In Silico Analysis of Synaptonemal Complex Protein 1 (SYCP1) and Acrosin Binding Protein (ACRBP) Antigens to Design Novel Multiepitope Peptide Cancer Vaccine Against Breast Cancer

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

Development of a Multiple-Epitope-Based Vaccine for Hepatitis C Virus Genotypes 1a and 1b: an in-silico reverse vaccinology approach

The Hepatitis C virus (HCV) is a blood-transmitted virus responsible for persistent inflammation, presenting a substantial worldwide health challenge. HCV, characterized by a positive-stranded ribonucleic acid genome, possesses an intricate genetic makeup encoding both structural and non-structural proteins, crucial for sustaining its life cycle. The Direct Acting Antivirals have revolutionized the treatment landscape of HCV promoting higher Sustained Virological Response rates. Despite significant advancements in treatment, no vaccines are currently available against HCV. The development of effective HCV vaccines becomes challenging as the genetic diversity of HCV virus and its complex nature of the immune response required for protection. In this work, the immunoinformatics methods were utilized to develop a multiple-epitope-based vaccine towards an effective treatment against the viral HCV polyprotein. The vaccine was constructed by T-cell epitopes extracted from the viral polyprotein of HCV genotypes 1a and 1b. The vaccine was highly antigenic, non-toxic, and non-allergenic. Effective binding of the designed vaccine construct was studied by forming complexes with the human immune Toll-Like Receptors; TLR3 and TLR8. The MD simulation of these receptor-vaccine complexes were performed for 50ns and the immunological simulation of modeled vaccine in presence of receptors for 365 days timeline validated the stability of the constructed vaccine. The in-silico vaccine construct developed from this work might be beneficial as prophylactic measures against the HCV variants, if explored further in in vivo and in vitro methods. Consequently, this research outcome is presumed to have implications in the development of safer and more efficient vaccines for lethal diseases.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00275-4.

Recent Advances in Bacterium-Based Therapeutic Modalities for Melanoma Treatment

Melanoma is one of the most severe skin cancer indications with rapid progression and a high risk of metastasis. However, despite the accumulated advances in melanoma treatment including adjuvant radiation, chemotherapy, and immunotherapy, the overall melanoma treatment efficacy in the clinics is still not satisfactory. Interestingly, bacterial therapeutics have demonstrated unique properties for tumor-related therapeutic applications, such as tumor-targeted motility, tailorable cytotoxicity, and immunomodulatory capacity of the tumor microenvironment, which have emerged as a promising platform for melanoma therapy. Indeed, the recent advances in genetic engineering and nanotechnologies have boosted the application potential of bacterium-based therapeutics for treating melanoma by further enhancing their tumor-homing, cell-killing, drug delivery, and immunostimulatory capacities. This review provides a comprehensive summary of the state-of-the-art bacterium-based anti-melanoma modalities, which are categorized according to their unique functional merits, including tumor-specific cytotoxins, tumor-targeted drug delivery platforms, and immune-stimulatory agents. Furthermore, a perspective is provided discussing the potential challenges and breakthroughs in this area. The insights in this review may facilitate the development of more advanced bacterium-based therapeutic modalities for improved melanoma treatment efficacy.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Novel prophylactic and therapeutic multi-epitope vaccine based on Ag85A, Ag85B, ESAT-6, and CFP-10 of Mycobacterium tuberculosis using an immunoinformatics approach

BACKGROUND

Current tuberculosis (TB) control strategies face limitations, such as low antibiotic treatment compliance and a rise in multidrug resistance. Furthermore, the lack of a safe and effective vaccine compounds these challenges. The limited efficacy of existing vaccines against TB underscores the urgency for innovative strategies, such as immunoinformatics. Consequently, this study aimed to design a targeted multi-epitope vaccine against TB infection utilizing an immunoinformatics approach.

METHODS

The multi-epitope vaccine targeted Ag85A, Ag85B, ESAT-6, and CFP-10 proteins. The design adopted various immunoinformatics tools for cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B lymphocyte (LBL) epitope prediction, the assessment of vaccine characteristics, structure modeling, population coverage analysis, disulfide engineering, solubility prediction, molecular docking/dynamics with toll-like receptors (TLRs), codon optimization/cloning, and immune simulation.

RESULTS

The multi-epitope vaccine, which was assembled using 12 CTL, 25 HTL, and 21 LBL epitopes associated with CpG adjuvants, showed promising characteristics. The immunoinformatics analysis confirmed the antigenicity, immunogenicity, and lack of allergenicity. Physicochemical evaluations indicated that the proteins were stable, thermostable, hydrophilic, and highly soluble. Docking simulations suggested high-affinity binding to TLRs, including TLR2, TLR4, and TLR9. In silico immune simulation predicted strong T cell (cytokine release) and B cell (immunoglobulin release) responses.

CONCLUSION

This immunoinformatics-designed multi-epitope vaccine targeting Ag85A, Ag85B, ESAT-6, and CFP-10 proteins showed promising characteristics in terms of stability, immunogenicity, antigenicity, solubility, and predicted induction of humoral and adaptive immune responses. This suggests its potential as a prophylactic and therapeutic vaccine against TB.

Bioinformatics design of a peptide vaccine containing sarcoma antigen NY-SAR-35 epitopes against breast cancer and evaluation of its immunological function in BALB/c mouse model

The development of a cancer vaccine has become an essential focus in the field of medical biotechnology and immunology. In our study, the NY-SAR-35 cancer/testis antigen was targeted to design a novel peptide vaccine using bioinformatics tools, and BALB/c mice were used to evaluate the vaccine's immunological function. This evaluation involved assessing peptide-specific IgG levels in the serum via ELISA and measuring the levels of IFN-γ, IL-4, and granzyme B in the supernatant of cultured splenocytes. The final vaccine construct consisted of two T lymphocyte epitopes linked by the AAY linker. This construct displayed high antigenicity, non-allergenicity, non-toxicity, stability, and ability to induce IFN-γ and IL-4. It showed stable dynamics with both human MHC-I and II molecules, as well as mouse MHC-II molecules, and revealed strong Van der Waals and electrostatic energies. Emulsifying our peptide vaccine in incomplete Freund's adjuvant resulted in a remarkable increase in the levels of IgG. The splenocytes of mice that received the combination of peptide and adjuvant displayed a noteworthy increase in IFN-γ, IL-4, and granzyme B secretion. Additionally, their lymphocytes exhibited higher proliferation rates compared to the control group. Our data demonstrated that our vaccine could stimulate a robust immune response, making it a promising candidate for cancer prevention. However, clinical trials are necessary to assess its efficacy in humans.

Subtractive proteomics-guided vaccine targets identification and designing of multi-epitopes vaccine for immune response instigation against Burkholderia pseudomallei

In this study, a methodical workflow using subtractive proteomics, vaccine designing, molecular simulation, and agent-based modeling approaches were used to annotate the whole proteome of Burkholderia pseudomallei (strain K96243) for vaccine designing. Among the total 5717 proteins in the whole proteome, 505 were observed to be essential for the pathogen's survival and pathogenesis predicted by the Database of Essential Genes. Among these, 23 vaccine targets were identified, of which fimbrial assembly chaperone (Q63UH5), Outer membrane protein (Q63UH1), and Hemolysin-like protein (Q63UE4) were selected for the subsequent analysis based on the systematic approaches. Using immunoinformatic approaches CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), IFN-positive, and B cell epitopes were predicted for these targets. A total of 9 CTL epitopes were added using the GSS linker, 6 HTL epitopes using the GPGPG linker, and 6 B cell epitopes using the KK linker. An adjuvant was added for enhanced antigenicity, an HIV-TAT peptide for improved delivery, and a PADRE sequence was added to form a 466 amino acids long vaccine construct. The construct was classified as non-allergenic, highly antigenic, and experimentally feasible. Molecular docking results validated the robust interaction of MEVC with immune receptors such as TLR2/4. Furthermore, molecular simulation revealed stable dynamics and compact nature of the complexes. The binding free energy results further validated the robust binding. In silico cloning, results revealed GC contents of 50.73 % and a CIA value of 0.978 which shows proper downstream processing. Immune simulation results reported that after the three injections of the vaccine a robust secondary immune response, improved antigen clearance, and effective immune memory generation were observed highlighting its potential for effective and sustained immunity. Future directions should encompass experimental validations, animal model studies, and clinical trials to substantiate the vaccine's efficacy, safety, and immunogenicity.

Design of a novel multi-epitope vaccine candidate against Chlamydia trachomatis using structural and nonstructural proteins: an immunoinformatics study

Chlamydia trachomatis (C. trachomatis) is an obligate intracellular bacterium which causes eye and sexually transmitted infections. During pregnancy, the bacterium is associated with preterm complications, low weight of neonates, fetal demise and endometritis leading to infertility. The aim of our study was design of a multi-epitope vaccine (MEV) candidate against C. trachomatis. After protein sequence adoption from the NCBI, potential epitopes toxicity, antigenicity, allergenicity, MHC-I and MHC-II binding, cytotoxic T lymphocytes (CTLs), Helper T lymphocytes (HTLs) and interferon-γ (IFN-γ)- induction were predicted. The adopted epitopes were fused together using appropriate linkers. In the next step, the MEV structural mapping and characterization, three-dimensional (3D) structure homology modeling and refinement were also performed. The MEV candidate interaction with the toll-like receptor 4 (TLR4) was also docked. The immune responses simulation was assessed using the C-IMMSIM server. Molecular dynamic (MD) simulation verified the structural stability of the TLR4-MEV complex. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach demonstrated the MEV high affinity of binding to the TLR4, MHC-I and MHC-II. The MEV construct was also stable and water soluble and had enough antigenicity and lacked allergenicity with stimulation of T cells and B cells and INF-γ release. The immune simulation confirmed acceptable responses of both the humoral and cellular arms. It is proposed that in vitro and in vivo studies are needed to evaluate the findings of this study.Communicated by Ramaswamy H. Sarma.

Immunoinformatics-Based Designing of Novel and Potent Multi-Epitope PSA D15 and Cag11 Immunogens for Helicobacter pylori Immunodiagnostic Assay Development

Helicobacter pylori (H. pylori) strain is the most genetically diverse pathogenic bacterium and now alarming serious human health concern ranging from chronic gastritis to gastric cancer and human death all over the world. Currently, the majority of commercially available diagnostic assays for H. pylori is a challenging task due to the heterogeneity of virulence factors in various geographical regions. In this concern, designing of universal multi-epitope immunogenic biomarker targeted for all H. pylori strains would be crucial to successfully immunodiagnosis assay and vaccine development for H. pylori infection. Hence, the present study aimed to explore the potential immunogenic epitopes of PSA D15 and Cag11 proteins of H. pylori, using immunoinformatics web tools in order to design novel immune-reactive multi-epitope antigens for enhanced immunodiagnosis in humans. Through an in silico immunoinformatics approach, high-ranked B-cell, MHC-I, and MHC-II epitopes of PSA D15 and Cag11 proteins were predicted, screened, and selected. Subsequently, a novel multi-epitope PSA D15 and Cag11 antigens were designed by fused the high-ranked B-cell, MHC-I, and MHC-II epitopes and 50S ribosomal protein L7/L12 adjuvant using linkers. The antigenicity, solubility, physicochemical properties, secondary and tertiary structures, 3D model refinement, and validations were carried. Furthermore, the designed multi-epitope antigens were subjected to codon adaptation and in silico cloning, immune response simulation, and molecular docking with receptor molecules. A novel, stable multi-epitope PSA D15 and Cag11 H. pylori antigens were developed and immune simulation of the designed antigens showed desirable levels of immunological response. Molecular docking of designed antigens with immune receptors (B-cell, MHC-I, MHC-II, and TLR-2/4) revealed robust interactions and stable binding affinity to the receptors. The codon optimized and in silico cloned showed that the designed antigens were successfully expressed (CAI value of 0.95 for PSA D15 and 1.0 for Cag11) after inserted into pET-32ba (+) plasmid of the E. coli K12 strain. In conclusion, this study revealed that the designed multi-epitope antigens have a huge immunological potential candidate biomarker and useful in developing immunodiagnostic assays and vaccines for H. pylori infection.

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

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

Immunoinformatics-based potential multi-peptide vaccine designing against Jamestown Canyon Virus (JCV) capable of eliciting cellular and humoral immune responses

Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.

Computational design of a multi-epitope vaccine candidate against Langya henipavirus using surface proteins

In July 2022, Langya henipavirus (LayV) was identified in febrile patients in China. There is currently no approved vaccine against this virus. Therefore, this research aimed to design a multi-epitope vaccine against LayV using reverse vaccinology. The best epitopes were selected from LayV's fusion protein (F) and glycoprotein (G), and a multi-epitope vaccine was designed using these epitopes, adjuvant, and appropriate linkers. The physicochemical properties, antigenicity, allergenicity, toxicity, and solubility of the vaccine were evaluated. The vaccine's secondary and 3D structures were predicted, and molecular docking and molecular dynamics (MD) simulations were used to assess the vaccine's interaction and stability with toll-like receptor 4 (TLR4). Immune simulation, codon optimization, and in silico cloning of the vaccine were also performed. The vaccine candidate showed good physicochemical properties, as well as being antigenic, non-allergenic, and non-toxic, with acceptable solubility. Molecular docking and MD simulation revealed that the vaccine and TLR4 have stable interactions. Furthermore, immunological simulation of the vaccine indicated its ability to elicit immune responses against LayV. The vaccine's increased expression was also ensured using codon optimization. This study's findings were encouraging, but in vitro and in vivo tests are needed to confirm the vaccine's protective effect.Communicated by Ramaswamy H. Sarma.

Natural furin inhibitor(s) as potent therapeutic molecule to mitigate SARS-CoV-2 infection

The COVID 2019 has had been going pandemic as per WHO situation reports. The major differentiating point in this virus is the presence of a unique furin cleavage site. Our insilico study points out to the effectiveness of a potent plant origin furin inhibitor. We exploited the aspect of the cleavage machinery of furin subunits which is critical and indispensible to the entry of SARS-CoV-2 in human cells and subsequent massive contagion. In-silico analysis was done to observe the interactions of proposed analogs of protease inhibitor of plant origin against furin protein as well as the furin spike glycoprotein (SGP) binding machinery. This was done by docking protocols using Hex 6.0 software followed by molecular Dynamic simulation (MDS) analysis in 100 ns scale using Amber94. Further, the images were analysed with PyMol software. The analogs I, II and III included in our study showed strong interactivity against furin individually, as well as the furin-Spike Glycoprotein 1 binding machinery. The findings were confirmed using molecular dynamic simulation analysis which indicated good structural stability and ability to neutralise furin and furin-spike glycoprotein 1. Analog II was found to be the best interactive molecule against furin, showing the least deviation (1.484 ± 0.0064). Also, it was the most effective against furin + Spike glycoprotein I machinery [1.575 ± 0.01]. We report the first of its kind of natural furin inhibitor(s) which would disrupt the furin machinery of SARS-CoV-2 and help in controlling the COVID-19 contagion.Communicated by Ramaswamy H. Sarma.

In silico analysis of the conserved surface-exposed epitopes to design novel multiepitope peptide vaccine for all variants of the SARS-CoV-2

Recently the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pervasive threat to generic health. The SARS-CoV-2 spike (S) glycoprotein plays a fundamental role in binds and fusion to the angiotensin-converting enzyme 2 (ACE2). The multi-epitope peptide vaccines would be able to elicit both long-lasting humoral and cellular immune responses, resulting the eliminating SARS-CoV-2 infections as asymptomatic patients are in large numbers. Recently, the omicron variant of the SARS-CoV-2 became a variant of concern that contained just 15-point mutations in the receptor-binding domain of the spike protein. In order to eliminate new evidence on coronavirus variants of concern detected through epidemic intelligence, the conserved epitopes of the receptor-binding domain (RBD) and spike cleavage site is the most probable target for vaccine development to inducing binds and fusion inhibitors neutralizing antibodies respectively. In this study, we utilized bioinformatics tools for identifying and analyzing the spike (S) glycoprotein sequence, e.g. the prediction of the potential linear B-cell epitopes, B-cell multi‑epitope design, secondary and tertiary structures, physicochemical properties, solubility, antigenicity, allergenicity, the molecular docking and molecular dynamics simulation for the promising vaccine candidate against all variant of concern of SARS-CoV-2. Among the epitopes of the RBD region are surface-exposed epitopes SVYAWNRKRISNCV and ATRFASVYAWNRKR as the conserved sequences in all variants of concern can be a good candidate to induce an immune response.Communicated by Ramaswamy H. Sarma.

Novel multi-epitope vaccine against bovine brucellosis: approach from immunoinformatics to expression

Brucellosis is a zoonotic caused by the Brucella which is a well-known infectious disease agent in domestic animals and if transmitted, it can cause infection in humans. Because brucellosis is contagious, its control depends on the eradication of the animal disease in farms. There are two vaccines based on the killed and/or weakened bacteria against B. melitensis and B. abortus, but no recombinant vaccine is available for preventing the disease. The present study was designed to develop a multi-epitope vaccine against of B. melitensis and B. abortus using virB10, Omp31 and Omp16 antigens by the prediction of T lymphocytes, T cell cytotoxicity and IFN-γ epitopes. 50S L7/L12 Ribosomal protein from Mycobacterium tuberculosis was used as a bovine TLR4 and TLR9 agonist. GPGPG, AAY and KK linkers were used as a linker. Brucella construct was well-integrated in the pET-32a Shuttle vector with BamHI and HindIII restriction enzymes. The final construct contained 769 amino acids, that it was soluble protein of about ∼82 kDa after expression in the Escherichia coli SHuffle host. Modeled protein analysis based on the tertiary structure validation, molecular docking studies, molecular dynamics simulations results like RMSD, Gyration and RMSF as well as MM/PBSA analysis showed that this protein has a stable construct and is capable being in interaction with bovine TLR4 and TLR9. Analysis of the data obtained suggests that the proposed vaccine can induce the immune response by stimulating T- and B-cells, and may be used for prevention and remedial purposes, against B. melitensis and B. abortus.Communicated by Ramaswamy H. Sarma.

Immunoinformatics analysis for design of multi-epitope subunit vaccine by using heat shock proteins against Schistosoma mansoni

The development of T cell and B cell that able provide long-term immune response against the schistosomiasisis to the people belongs to the epidemic area. Heat Shock Proteins (HSPs) are up-regulated in schistosomes as their environment changes owing to the developmental cycle, assisting the parasite in living with the adverse circumstances related with its life cycle. Schistosomiasis is still a severe health problem in the people of many countries in worldwide. In this work, to develop a chimeric antigen, we used an advanced and powerful immunoinformatics technique that targeted Schistosoma mansoni (S. mansoni) Heat shock protein (HSPs). Antigenicity, immunogenicity, allergenicity, and physicochemical characteristics were all assessed in silico for the developed subunit vaccine. The 3D structure of the vaccine was constructed and the stability of the vaccine construct was increased by using disulphide engineering. The protein-protein docking and simulation were performed between the vaccine construct and Toll-like receptor-4. The antigenicity probability value obtained for the vaccine construct was 0.93, which indicates that vaccine is non-allergenic and safe for human consumption. Communicated by Ramaswamy H. Sarma.

Computational design of candidate multi-epitope vaccine against SARS-CoV-2 targeting structural (S and N) and non-structural (NSP3 and NSP12) proteins

The COVID-19 pandemic caused by SARS-CoV-2 virus has created a global damage and has exposed the vulnerable side of scientific research towards novel diseases. The intensity of the pandemic is huge, with mortality rates of more than 6 million people worldwide in a span of 2 years. Considering the gravity of the situation, scientists all across the world are continuously attempting to create successful therapeutic solutions to combat the virus. Various vaccination strategies are being devised to ensure effective immunization against SARS-CoV-2 infection. SARS-CoV-2 spreads very rapidly, and the infection rate is remarkably high than other respiratory tract viruses. The viral entry and recognition of the host cell is facilitated by S protein of the virus. N protein along with NSP3 is majorly responsible for viral genome assembly and NSP12 performs polymerase activity for RNA synthesis. In this study, we have designed a multi-epitope, chimeric vaccine considering the two structural (S and N protein) and two non-structural proteins (NSP3 and NSP12) of SARS-CoV-2 virus. The aim is to induce immune response by generating antibodies against these proteins to target the viral entry and viral replication in the host cell. In this study, computational tools were used, and the reliability of the vaccine was verified using molecular docking, molecular dynamics simulation and immune simulation studies in silico. These studies demonstrate that the vaccine designed shows steady interaction with Toll like receptors with good stability and will be effective in inducing a strong and specific immune response in the body.Communicated by Ramaswamy H. Sarma.

Exploring staphylococcal superantigens to design a potential multi-epitope vaccine against Staphylococcus aureus: an in-silico reverse vaccinology approach

Staphylococcus aureus is a horrifying bacteria capable of causing millions of deaths yearly across the globe. A major contribution to the success of S. aureus as an ESKAPE pathogen is the abundance of virulence factors that can manipulate the innate and adaptive immune system of the individual. Currently, no vaccine is available to treat S. aureus-mediated infections. In this study, we present in-silico approaches to design a stable, safe and immunogenic vaccine that could help to control the infections associated with the bacteria. Three vital pathogenic secreted toxins of S. aureus, such as staphylococcal enterotoxin A (SEA), staphylococcal enterotoxin B (SEB), Toxic-shock syndrome toxin (TSST-1), were selected using the reverse vaccinology approach to design the multi-epitope vaccine (MEV). Linear B-lymphocyte, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. For designing the multi-epitope vaccine (MEV), B-cell epitopes were joined with the KK linker, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Finally, to increase the immune response to the vaccine, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminus of the MEV construct. The final MEV was found to be antigenic and non-allergen in nature. In-silico immune simulation and cloning analysis predicted the immune-stimulating potential of the designed MEV construct along with the cloning feasibility in the pET28a(+) vector with the E. coli expression system. This immunoinformatics study provides a platform for designing a suitable, safe and effective vaccine against S. aureus.Communicated by Ramaswamy H. Sarma.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses.

METHODS

Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed.

RESULTS

A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

Reverse vaccinology assisted design of a novel multi-epitope vaccine to target Wuchereria bancrofti cystatin: An immunoinformatics approach

Proteases are the critical mediators of immunomodulation exerted by the filarial parasites to bypass and divert host immunity. Cystatin is a small (∼15 kDa) immunomodulatory filarial protein and known to contribute in the immunomodulation strategy by inducing anti-inflammatory response through alternative activation of macrophages. Recently, Wuchereria bancrofti cystatin has been discovered as a ligand of human toll-like receptor 4 which is key behind the cystatin-induced anti-inflammatory response in major human antigen-presenting cells. Considering the pivotal role of cystatin in the immunobiology of filariasis, cystatin could be an efficacious target for developing vaccine. Herein, we present the design and in-silico analyses of a multi-epitope-based peptide vaccine to target W. bancrofti cystatin through immune-informatics approaches. The 262 amino acid long antigen construct comprises 9 MHC-I epitopes and MHC-II epitopes linked together by GPGPG peptide alongside an adjuvant (50S ribosomal protein L7/L12) at N terminus and 6 His tags at C terminus. Molecular docking study reveals that the peptide could trigger TLR4-MD2 to induce protective innate immune responses while the induced adaptive responses were found to be mediated by IgG, IgM and Th1 mediated responses. Notably, the designed vaccine exhibits high stability and no allergenicity in-silico. Furthermore, the muti epitope-vaccine was also predicted for its RNA structure and cloned in pET30ax for further experimental validation. Taken together, this study presents a novel multi-epitope peptide vaccine for triggering efficient innate and adaptive immune responses against W. bancrofti to intervene LF through immunotherapy.

In silico design of a multi-epitope vaccine against the spike and the nucleocapsid proteins of the Omicron variant of SARS-CoV-2

Computational studies can comprise an effective approach to treating and preventing viral infections. Since 2019, the world has been dealing with the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most important achievement in this short period of time in the effort to reduce morbidity and mortality was the production of vaccines and effective antiviral drugs. Although the virus has been significantly suppressed, it continues to evolve, spread, and evade the host's immune system. Recently, researchers have turned to immunoinformatics tools to reduce side effects and save the time and cost of traditional vaccine production methods. In the present study, an attempt has been made to design a multi-epitope vaccine with humoral and cellular immune response stimulation against the Omicron variant of SARS-CoV-2 by investigating new mutations in spike (S) and nucleocapsid (N) proteins. The population coverage of the vaccine was evaluated as appropriate compared to other studies. The results of molecular dynamics simulation and molecular mechanics/generalized Born surface area (MM/GBSA) calculations predict the stability and proper interaction of the vaccine with Toll-like receptor 4 (TLR-4) as an innate immune receptor. The results of the immune simulation show a significant increase in the coordinated response of IgM and IgG after the third injection of the vaccine. Also, in the continuation of the research, spike proteins from BA.4 and BA.5 lineages were screened by immunoinformatics filters and effective epitopes were suggested for vaccine design. Despite the high precision of computational studies, in-vivo and in-vitro research is needed for final confirmation.Communicated by Ramaswamy H. Sarma.

Design of a multi-epitope-based vaccine consisted of immunodominant epitopes of structural proteins of SARS-CoV-2 using immunoinformatics approach

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown rapid global spread and has resulted in a significant death toll worldwide. In this study, we aimed to design a multi-epitope vaccine against SARS-CoV-2 based on structural proteins S, M, N, and E. We identified B- and T-cell epitopes and then the antigenicity, toxicity, allergenicity, and similarity of predicted epitopes were analyzed. T-cell epitopes were docked with corresponding HLA alleles. Consequently, the selected T- and B-cell epitopes were included in the final construct. All selected epitopes were connected with different linkers and flagellin and pan-HLA DR binding epitopes (PADRE) as an adjuvant were used in the vaccine construct. Furthermore, molecular docking was used to evaluate the complex between the final vaccine construct and two alleles, HLA-A*02:01 and HLA-DRB1*01:01. Finally, codons were optimized for in silico cloning into pET28a(+) vector using SnapGene. The final vaccine construct comprised 11 CTL, HTL, and B-cell epitopes corresponding to 394 amino acid residues. In silico evaluation showed that the designed vaccine might potentially promote an immune response. Further in vivo preclinical and clinical testing is required to determine the safety and efficacy of the designed vaccine.

In silico design and validation of a novel multi-epitope vaccine candidate against structural proteins of Chikungunya virus using comprehensive immunoinformatics analyses

Chikungunya virus (CHIKV) is an emerging viral infectious agent with the potential of causing pandemic. There is neither a protective vaccine nor an approved drug against the virus. The aim of this study was design of a novel multi-epitope vaccine (MEV) candidate against the CHIKV structural proteins using comprehensive immunoinformatics and immune simulation analyses. In this study, using comprehensive immunoinformatics approaches, we developed a novel MEV candidate using the CHIKV structural proteins (E1, E2, 6 K, and E3). The polyprotein sequence was obtained from the UniProt Knowledgebase and saved in FASTA format. The helper and cytotoxic T lymphocytes (HTLs and CTLs respectively) and B cell epitopes were predicted. The toll-like receptor 4 (TLR4) agonist RS09 and PADRE epitope were employed as promising immunostimulatory adjuvant proteins. All vaccine components were fused using proper linkers. The MEV construct was checked in terms of antigenicity, allergenicity, immunogenicity, and physicochemical features. The docking of the MEV construct and the TLR4 and molecular dynamics (MD) simulation were also performed to assess the binding stability. The designed construct was non-allergen and was immunogen which efficiently stimulated immune responses using the proper synthetic adjuvant. The MEV candidate exhibited acceptable physicochemical features. Immune provocation included prediction of HTL, B cell, and CTL epitopes. The docking and MD simulation confirmed the stability of the docked TLR4-MEV complex. The high-level protein expression in the Escherichia coli (E. coli) host was observed through in silico cloning. The in vitro, in vivo, and clinical trial investigations are required to verify the findings of the current study.

In silico approach to design a multi-epitopic vaccine candidate targeting the non-mutational immunogenic regions in envelope protein and surface glycoprotein of SARS-CoV-2

The novel corona virus (COVID-19) is a causative agent for severe acute respiratory syndrome (SARS-CoV-2) and responsible for the current human pandemic situation which has caused global social and economic commotion. The currently available vaccines use whole viruses whereas there is scope for peptide based vaccines. Thus, the global raise in statistics of this infection at an alarming rate evoked us to determine a novel and effective vaccine candidate against SARS-CoV-2. To find the potential vaccine candidate targets, immunoinformatics approaches were used to analyze the mutations in the envelope protein and surface glycoprotein and determine the conserved region; further specific T-cell epitopes VSLVKPSFY, SLVKPSFYV, RVKNLNSSR, SEETGTLIV, LVKPSFYVY, LTDEMIAQY, YLQPRTFLL, RLFRKSNLK, SPRRARSVA, AEIRASANL, TLLALHRSY, YSRVKNLNS and FELLHAPAT and B-cells epitopes TLAILTALRLCAYCCN and AGTITSGWTFGAGAAL were identified. The 3 D structure of epitope was predicted, refined and validated. The molecular docking analysis of multi-epitope vaccine candidates with TLR receptors, predicted effective binding. Overall, using bioinformatics approach this multi-epitopic target facilitates the proof of concept for SARS-CoV-2 conserved epitopic vaccine design.Communicated by Ramaswamy H. Sarma.

Immunoinformatic approach employing modeling and simulation to design a novel vaccine construct targeting MDR efflux pumps to confer wide protection against typhoidal Salmonella serovars

Overcoming multi drug resistance is one of the crucial challenges to control enteric typhoid fever caused by Salmonella typhi and Salmonella paratyphi. Overexpression of efflux pumps predominantly causes drug resistance in microorganisms. Therefore, immunotherapy targeting the various efflux pumps antigens could be a promising strategy to increase the success of vaccines. An immunoinformatic approach was employed to design a Salmonellosis multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of multidrug resistance protein families including ATP Binding Cassette (ABC), major facilitator superfamily (MFS), resistance nodulation cell division (RND), small multidrug resistance (SMR), and multidrug and toxin extrusion (MATE). The selected epitopes exhibited conservation in both S. typhi and S. paratyphi and thus could be helpful for cross-protection. Further, the final vaccine construct encompassing the peptides, adjuvants and specific linker sequences showed high immunogenicity, solubility, non-allergenic, nontoxic, and wide population coverage due to strong binding affinity to maximum HLA alleles. The three-dimensional structure was predicted, and validated using various structure validation tools. Additionally, protein-protein docking of the chimeric vaccine construct with the TLR-2 protein and molecular dynamics demonstrated stable and efficient binding. Conclusively, the immunoinformatic study showed that the novel multi epitopic vaccine construct can simulate the both T-cell and B-cell immune responses in typhoidal Salmonella serovars and could potentially be used for prophylactic or therapeutic applications.Communicated by Ramaswamy H. Sarma.

Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus

Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.

Epitope-based minigene vaccine targeting fibroblast activation protein α induces specific immune responses and anti-tumor effects in 4 T1 murine breast cancer model

Fibroblast activation protein (FAPα) is a tumor stromal antigen expressed by cancer-associated fibroblasts (CAFs) in more than 90 % of malignant epithelial carcinomas. FAPα-based immunotherapy has been reported and showed that FAPα-specific immune response can remold immune microenvironment and contribute to tumor regression. Many FAPα-based vaccines have been investigated in preclinical trials, which can elicit strong and durable cytolytic T lymphocytes (CTL) with good safety. However, epitope-based FAPα vaccines are rarely reported. To break tolerance against self-antigens, analogue epitopes with modified peptides at the anchor residues are typically used to improve epitope immunogenicity. To investigate the feasibility of a FAPα epitope-based vaccine for cancer immunotherapy in vivo, we conducted a preclinical study to identify a homologous CTL epitope of human and mouse FAPα and obtained its analogue epitope in BALB/c mice, and explored the anti-tumor activity of their minigene vaccines in 4 T1 tumor-bearing mice. By using in silico epitope prediction tools and immunogenicity assays, immunodominant epitope FAP.291 (YYFSWLTWV) and its analogue epitope FAP.291I9 (YYFSWLTWI) were identified. The FAP.291-based epitope minigene vaccine successfully stimulated CTLs targeting CAFs and exhibited anti-tumor activity in a 4 T1 murine breast cancer model. Furthermore, although the analogue epitope FAP.291I9 enhanced FAP.291-specific immune responses, improvement of anti-tumor immunity effects was not observed. Check of immunosuppressive factors revealed that the high levels of IL-10, IL-13, myeloid-derived suppressor cells and iNOS induced by FAP.291I9 increased, which considered the main cause of the failure of the analogue epitope-based vaccine. Thus, we demonstrated for the first time that the FAP.291 minigene vaccine could induce mouse CTLs and also function as a tumor regression antigen, providing the basis for future studies of FAPα epitope-based vaccines. This study may also be valuable for further improvement of the immunogenicity of analogue epitope vaccines.

Vaccinomics to Design a Multiepitope Vaccine against Legionella pneumophila

Legionella pneumophila is found in the natural aquatic environment and can resist a wide range of environmental conditions. There are around fifty species of Legionella, at least twenty-four of which are directly linked to infections in humans. L. pneumophila is the cause of Legionnaires' disease, a potentially lethal form of pneumonia. By blocking phagosome-lysosome fusion, L. pneumophila lives and proliferates inside macrophages. For this disease, there is presently no authorized multiepitope vaccine available. For the multi-epitope-based vaccine (MEBV), the best antigenic candidates were identified using immunoinformatics and subtractive proteomic techniques. Several immunoinformatics methods were utilized to predict B and T cell epitopes from vaccine candidate proteins. To construct an in silico vaccine, epitopes (07 CTL, 03 HTL, and 07 LBL) were carefully selected and docked with MHC molecules (MHC-I and MHC-II) and human TLR4 molecules. To increase the immunological response, the vaccine was combined with a 50S ribosomal adjuvant. To maximize vaccine protein expression, MEBV was cloned and reverse-translated in Escherichia coli. To prove the MEBV's efficacy, more experimental validation is required. After its development, the resulting vaccine is greatly hoped to aid in the prevention of L. pneumophila infections.

Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine Against Clostridium difficile

Clostridium difficile (C.difficile) is an exclusively anaerobic, spore-forming, and Gram-positive pathogen that is the most common cause of nosocomial diarrhea and is becoming increasingly prevalent in the community. Because C. difficile is strictly anaerobic, spores that can survive for months in the external environment contribute to the persistence and diffusion of C. difficile within the healthcare environment and community. Antimicrobial therapy disrupts the natural intestinal flora, allowing spores to develop into propagules that colonize the colon and produce toxins, thus leading to antibiotic-associated diarrhea and pseudomembranous enteritis. However, there is no licensed vaccine to prevent Clostridium difficile infection (CDI). In this study, a multi-epitope vaccine was designed using modern computer methods. Two target proteins, CdeC, affecting spore germination, and fliD, affecting propagule colonization, were chosen to construct the vaccine so that it could simultaneously induce the immune response against two different forms (spore and propagule) of C. difficile. We obtained the protein sequences from the National Center for Biotechnology Information (NCBI) database. After the layers of filtration, 5 cytotoxic T-cell lymphocyte (CTL) epitopes, 5 helper T lymphocyte (HTL) epitopes, and 7 B-cell linear epitopes were finally selected for vaccine construction. Then, to enhance the immunogenicity of the designed vaccine, an adjuvant was added to construct the vaccine. The Prabi and RaptorX servers were used to predict the vaccine's two- and three-dimensional (3D) structures, respectively. Additionally, we refined and validated the structures of the vaccine construct. Molecular docking and molecular dynamics (MD) simulation were performed to check the interaction model of the vaccine-Toll-like receptor (TLR) complexes, vaccine-major histocompatibility complex (MHC) complexes, and vaccine-B-cell receptor (BCR) complex. Furthermore, immune stimulation, population coverage, and in silico molecular cloning were also conducted. The foregoing findings suggest that the final formulated vaccine is promising against the pathogen, but more researchers are needed to verify it.

Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants

The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF-NYNYLYRLFR-MFVFLVLLPLVSSQC), M (MWLSYFIASF-GLMWLSYFIASFRLF), E (LTALRLCAY-LLFLAFVVFLLVTLA), and N (SPRWYFYYL-AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2-TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants.Communicated by Ramaswamy H. Sarma.

Neospora caninum SRS2 Protein: Essential Vaccination Targets and Biochemical Features for Next-Generation Vaccine Design

Vaccination is a standout preventive measure to combat neosporosis among cattle herds. The present in silico study was done to evaluate the physicochemical properties and potent immunogenic epitopes of N. caninum SRS2 protein as a possible vaccine candidate. Web-based tools were used to predict physicochemical properties, antigenicity, allergenicity, solubility, posttranslational modification (PTM) sites, transmembrane domains and signal peptide, and secondary and tertiary structures as well as intrinsically disordered regions, followed by identification and screening of potential linear and conformational B-cell epitopes and those peptides having affinity to bind mouse major histocompatibility complex (MHC) and cytotoxic T lymphocyte (CTL). The protein had 401 residues with a molecular weight of 42 kDa, representing aliphatic index of 69.35 (thermotolerant) and GRAVY score of -0.294 (hydrophilic). There were 53 PTM sites without a signal peptide in the sequence. Secondary structure comprised mostly by extended strand, followed by helices and coils. The Ramachandran plot of the refined model showed 90.2%, 8.8%, 0.5%, and 0.5% residues in the favored, additional allowed, generously allowed, and disallowed regions, correspondingly. Additionally, various potential B-cell (linear and conformational), CTL, and MHC-binding epitopes were predicted for N. caninum SRS2. These epitopes could be further utilized in the multiepitope vaccine constructs directed against neosporosis.

A Next-Generation Vaccine Candidate Using Alternative Epitopes to Protect against Wuhan and All Significant Mutant Variants of SARS-CoV-2: An Immunoinformatics Approach

Newly emerging significant SARS-CoV-2 variants such as B.1.1.7, B.1.351, and B.1.1.28 are the variant of concern (VOC) for the human race. These variants are getting challenging to contain from spreading worldwide. Because of these variants, the second wave has started in various countries and is threatening human civilization. Thus, we require efficient vaccines that can combat all emerging variants of SARS-CoV-2. Therefore, we took the initiative to develop a peptide-based next-generation vaccine using four variants (Wuhan variant, B.1.1.7, B.1.351, and B.1.1.28) that could potentially combat SARS-CoV-2 variants. We applied a series of computational tools, servers, and software to identify the most significant epitopes present on the mutagenic regions of SARS-CoV-2 variants. The immunoinformatics approaches were used to identify common B cell derived T cell epitopes, influencing the host immune system. Consequently, to develop a novel vaccine candidate, the antigenic epitopes were linked with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end. 3D vaccine candidate structure was refined, and quality was assessed using web servers. The physicochemical properties and safety parameters of the vaccine construct were assessed through bioinformatics and immunoinformatics tools. The molecular docking analysis between TLR4/MD2 and the proposed vaccine candidate demonstrated a satisfactory interaction. The molecular dynamics studies confirmed the stability of the vaccine candidate. Finally, we optimized the proposed vaccine through codon optimization and in silico cloning to study the expression. Our multi-epitopic next-generation peptide vaccine construct can boost immunity against the Wuhan variant and all significant mutant variants of SARS-CoV-2.

Designing of Potential Polyvalent Vaccine Model for Respiratory Syncytial Virus by System Level Immunoinformatics Approaches

BACKGROUND

Respiratory syncytial virus (RSV) infection is a public health epidemic, leading to around 3 million hospitalization and about 66,000 deaths each year. It is a life-threatening condition exclusive to children with no effective treatment.

METHODS

In this study, we used system-level and vaccinomics approaches to design a polyvalent vaccine for RSV, which could stimulate the immune components of the host to manage this infection. Our framework involves data accession, antigenicity and subcellular localization analysis, T cell epitope prediction, proteasomal and conservancy evaluation, host-pathogen-protein interactions, pathway studies, and in silico binding affinity analysis.

RESULTS

We found glycoprotein (G), fusion protein (F), and small hydrophobic protein (SH) of RSV as potential vaccine candidates. Of these proteins (G, F, and SH), we found 9 epitopes for multiple alleles of MHC classes I and II bear significant binding affinity. These potential epitopes were linked to form a polyvalent construct using AAY, GPGPG linkers, and cholera toxin B adjuvant at N-terminal with a 23.9 kDa molecular weight of 224 amino acid residues. The final construct was a stable, immunogenic, and nonallergenic protein containing cleavage sites, TAP transport efficiency, posttranslation shifts, and CTL epitopes. The molecular docking indicated the optimum binding affinity of RSV polyvalent construct with MHC molecules (-12.49 and -10.48 kcal/mol for MHC classes I and II, respectively). This interaction showed that a polyvalent construct could manage and control this disease.

CONCLUSION

Our vaccinomics and system-level investigation could be appropriate to trigger the host immune system to prevent RSV infection.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations.Communicated by Ramaswamy H. Sarma.

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 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.

Efficacy of co-immunization with the DNA and peptide vaccines containing SYCP1 and ACRBP epitopes in a murine triple-negative breast cancer model

Multiepitope cancer vaccines have gained lots of attention for prophylactic and therapeutic purposes in cancer patients. In our previous study, multiepitope DNA and peptide cancer vaccines consisted of the most immunodominant epitopes of ACRBP and SYCP1 antigens were designed by bioinformatic tools. In this study, the effect of prophylactic co-immunization with these DNA and peptide cancer vaccines in the 4T1 breast cancer animal model was assessed. Serum levels of the peptide-specific IgG total, IgG2a and IgG1 were measured by enzyme-linked immunosorbent assay (ELISA). Also, the efficacy of the immunized mice splenocytes' for producing interleukin-4 (IL-4) and interferon-γ (IFN-γ) was evaluated. The co-immunization caused a significant (P < .05) increase in the serum levels of IgG1 and IgG2a. The co-immunized mice splenocytes exhibited significantly enhanced IL-4 (6.6-fold) and IFN-γ (19-fold) production. Also, their lymphocytes exhibited higher proliferation rate (3-fold) and granzyme B production (6.5-fold) in comparison with the control. The prophylactic co-immunization significantly decreased the breast tumors' volume (78%) and increased the tumor-bearing mice survival time (37.5%) in comparison with the control. Taking together, prophylactic co-immunization with these multiepitope DNA and peptide cancer vaccines can activate the immune system against breast cancer. However, further experiments are needed to evaluate their efficacy from different angles.