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Abstract The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Objectives: This study aimed to identify the optimal protein construction for designing a multiepitope 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. [ABSTRACT FROM AUTHOR]

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively. [ABSTRACT FROM AUTHOR]

[Display omitted] Dengue becomes the most common life-threatening infectious arbovirus disease globally, with prevalence in the tropical and subtropical areas. The major clinical features include dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), a condition of hypovolemic shock. Four different serotypes of the dengue virus, known as dengue virus serotype (DENV)- 1, 2, 3 and 4 can infect humans. Only one vaccine is available in the market, named Dengvaxia by Sanofi Pasteur, but there is no desired outcome of this treatment due the antibody dependent enhancement (ADE) of the multiple dengue serotypes. As of now, there is no cure against dengue disease. Our goal in this work was to create a subunit vaccine based on several epitopes that would be effective against every serotype of the dengue virus. Here, computational methods like- immunoinformatics and bioinformatics were implemented to find out possible dominant epitopes. A total of 21 epitopes were chosen using various in-silico techniques from the expected 133 major histocompatibility complex (MHC)- I and major histocompatibility complex (MHC)- II epitopes, along with 95 B-cell epitopes which were greatly conserved. Immune stimulant, non-allergenic and non-toxic immunodominant epitopes (super epitopes) with a suitable adjuvant (Heparin-Binding Hemagglutinin Adhesin, HBHA) were used to construct the vaccine. Following the physicochemical analysis, vaccine construct was docked with Toll-like receptors (TLRs) to predict the immune stimulation. Consequently, the optimal docked complex that demonstrated the least amount of ligand-receptor complex deformability was used to conduct the molecular dynamics analysis. By following the codon optimization, the final vaccine molecule was administered into an expressing vector to perform in-silico cloning. The robust immune responses were generated in the in-silico immune simulation analysis. Hence, this study provides a hope to control the dengue infections. For validation of the immune outcomes, in-vitro as well as in-vivo investigations are essential. [ABSTRACT FROM AUTHOR]

Kingella kingae is a Gram-negative bacterium that primarily causes pediatric infections such as septicemia, endocarditis, and osteoarticular infections. Its virulence is attributed to the outer membrane proteins having implications in bacterial adhesion, invasion, nutrition, and host tissue damage. TonB-dependent receptors (TBDRs) play an important role in nutrition and were previously implicated as vaccine targets in other bacteria. Therefore, we targeted the conserved β-barrel TBDR domain of these proteins for designing a vaccine construct that could elicit humoral and cellular immune responses. We used bioinformatic tools to mine TBDR-containing proteins from K. kingae ATCC 23330 and then predict B- and T-cell epitopes from their conserved β-barrel TDR domain. A chimeric vaccine construct was designed using three antigenic epitopes, covering >98% of the world population and capable of inciting humoral and adaptive immune responses. The final construct elicited a robust immune response. Docking and dynamics simulation showed good binding affinity of the vaccine construct to various receptors of the immune system. Additionally, the vaccine was predicted to be safe and non-allergenic, making it a promising candidate for further development. In conclusion, our study demonstrates the potential of immunoinformatics approaches in designing chimeric vaccines against K. kingae infections. The chimeric vaccine we designed can serve as a blueprint for future experimental studies to develop an effective vaccine against this pathogen, which can serve as a potential strategy to prevent K. kingae infections.

Background. The coronavirus epidemic continues, but there is evidence that the society has already mastered effective measures for the prevention and treatment of this disease. The unresolved problems are prevention, early diagnosis and timely treatment of new viral epidemics, prevention and treatment of postcovid complications, the mortality from which manifests itself latently under the guise of other diseases and does not fall into the statistics of the coronavirus pandemic. Materials and methods. Based on the developed original data processing technique, designed for analyzing coronavirus genomic data, models of the anti-coronavirus multi-epitope vaccine were computed and tested in silico. In a series of computational experiments, evidence of their possible efficiency and safety was obtained. Results and conclusions. Based on research experiments and analysis of scientific literature, recommendations are formulated for the development and application of epitope antiviral vaccines using the example of the anti-coronavirus vaccine.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time. [ABSTRACT FROM AUTHOR]

(1) Background: Many co-infections of Mycobacterium tuberculosis (MTB) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have emerged since the occurrence of the SARS-CoV-2 pandemic. This study aims to design an effective preventive multi-epitope vaccine against the co-infection of MTB and SARS-CoV-2. (2) Methods: The three selected proteins (spike protein, diacylglycerol acyltransferase, and low molecular weight T-cell antigen TB8.4) were predicted using bioinformatics, and 16 epitopes with the highest ranks (10 helper T lymphocyte epitopes, 2 CD8+ T lymphocytes epitopes, and 4 B-cell epitopes) were selected and assembled into the candidate vaccine referred to as S7D5L4. The toxicity, sensitization, stability, solubility, antigenicity, and immunogenicity of the S7D5L4 vaccine were evaluated using bioinformatics tools. Subsequently, toll-like receptor 4 docking simulation and discontinuous B-cell epitope prediction were performed. Immune simulation and codon optimization were carried out using immunoinformatics and molecular biology tools. (3) Results: The S7D5L4 vaccine showed good physical properties, such as solubility, stability, non-sensitization, and non-toxicity. This vaccine had excellent antigenicity and immunogenicity and could successfully simulate immune responses in silico. Furthermore, the normal mode analysis of the S7D5L4 vaccine and toll-like receptor 4 docking simulation demonstrated that the vaccine had docking potential and a stable reaction. (4) Conclusions: The S7D5L4 vaccine designed to fight against the co-infection of MTB and SARS-CoV-2 may be safe and effective. The protective efficacy of this promising vaccine should be further verified using in vitro and in vivo experiments. [ABSTRACT FROM AUTHOR]

(1) Background: The monkeypox virus is a zoonotic orthopox DNA virus that is closely linked to the virus. In light of the growing concern about this virus, the current research set out to use bioinformatics and immunoinformatics to develop a potential vaccine against the virus. (2) Methods: A multiepitope vaccine was constructed from the B-cell and T-cell epitopes of the MPXVgp181 strain using adjuvant and different linkers. The constructed vaccine was predicted for antigenicity, allergenicity, toxicity, and population coverage. In silico immune simulation studies were also carried out. Expression analysis and cloning of the constructed vaccine was carried out in the pET-28a(+) vector using snapgene. (3) Results: The constructed vaccine was predicted to be antigenic, non-allergenic, and non-toxic. It was predicted to have excellent global population coverage and produced satisfactory immune response. The in silico expression and cloning studies were successful in E. coli, which makes the vaccine construct suitable for mass production in the pharmaceutical industry. (4) Conclusion: The constructed vaccine is based on the B-cell and T-cell epitopes obtained from the MPXVgp181 strain. This research can be useful in developing a vaccine to combat the monkeypox virus globally after performing in-depth in vitro and in vivo studies. [ABSTRACT FROM AUTHOR]

B‐cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred‐3.0, a sequence‐based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user‐friendly interface to navigate the results. [ABSTRACT FROM AUTHOR]

Human African trypanosomiasis (HAT) is a neglected tropical disease that is caused by flagellated parasites of the genus Trypanosoma. HAT imposes a significant socio-economic burden on many countries in sub-Saharan Africa and its control is hampered by several drawbacks ranging from the ineffectiveness of drugs, complex dosing regimens, drug resistance, and lack of a vaccine. Despite more than a century of research and investigations, the development of a vaccine to tackle HAT is still challenging due to the complex biology of the pathogens. Advancements in computational modeling coupled with the availability of an unprecedented amount of omics data from different organisms have allowed the design of new generation vaccines that offer better antigenicity and safety profile. One of such new generation approaches is a multi-epitope vaccine (MEV) designed from a collection of antigenic peptides. A MEV can stimulate both cellular and humoral immune responses as well as avoiding possible allergenic reactions. Herein, we take advantage of this approach to design a MEV from conserved hypothetical plasma membrane proteins of Trypanosoma brucei gambiense, the trypanosome subspecies that is responsible for the west and central African forms of HAT. The designed MEV is 402 amino acids long (41.5 kDa). It is predicted to be antigenic, non-toxic, to assume a stable 3D conformation, and to interact with a key immune receptor. In addition, immune simulation foresaw adequate immune stimulation by the putative antigen and a lasting memory. Therefore, the designed chimeric vaccine represents a potential candidate that could be used to target HAT.

Whooping cough is a highly contagious respiratory disease caused by the gram-negative bacterium Bordetella pertussis, which was first reported more than a century ago. Recent data indicate that the reoccurrence of this disease is linked to bacterial resistance against the immune system's defense mechanisms and reduced effectiveness of vaccines. Therefore, the development of new and efficient vaccines and medications is urgently required. This study employed an immunoinformatics approach to identify potential vaccine targets from the proteome of B. pertussis strain NC_018518.1, aiming to create a multi-epitope subunit vaccine against B. pertussis. Following immunization steps, suitable extracellular and outer-membrane proteins with a peptide signal were carefully selected. To construct a multi-epitope vaccine, proteins involved in pathogenicity but not found in human hosts were chosen. T and B cell epitopes were extracted from these selected proteins and screened using various immunoinformatics tools. The epitopes commonly predicted by multiple servers were added to the candidate list based on their immunogenicity, antigenicity, and toxicity analysis. The candidate vaccine was then designed by incorporating an appropriate linker, adjuvant, and graft with the selected epitopes. Various bioinformatics tools were employed to evaluate the antigenicity, allergenicity, solubility, and physicochemical properties of the candidate vaccine. The prediction of its secondary and tertiary structures and subsequent validation steps were conducted. Our findings demonstrate that the candidate vaccine elicits both humoral and cell-mediated immune responses, suggesting its potential as a pertussis vaccine. However, further experimental validation is necessary to fully assess the proposed vaccine's potential.

BackgroundWith the increasing incidence of tuberculosis (TB) and the shortcomings of existing TB vaccines to prevent TB in adults, new TB vaccines need to be developed to address the complex TB epidemic.MethodThe dominant epitopes were screened from antigens to construct a novel epitope vaccine termed HP13138PB. The immune properties, structure, and function of HP13138PB were predicted and analyzed with bioinformatics and immunoinformatics. Then, the immune responses induced by the HP13138PB were confirmed by enzyme-linked immunospot assay (ELISPOT) and Th1/Th2/Th17 multi-cytokine detection kit.ResultThe HP13138PB vaccine consisted of 13 helper T lymphocytes (HTL) epitopes, 13 cytotoxic T lymphocytes (CTL) epitopes, and 8 B-cell epitopes. It was found that the antigenicity, immunogenicity, and solubility index of the HP13138PB vaccine were 0.87, 2.79, and 0.55, respectively. The secondary structure prediction indicated that the HP13138PB vaccine had 31% of α-helix, 11% of β-strand, and 56% of coil. The tertiary structure analysis suggested that the Z-score and the Favored region of the HP13138PB vaccine were -4.47 88.22%, respectively. Furthermore, the binding energies of the HP13138PB to toll-like receptor 2 (TLR2) was -1224.7 kcal/mol. The immunoinformatics and real-world experiments showed that the HP13138PB vaccine could induce an innate and adaptive immune response characterized by significantly higher levels of cytokines such as interferon-gamma (IFN-γ), tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), and IL-10.ConclusionThe HP13138PB is a potential vaccine candidate to prevent TB, and this study preliminarily evaluated the ability of the HP13138PB to generate an immune response, providing a precursor target for developing TB vaccines.

Background and Aim: Mycobacterium tuberculosis is a health problem in countries. Despite the global prevalence of tuberculosis and the lack of appropriate drugs, further progress is still needed with the help of modern methods of preparing epitope-based vaccines for tuberculosis. Materials and Methods: In this study, specific T and B cell epitopes required for producing chimeric vaccines with the help of servers such as IEDB were determined. The antigenicity, allergenicity, and toxicity of the selected epitopes by various other servers such as VaxiJenv2.0, AllerTOP, and Toxinpred were determined, and the vaccine of the epitope was then configured with the help of special linkers. Then, analyze the structure vaccine by some other bioinformatics servers such as PRABI, SWISS-MODEL, PROCHECK, and PEPCALC, was investigated and finally detected using docking techniques to evaluate the interaction with the epitope through MVD software. Results: The results showed that the vaccine, in terms of in silico evaluations of two-dimensional and three-dimensional structures, has a good condition. Also, the percentage of optimal placement of amino acids and bonds by PROCHECK server, % 99 percent of optimal placement of amino acids in the chimer structure was established. Also, it was non-toxic and nonallergenic and had the desired antigenicity. Conclusion: In general, the vaccine that was able to have a favorable interaction with some components of the immune system (HLA) in the docking process, which indicates the optimal identification of this structure by the humoral and cellular immune system, of course, more reliable proof of it requires clinical phase processes. [ABSTRACT FROM AUTHOR]

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines. [ABSTRACT FROM AUTHOR]

Background and Aim: Cutaneous leishmaniasis is a significant public health issue worldwide. Cutaneous leishmaniasis is the most prevalent in the world among the different types of leishmaniasis. Currently, available medications have had no discernible influence on the disease's progression. Up to now, there has been no approved cutaneous leishmaniasis vaccine. New developments in vaccination might be a potential way to come up with a vaccination that is successful for the treatment of cutaneous leishmaniasis. Materials and Methods: This research was conducted to learn more about an effective vaccine for Leishmania major, the ailment's primary cause of CL, which was designed using computational methods. Thus, a multiepitope protein was designed by utilizing potential immune system epitopes, including predicted MHC class I, MHC class II, Cytotoxic T lymphocytes, Bcell, and Interferon-gamma epitopes of Cysteine protease b (CPB), Leishmania homologue of activated C kinase (LACK), and Kinetoplastid membrane protein-11 (KMP-11) antigenic proteins. In order to enhance vaccine immunogenicity, two resuscitation-promoting factors of Mycobacterium tuberculosis were used as adjuvants. Final epitopes were matched with suitable linkers to construct the recombinant structure. The physicochemical and immune-based characteristics of the designed vaccine have been forecasted by using different tools. Moreover, homogeneity modeling was performed to obtain a high-quality 3D structure, followed by refinement and validation. Finally, the codon optimization based on E. coli resulted in a higher CAI value and optimal GC content, followed by combining it in the pET-14b cloning vector. Results: Evaluation of the various characteristics of the designed vaccine showed that it is an immunogenic and nonallergenic antigen that can induce immune responses against Leishmania major infection, which could be promising for cutaneous leishmaniasis. Conclusion: Research shows that a recombinant vaccine can be an effective candidate against cutaneous leishmaniasis. [ABSTRACT FROM AUTHOR]

Immunoinformatics, as a discipline, concerns the application of computational systems to study the immune system. Recently, the principles of reverse vaccinology have united with immunoinformatics to construct and evaluate novel vaccines in silico, enabling an economical response to emergent pathogens. In this tutorial, the basic procedure for in silico vaccine design is outlined. Because theoretical methods often suffer from a lack of transparency, limiting their practicality, this guide is written to minimize these discrepancies. Three concise phases – target selection and epitope identification, vaccine construction, and vaccine analysis and refinement – are detailed. To demonstrate, a simple vaccine against Plasmodium vivax is composed and validated. It exhibited a population coverage of 99.95% and favorable biophysical properties, making it a promising candidate for malaria prevention. This tutorial renders efficient in silico vaccine design accessible, introducing an opportunity to enhance the future of immunology.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Background: Since the first appearance of SARS-CoV-2 in China in December 2019, the world witnessed the emergence of the SARS-CoV-2 outbreak. Due to the high transmissibility rate of the virus, there is an urgent need to design and develop vaccines against SARS-CoV-2 to prevent more cases affected by the virus. Objective: A computational approach is proposed for vaccine design against the SARS-CoV-2 spike (S) protein, as the key target for neutralizing antibodies, and envelope (E) protein, which contains a conserved sequence feature. Methods: We used previously reported epitopes of S protein detected experimentally and further identified a collection of predicted B-cell and major histocompatibility (MHC) class II–restricted T-cell epitopes derived from E proteins with an identical match to SARS-CoV-2 E protein. Results: The in silico design of our candidate vaccine against the S and E proteins of SARS-CoV-2 demonstrated a high affinity to MHC class II molecules and effective results in immune response simulations. Conclusions: Based on the results of this study, the multiepitope vaccine designed against the S and E proteins of SARS-CoV-2 may be considered as a new, safe, and efficient approach to combatting the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

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

Presently, immunoinformatics and bioinformatics approaches are contributing actively to COVID-19 vaccine research. The first immunoinformatics-based vaccine construct against SARS-CoV-2 was published in February 2020. Following this, immunoinformatics and bioinformatics approaches have created a new direction in COVID-19 vaccine research. Several researchers have designed the next-generation COVID-19 vaccines using these approaches. Presently, immunoinformatics has accelerated immunology research immensely in the area of COVID-19. Hence, we have tried to depict the current scenario of immunoinformatics and bioinformatics in COVID-19 vaccine research. [ABSTRACT FROM AUTHOR]

Background and Aim: SARS-CoV-2 is the causative agent of Coronavirus 2019 or COVID-19 in the world. Novel coronavirus disease is a respiratory disease. To date, there have been challenges in the treatment for COVID-19 and emerged new variants like UK B1.1.7. Accordingly, an effective prevention regime is needed for this infection, which covers most variants. The purpose of this research was to predict the conserved epitopes of Spike and Nucleocapsid proteins from SARS-CoV-2 for the design of a novel coronavirus 2019 multi-epitope vaccine using in silico tools. Materials and Methods: Computational analysis and immunoinformatics approaches include identification of potential conserve epitopes and selection of epitopes based on allergenicity, toxicity, antigenicity, and molecular docking were used for epitope prediction and screening. In the next step, selected segments of the epitopes were attached by the suitable linkers. Finally, Maltese-bound protein (MBP) as an adjuvant was added to the novel vaccine structure. The secondary and third structures of the designed multi-epitope vaccine were predicted via immunoinformatics algorithms. Predicted structure refined and validated for attaining best stability. In the end, immunoinformatics evaluation, molecular docking, and molecular dynamics were performed to confirm vaccine efficiency. Codon optimization and in silico cloning were done to ensure the expression yield of the novel multi-epitope vaccine in the target host. Results: This study showed that our data support the suggestion that the designed vaccine could induce immune responses against SARS-CoV-2 variants. Conclusion: The structure designed had acceptable quality with software reviews. Further in vitro and in vivo experiments are needed to confirm the safety and immunogenicity of the candidate vaccine. [ABSTRACT FROM AUTHOR]

(1) Background: The monkeypox virus is a zoonotic orthopox DNA virus that is closely linked to the virus. In light of the growing concern about this virus, the current research set out to use bioinformatics and immunoinformatics to develop a potential vaccine against the virus. (2) Methods: A multiepitope vaccine was constructed from the B-cell and T-cell epitopes of the MPXVgp181 strain using adjuvant and different linkers. The constructed vaccine was predicted for antigenicity, allergenicity, toxicity, and population coverage. In silico immune simulation studies were also carried out. Expression analysis and cloning of the constructed vaccine was carried out in the pET-28a(+) vector using snapgene. (3) Results: The constructed vaccine was predicted to be antigenic, non-allergenic, and non-toxic. It was predicted to have excellent global population coverage and produced satisfactory immune response. The in silico expression and cloning studies were successful in E. coli, which makes the vaccine construct suitable for mass production in the pharmaceutical industry. (4) Conclusion: The constructed vaccine is based on the B-cell and T-cell epitopes obtained from the MPXVgp181 strain. This research can be useful in developing a vaccine to combat the monkeypox virus globally after performing in-depth in vitro and in vivo studies.

After the outbreak of SARS-CoV-2 by the end of 2019, the vaccine development strategies became a worldwide priority. Furthermore, the appearances of novel SARS-CoV-2 variants challenge researchers to develop new pharmacological or preventive strategies. However, vaccines still represent an efficient way to control the SARS-CoV-2 pandemic worldwide. This review describes the importance of bioinformatic and immunoinformatic tools (in silico) for guide vaccine design. In silico strategies permit the identification of epitopes (immunogenic peptides) which could be used as potential vaccines, as well as nonacarriers such as: vector viral based vaccines, RNA-based vaccines and dendrimers through immunoinformatics. Currently, nucleic acid and protein sequential as well structural analyses through bioinformatic tools allow us to get immunogenic epitopes which can induce immune response alone or in complex with nanocarriers. One of the advantages of in silico techniques is that they facilitate the identification of epitopes, while accelerating the process and helping to economize some stages of the development of safe vaccines.

Epsilon toxin (ETX) is one of four lethal toxins of Clostridium perfringens produced by types B and D of the pathogen. This pore-forming toxin is one of the most potent bioterrorism agents with economic importance. Although an effective vaccine and an equine antitoxin are available to protect livestock against ETX, no approved vaccine or antitoxin is available for humans. In the current study, an integrative, simple, fast, and reliable approach is availed to design a safe and minimized construct based on preexisting experimental linear B cell epitopes. This guideline is designed based on the surface accessibility, flexibility, hydrophilicity, content of beta-turn structure, and antigenicity of potential epitopes. Experimental linear B-cell epitopes were analyzed with respect to antigenicity. Two antigenic regions were introduced based on the distribution of epitopes. Moreover, three constructs were designed based on top ranking epitopes. These constructs were evaluated for their antigenicity, surface accessibility, flexibility, hydrophilicity, and beta-turn content and were compared to the ETX sequence. The 114-aa construct with an antigenicity score of 0.8592 was the most stable, antigenic, hydrophilic, flexible, and surface accessible antigen within the ETX sequence, the selected regions, and the designed constructs. Although the performed in silico analyses revealed that the designed construct could serve as a safe antigen triggering highly reactive and neutralizing anti-ETX antibodies, it should be verified by experimental assays in future studies. [ABSTRACT FROM AUTHOR]

There are many challenges in the field of public health sciences. Rational decisions are required in order to treat different diseases, gain knowledge and wealth regarding research, and produce biological or synthetic products. Various advances in the basic laboratory science, computer science, and the engineering of biological production processes can help solve the occurring problems. Bioinformatics is defined as a field of science combined of biology, mathematics, physics, chemistry, and computer sciences. Recently, bioinformatics has been extensively used in the designing of the epitope, vaccines, antibodies, adjuvants, diagnostic kits, and therapeutic purposes (e.g., proteins, peptides, or small molecules). Moreover, bioinformatics includes chemoinformatics that has been employed to produce various biological or chemical products to target and combat pathogens. Bioinformatics is involved in other areas of data analysis and prediction, such as structural biology, system biology, phylogeny, population genetics, and next-generation data sequencing. To the best of our knowledge, no published study coherently described the benefits of bioinformatics fields applied for medication development or diagnostic aims in bio-productive and pharmaceutical/vaccine companies. Therefore, in the current review, we attempted to present the available bioinformatics resources, practical experiences, and other findings in the mentioned field along with providing a harmonized and applied model(s). The key points presented in the current review may help to elevate production and reduce the costs for the development of novel vaccines, medicines, and antibodies. In addition, these methods can facilitate the identification of organisms and may guarantee the quality of biological products.

Schistosomiasis remains a serious health issue nowadays for an estimated one billion people in 79 countries around the world. Great efforts have been made to identify good vaccine candidates during the last decades, but only three molecules reached clinical trials so far. The reverse vaccinology approach has become an attractive option for vaccine design, especially regarding parasites like Schistosoma spp. that present limitations for culture maintenance. This strategy also has prompted the construction of multi-epitope based vaccines, with great immunological foreseen properties as well as being less prone to contamination, autoimmunity, and allergenic responses. Therefore, in this study we applied a robust immunoinformatics approach, targeting S. mansoni transmembrane proteins, in order to construct a chimeric antigen. Initially, the search for all hypothetical transmembrane proteins in GeneDB provided a total of 584 sequences. Using the PSORT II and CCTOP servers we reduced this to 37 plasma membrane proteins, from which extracellular domains were used for epitope prediction. Nineteen common MHC-I and MHC-II binding epitopes, from eight proteins, comprised the final multi-epitope construct, along with suitable adjuvants. The final chimeric multi-epitope vaccine was predicted as prone to induce B-cell and IFN-γ based immunity, as well as presented itself as stable and non-allergenic molecule. Finally, molecular docking and molecular dynamics foresee stable interactions between the putative antigen and the immune receptor TLR 4. Our results indicate that the multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against schistosomiasis.

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. In parallel, we explored the future perspectives and challenges that may be faced with more accurate quantitative infiltration of immune cells in the future. Together, our study provides a little guide for scientists in the field of clinical and experimental immunology to look for dedicated resources and more competent tools for accelerating the unraveling of tumor-immune interactions with the implication in precision immunotherapy. [ABSTRACT FROM AUTHOR]

Schistosomiasis remains a serious health issue nowadays for an estimated one billion people in 79 countries around the world. Great efforts have been made to identify good vaccine candidates during the last decades, but only three molecules reached clinical trials so far. The reverse vaccinology approach has become an attractive option for vaccine design, especially regarding parasites like Schistosoma spp. that present limitations for culture maintenance. This strategy also has prompted the construction of multi-epitope based vaccines, with great immunological foreseen properties as well as being less prone to contamination, autoimmunity, and allergenic responses. Therefore, in this study we applied a robust immunoinformatics approach, targeting S. mansoni transmembrane proteins, in order to construct a chimeric antigen. Initially, the search for all hypothetical transmembrane proteins in GeneDB provided a total of 584 sequences. Using the PSORT II and CCTOP servers we reduced this to 37 plasma membrane proteins, from which extracellular domains were used for epitope prediction. Nineteen common MHC-I and MHC-II binding epitopes, from eight proteins, comprised the final multi-epitope construct, along with suitable adjuvants. The final chimeric multi-epitope vaccine was predicted as prone to induce B-cell and IFN-γ based immunity, as well as presented itself as stable and non-allergenic molecule. Finally, molecular docking and molecular dynamics foresee stable interactions between the putative antigen and the immune receptor TLR 4. Our results indicate that the multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against schistosomiasis. [ABSTRACT FROM AUTHOR]

High-throughput T-cell receptor repertoire sequencing constitutes a powerful tool to study T cell responses at the clonal level. However, it does not give information on the functional phenotype of the responding clones and lacks a statistical framework for quantitative evaluation. To overcome this, we combined datasets from different experiments, all starting from the same blood samples. We used a novel, sensitive, UMI-based protocol to perform repertoire analysis on experimental replicates. Applying established bioinformatic routines for transcriptomic expression analysis we explored the dynamics of antigen-induced clonal expansion after in vitro stimulation, identified antigen-responsive clones, and confirmed their activation status using the expression of activation markers upon antigen re-challenge. We demonstrate that the addition of IL-4 after antigen stimulation drives the expansion of T cell clones encoding unique receptor sequences. We show that our approach represents a scalable, high-throughput immunological tool, which can be used to identify and characterize antigen-responsive T cells at clonal level.

High-throughput T-cell receptor repertoire sequencing constitutes a powerful tool to study T cell responses at the clonal level. However, it does not give information on the functional phenotype of the responding clones and lacks a statistical framework for quantitative evaluation. To overcome this, we combined datasets from different experiments, all starting from the same blood samples. We used a novel, sensitive, UMI-based protocol to perform repertoire analysis on experimental replicates. Applying established bioinformatic routines for transcriptomic expression analysis we explored the dynamics of antigen-induced clonal expansion after in vitro stimulation, identified antigen-responsive clones, and confirmed their activation status using the expression of activation markers upon antigen re-challenge. We demonstrate that the addition of IL-4 after antigen stimulation drives the expansion of T cell clones encoding unique receptor sequences. We show that our approach represents a scalable, high-throughput immunological tool, which can be used to identify and characterize antigen-responsive T cells at clonal level. [ABSTRACT FROM AUTHOR]

Contagious human coronavirus belong to family Coronaviridae and infects human respiratory system causing the disease known as COVID-19 (WHO). To eradicate the severe acute respiratory syndrome coronavirus 2 pandemic, an effective vaccine should be developed. In the current study immunoinformatics procedures were employed to introduce a novel multi-epitope subunit vaccine. This multi-epitope vaccine can activate equally class I and II human leukocyte antigen and antibody mediated immune responses. Spike glycoprotein (PDB Id: 6VSB) of the coronavirus was selected and analysed using immune epitope database server for prediction of potential immunogenic B and T-cell epitopes. Population conservation studies of the selected protein and predicted epitopes were also performed using population conservancy analysis tool of immune epitope database resource. The two dimensional and three dimensional structure of multi-epitope vaccine were predicted and authenticated using PROCheck and Raptor-X servers. Docking results of the multi-epitope vaccine peptide with human leukocyte antigen class I and II alleles predicted efficient binding and the resulted docked models were stable during simulation. In silico immune evaluation using C-ImmSim server showed that the peptide could concurrently elicit cell-mediated and humoral immune responses. Immune simulation studies significantly anticipated high levels of IgM and IgG, T-helper, T-cytotoxic cells, INF-γ. [ABSTRACT FROM AUTHOR]

Rotavirus A (RVA) remains one of the most widespread causes of diarrheal disease and mortality in piglets despite decades of research and efforts to boost lactogenic immunity for passive protection. Genetic changes at B cell epitopes (BCEs) may be driving failure of lactogenic immunity, which relies on production of IgA antibodies to passively neutralize RVA within the piglet gut, yet little research has mapped epitopes to swine-specific strains of RVA. Here we describe a bioinformatic approach to predict BCEs on the VP7 outer capsid protein using sequence data alone. We first validated the approach using a previously published dataset of VP7-specific cross-neutralization titers, and found that amino acid changes at predicted BCEs on the VP7 protein allowed for accurate recapitulation of antigenic relationships among the strains. Applying the approach to a dataset of swine RVA sequences identified 9 of the 11 known BCEs previously mapped to swine strains, indicating that epitope prediction can identify sites that are known to drive neutralization escape in vitro. Additional genotype-specific BCEs were also predicted that may be the cause of antigenic differences among strains of RVA on farms and should be targeted for further confirmatory work. The results of this work lay the groundwork for high throughput, immunologically-relevant analysis of swine RVA sequence data, and provide potential sites that can be targeted with vaccines to reduce piglet mortality and support farm health.

Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. “Rapid-Fire” applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.

Rotavirus A (RVA) remains one of the most widespread causes of diarrheal disease and mortality in piglets despite decades of research and efforts to boost lactogenic immunity for passive protection. Genetic changes at B cell epitopes (BCEs) may be driving failure of lactogenic immunity, which relies on production of IgA antibodies to passively neutralize RVA within the piglet gut, yet little research has mapped epitopes to swine-specific strains of RVA. Here we describe a bioinformatic approach to predict BCEs on the VP7 outer capsid protein using sequence data alone. We first validated the approach using a previously published dataset of VP7-specific cross-neutralization titers, and found that amino acid changes at predicted BCEs on the VP7 protein allowed for accurate recapitulation of antigenic relationships among the strains. Applying the approach to a dataset of swine RVA sequences identified 9 of the 11 known BCEs previously mapped to swine strains, indicating that epitope prediction can identify sites that are known to drive neutralization escape in vitro. Additional genotype-specific BCEs were also predicted that may be the cause of antigenic differences among strains of RVA on farms and should be targeted for further confirmatory work. The results of this work lay the groundwork for high throughput, immunologically-relevant analysis of swine RVA sequence data, and provide potential sites that can be targeted with vaccines to reduce piglet mortality and support farm health. [ABSTRACT FROM AUTHOR]

Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. "Rapid-Fire" applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 has infected over 109 000 000 people with 2 423 443 deaths as of February 17, 2021. Currently, there are no approved or consistently effective treatments, and conventional vaccines may take several years for development and testing. In silico methods of bioinformatics, vaccinogenomics, immunoinformatics, structural biology, and molecular simulations can be used for more rapid and precise vaccine design. This paper highlights two major immunoinformatics strategies that are used in designing novel and effective vaccines and therapeutics: reverse vaccinology and structural vaccinology. [ABSTRACT FROM AUTHOR]

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With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019), as a global pandemic. Additionally, the positive cases are still following an upward trend worldwide and as a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication and interferes with host immune responses. We comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we predicted the most immunogenic epitope for the T-cell and B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

West Nile virus (WNV) is a major pathogenic flavivirus which causes human neuro-invasive disease, worldwide. Still successful vaccine and therapeutic treatment against WNV infection is not available, which demands the development of more potential WNV vaccines. The present study used immunoinformatics methods viz. Matrix and Artificial Neural Network (ANN) based algorithm to identify the promiscuous and conserve T cell epitopes from entire WNV proteome followed by structure based analysis of identified epitopes. The epitopes were also taken for TAP binding analysis and epitope conservancy analysis. Among 89 identified epitopes, eight epitopes showed high potential and conserve nature but two epitopes viz. capsid 40FVLALLAFF48 and NS2B 9LMFAIVGGL17 were found most promiscuous and having high population coverage in comparison of other identified epitopes and known antigenic positive control epitopes. Further, Autodock 4.2 and NAMD-VMD molecular dynamics simulation were used for docking and molecular dynamics simulation respectively, to validate epitope and allele complex stability. The 3D structure models were generated for epitopes and corresponding HLA allele by Pepstr and Modeller 9.10, respectively. Epitope FVLALLAFF-B*3501 allele and epitope LMFAIVGGL-B*5101 HLA allele complexes have shown best energy minimization and stable complexes during simulation. The study also showed the optimum binding epitopes FVLALLAFF and LMFAIVGGL with cTAP1 (PDB ID: 1JJ7) cavity, as revealed by Autodock 4.2, concluding favored passage through the ER membrane from cytosol to the ER lumen during cytosolic processing. The docking experiment of epitopes FVLALLAFF, LMFAIVGGL with cTAP1 very well show 1 H-bond state with a binding energy of −1.62 and −0.23 kcal/mol, respectively. These results show a smooth pass through of the epitope across the channel of cTAP1 via being weakly bonded and released into the ER lumen through the cavity of cTAP1. Overall, identified peptides have potential application in the development of short peptide based vaccines and diagnostic agents for West Nile virus. [ABSTRACT FROM AUTHOR]

Current T cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T cell receptor (TCR) is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T cell receptor and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of TCRs that each bind to a known peptide and (2) retrieving TCRs that bind to a given peptide from a large pool of TCRs. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particular importance as they show that prediction of T cell epitope and T cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T cell epitopes but also paves the way for more general and high-performing models. [ABSTRACT FROM AUTHOR]