen molecules instead of extracts is a promising strategy in AIT. In this study, we constructed a peptide hybrid vaccine against the major mite allergen Der f 35 and verified its hypoallergenicity, making it to be a promising candidate for AIT of mite allergy.

METHODS

The gene encoding Der f 35 was retrieved and synthesized. The hypoallergenic peptide fragments derived from the B-cell epitopes were synthesized based on the predicted profiles of B-cell or T helper-cell epitopes in Der f 35, they were verified by immunoglobulin E (IgE)-reaction test and fused to non-allergenic protein carrier to form the hybrid vaccine. Both the wild-type Der f 35 and the designed vaccine were expressed in Escherichia coli and purified by chromatography; their IgE-binding activity was compared by indirect enzyme-linked immunosorbent assay (ELISA), Western blot, inhibition ELISA, and basophil activation test (BAT). The blocking immunoglobulin G (IgG) against the designed vaccine was raised in rabbits and its ability to inhibit IgE binding of Der f 35 was evaluated by ELISA. The vaccine's effects on peripheral blood mononuclear cells (PBMCs) were investigated.

RESULTS

A total of 29 out of 60 (48.33%) IgE-positive sera against Der f 35 were screened. Five peptide fragments (residue 39-42, 60-67, 73-107, 111-118, 126-143) from Der f 35 were selected as candidates, in which four peptides exhibited almost no IgE reactivity and the fragment 73-107 had weak reactions. Only 5.98-24.02% inhibition rates could be achieved by the peptides when compared with Der f 35 (97.32%). The designed vaccine migrated at approximately 30 kDa by SDS-PAGE. The IgE-ELISA revealed a significant reduction in IgE-binding activity to the vaccine when compared to wild-type Der f 35 (p < 0.0001); the decreased allergenicity was further confirmed by IgE-Western blot, inhibition ELISA, and BAT, respectively. The IgE-reactivity of Der f 35 could be blocked by the vaccine-induced IgG (p < 0.01). The levels of IL-5 and IL-13 from PBMCs were significantly decreased after stimulation by the vaccine than that by Der f 35 (p < 0.05).

CONCLUSION

The designed B-cell epitope vaccine of Der f 35 showed greatly diminished allergenicity and Th2 activity. It could be an effective and safe candidate to prevent allergic adverse reactions during the immunotherapy of mite allergy and merits the further study.

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

In silico and immunoinformatics based multiepitope subunit vaccine design for protection against visceral leishmaniasis

Visceral leishmaniasis (VL) is a vector-borne neglected tropical protozoan disease with high fatality and no certified vaccine. Conventional vaccine preparation is challenging and tedious. Here in this work, we created a global multiepitope subunit vaccination against VL utilizing innovative immunoinformatics technique based on the extensively conserved epitopic regions of the PrimPol protein of Leishmania donovani consisting of four subunits which were analyzed and studied, out of which DNA primase large subunit and DNA polymerase α subunit B were evaluated as antigens by Vaxijen 2.0. The multiepitope vaccine design includes a single adjuvant β-defensins, eight CTL epitopes, eight HTL epitopes, seven linear BCL epitopes and one discontinuous BCL epitope to induce innate, cellular and humoral immune responses against VL. The Expasy ProtParam tool characterized the physiochemical parameters of the vaccine. At the same time, SOLpro evaluated our vaccine constructs to be soluble upon expression. We also modeled the stable tertiary structure of our vaccine construct through Robetta modeling for molecular docking studies with toll-like receptor proteins through HADDOCK 2.4. Simulations based on molecular dynamics revealed an intact vaccine and TLR8 complex, supporting our vaccine design's immunogenicity. Also, the immune simulation of our vaccine by the C-ImmSim server demonstrated the potency of the multiepitope vaccine construct to induce proper immune response for host defense. Codon optimization and in silico cloning of our vaccine further assured high expression. The outcomes of our study on multiepitope vaccine design significantly produced a potential candidate against VL and can potentially eradicate the disease in the future after clinical investigations.Communicated by Ramaswamy H. Sarma.

Rational design of a multi-epitope vaccine against heartland virus (HRTV) using immune-informatics, molecular docking and dynamics approaches

Heartland virus (HRTV) is a single-stranded negative-sense RNA virus that infects human beings. Because there are no antiviral medications available to treat HRTV infection, supportive care management is used in cases of severe disease. Therefore, it has spurred research into developing a multi-epitope vaccine capable of providing effective protection against HRTV infection. A multi-epitope vaccine was created using a combination of immuno-informatics, molecular docking and molecular dynamics simulation in this investigation. The HRTV proteome was utilized to predict B-cell, T-cell (HTL and CTL), and IFN-epitopes. Following prediction, highly antigenic, non-allergenic and immunogenic epitopes were chosen, including 6 CTL, 8 HTL, and 5 LBL epitopes that were connected to the final peptide by AAY, GPGPG, and KK linkers, respectively. An adjuvant was introduced to the vaccine's N-terminal through the EAAAK linker to increase its immunogenicity. Following the inclusion of linkers and adjuvant, the final construct has 359 amino acids. The presence of B-cell and IFN-γ-epitopes validates the construct's acquired humoral and cell-mediated immune responses. To ensure the vaccine's safety and immunogenicity profile, its allergenicity, antigenicity, and various physicochemical characteristics were assessed. Docking was used to assess the binding affinity and molecular interaction between the vaccination and TLR-3. In silico cloning was used to confirm the construct's validity and expression efficiency. The results of these computer assays demonstrated that the designed vaccine is highly promising in terms of developing protective immunity against HRTV; nevertheless, additional in vivo and in vitro investigations are required to validate its true immune-protective efficiency.

T-Cell Epitope-Based Vaccines: A Promising Strategy for Prevention of Infectious Diseases

With the development of novel vaccine strategies, T-cell epitope-based vaccines have become promising prophylactic and therapeutic tools against infectious diseases that cannot be controlled via traditional vaccines. T-cell epitope-based vaccines leverage specific immunogenic peptides to elicit protective T-cell responses against infectious pathogens. Compared to traditional vaccines, they provide superior efficacy and safety, minimizing the risk of adverse side effects. In this review, we summarized and compared the prediction and identification methods of T-cell epitopes. By integrating bioinformatic prediction and experimental validation, efficient and precise screening of T-cell epitopes can be achieved. Importantly, we delved into the development approaches to diverse T-cell epitope-based vaccines, comparing their merits and demerits, as well as discussing the prevalent challenges and perspectives in their applications. This review offers fresh perspectives for the formulation of safe and efficacious epitope-based vaccines for the devastating diseases against which no vaccines are currently available.

Prediction of antigenic peptides of SARS- CoV-2 pathogen using machine learning

Antigenic peptides (APs), also known as T-cell epitopes (TCEs), represent the immunogenic segment of pathogens capable of inducing an immune response, making them potential candidates for epitope-based vaccine (EBV) design. Traditional wet lab methods for identifying TCEs are expensive, challenging, and time-consuming. Alternatively, computational approaches employing machine learning (ML) techniques offer a faster and more cost-effective solution. In this study, we present a robust XGBoost ML model for predicting TCEs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus as potential vaccine candidates. The peptide sequences comprising TCEs and non-TCEs retrieved from Immune Epitope Database Repository (IEDB) were subjected to feature extraction process to extract their physicochemical properties for model training. Upon evaluation using a test dataset, the model achieved an impressive accuracy of 97.6%, outperforming other ML classifiers. Employing a five-fold cross-validation a mean accuracy of 97.58% was recorded, indicating consistent and linear performance across all iterations. While the predicted epitopes show promise as vaccine candidates for SARS-CoV-2, further scientific examination through in vivo and in vitro studies is essential to validate their suitability.

Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

Platelet dysfunction caused by differentially expressed genes as key pathogenic mechanisms in COVID-19

At the end of 2019, the novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became prevalent worldwide, which brought a heavy medical burden and tremendous economic losses to the world population. In addition to the common clinical respiratory symptoms such as fever, cough and headache, patients with COVID-19 often have hematological diseases, especially platelet dysfunction. Platelet dysfunction usually leads to multiple organ dysfunction, which is closely related to patient severity or mortality. In addition, studies have confirmed significant changes in the gene expression profile of circulating platelets under SARS-CoV-2 infection, which will further lead to changes in platelet function. At the same time, studies have shown that platelets may absorb SARS-COV-2 mRNA independently of ACE2, which further emphasizes the importance of the stability of platelet function in defense against SARS-CoV-2 infection. This study reviewed the relationship between COVID-19 and platelet and SARS-CoV-2 damage to the circulatory system, and further analyzed the significantly differentially expressed mRNA in platelets after infection with SARS-CoV-2 on the basis of previous studies. The top eight hub genes were identified as NLRP3, MT-CO1, CD86, ICAM1, MT-CYB, CASP8, CXCL8 and CXCR4. Subsequently, the effects of SARS-CoV-2 infection on platelet transcript abnormalities and platelet dysfunction were further explored on the basis of 8 hub genes. Finally, the treatment measures of complications caused by platelet dysfunction in patients with COVID-19 were discussed in detail, so as to provide reference for the prevention, diagnosis and treatment of COVID-19.

Anticipatory in silico vaccine designing based on specific antigenic epitopes from Streptococcus mutans against diabetic pathogenesis

The metabolic disorder Type 2 Diabetes Mellitus (T2DM) is characterized by hyperglycaemia, causing increased mortality and healthcare burden globally. Recent studies emphasize the impact of metabolites in the gut microbiome on T2DM pathogenesis. One such microbial metabolite, imidazole propionate (Imp) derived from histidine metabolism, is shown to interfere with insulin signalling and other key metabolic processes. The key enzyme urocanate reductase (UrdA) is involved in ImP production. Hence, we propose to develop a novel therapeutic vaccine against the gut microbe producing Imp based on UrdA as a target for treating T2DM using immunoinformatics approach. Antigenic, non-allergic, non-toxic, and immunogenic B cell and T cell potential epitopes were predicted using immunoinformatics servers and tools. These epitopes were adjoined using linker sequences, and to increase immunogenicity, adjuvants were added at the N-terminal end of the final vaccine construct. Further, to confirm the vaccine's safety, antigenic and non-allergic characteristics of the developed vaccine construct were assessed. The tertiary structure of the UrdA vaccine sequence was predicted using molecular modelling tools. A molecular docking study was utilized to understand the vaccine construct interaction with immune receptors, followed by molecular dynamics simulation and binding free energy calculations to assess stability of the complex. In silico cloning techniques were employed to evaluate the expression and translation effectiveness of the developed vaccine in pET vector. In conclusion, this study developed an in silico epitope-based vaccine construct as a novel adjunct therapeutic for T2DM.

Graphical Abstract

Development of an ancestral DC and TLR4-inducing multi-epitope peptide vaccine against the spike protein of SARS-CoV and SARS-CoV-2 using the advanced immunoinformatics approaches

The oldest human coronavirus that started pandemics is severe acute respiratory syndrome virus (SARS-CoV). While SARS-CoV was eradicated, its new version, SARS-CoV2, caused the global pandemic of COVID-19. Evidence highlights the harmful events orchestrated by these viruses are mediated by Spike (S)P protein. Experimental epitopes of the S protein which were overlapping and ancestral between SARS-CoV and SARS-CoV-2 were obtained from the immune epitopes database (IEDB). The epitopes were then assembled in combination with a 50 S ribosomal protein L7/L12 adjuvant, a Mycobacterium tuberculosis-derived element and mediator of dendritic cells (DCs) and toll-like receptor 4 (TLR4). The immunogenic sequence was modeled by the GalaxyWeb server. After the improvement and validation of the protein structure, the physico-chemical properties and immune simulation were performed. To investigate the interaction with TLR3/4, Molecular Dynamics Simulation (MDS) was used. By merging the 17 B- and T-lymphocyte (HTL/CTL) epitopes, the vaccine sequence was created. Also, the Ramachandran plot presented that most of the residues were located in the most favorable and allowed areas. Moreover, SnapGene was successful in cloning the DNA sequence linked to our vaccine in the intended plasmid. A sequence was inserted between the XhoI and SacI position of the pET-28a (+) vector, and simulating the agarose gel revealed the existence of the inserted gene in the cloned plasmid with SARS vaccine (SARSV) construct, which has a 6565 bp in length overall. In terms of cytokines/IgG response, immunological simulation revealed a strong immune response. The stabilized vaccine showed strong interactions with TLR3/4, according to Molecular Dynamics Simulation (MDS) analysis. The present ancestral vaccine targets common sequences which seem to be valuable targets even for the new variant SARS-CoV-2.

Engineering receptor-binding domain and heptad repeat domains towards the development of multi-epitopes oral vaccines against SARS-CoV-2 variants

The inability of existing vaccines to cope with the mutation rate has highlighted the need for effective preventative strategies for COVID-19. Through the secretion of immunoglobulin A, mucosal delivery of vaccines can effectively stimulate mucosal immunity for better protection against SARS-CoV-2 infection. In this study, various immunoinformatic tools were used to design a multi-epitope oral vaccine against SARS-CoV-2 based on its receptor-binding domain (RBD) and heptad repeat (HR) domains. T and B lymphocyte epitopes were initially predicted from the RBD and HR domains of SARS-CoV-2, and potential antigenic, immunogenic, non-allergenic, and non-toxic epitopes were identified. Epitopes that are highly conserved and have no significant similarity to human proteome were selected. The epitopes were joined with appropriate linkers, and an adjuvant was added to enhance the vaccine efficacy. The vaccine 3D structure constructs were docked with toll-like receptor 4 (TLR-4) and TLR1-TLR2, and the binding affinity was calculated. The designed multi-epitope vaccine construct (MEVC) consisted of 33 antigenic T and B lymphocyte epitopes. The results of molecular dockings and free binding energies confirmed that the MEVC effectively binds to TLR molecules, and the complexes were stable. The results suggested that the designed MEVC is a potentially safe and effective oral vaccine against SARS-CoV-2. This in silico study presents a novel approach for creating an oral multi-epitope vaccine against the rapidly evolving SARS-CoV-2 variants. These findings offer valuable insights for developing an effective strategy to combat COVID-19. Further preclinical and clinical studies are required to confirm the efficacy of the MEVC vaccine.

Design of Cryptococcus neoformans multi-epitope vaccine based on immunoinformatics method

Cryptococcus neoformans is a widely distributed opportunistic pathogenic fungus. While C. neoformans commonly infects immunocompromised individuals, it can also affect those who are immunocompetent. Transmission of C. neoformans primarily occurs through the respiratory tract, leading to the development of meningitis. The mortality rate of Cryptococcal meningitis is high, and treatment options are limited. Cryptococcus neoformans infections pose a significant public health threat and currently lack targeted and effective response strategies. This study aimed to screen T lymphocyte (cytotoxic T lymphocyte and helper T lymphocyte) and B lymphocyte epitopes derived from four C. neoformans antigens and develop two multi-epitope vaccines by combining them with various adjuvants. Molecular docking results demonstrated that the vaccines bind stably to Toll-like receptor 4 ( and induce innate immunity. The credibility of the molecular docking results was validated through subsequent molecular dynamics simulations. Furthermore, the results of immune simulation analyses underscored the multi-epitope vaccine's capability to effectively induce robust humoral and cellular immune responses within the host organism. These two vaccines have demonstrated theoretical efficacy against C. neoformans infection as indicated by computer analysis. Nevertheless, additional experimental validation is essential to substantiate the protective efficacy of the vaccines.

Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer

BACKGROUND

This study aimed to identify the optimal protein construction for designing a multi-epitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process involved using capsid epitopes L1 and L2, as well as oncoproteins E5, E6, and E7 from human papillomavirus (HPV) types 16 and 18.

METHODS

An experimental in silico analysis with an immunoinformatics approach was used to develop 2 multi-epitope vaccine constructs (A and B). Further analysis was then conducted to compare the constructs and select the one with the highest potential against cervical cancer.

RESULTS

This study produced 2 antigenic, non-allergenic, and nontoxic multi-epitope vaccine constructs (A and B), which exhibited the ideal physicochemical properties for a vaccine. Further analysis revealed that construct B effectively induced both cellular and humoral immune responses.

CONCLUSION

The multi-epitope vaccine construct B for HPV 16 and 18, designed for both prophylactic and therapeutic purposes, met the development criteria for a cervical cancer vaccine. However, these findings need to be validated through in vitro and in vivo experiments.

A broad-spectrum multiepitope vaccine against seasonal influenza A and B viruses in mice

BACKGROUND

Influenza viruses pose a persistent threat to global public health, necessitating the development of innovative and broadly effective vaccines.

METHODS

This study focuses on a multiepitope vaccine (MEV) designed to provide broad-spectrum protection against different influenza viruses. The MEV, containing 19 B-cell linear epitopes, 7 CD4+ T cells, and 11 CD8+ T cells epitopes identified through enzyme-linked immunospot assay (ELISPOT) in influenza viruses infected mice, was administered through a regimen of two doses of DNA vaccine followed by one dose of a protein vaccine in C57BL/6 female mice.

FINDINGS

Upon lethal challenge with both seasonal circulating strains (H1N1, H3N2, BV, and BY) and historical strains (H1N1-PR8 and H3N2-X31), MEV demonstrated substantial protection against different influenza seasonal strains, with partial efficacy against historical strains. Notably, the increased germinal centre B cells and antibody-secreting cells, along with robust T cell immune responses, highlighted the comprehensive immune defence elicited by MEV. Elevated hemagglutinin inhibition antibody was also observed against seasonal circulating and historical strains. Additionally, mice vaccinated with MEV exhibited significantly lower counts of inflammatory cells in the lungs compared to negative control groups.

INTERPRETATION

Our results demonstrated the efficacy of a broad-spectrum MEV against influenza viruses in mice. Conducting long-term studies to evaluate the durability of MEV-induced immune responses and explore its potential application in diverse populations will offer valuable insights for the continued advancement of this promising vaccine.

FUNDING

Funding bodies are described in the Acknowledgments section.

Putative new combination vaccine candidates identified by reverse vaccinology and genomic approaches to control enteric pathogens

OBJECTIVES

The pathogenic microorganisms that cause intestinal diseases can significantly jeopardize people's health. Currently, there are no authorized treatments or vaccinations available to combat the germs responsible for intestinal disease.

METHODS

Using immunoinformatics, we developed a potent multi-epitope Combination (combo) vaccine versus Salmonella and enterohemorrhagic E. coli. The B and T cell epitopes were identified by performing a conservancy assessment, population coverage analysis, physicochemical attributes assessment, and secondary and tertiary structure assessment of the chosen antigenic polypeptide. The selection process for vaccine development included using several bioinformatics tools and approaches to finally choose two linear B-cell epitopes, five CTL epitopes, and two HTL epitopes.

RESULTS

The vaccine had strong immunogenicity, cytokine production, immunological properties, non-toxicity, non-allergenicity, stability, and potential efficacy against infections. Disulfide bonding, codon modification, and computational cloning were also used to enhance the stability and efficacy of expression in the host E. coli. The vaccine's structure has a strong affinity for the TLR4 ligand and is very durable, as shown by molecular docking and molecular modeling. The results of the immunological simulation demonstrated that both B and T cells had a heightened response to the vaccination component.

CONCLUSIONS

The comprehensive in silico analysis reveals that the proposed vaccine will likely elicit a robust immune response against pathogenic bacteria that cause intestinal diseases. Therefore, it is a promising option for further experimental testing.

Design of multi-epitope vaccine against porcine rotavirus using computational biology and molecular dynamics simulation approaches

Porcine Rotavirus (PoRV) is a significant pathogen affecting swine-rearing regions globally, presenting a substantial threat to the economic development of the livestock sector. At present, no specific pharmaceuticals are available for this disease, and treatment options remain exceedingly limited. This study seeks to design a multi-epitope peptide vaccine for PoRV employing bioinformatics approaches to robustly activate T-cell and B-cell immune responses. Two antigenic proteins, VP7 and VP8*, were selected from PoRV, and potential immunogenic T-cell and B-cell epitopes were predicted using immunoinformatic tools. These epitopes were further screened according to non-toxicity, antigenicity, non-allergenicity, and immunogenicity criteria. The selected epitopes were linked with linkers to form a novel multi-epitope vaccine construct, with the PADRE sequence (AKFVAAWTLKAAA) and RS09 peptide attached at the N-terminus of the designed peptide chain to enhance the vaccine's antigenicity. Protein-protein docking of the vaccine constructs with toll-like receptors (TLR3 and TLR4) was conducted using computational methods, with the lowest energy docking results selected as the optimal predictive model. Subsequently, molecular dynamics (MD) simulation methods were employed to assess the stability of the protein vaccine constructs and TLR3 and TLR4 receptors. The results indicated that the vaccine-TLR3 and vaccine-TLR4 docking models remained stable throughout the simulation period. Additionally, the C-IMMSIM tool was utilized to determine the immunogenic triggering capability of the vaccine protein, demonstrating that the constructed vaccine protein could induce both cell-mediated and humoral immune responses, thereby playing a role in eliciting host immune responses. In conclusion, this study successfully constructed a multi-epitope vaccine against PoRV and validated the stability and efficacy of the vaccine through computational analysis. However, as the study is purely computational, experimental evaluation is required to validate the safety and immunogenicity of the newly constructed vaccine protein.

Formulation of next-generation polyvalent vaccine candidates against three important poxviruses by targeting DNA-dependent RNA polymerase using an integrated immunoinformatics and molecular modeling approach

BACKGROUND

Poxviruses comprise a group of large double-stranded DNA viruses and are known to cause diseases in humans, livestock animals, and other animal species. The Mpox virus (MPXV; formerly Monkeypox), variola virus (VARV), and volepox virus (VPXV) are among the prevalent poxviruses of the Orthopoxviridae genera. The ongoing Mpox infectious disease pandemic caused by the Mpox virus has had a major impact on public health across the globe. To date, only limited repurposed antivirals and vaccines are available for the effective treatment of Mpox and other poxviruses that cause contagious diseases.

METHODS

The present study was conducted with the primary goal of formulating multi-epitope vaccines against three evolutionary closed poxviruses i.e., MPXV, VARV, and VPXV using an integrated immunoinformatics and molecular modeling approach. DNA-dependent RNA polymerase (DdRp), a potential vaccine target of poxviruses, has been used to determine immunodominant B and T-cell epitopes followed by interactions analysis with Toll-like receptor 2 at the atomic level.

RESULTS

Three multi-epitope vaccine constructs, namely DdRp_MPXV (V1), DdRp_VARV (V2), and DdRp_VPXV (V3) were designed. These vaccine constructs were found to be antigenic, non-allergenic, non-toxic, and soluble with desired physicochemical properties. Protein-protein docking and interaction profiling analysis depicts a strong binding pattern between the targeted immune receptor TLR2 and the structural models of the designed vaccine constructs, and manifested a number of biochemical bonds (hydrogen bonds, salt bridges, and non-bonded contacts). State-of-the-art all-atoms molecular dynamics simulations revealed highly stable interactions of vaccine constructs with TLR2 at the atomic level throughout the simulations on 300 nanoseconds. Additionally, the outcome of the immune simulation analysis suggested that designed vaccines have the potential to induce protective immunity against targeted poxviruses.

CONCLUSIONS

Taken together, formulated next-generation polyvalent vaccines were found to have good efficacy against closely related poxviruses (MPXV, VARV, and VPXV) as demonstrated by our extensive immunoinformatics and molecular modeling evaluations; however, further experimental investigations are still needed.

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.

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

Immunoinformatic of novel self-amplifying mRNA vaccine lipid nanoparticle against SARS-CoV-2

We developed innovative self-amplifying mRNA (sa-mRNA) vaccine based on the derivative of S and Nsp3 proteins, which are considered crucial adhering to human host cells. We performed B-cell, Major histocompatibility complex (MHC) I, and II epitope which were merged with the KK and GPGPG linker. We also incorporated 5' cap sequence, Kozak sequence, replicase sequence, 3'/5' UTR, and poly A tail within the vaccine structure. The vaccine structure was subsequently docked and run the molecular dynamic simulation with TLR7 molecules. As the results of immune response simulation, the immune response was accelerated drastically up to >10-fold for immunoglobulin, interferon-γ, interleukin-2, immunoglobulin M (IgM) + immunoglobulin G (IgG) isotype, IgM isotype, and IgG1 isotype in secondary and tertiary dose, whereas natural killer cells, macrophages, and dendritic cells showed relatively high concentrations after the first dose. As our finding, the IgM + IgG, IgG1 + IgG2, and IgM level (induced by sa-mRNA vaccine) ensued three times with two-fold increase in days 25, and 50, then decreased after days 70-150. However, 150-350 days demonstrated constantly in the range of 20,000-21,000.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV's ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (-1,023.4 kcal/mol, -923.2 kcal/mol, and -988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes' remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine's efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.

Design of a multi-epitope vaccine against goatpox virus using an immunoinformatics approach

Introduction

Goatpox, a severe infectious disease caused by goatpox virus (GTPV), leads to enormous economic losses in the livestock industry. Traditional live attenuated vaccines cause serious side effects and exist a risk of dispersal. Therefore, it is urgent to develop efficient and safer vaccines to prevent and control of GTPV.

Methods

In the present study, we are aimed to design a multi-epitope subunit vaccine against GTPV using an immunoinformatics approach. Various immunodominant cytotoxic T lymphocytes (CTL) epitopes, helper T lymphocytes (HTL) epitopes, and B-cell epitopes from P32, L1R, and 095 proteins of GTPV were screened and liked by the AAY, GPGPG, and KK connectors, respectively. Furthermore, an adjuvant β-defensin was attached to the vaccine's N-terminal using the EAAAK linker to enhance immunogenicity.

Results

The constructed vaccine was soluble, non-allergenic and non-toxic and exhibited high levels of antigenicity and immunogenicity. The vaccine's 3D structure was subsequently predicted, refined and validated, resulting in an optimized model with a Z-value of -3.4. Molecular docking results demonstrated that the vaccine had strong binding affinity with TLR2(-27.25 kcal/mol), TLR3(-39.84 kcal/mol), and TLR4(-59.42 kcal/mol). Molecular dynamics simulation results indicated that docked vaccine-TLR complexes were stable. Immune simulation analysis suggested that the vaccine can induce remarkable increase in antibody titers of IgG and IgM, higher levels of IFN-γ and IL-2.

Conclusion

The designed GTPV multi-epitope vaccine is structurally stable and can induce robust humoral and cellular immune responses, which may be a promising vaccine candidate against GTPV.

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Streptococcus gallolyticus is a non-motile, gram-positive bacterium that causes infective endocarditis. S. gallolyticus has developed resistance to existing antibiotics, and no vaccine is currently available. Therefore, it is essential to develop an effective S. gallolyticus vaccine. Core proteomics was used in this study together with subtractive proteomics and reverse vaccinology approach to find antigenic proteins that could be utilized for the design of the S. gallolyticus multi-epitope vaccine. The pipeline identified two antigenic proteins as potential vaccine targets: penicillin-binding protein and the ATP synthase subunit. T and B cell epitopes from the specific proteins were forecasted employing several immunoinformatics and bioinformatics resources. A vaccine (360 amino acids) was created using a combination of seven cytotoxic T cell lymphocyte (CTL), three helper T cell lymphocyte (HTL), and five linear B cell lymphocyte (LBL) epitopes. To increase immune responses, the vaccine was paired with a cholera enterotoxin subunit B (CTB) adjuvant. The developed vaccine was highly antigenic, non-allergenic, and stable for human use. The vaccine's binding affinity and molecular interactions with the human immunological receptor TLR4 were studied using molecular mechanics/generalized Born surface area (MMGBSA), molecular docking, and molecular dynamic (MD) simulation analyses. Escherichia coli (strain K12) plasmid vector pET-28a ( +) was used to examine the ability of the vaccine to be expressed. According to the outcomes of these computer experiments, the vaccine is quite promising in terms of developing a protective immunity against diseases. However, in vitro and animal research are required to validate our findings.

Revolutionizing and identifying novel drug targets in Citrobacter koseri via subtractive proteomics and development of a multi-epitope vaccine using reverse vaccinology and immuno-informatics

Citrobacter koseri is a gram-negative rod that has been linked to infections in people with significant comorbidities and immunocompromised immune systems. It is most commonly known to cause urinary tract infections. Thus, the development of an efficacious C. koseri vaccine is imperative, as the pathogen has acquired resistance to current antibiotics. Subtractive proteomics was employed during this research to identify potential antigenic proteins to design an effective vaccine against C. koseri. The pipeline identified two antigenic proteins as potential vaccine targets: DP-3-O-acyl-N-acetylglucosamine deacetylase and Arabinose 5-phosphate isomerase. B and T cell epitopes from the specific proteins were forecasted employing several immunoinformatic and bioinformatics resources. A vaccine was created using a combination of seven cytotoxic T cell lymphocytes (CTL), five helper T cell lymphocyte (HTL), and seven linear B cell lymphocyte (LBL) epitopes. An adjuvant (β-defensin) was added to the vaccine to enhance immunological responses. The created vaccine was stable for use in humans, highly antigenic, and non-allergenic. The vaccine's molecular and interactions binding affinity with the human immunological receptor TLR3 were studied using MMGBSA, molecular dynamics (MD) simulations, and molecular docking analyses. E. coli (strain-K12) plasmid vector pET-28a (+) was used to examine the ability of the vaccine to be expressed. The vaccine shows great promise in terms of developing protective immunity against diseases, based on the results of these computer experiments. However, in vitro and animal research are required to validate our findings.

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.

Elucidation of conserved multi-epitope vaccine against Leishmania donovani using reverse vaccinology

Visceral leishmaniasis (VL) is a tropical disease that causes severe public health problems in humans when untreated. As no licensed vaccine exists against VL, we aimed to formulate a potential MHC-restricted chimeric vaccine construct against this dreadful parasitic disease. Amastin-like protein derived from L. donovani is considered to be stable, immunogenic and non-allergic. A comprehensive established framework was used to explore the set of immunogenic epitopes with estimated population coverage of 96.08% worldwide. The rigorous assessment revealed 6 promiscuous T-epitopes which can plausibly be presented by more than 66 diverse HLA alleles. Further docking and simulation study of peptide receptor complexes identified a strong and stable binding interaction with better structural compactness. The predicted epitopes were combined with appropriate linkers and adjuvant molecules and their translation efficiency was evaluated in pET28+(a), an bacterial expression vector using in-silico cloning. Molecular docking followed by MD simulation study revealed a stable interaction between chimeric vaccine construct with TLRs. Immune simulation of the chimeric vaccine constructs showed an elevated Th1 immune response against both B and T epitopes. With this, the detailed computational analysis suggested that the chimeric vaccine construct can evoke a robust immune response against Leishmania donovani infection. Future studies are required to validate the role of amastin as a promising vaccine target.Communicated by Ramaswamy H. Sarma.

In silico analysis of virulence factors of Streptococcus uberis for a chimeric vaccine design

Streptococcus uberis is one of the causative agents of bovine mastitis, which has detrimental effects on animal health and the dairy industry. Despite decades of research, the requirement for effective vaccines against the disease remains unmet. The goal of this study was to create a multi-epitope vaccine using five virulence factors of S. uberis through the reverse vaccinology approach, which has been employed due to its high efficiency and applicability. Plasminogen activator A (PauA), glyceraldehyde-3-phosphate dehydrogenase C (GapC), C5a peptidase, S. uberis adhesion molecule (SUAM), and sortase A (SrtA) were selected for the T cytotoxic (CTL) and B cell epitope analyses as they were extensively studied in S. uberis or other pathogens. Eighteen CTL and ten B cell epitopes that were antigenic, non-toxic, and non-allergenic were selected in order to design a chimeric vaccine candidate that in silico analysis revealed to be potentially immunogenic, non-allergenic, and stable. Molecular docking analysis of the vaccine candidate with Toll-like receptor (TLR) 2 and TLR 4 revealed stable interactions between the candidate and the immune receptors. Meanwhile, the stability of the docked complexes was confirmed using normal mode analysis. Additionally, in silico immune simulation of the vaccine candidate demonstrated the stimulation of primary immune responses, indicating that the chimeric protein can hold promise as a viable vaccine candidate for preventing S. uberis mastitis. Moreover, the current study can provide a background for designing epitope-based vaccines based on the explored epitopes.

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

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

Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach

Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.

Immunoinformatics-aided rational design of multiepitope-based peptide vaccine (MEBV) targeting human parainfluenza virus 3 (HPIV-3) stable proteins

BACKGROUND

Human parainfluenza viruses (HPIVs) are common RNA viruses responsible for respiratory tract infections. Human parainfluenza virus 3 (HPIV-3) is particularly pathogenic, causing severe illnesses with no effective vaccine or therapy available.

RESULTS

The current study employed a systematic immunoinformatic/reverse vaccinology approach to design a multiple epitope-based peptide vaccine against HPIV-3 by analyzing the virus proteome. On the basis of a number of therapeutic features, all three stable and antigenic proteins with greater immunological relevance, namely matrix protein, hemagglutinin neuraminidase, and RNA-directed RNA polymerase L, were chosen for predicting and screening suitable T-cell and B-cell epitopes. All of our desired epitopes exhibited no homology with human proteins, greater population coverage (99.26%), and high conservancy among reported HPIV-3 isolates worldwide. All of the T- and B-cell epitopes are then joined by putative ligands, yielding a 478-amino acid-long final construct. Upon computational refinement, validation, and thorough screening, several programs rated our peptide vaccine as biophysically stable, antigenic, allergenic, and non-toxic in humans. The vaccine protein demonstrated sufficiently stable interaction as well as binding affinity with innate immune receptors TLR3, TLR4, and TLR8. Furthermore, codon optimization and virtual cloning of the vaccine sequence in a pET32a ( +) vector showed that it can be readily expressed in the bacterial system.

CONCLUSION

The in silico designed HPIV-3 vaccine demonstrated potential in evoking an effective immune response. This study paves the way for further preclinical and clinical evaluation of the vaccine, offering hope for a future solution to combat HPIV-3 infections.

Systematic review of reverse vaccinology and immunoinformatics data for non-viral sexually transmitted infections

Sexually Transmitted Infections (STIs) are a public health burden rising in developed and developing nations. The World Health Organization estimates nearly 374 million new cases of curable STIs yearly. Global efforts to control their spread have been insufficient in fulfilling their objective. As there is no vaccine for many of these infections, these efforts are focused on education and condom distribution. The development of vaccines for STIs is vital for successfully halting their spread. The field of immunoinformatics is a powerful new tool for vaccine development, allowing for the identification of vaccine candidates within a bacterium's genome and allowing for the design of new genome-based vaccine peptides. The goal of this review was to evaluate the usage of immunoinformatics in research focused on non-viral STIs, identifying fields where research efforts are concentrated. Here we describe gaps in applying these techniques, as in the case of Treponema pallidum and Trichomonas vaginalis.

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

Visceral Leishmaniasis (VL) is a neglected tropical disease of public health importance in the Indian subcontinent. Despite consistent elimination initiatives, the disease has not yet been eliminated and there is an increased risk of resurgence from active VL reservoirs including asymptomatic, post kala azar dermatitis leishmaniasis (PKDL) and HIV-VL co-infected individuals. To achieve complete elimination and sustain it in the long term, a prophylactic vaccine, which can elicit long lasting immunity, is desirable. In this study, we employed immunoinformatic tools to design a multi-subunit epitope vaccine for the Indian population by targeting antigenic secretory proteins screened from the Leishmania donovani proteome. Out of 8014 proteins, 277 secretory proteins were screened for their cellular location and proteomic evidence. Through NCBI BlastP, unique fragments of the proteins were cropped, and their antigenicity was evaluated. B-cell, HTL and CTL epitopes as well as IFN-ɣ, IL-17, and IL-10 inducers were predicted, manually mapped to the fragments and common regions were tabulated forming a peptide ensemble. The ensemble was evaluated for Class I MHC immunogenicity and toxicity. Further, immunogenic peptides were randomly selected and used to design vaccine constructs. Eight vaccine constructs were generated by linking random peptides with GS linkers. Synthetic TLR-4 agonist, RS09 was used as an adjuvant and linked with the constructs using EAAK linkers. The predicted population coverage of the constructs was ∼99.8 % in the Indian as well as South Asian populations. The most antigenic, nontoxic, non-allergic construct was chosen for the prediction of secondary and tertiary structures. The 3D structures were refined and analyzed using Ramachandran plot and Z-scores. The construct was docked with TLR-4 receptor. Molecular dynamic simulation was performed to check for the stability of the docked complex. Comparative in silico immune simulation studies showed that the predicted construct elicited humoral and cell-mediated immunity in human host comparable to that elicited by Leish-F3, which is a promising vaccine candidate for human VL.

Development of a tetravalent subunit vaccine against dengue virus through a vaccinomics approach

Dengue virus infection (DVI) is a mosquito-borne disease that can lead to serious morbidity and mortality. Dengue fever (DF) is a major public health concern that affects approximately 3.9 billion people each year globally. However, there is no vaccine or drug available to deal with DVI. Dengue virus consists of four distinct serotypes (DENV1-4), each raising a different immunological response. In the present study, we designed a tetravalent subunit multi-epitope vaccine, targeting proteins including the structural protein envelope domain III (EDIII), precursor membrane proteins (prM), and a non-structural protein (NS1) from each serotype by employing an immunoinformatic approach. Only conserved sequences obtained through a multiple sequence alignment were used for epitope mapping to ensure efficacy against all serotypes. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, overall, 11 B-cell epitopes, 10 HTL epitopes, and 10 CTL epitopes from EDIII, prM, and NS1 proteins targeting all serotypes were selected and joined via KK, AAY, and GGGS linkers, respectively. We incorporated a 45-amino-acid-long B-defensins adjuvant in the final vaccine construct for a better immunogenic response. The vaccine construct has an antigenic score of 0.79 via VaxiJen and is non-toxic and non-allergenic. Our refined vaccine structure has a Ramachandran score of 96.4%. The vaccine has shown stable interaction with TLR3, which has been validated by 50 ns of molecular dynamics (MD) simulation. Our findings propose that a designed multi-epitope vaccine has substantial potential to elicit a strong immune response against all dengue serotypes without causing any adverse effects. Furthermore, the proposed vaccine can be experimentally validated as a probable vaccine, suggesting it may serve as an effective preventative measure against dengue virus infection.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

Recognition of immune reactive proteins as a potential multiepitope vaccine candidate of Taenia solium cysticerci through proteomic approach

Metacestode, the larva of Taenia solium, is the causative agent for neurocysticercosis (NCC), which causes epilepsy. The unavailability of a vaccine against human NCC is a major cause for its widespread prevalence across the globe. Therefore, the development of a reliable vaccine against NCC is the need of the hour. Employing a combination of proteomics and immunoinformatics, we endeavored to formulate a vaccine candidate. The immune reactive cyst fluid antigens of T. solium were identified by immune-blotting two-dimensional gels with NCC patient's sera, followed by Matrix-assisted laser desorption-ionization analysis. We performed a detailed proteomic study of these immune reactive proteins by utilizing immune-informatics tools, identified the nontoxic, nonallergic, B-cell epitopes, and collected epitopes with the least sequence homology with human and other Taenia species. These epitopes were joined through linkers to construct a multiepitope vaccine. Different physiochemical parameters such as molecular weight (23.82 kDa), instability (39.91), and aliphatic index (49.61) were calculated to ensure the stability of the linked peptides vaccine. The vaccine demonstrated stable interactions with different immune receptors like Toll-like receptor 4 and IgG confirming that it will effectively stimulate the host immune response. We anticipate that our designed B-cell linear epitope-based vaccine will show promising results in in vitro and in vivo assays. This study provides a platform that would be useful to develop other suitable vaccine candidates to prevent helminthic neglected tropical diseases in near future.

Use of immunoinformatics and the simulation approach to identify Helicobacter pylori epitopes to design a multi-epitope subunit vaccine for B- and T-cells

BACKGROUND

Helicobacter pylori cause a variety of gastric malignancies, gastric ulcers, and cause erosive diseases. The extreme nature of the bacterium and the implantation of this bacterium protects it against designing a potent drug against it. Therefore, employing a precise and effective design for a more safe and stable antigenic vaccine against this pathogen can effectively control its associated infections. This study, aimed at improving the design of multiple subunit vaccines against H. pylori, adopts multiple immunoinformatics approaches in combination with other computational approaches.

RESULTS

In this regard, 10 HTL, and 11 CTL epitopes were employed based on appropriate adopted MHC binding scores and c-terminal cut-off scores of 4 main selected proteins (APO, LeoA, IceA1, and IceA2). An adjuvant was added to the N end of the vaccine to achieve higher stability. For validation, immunogenicity and sensitization of physicochemical analyses were performed. The vaccine could be antigenic with significantly strong interactions with TOLK-2, 4, 5, and 9 receptors. The designed vaccine was subjected to Gromacs simulation and immune response prediction modelling that confirmed expression and immune-stimulating response efficiency. Besides, the designed vaccine showed better interactions with TLK-9.

CONCLUSIONS

Based on our analyses, although the suggested vaccine could induce a clear response against H. pylori, precise laboratory validation is required to confirm its immunogenicity and safety status.

Designing and development of multi-epitope chimeric vaccine against Helicobacter pylori by exploring its entire immunogenic epitopes: an immunoinformatic approach

BACKGROUND

Helicobacter pylori is a prominent causative agent of gastric ulceration, gastric adenocarcinoma and gastric lymphoma and have been categorised as a group 1 carcinogen by WHO. The treatment of H. pylori with proton pump inhibitors and antibiotics is effective but also leads to increased antibiotic resistance, patient dissatisfaction, and chances of reinfection. Therefore, an effective vaccine remains the most suitable prophylactic option for mass administration against this infection.

RESULTS

We modelled a multi-chimera subunit vaccine candidate against H. pylori by screening its secretory/outer membrane proteins. We identified B-cell, MHC-II and IFN-γ-inducing epitopes within these proteins. The population coverage, antigenicity, physiochemical properties and secondary structure were evaluated using different in-silico tools, which showed it can be a good and effective vaccine candidate. The 3-D construct was predicted, refined, validated and docked with TLRs. Finally, we performed the molecular docking/simulation and immune simulation studies to validate the stability of interaction and in-silico cloned the epitope sequences into a pET28b(+) plasmid vector.

CONCLUSION

The multiepitope-constructed vaccine contains T- cells, B-cells along with IFN-γ inducing epitopes that have the property to generate good cell-mediated immunity and humoral response. This vaccine can protect most of the world's population. The docking study and immune simulation revealed a good binding with TLRs and cell-mediated and humoral immune responses, respectively. Overall, we attempted to design a multiepitope vaccine and expect this vaccine will show an encouraging result against H. pylori infection in in-vivo use.

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

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

Designing a multi-epitope vaccine to control porcine epidemic diarrhea virus infection using immunoinformatics approaches

Porcine epidemic diarrhea virus (PEDV), a continuously evolving pathogen, causes severe diarrhea in piglets with high mortality rates. However, current vaccines cannot provide complete protection against PEDV, so vaccine development is still necessary and urgent. Here, with the help of immunoinformatics approaches, we attempted to design a multi-epitope vaccine named rPMEV to prevent and control PEDV infection. The epitopes of rPMEV were constructed by 9 cytotoxic T lymphocyte epitopes (CTLs), 11 helper T lymphocyte epitopes (HTLs), 6 linear B cell epitopes (LBEs), and 4 conformational B cell epitopes (CBEs) based on the S proteins from the four representative PEDV G2 strains. To enhance immunogenicity, porcine β-defensin-2 (PBD-2) was adjoined to the N-terminal of the vaccine as an adjuvant. All of the epitopes and PBD-2 were joined by corresponding linkers and recombined into the multivalent vaccine, which is stable, antigenic, and non-allergenic. Furthermore, we adopted molecular docking and molecular dynamics simulation methods to analyze the interaction of rPMEV with the Toll-like receptor 4 (TLR4): a stable interaction between them created by 13 hydrogen bonds. In addition, the results of the immune simulation showed that rPMEV could stimulate both cellular and humoral immune responses. Finally, to raise the expression efficiency, the sequence of the vaccine protein was cloned into the pET28a (+) vector after the codon optimization. These studies indicate that the designed multi-epitope vaccine has a potential protective effect, providing a theoretical basis for further confirmation of its protective effect against PEDV infection in vitro and in vivo studies.