Computational tools for modern vaccine development

Computational Approaches for the Structure-Based Identification of Novel Inhibitors Targeting Nucleoid-Associated Proteins in Mycobacterium Tuberculosis

Implementation of computational tools in the identification of novel drug targets for Tuberculosis (TB) has been a promising area of research. TB has been a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb) localized primarily on the lungs and it has been one of the most successful pathogen in the history of mankind. Extensively arising drug resistivity in TB has made it a global challenge and need for new drugs has become utmost important.The involvement of Nucleoid-Associated Proteins (NAPs) in maintaining the structure of the genomic material and regulating various cellular processes like transcription, DNA replication, repair and recombination makes significant, has opened a new arena to find the drugs targeting Mtb. The current study aims to identify potential inhibitors of NAPs through a computational approach. In the present work we worked on the eight NAPs of Mtb, namely, Lsr2, EspR, HupB, HNS, NapA, mIHF and NapM. The structural modelling and analysis of these NAPs were carried out. Moreover, molecular interaction were checked and binding energy was identified for 2500 FDA-approved drugs that were selected for antagonist analysis to choose novel inhibitors targeting NAPs of Mtb. Drugs including Amikacin, streptomycin, kanamycin, and isoniazid along with eight FDA-approved molecules that were found to be potential novel targets for these mycobacterial NAPs and have an impact on their functions. The potentiality of several anti-tubercular drugs as therapeutic agents identified through computational modelling and simulation unlocks a new gateway for accomplishing the goal to treat TB.

Structural vaccinology, molecular simulation and immune simulation approaches to design multi-epitopes vaccine against John Cunningham virus

The JCV (John Cunningham Virus) is known to cause progressive multifocal leukoencephalopathy, a condition that results in the formation of tumors. Symptoms of this condition such as sensory defects, cognitive dysfunction, muscle weakness, homonosapobia, difficulties with coordination, and aphasia. To date, there is no specific and effective treatment to completely cure or prevent John Cunningham polyomavirus infections. Since the best way to control the disease is vaccination. In this study, the immunoinformatic tools were used to predict the high immunogenic and non-allergenic B cells, helper T cells (HTL), and cytotoxic T cells (CTL) epitopes from capsid, major capsid, and T antigen proteins of JC virus to design the highly efficient subunit vaccines. The specific immunogenic linkers were used to link together the predicted epitopes and subjected to 3D modeling by using the Robetta server. MD simulation was used to confirm that the newly constructed vaccines are stable and properly fold. Additionally, the molecular docking approach revealed that the vaccines have a strong binding affinity with human TLR-7. The codon adaptation index (CAI) and GC content values verified that the constructed vaccines would be highly expressed in E. coli pET28a (+) plasmid. The immune simulation analysis indicated that the human immune system would have a strong response to the vaccines, with a high titer of IgM and IgG antibodies being produced. In conclusion, this study will provide a pre-clinical concept to construct an effective, highly antigenic, non-allergenic, and thermostable vaccine to combat the infection of the John Cunningham virus.

Expression and purification of TolC as a recombinant protein vaccine against Shigella flexneri and evaluation of immunogenic response in mice

BACKGROUND

Shigella is one of the major causes of dysenteric diarrhea, which is known shigelosis. Shigelosis causes 160,000 deaths annually of diarrheal disease in the global scale especially children less than 5 years old. No licensed vaccine is available against shigelosis, therefore, efforts for develop an effective and safe vaccine against Shigella as before needed. The reverse vaccinology (RV) is a novel strategy that evaluate genome or proteome of the organism to find a new promising vaccine candidate. In this study, immunogenicity of a designed-recombinant antigen is evaluated through the in silico studies and animal experiments to predict a new immunogenic candidate against Shigella.

METHODS

In the first step, proteome of Shigella flexneri was obtained from UniProtKB and then the outer membrane and extracellular proteins were predicted. In this study TolC as an outer membrane protein was selected and confirmed among candidates. In next steps, pre-selected protein was evaluated for transmembrane domains, homology, conservation, antigenicity, solubility, and B- and T-cell prediction by different online servers.

RESULT

TolC as a conserved outer membrane protein, using different immune-informatics tools had acceptable scores and was selected as the immunogenic antigen for animal experiment studies. Recombinant TolC protein after expression and purification, was administered to BALB/c mice over three intraperitoneal routes. The sera of mice was used to evaluate the IgG1 production assay by indirect-ELISA. The immunized mice depicted effective protection against 2LD50 of Shigella. Flexneri ATCC12022 (challenge study).

CONCLUSION

Therefore, the reverse vaccinology approach and experimental test results demonstrated that TolC as a novel effective and immunogenic antigen is capable for protection against shigellosis.

Technological advancements in viral vector designing and optimization for therapeutic applications

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.

In silico designing and characterization of outer membrane protein K (OmpK) from Vibrio anguillarum and its expression in Nicotiana tabacum for the development of a plant-based vaccine against fish vibriosis

Vibriosis is caused by Vibrio anguillarum in various species of aquaculture. A novel, secure, and stable vaccine is needed to eradicate vibriosis. Here, for reverse vaccinology and plant-based expression, the outer membrane protein K (OmpK) of V. anguillarum was chosen due to its conserved nature in all Vibrio species. OmpK, an ideal vaccine candidate against vibriosis, demonstrated immunogenic, non-allergic, and non-toxic behavior by using various bioinformatics tools. Docking showed the interaction of the OmpK model with TLR-5. In comparison to costly platforms, plants can be used as alternative and economic bio-factories to produce vaccine antigens. We expressed OmpK antigen in Nicotiana tabacum using Agrobacterium-mediated transformation. The expression vector was constructed using Gateway® cloning. Transgene integration was verified by polymerase chain reaction (PCR), and the copy number via qRT-PCR, which showed two copies of transgenes. Western blotting detected monomeric form of OmpK protein. The total soluble protein (TSP) fraction of OmpK was equivalent to 0.38% as detected by ELISA. Mice and fish were immunized with plant-derived OmpK antigen, which showed a significantly high level of anti-OmpK antibodies. The present study is the first report of OmpK antigen expression in higher plants for the potential use as vaccine in aquaculture against vibriosis, which could provide protection against multiple Vibrio species due to the conserved nature OmpK antigen.

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.

Recruiting In Vitro Transcribed mRNA against Cancer Immunotherapy: A Contemporary Appraisal of the Current Landscape

Over 100 innovative in vitro transcribed (IVT)-mRNAs are presently undergoing clinical trials, with a projected substantial impact on the pharmaceutical market in the near future. Τhe idea behind this is that after the successful cellular internalization of IVT-mRNAs, they are subsequently translated into proteins with therapeutic or prophylactic relevance. Simultaneously, cancer immunotherapy employs diverse strategies to mobilize the immune system in the battle against cancer. Therefore, in this review, the fundamental principles of IVT-mRNA to its recruitment in cancer immunotherapy, are discussed and analyzed. More specifically, this review paper focuses on the development of mRNA vaccines, the exploitation of neoantigens, as well as Chimeric Antigen Receptor (CAR) T-Cells, showcasing their clinical applications and the ongoing trials for the development of next-generation immunotherapeutics. Furthermore, this study investigates the synergistic potential of combining the CAR immunotherapy and the IVT-mRNAs by introducing our research group novel, patented delivery method that utilizes the Protein Transduction Domain (PTD) technology to transduce the IVT-mRNAs encoding the CAR of interest into the Natural Killer (NK)-92 cells, highlighting the potential for enhancing the CAR NK cell potency, efficiency, and bioenergetics. While IVT-mRNA technology brings exciting progress to cancer immunotherapy, several challenges and limitations must be acknowledged, such as safety, toxicity, and delivery issues. This comprehensive exploration of IVT-mRNA technology, in line with its applications in cancer therapeutics, offers valuable insights into the opportunities and challenges in the evolving landscape of cancer immunotherapy, setting the stage for future advancements in the field.

Advances of Reverse Vaccinology for mRNA Vaccine Design against SARS-CoV-2: A Review of Methods and Tools

mRNA vaccines are a new class of vaccine that can induce potent and specific immune responses against various pathogens. However, the design of mRNA vaccines requires the identification and optimization of suitable antigens, which can be challenging and time consuming. Reverse vaccinology is a computational approach that can accelerate the discovery and development of mRNA vaccines by using genomic and proteomic data of the target pathogen. In this article, we review the advances of reverse vaccinology for mRNA vaccine design against SARS-CoV-2, the causative agent of COVID-19. We describe the steps of reverse vaccinology and compare the in silico tools used by different studies to design mRNA vaccines against SARS-CoV-2. We also discuss the challenges and limitations of reverse vaccinology and suggest future directions for its improvement. We conclude that reverse vaccinology is a promising and powerful approach to designing mRNA vaccines against SARS-CoV-2 and other emerging pathogens.

Immunogenic Potential and Therapeutic Efficacy of Multi-Epitope Encapsulated Silk Fibroin Nanoparticles against Pseudomonas aeruginosa-Mediated Urinary Tract Infections

Pseudomonas aeruginosa (P. aeruginosa) causing urinary tract infections (UTIs) are a major concern among hospital-acquired infections. The need for an effective vaccine that reduces the infections is imperative. This study aims to evaluate the efficacy of a multi-epitope vaccine encapsulated in silk fibroin nanoparticles (SFNPs) against P. aeruginosa-mediated UTIs. A multi-epitope is constructed from nine proteins of P. aeruginosa using immunoinformatic analysis, expressed, and purified in BL21 (DE3) cells. The encapsulation efficiency of the multi-epitope in SFNPs is 85% with a mean particle size of 130 nm and 24% of the encapsulated antigen is released after 35 days. The vaccine formulations adjuvanted with SFNPs or alum significantly improve systemic and mucosal humoral responses and the cytokine profile (IFN-γ, IL-4, and IL-17) in mice. Additionally, the longevity of the IgG response is maintained for at least 110 days in a steady state. In a bladder challenge, mice treated with the multi-epitope admixed with alum or encapsulated in SFNPs demonstrate significant protection of the bladder and kidneys against P. aeruginosa. This study highlights the promising therapeutic potential of a multi-epitope vaccine encapsulated in SFNPs or adjuvanted with alum against P. aeruginosa infections.

Designing a bioadjuvant candidate vaccine targeting infectious bursal disease virus (IBDV) using viral VP2 fusion and chicken IL-2 antigenic epitope: A bioinformatics approach

Infectious Bursal Disease (IBD) is a common and contagious viral infection that significantly affects the poultry industry. This severely suppresses the immune system in chickens, thereby threating their health and well-being. Vaccination is the most effective strategy for preventing and controlling this infectious agent. The development of VP2-based DNA vaccines combined with biological adjuvants has recently received considerable attention due to their effectiveness in eliciting both humoral and cellular immune responses. In this study, we applied bioinformatics tools to design a fused bioadjuvant candidate vaccine from the full-length sequence of the VP2 protein of IBDV isolated in Iran using the antigenic epitope of chicken IL-2 (chiIL-2). Furthermore, to improve the antigenic epitope presentation and to maintain the three-dimensional structure of the chimeric gene construct, the P2A linker (L) was used to fuse the two fragments. Our in-silico analysis for the design of a candidate vaccine indicates that a continuous sequence of amino acid residues ranging from 105 to 129 in chiIL-2 is proposed as a B cell epitope by epitope prediction servers. The final 3D structure of the VP2-L-chiIL-2105-129 was subjected to physicochemical property determination, molecular dynamic simulation, and antigenic site determination. The results of these analyses led to the development of a stable candidate vaccine that is non-allergenic and has the potential for antigenic surface display potential and adjuvant activity. Finally, it is necessary to investigate the immune response induced by our proposed vaccine in avian hosts. Notably, increasing the immunogenicity of DNA vaccines can be achieved by combining antigenic proteins with molecular adjuvants using the principle of rational vaccine design.

Efficient strategy to isolate exosomes using anti-CD63 antibodies conjugated to gold nanoparticles

Exosomes, a subpopulation of Extracellular vesicles (EVs), are cell-secreted vesicles found in the majority of biological fluids, including breast milk, tears, sweat, blood and, urine. The density and size of these vesicles depend on a variety of factors, including age, gender and the biological condition of the individual. Researchers are now focusing on the selective extraction of exosomes from bodily fluids due to the unique biomolecule composition of exosomes, which is critical for diagnosis, disease, and regeneration. Furthermore, current approaches for exosome isolation have limitations, necessitating the development of a simpler and more effective technique to achieve this goal. In this study, we investigated a quick and effective strategy for isolating exosomes from serum using a bench-top centrifuge. This was accomplished by raising antibodies against exosome surface tetraspanins (CD9, CD63 & CD81) in Leghorn chickens due to their phylogenetic distance from humans and cost-effectiveness for commercial use. In order to separate exosomes from a complex biological fluid, the antibodies were further coupled with gold nanoparticles (AuNPs). The findings were validated using ELISA, spectrophotometry, and transmission electron microscopy (TEM). Using this technique, exosome isolation from serum was achieved rapidly and these were captured by using anti CD63 antibodies bound to AuNPs. To summarize, exosomes were purified from serum using anti-CD63 antibodies conjugated to gold nanoparticles (IgY@AuNPs). Consequently, the approach for exosome isolation from biological fluid could be useful for clinically monitoring the biological state of the patients.

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus

Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.

Computational formulation of a multiepitope vaccine unveils an exceptional prophylactic candidate against Merkel cell polyomavirus

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%-100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine's effectiveness and potential for further development.

Staphylococcus aureus vaccine strategy: Promise and challenges

Staphylococcus aureus (S. aureus) is a leading and crucial infectious threat to global public health due to the widespread emergence of antibiotic-resistant strains such as Methicillin-Resistant S. aureus (MRSA). MRSA infects immunocompromised patients and healthy individuals and has rapidly spread from the healthcare setting to the outside community. The development of flawless vaccines become a medical need worldwide against multi-drug resistant S. aureus. Therefore, protection by an immune-based strategy may provide valuable measures to contain the spread of invasive S. aureus infections. Several vaccine candidates have been prepared which are either in the preclinical phase or in the early clinical phase, whereas several candidates have failed to show a protective efficacy in human subjects. Currently, research is focusing on identifying novel vaccine formulations able to elicit potent humoral and cellular immune responses. Several approaches have also been made to the development of monoclonal or polyclonal antibodies for passive immunization to protect against S. aureus infections. In recent years, a multi-epitope vaccine has emerged as a novel platform for subunit vaccine design by using computational approaches. Therefore, in this review, we have summarized and discussed the mechanistic overview of different strategies used to develop potential vaccine candidates and passive interventions which are in different stages of clinical trials to fight multi-drug resistant S. aureus infections.

In silico design and immunoinformatics analysis of a chimeric vaccine construct based on Salmonella pathogenesis factors

Currently, there are two vaccines based on killed and/or weakened Salmonella bacteria, but no recombinant vaccine is available for preventing or treating the disease. We used an in silico approach to design a multi-epitope vaccine against Salmonella using OmpA, OmpS, SopB, SseB, SthA and FilC antigens. We predicted helper T lymphocyte, cytotoxic T lymphocyte, and IFN-γ epitopes. The FilC sequence was used as a bovine TLR5 agonist, and the linkers KK, AAY, GPGPG and EAAAK were used to connect epitopes. The final sequence consisted of 747 amino acid residues, and the expressed soluble protein (∼79.6 kDa) was predicted to be both non-allergenic and antigenic. The tertiary structure of modeled protein was refined and validated, and the interactions of vaccine 3D structure were evaluated using molecular docking, and molecular dynamics simulation (RMSD, RMSF and Gyration). This structurally stable protein could interact with human TLR5. The C-ImmSim server predicted that this proposed vaccine likely induces an immune response by stimulating T and B cells, making it a potential candidate for further evaluation for the prevention and treatment of Salmonella infection.

Tracing the recent updates on vaccination approaches and significant adjuvants being developed against HIV

INTRODUCTION

Human Immunodeficiency Virus type 1 (HIV1); the causative agent of Acquired Immunodeficiency Syndrome (AIDS), has been a major target of the scientific community to develop an anti-viral therapy. Some successful discoveries have been made during the last two decades in the form of availability of antiviral therapy in endemic regions. Nevertheless, a total cure and safety vaccine has not yet been designed to eradicate HIV from the world.

AREAS COVERED

The purpose of this comprehensive study is to compile recent data regarding therapeutic interventions against HIV and to determine future research needs in this field. A systematic research strategy has been used to gather data from recent, most advanced published electronic sources. Literature based results show that experiments at the invitro level and animal models are continuously in research annals and are providing hope for human trials.

EXPERT OPINION

There is still a gap and more work is needed in the direction of modern drug and vaccination designs. Moreover coordination is necessary among researchers, educationists, public health workers, and the general community to communicate and coordinate the repercussions associated with the deadly disease. It is important for taking timely measures regarding mitigation and adaptation with HIV in future.

Oral vaccination with novel Lactococcus lactis mucosal live vector-secreting Brucella lumazine synthase (BLS) protein induces humoral and cellular immune protection against Brucella abortus

This work aimed to provide recombinant Lactococcus lactis as a potential live vector for the manufacture of recombinant Brucella abortus (rBLS-Usp45). The sequences of the genes were collected from the GenBank database. Using Vaxijen and ccSOL, the proteins' immunogenicity and solubility were evaluated. Mice were given oral vaccinations with recombinant L. lactis. Anti-BLS-specific IgG antibodies were measured by ELISA assay. Cytokine reactions were examined using real-time PCR and the ELISA technique. The BLS protein was chosen for immunogenicity based on the vaccinology screening findings since it had maximum solubility and antigenic values ​​of 99% and 0.75, respectively. The BLS gene, digested at 477 bp, was electrophoretically isolated to demonstrate that the recombinant plasmid was successfully produced. Protein-level antigen expression showed that the target group produced the 18 kDa-sized BLS protein, whereas the control group did not express any proteins. In the sera of mice given the L. lactis-pNZ8148-BLS-Usp45 vaccine 14 days after priming, there was a significant level of BLS-specific IgG1, IgG2a (P < 0.001) compared to the PBS control group. Vaccinated mice showed higher levels of IFN-γ, TNFα, IL-4, and IL-10 in samples obtained on days 14 and 28, after receiving the L. lactis-pNZ8148-BLS-Usp45 and IRBA vaccines (P < 0.001). The inflammatory reaction caused less severe spleen injuries, alveolar edema, lymphocyte infiltration, and morphological damage in the target group's spleen sections. Based on our findings, an oral or subunit-based vaccine against brucellosis might be developed using L. lactis-pNZ8148-BLS-Usp45 as a novel, promising, and safe alternative to the live attenuated vaccines now available.

Reducing the Immunogenicity of Pulchellin A-Chain, Ribosome-Inactivating Protein Type 2, by Computational Protein Engineering for Potential New Immunotoxins

Pulchellin is a plant biotoxin categorized as a type 2 ribosome-inactivating protein (RIPs) which potentially kills cells at very low concentrations. Biotoxins serve as targeting immunotoxins (IT), consisting of antibodies conjugated to toxins. ITs have two independent protein components, a human antibody and a toxin with a bacterial or plant source; therefore, they pose unique setbacks in immunogenicity. To overcome this issue, the engineering of epitopes is one of the beneficial methods to elicit an immunological response. Here, we predicted the tertiary structure of the pulchellin A-chain (PAC) using five common powerful servers and adopted the best model after refining. Then, predicted structure using four distinct computational approaches identified conformational B-cell epitopes. This approach identified some amino acids as a potential for lowering immunogenicity by point mutation. All mutations were then applied to generate a model of pulchellin containing all mutations (so-called PAM). Mutants’ immunogenicity was assessed and compared to the wild type as well as other mutant characteristics, including stability and compactness, were computationally examined in addition to immunogenicity. The findings revealed a reduction in immunogenicity in all mutants and significantly in N146V and R149A. Furthermore, all mutants demonstrated remarkable stability and validity in Molecular Dynamic (MD) simulations. During docking and simulations, the most homologous toxin to pulchellin, Abrin-A was applied as a control. In addition, the toxin candidate containing all mutations (PAM) disclosed a high level of stability, making it a potential model for experimental deployment. In conclusion, by eliminating B-cell epitopes, our computational approach provides a potential less immunogenic IT based on PAC.

tuberculosis

Tuberculosis (TB) is still the leading global cause of death from an infectious bacterial agent. Limiting tuberculosis epidemic spread is therefore an urgent global public health priority. As stated by the WHO, to stop the spread of the disease we need a new vaccine, with better coverage than the current Mycobacterium bovis BCG vaccine. This vaccine was first used in 1921 and, since then, there are still no new licensed tuberculosis vaccines. However, there is extremely active research in the field, with a steep acceleration in the past decades, due to the advance of technologies and more rational vaccine design strategies. This review aims to gather latest updates in vaccine development in the various clinical phases and to underline the contribution of Structural Vaccinology (SV) to the development of safer and effective antigens. In particular, SV and the development of vaccine adjuvants is making the use of subunit vaccines, which are the safest albeit the less antigenic ones, an achievable goal. Indeed, subunit vaccines overcome safety concerns but need to be rationally re-engineered to enhance their immunostimulating effects. The larger availability of antigen structural information as well as a better understanding of the complex host immune response to TB infection is a strong premise for a further acceleration of TB vaccine development.

COVID-19 Vaccines: Computational tools and Development

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Review: Unravelling the Role of DNA Sensing in Alum Adjuvant Activity

Public interest in vaccines is at an all-time high following the SARS-CoV-2 global pandemic. Currently, over 6 billion doses of various vaccines are administered globally each year. Most of these vaccines contain Aluminium-based adjuvants (alum), which have been known and used for almost 100 years to enhance vaccine immunogenicity. However, despite the historical use and importance of alum, we still do not have a complete understanding of how alum works to drive vaccine immunogenicity. In this article, we critically review studies investigating the mechanisms of action of alum adjuvants, highlighting some of the misconceptions and controversies within the area. Although we have emerged with a clearer understanding of how this ubiquitous adjuvant works, we have also highlighted some of the outstanding questions in the field. While these may seem mainly of academic interest, developing a more complete understanding of these mechanisms has the potential to rationally modify and improve the immune response generated by alum-adjuvanted vaccines.

Immunoinformatic-based design of immune-boosting multiepitope subunit vaccines against monkeypox virus and validation through molecular dynamics and immune simulation

Monkeypox virus is the causative agent of monkeypox disease, belonging to an orthopoxvirus genus, with a disease pattern similar to that of smallpox. The number of monkeypox cases have robustly increased recently in several countries around the world, potentially causing an international threat. Therefore, serious measures are indispensable to be taken to mitigate the spread of the disease and hence, under these circumstances, vaccination is the best choice to neutralize the monkeypox virus. In the current study, we used immunoinformatic approaches to target the L1R, B5R, and A33R proteins of the monkeypox virus to screen for immunogenic cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes to construct multiepitope subunit vaccines. Various online tools predicted the best epitope from immunogenic targets (L1R, B5R, and A33R) of monkeypox virus. The predicted epitopes were joined together by different linkers and subjected to 3D structure prediction. Molecular dynamics simulation analysis confirmed the proper folding of the modeled proteins. The strong binding of the constructed vaccines with human TLR-2 was verified by the molecular docking and determination of dissociation constant values. The GC content and codon adaptation index (CAI) values confirmed the high expression of the constructed vaccines in the pET-28a (+) expression vector. The immune response simulation data delineated that the injected vaccines robustly activated the immune system, triggering the production of high titers of IgG and IgM antibodies. In conclusion, this study provided a solid base of concept to develop dynamic and effective vaccines that contain several monkeypox virus-derived highly antigenic and nonallergenic peptides to control the current pandemic of monkeypox virus.

Structural and Functional Annotation and Molecular Docking Analysis of a Hypothetical Protein from Neisseria gonorrhoeae: An In-Silico Approach

Background

Worldwide, Neisseria gonorrhoeae-related sexually transmitted infections (STIs) continue to be of significant public health concern. This obligate-human pathogen has developed a number of defenses against both innate and adaptive immune responses during infection, some of which are mediated by the pathogen's proteins. Hence, the uncharacterized proteins of N. gonorrhoeae can be annotated to get insight into the unique functions of this organism related to its pathogenicity and to find a more efficient therapeutic target.

Methods

In this study, a hypothetical protein (HP) of N. gonorrhoeae was chosen for analysis and an in-silico approach was used to explore various properties such as physicochemical characteristics, subcellular localization, secondary structure, 3D structures, and functional annotation of that HP. Finally, a molecular docking analysis was performed to design an epitope-based vaccine against that HP.

Results

This study has identified the potential role of the chosen HP of N. gonorrhoeae in plasmid transfer, cell cycle control, cell division, and chromosome partitioning. Acidic nature, thermal stability, cytoplasmic localization of the protein, and some of its other physicochemical properties have also been identified through this study. Molecular docking analysis has demonstrated that one of the T cell epitopes of the protein has a significant binding affinity with the human leukocyte antigen HLA-B∗15 : 01.

Conclusions

The in-silico characterization of this protein will help us understand molecular mechanism of action of N. gonorrhoeae and get an insight into novel therapeutic identification processes. This research will, therefore, enhance our knowledge to find new medications to tackle this potential threat to humankind.

The progress of peptide vaccine clinical trials in gynecologic oncology

Peptide vaccine are a type of immunotherapy that are synthesized according to the amino acid sequence of known or predicted tumor antigen epitopes. They are safe and well tolerated and have shown exciting results in gynecologic oncology. However, no peptide vaccine has yet been licensed in this field. This review examines peptide vaccine clinical trials in gynecology registered on ClinicalTrials.gov through January 1, 2022, analyzes the global progress and current achievements of peptide vaccines in gynecology, and explores the efforts focused on devising new methods to boost immunotherapeutic outcomes, including the use of adjuvants, multi-epitope vaccines, combinations of helper T cell epitopes, personalized peptide vaccines, synthetic long peptides, new peptide delivery, and combination therapy.

Recent Advances in Vaccine Technology and Design

If up until three years ago, infectious diseases were a lesser concern when compared to non-communicable diseases in Western countries, the ongoing pandemic has reminded us that things are not so clean-cut [...].

Toward rational vaccine engineering

Technological revolutions in several fields have pushed the boundaries of vaccine design and provided new avenues for vaccine development. Next-generation vaccine platforms have shown promise in targeting challenging antigens, for which traditional approaches have been ineffective. With advances in protein engineering, structural biology, computational biology and immunology, the structural vaccinology approach, which uses protein structure information to develop immunogens, holds promise for future vaccine design. In this review, we highlight various vaccine development strategies, along with their advantages and limitations. We discuss the rational vaccine design approach, which focuses on structure-based vaccine design. Finally, we discuss antigen engineering using the epitope-scaffold approach, gaps in structural vaccinology, and remaining challenges in vaccine design.

Immunoinformatic Approaches for Vaccine Designing for Pathogens with Unclear Pathogenesis

Designing a vaccine against a pathogen has been the toughest challenge to fight against any infectious diseases. To overcome this problem, use of artificial neural network with immuno-informatics is emerging as a front runner solution. For a successful designing of a potent vaccine, prediction of T-cell/B-cell epitopes, antigen processing and presentation analysis, antigenic potential analysis of epitopes, usages of linkers, population coverage, codon optimization, allergenicity assessment, toxicity prediction of construct, and finally protein-peptide docking for stability of vaccine are important steps. To achieve this, several bioinformatics software, tools and online web servers have been developed for each application, which have their own advantages and limitations. Scientists must evaluate these parameters and should take the decision to apply more suitable and precise servers for each analysis and prediction based on their accuracy, suitability, and robustness.

An overview of progress from empirical to rational design in modern vaccine development, with an emphasis on computational tools and immunoinformatics approaches

Vaccination remains the most effective strategy for preventing and controlling infectious diseases. Numerous conventional vaccines, especially live attenuated, inactivated (killed) microorganisms and subunit vaccines, lead to an effective induction of protective immune responses, mainly antibody-mediated responses against pathogens. However, it has become known that a wide range of highly dangerous pathogens are uncontrollable via conventional vaccination strategies. Recent advances in molecular biology, immunology, genetics, biochemistry, and bioinformatics have provided new prospects for vaccine development. As a result of these advances, several new strategies for vaccine design, development, and production have appeared. These strategies show advantages over conventional vaccines. In this review, we discuss some of the major novel approaches, including recombinant protein vaccines, live recombinant viral and bacterial vectors, DNA and RNA vaccines, reverse vaccinology and reverse genetics approaches. Moreover, we have described the recent progresses on computational tools and immunoinformatics approaches for identifying, designing, and developing new candidate vaccines.

Severe acute respiratory syndrome coronavirus-2: An era of struggle and discovery leading to the emergency use authorization of treatment and prevention measures based on computational analysis

Severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), a novel betacoronavirus, has surprised the world with its disease spread and mortality rate. SARS-CoV-2 is a positive-sense, enveloped RNA virus that can infect various organs of the body, potentially leading to multiple organ dysfunction and eventual death. While various medications have received emergency use authorizations (EUAs) for the treatment of Coronavirus disease-2019 (COVID-19), as of April 30, 2021, only one drug has been Food and Drug Administration (FDA)-approved: remdesivir. Currently, three vaccines have received EUAs in the United States, but none are FDA-approved. This shortage of treatments and prevention measures is extremely problematic. Thus computational approaches would provide important data about drug resistance and variants. Such data will be useful for the development of drugs and vaccines. This chapter is a synopsis of SARS-CoV-2 clinical presentation, COVID-19 symptomology, treatment, prevention mechanisms, and SARS-CoV-2 variants using computational analysis.

Animal models in vaccinology: state of the art and future perspectives for an animal-free approach

Vaccine discovery and development is mainly driven by studies on immunogenicity and safety based on the appropriate animal models. In this review we will describe the importance of animal models in vaccinology, from research and development to pre-licensure and post-licensure commitments with particular emphasis on the advantages and limitations of each animal species. Finally, we will describe the most modern technologies, the new in vitro and ex vivo models and the new advances in the field which may drive into a new era of 'animal free' vaccinology.

Development of synthetic antigen vaccines for COVID-19

The current pandemic called COVID-19 caused by the SARS-CoV-2 virus brought the need for the search for fast alternatives to both control and fight the SARS-CoV-2 infection. Therefore, a race for a vaccine against COVID-19 took place, and some vaccines have been approved for emergency use in several countries in a record time. Ongoing prophylactic research has sought faster, safer, and precise alternatives by redirecting knowledge of other vaccines, and/or the development of new strategies using available tools, mainly in the areas of genomics and bioinformatics. The current review highlights the development of synthetic antigen vaccines, focusing on the usage of bioinformatics tools for the selection and construction of antigens on the different vaccine constructions under development, as well as strategies to optimize vaccines for COVID-19.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

Engineering an efficient poly-epitope vaccine against Toxoplasma gondii infection: A computational vaccinology study

Toxoplasmosis is a zoonotic disease caused by Toxoplasma gondii. Despite the importance of toxoplasmosis, there is no comprehensive strategy to control this disease. Hence, applying the new methods such as the poly-epitope vaccine can be successful. In the current project, to engineer a potent poly-epitope vaccine, 10 antigenic proteins including BiP, GRA1, GRA2, GRA5, MIC8, MIC13, P30, PI1, SOD and Rop2 were selected based on the database. Then, B cell, MHCI and MHCII epitopes of the selected antigenic proteins were isolated by the most accurate servers. The best predicted epitopes along with a molecular adjuvant were employed to engineer a poly-epitope vaccine. After engineering, different physicochemical features, secondary and tertiary structures, molecular docking of the designed vaccine were assessed. The results of this project revealed that the designed vaccine with 730 amino acids in length and molecular weight of 77.67 kDa was a soluble protein which could bind to its receptor with an energy of 6223.43. According to the achievements of this study, it seems the designed vaccine can be an appropriate candidate to apply.

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches

SARS-CoV-2 causes a severe respiratory disease called COVID-19. Currently, global health is facing its devastating outbreak. However, there is no vaccine available against this virus up to now. In this study, a novel multi-epitope vaccine against SARS-CoV-2 was designed to provoke both innate and adaptive immune responses. The immunodominant regions of six non-structural proteins (nsp7, nsp8, nsp9, nsp10, nsp12 and nsp14) of SARS-CoV-2 were selected by multiple immunoinformatic tools to provoke T cell immune response. Also, immunodominant fragment of the functional region of SARS-CoV-2 spike (400-510 residues) protein was selected for inducing neutralizing antibodies production. The selected regions' sequences were connected to each other by furin-sensitive linker (RVRR). Moreover, the functional region of β-defensin as a well-known agonist for the TLR-4/MD complex was added at the N-terminus of the vaccine using (EAAAK)3 linker. Also, a CD4 + T-helper epitope, PADRE, was used at the C-terminal of the vaccine by GPGPG and A(EAAAK)2A linkers to form the final vaccine construct. The physicochemical properties, allergenicity, antigenicity, functionality and population coverage of the final vaccine construct were analyzed. The final vaccine construct was an immunogenic, non-allergen and unfunctional protein which contained multiple CD8 + and CD4 + overlapping epitopes, IFN-γ inducing epitopes, linear and conformational B cell epitopes. It could form stable and significant interactions with TLR-4/MD according to molecular docking and dynamics simulations. Global population coverage of the vaccine for HLA-I and II were estimated 96.2% and 97.1%, respectively. At last, the final vaccine construct was reverse translated to design the DNA vaccine. Although the designed vaccine exhibited high efficacy in silico, further experimental validation is necessary.

An immunoinformatics study on the spike protein of SARS-CoV-2 revealing potential epitopes as vaccine candidates

Background

The pandemic situation of SARS-CoV-2 infection has sparked global concern due to the disease COVID-19 caused by it. Since the first cluster of confirmed cases in China in December 2019, the infection has been reported across the continents and inflicted upon a substantial number of populations.

Method

This study is focused on immunoinformatics analyses of the SARS-CoV-2 spike glycoprotein (S protein) which is key for the viral attachment to human host cells. Computational analyses were carried out for the prediction of B-cell and T-cell (MHC class I and II) epitopes of S protein and the analyses were extended further for the prediction of their immunogenic properties. The interaction and binding affinity of T-cell epitopes with HLA-B7 were also investigated by molecular docking.

Result

Three distinct epitopes for vaccine design were predicted from the sequence of S protein. The potential B-cell epitope was KNHTSPDVDLG possessing the highest antigenicity score of 1.4039 among other B-cell epitopes. T-cell epitope for human MHC class I was VVVLSFELL with an antigenicity score of 1.0909 and binding ability to 29 MHC-I alleles. The predicted T-cell epitope for human MHC class II molecule was VVIGIVNNT with a corresponding 1.3063 antigenicity score, less digesting enzymes, and 7 MHC-II alleles binding ability. All these three peptides were predicted to be highly antigenic, non-allergenic, and non-toxic. Analyses of the physiochemical properties of these predicted epitopes indicate their stable nature for plausible vaccine design. Furthermore, molecular docking investigation between the MHC class-I epitopes and human HLA-B7 reflects the stable interaction with high affinity among them.

Conclusion

The present study posits three potential epitopes of S protein of SARS-CoV-2 predicted by immunoinformatic methods based on their immunogenic properties and interactions with the host counterpart that can facilitate the development of vaccine against SARS-CoV-2. This study can act as the springboard for the future development of the COVID-19 vaccine.

Computational approaches in epitope design using DNA binding proteins as vaccine candidate in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is a successful pathogen in the history of mankind. A high rate of mortality and morbidity raises the need for vaccine development. Mechanism of pathogenesis, survival strategy and virulence determinant are needed to be explored well for this pathogen. The involvement of DNA binding proteins in the regulation of virulence genes, transcription, DNA replication, repair make them more significant. In present work, we have identified 1453 DNA binding proteins (DBPs) in the 4173 genes of Mtb through the DNABIND tool and they were subjected for further screening by incorporating different bioinformatics tools. The eighteen DBPs were selected for the B-cell epitope prediction by using ABCpred server. Moreover, the B-cell epitope bearing the antigenic and non- allergenic property were selected for T-cell epitope prediction using ProPredI, and ProPred server. Finally, DGIGSAVSV (Rv1088), IRALPSSRH (Rv3923c), LTISPIANS (Rv3235), VQPSGKGGL (Rv2871) VPRPGPRPG (Rv2731) and VGQKINPHG (Rv0707) were identified as T-cell epitopes. The structural modelling of these epitopes and DBPs was performed to ensure the localization of these epitopes on the respective proteins. The interaction studies of these epitopes with human HLA confirmed their validation to be used as potential vaccine candidates. Collectively, these results revealed that the DBPs- Rv2731, Rv3235, Rv1088, Rv0707, Rv3923c and Rv2871 are the most appropriate vaccine candidates. In our knowledge, it is the first report of using the DBPs of Mtb for epitope prediction. Significantly, this study also provides evidence to be useful for designing a peptide-based vaccine against tuberculosis.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

Peptide-Based Vaccination Therapy for Rheumatic Diseases

Rheumatic diseases are extremely heterogeneous diseases with substantial risks of morbidity and mortality, and there is a pressing need in developing more safe and cost-effective treatment strategies. Peptide-based vaccination is a highly desirable strategy in treating noninfection diseases, such as cancer and autoimmune diseases, and has gained increasing attentions. This review is aimed at providing a brief overview of the recent advances in peptide-based vaccination therapy for rheumatic diseases. Tremendous efforts have been made to develop effective peptide-based vaccinations against rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), while studies in other rheumatic diseases are still limited. Peptide-based active vaccination against pathogenic cytokines such as TNF-α and interferon-α (IFN-α) is shown to be promising in treating RA or SLE. Moreover, peptide-based tolerogenic vaccinations also have encouraging results in treating RA or SLE. However, most studies available now have been mainly based on animal models, while evidence from clinical studies is still lacking. The translation of these advances from experimental studies into clinical therapy remains impeded by some obstacles such as species difference in immunity, disease heterogeneity, and lack of safe delivery carriers or adjuvants. Nevertheless, advances in high-throughput technology, bioinformatics, and nanotechnology may help overcome these impediments and facilitate the successful development of peptide-based vaccination therapy for rheumatic diseases.