AllerTOP--a server for in silico prediction of allergens

In silico analysis of the interaction of de novo peptides derived from Salvia hispanica with anticancer targetsEvaluation of the anticancer potential of de novo peptides derived from Salvia hispanica through molecular docking

Cancer is one of the leading causes of death worldwide. Conventional cancer therapies are not selective to cancer cells resulting in serious side effects on patients. Thus, the need for complementary treatments that improve the patient's response to cancer therapy is highly important. To predict and evaluate the physicochemical characteristics and potential anticancer activity of the peptides identified from S. hispanica protein fraction <1 kDa through the use of in silico tools. Peptides derived from Salvia hispanica's protein fraction <1 kDa were identified and analyzed for the prediction of their physicochemical properties. The characterized peptide sequences were then submitted to a multi-criteria decision analysis to identify the peptides that possess the characteristics to potentially exert anticancer activity. Through molecular docking analysis, the potential anticancer activity of the Potentially Anticancer Peptide (PAP)-1, PAP-2, PAP-3, PAP-4, and PAP-5 was estimated by their binding interactions with cancer and apoptosis-related molecules. All five evaluated PAPs exhibited strong binding interactions (< -100 kcal/mol). However, PAP-3 showed the lowest binding free energies with several of the targets. Thus, PAP-3 shows potential to be used as a nutraceutical or ingredient for functional foods that adjuvate in cancer treatment. Conclusions: Through the molecular docking studies, the binding of the PAPs to target molecules of interest for cancer treatment was successfully simulated, from which PAP-3 exhibited the lowest binding free energies. Further in vitro and in vivo studies are required to validate the predictions obtained by the in silico analysis.Communicated by Ramaswamy H. Sarma.

Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus

Monkeypox virus (MPXV) is an orthopoxvirus, causing zoonotic infections in humans with smallpox-like symptoms. The WHO reported MPXV cases in May 2022 and the outbreak caused significant morbidity threats to immunocompromised individuals and children. Currently, no clinically validated therapies are available against MPXV infections. The present study is based on immunoinformatics approaches to design mRNA-based novel vaccine models against MPXV. Three proteins were prioritized based on high antigenicity, low allergenicity, and toxicity values to predict T- and B-cell epitopes. Lead T- and B-cell epitopes were used to design vaccine constructs, linked with epitope-specific linkers and adjuvant to enhance immune responses. Additional sequences, including Kozak sequence, MITD sequence, tPA sequence, Goblin 5', 3' UTRs, and a poly(A) tail were added to design stable and highly immunogenic mRNA vaccine construct. High-quality structures were predicted by molecular modeling and 3D-structural validation of the vaccine construct. Population coverage and epitope-conservancy speculated broader protection of designed vaccine model against multiple MPXV infectious strains. MPXV-V4 was eventually prioritized based on its physicochemical and immunological parameters and docking scores. Molecular dynamics and immune simulations analyses predicted significant structural stability and binding affinity of the top-ranked vaccine model with immune receptors to elicit cellular and humoral immunogenic responses against the MPXV. The pursuance of experimental and clinical follow-up of these prioritized constructs may lay the groundwork to develop safe and effective vaccine against MPXV.Communicated by Ramaswamy H. Sarma.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

Prediction of the 3D conformation of a small peptide vaccine targeting Aβ42 oligomers

The original etiology of Alzheimer's disease (AD) is the deposition of amyloid-beta (Aβ) proteins, which starts from the aggregation of the Aβ oligomers. The optimal therapeutic strategy targeting Aβ oligomer aggregation is the development of AD vaccines. Despite the fact that positive progress has been made for experimental attempts at AD vaccines, the physicochemical and even structural properties of these AD vaccines remain unclear. In this study, through immunoinformatic and molecular dynamics (MD) simulations, we first designed and simulated an alternative of vaccine TAPAS and found that the structure of the alternative can reproduce the 3D conformation of TAPAS determined experimentally. Meanwhile, immunoinformatic methods were used to analyze the physicochemical properties of TAPAS, including immunogenicity, antigenicity, thermal stability, and solubility, which confirm well the efficacy and safety of the vaccine, and validate the scheme reliability of immunoinformatic and MD simulations in designing and simulating the TAPAS vaccine. Using the same scheme, we predicted the 3D conformation of the optimized ACI-24 peptide vaccine, an Aβ peptide with the first 15 residues of Aβ42 (Aβ1-15). The vaccine was verified once to be effective against both full-length Aβ1-42 and truncated Aβ4-42 aggregates, but an experimental 3D structure was absent. We have also explored the immune mechanism of the vaccine at the molecular level and found that the optimized ACI-24 and its analogues can block the growth of either full-length Aβ1-42 or truncated Aβ4-42 pentamer by contacting the hydrophobic residues within the N-terminus and β1 region on the contact surface of either pentamer. Additionally, residues (D1, D7, S8, H13, and Q15) were identified as the key residues of the vaccine to contact either of the two Aβ oligomers. This work provides a feasible implementation scheme of immunoinformatic and MD simulations for the development of AD small peptide vaccines, validating the power of the scheme as a parallel tool to the experimental approaches and injecting molecular-level information into the understanding and design of anti-AD vaccines.

Design of an epitope-based peptide vaccine against Cryptococcus neoformans

Cryptococcus neoformans is the highest-ranked fungal pathogen in the Fungal Priority Pathogens List (FPPL) released by the World Health Organization (WHO). In this study, through in silico simulations, a multi-epitope vaccine against Cryptococcus neoformans was developed using the mannoprotein antigen (MP88) as a vaccine candidate. Following the retrieval of the MP88 protein sequences, these were used to predict antigenic B-cell and T-cell epitopes via the bepipred tool and the artificial neural network, respectively. Conserved B-cell epitopes AYSTPA, AYSTPAS, PASSNCK, and DSAYPP were identified as the most promising B-cell epitopes. While YMAADQFCL, VSYEEWMNY, and FQQRYTGTF were identified as the best candidates for CD8+ T-cell epitopes; and YARLLSLNA, ISYGTAMAV, and INQTSYARL were identified as the most promising CD4+ T-cell epitopes. The vaccine construct was modeled along with adjuvant and peptide linkers and the expasy protparam tool was used to predict the physiochemical properties. According to this, the construct vaccine was predicted to be antigenic, nontoxic, nonallergenic, soluble, stable, hydrophilic, and thermostable. Furthermore, the three-dimensional structure was also used in docking analyses with Toll-like receptor (TLR4). Finally, the cDNA of vaccine was successfully cloned into the E. coli pET-28a (+) expression vector. The results presented here could contribute towards the design of an effective vaccine against Cryptococcus neoformans.

A novel vaccine construct against Zika virus fever: insights from epitope-based vaccine discovery through molecular modeling and immunoinformatics approaches

The Zika virus (ZIKV) is an emerging virus associated with the Flaviviridae family that mainly causes infection in pregnant women and leads to several abnormalities during pregnancy. This virus has unique properties that may lead to pathological diseases. As the virus has the ability to evade immune response, a crucial effort is required to deal with ZIKV. Vaccines are a safe means to control different pathogenic infectious diseases. In the current research, a multi-epitope-based vaccination against ZIKV is being designed using in silico methods. For the epitope prediction and prioritization phase, ZIKV polyprotein (YP_002790881.1) and flavivirus polyprotein (>YP_009428568.1) were targeted. The predicted B-cell epitopes were used for MHC-I and MHC-II epitope prediction. Afterward, several immunoinformatics filters were applied and nine (REDLWCGSL, MQDLWLLRR, YKKSGITEV, TYTDRRWCF, RDAFPDSNS, KPSLGLINR, ELIGRARVS, AITQGKREE, and EARRSRRAV) epitopes were found to be probably antigenic in nature, non-allergenic, non-toxic, and water soluble without any toxins. Selected epitopes were joined using a particular GPGPG linker to create the base vaccination for epitopes, and an extra EAAAK linker was used to link the adjuvant. A total of 312 amino acids with a molecular weight (MW) of 31.62762 and an instability value of 34.06 were computed in the physicochemical characteristic analysis, indicating that the vaccine design is stable. The molecular docking analysis predicted a binding energy of -329.46 (kcal/mol) for TLR-3 and -358.54 (kcal/mol) for TLR-2. Moreover, the molecular dynamics simulation analysis predicted that the vaccine and receptor molecules have stable binding interactions in a dynamic environment. The C-immune simulation analysis predicted that the vaccine has the ability to generate both humoral and cellular immune responses. Based on the design, the vaccine construct has the best efficacy to evoke immune response in theory, but experimental analysis is required to validate the in silico base approach and ensure its safety.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features

Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

Computational design of novel chimeric multiepitope vaccine against bacterial and viral disease in tilapia (Oreochromis sp.)

Regarding several infectious diseases in fish, multiple vaccinations are not favorable. The chimeric multiepitope vaccine (CMEV) harboring several antigens for multi-disease prevention would enhance vaccine efficiency in terms of multiple disease prevention. Herein, the immunogens of tilapia's seven pathogens including E. tarda, F. columnare, F. noatunensis, S. iniae, S. agalactiae, A. hydrophila, and TiLV were used for CMEV design. After shuffling and annotating the B-cell epitopes, 5,040 CMEV primary protein structures were obtained. Secondary and tertiary protein structures were predicted by AlphaFold2 creating 25,200 CMEV. Proper amino acid alignment in the secondary structures was achieved by the Ramachandran plot. In silico determination of physiochemical and other properties including allergenicity, antigenicity, glycosylation, and conformational B-cell epitopes were determined. The selected CMEV (OSLM0467, OSLM2629, and OSLM4294) showed a predicted molecular weight (MW) of 70 kDa, with feasible sites of N- and O-glycosylation, and a number of potentially conformational B-cell epitope residues. Molecular docking, codon optimization, and in-silico cloning were tested to evaluate the possibility of protein expression. Those CMEVs will further elucidate in vitro and in vivo to evaluate the efficacy and specific immune response. This research will highlight the new era of vaccines designed based on in silico structural vaccine design.

Evaluation of humoral and cellular immune responses against Vibrio cholerae using oral immunization by multi-epitope-phage-based vaccine

INTRODUCTION

Cholera is a severe gastrointestinal disease that manifests with rapid onset of diarrhea, vomiting, and high mortality rates. Due to its widespread occurrence in impoverished communities with poor water sanitation, there is an urgent demand for a cost-effective and highly efficient vaccine. Multi-epitope vaccines containing dominant immunological epitopes and adjuvant compounds have demonstrated potential in boosting the immune response.

MATERIAL AND METHODS

B and T epitopes of OMPU, OMPW, TCPA, CTXA, and CTXB proteins were predicted using bioinformatics methods. Subsequently, highly antigenic multi-epitopes that are non-allergenic and non-toxic were synthesized. These multi-epitopes were then cloned into the pCOMB phagemid. A plasmid M13KO7ΔpIII containing all helper phage proteins except pIII was created to produce the recombinant phage. Female Balb/c mice were divided into three groups and immunized accordingly. The mice received the helper phage, recombinant phage or PBS via gavage feeding thrice within two weeks. Serum samples were collected before and after immunization for the ELISA test as well as evaluating immune system induction through ELISpot testing of spleen lymphocytes.

RESULTS

The titer of the recombinant phage was determined to be 1011 PFU/ml. The presence of the recombinant phage was confirmed through differences in optical density between sample and control groups in the ELISA phage technique, as well as by observing transduction activity, which demonstrated successful production of a recombinant phage displaying the Vibrio multi-epitope on M13 phage pIII. ELISA results revealed significant differences in phage antibodies before and after inoculation, particularly notable in the negative control mice. Mice treated with multi-epitope phages exhibited antibodies against Vibrio cholerae lysate. Additionally, ELISpot results indicated activation of cellular immunity in mice receiving both Vibrio and helper phage.

CONCLUSION

This study emphasizes the potential of multi-epitope on phage to enhance both cellular and humoral immunity in mice, demonstrating how phages can be used as adjuvants to stimulate mucosal immunity and act as promising candidates for oral vaccination.

From proteome to candidate vaccines: target discovery and molecular dynamics-guided multi-epitope vaccine engineering against kissing bug

Introduction

Trypanosoma cruzi is a protozoan parasite that causes the tropical ailment known as Chagas disease, which has its origins in South America. Globally, it has a major impact on health and is transported by insect vector that serves as a parasite. Given the scarcity of vaccines and the limited treatment choices, we conducted a comprehensive investigation of core proteomics to explore a potential reverse vaccine candidate with high antigenicity.

Methods

To identify the immunodominant epitopes, T. cruzi core proteomics was initially explored. Consequently, the vaccine sequence was engineered to possess characteristics of non-allergenicity, antigenicity, immunogenicity, and enhanced solubility. After modeling the tertiary structure of the human TLR4 receptor, the binding affinities were assessed employing molecular docking and molecular dynamics simulations (MDS).

Results

Docking of the final vaccine design with TLR4 receptors revealed substantial hydrogen bond interactions. A server-based methodology for immunological simulation was developed to forecast the effectiveness against antibodies (IgM + IgG) and interferons (IFN-g). The MDS analysis revealed notable levels of structural compactness and binding stability with average RMSD of 5.03 Aring;, beta-factor 1.09e+5 Å, Rg is 44.7 Aring; and RMSF of 49.50 Aring;. This is followed by binding free energies calculation. The system stability was compromised by the complexes, as evidenced by their corresponding Gibbs free energies of -54.6 kcal/mol.

Discussion

Subtractive proteomics approach was applied to determine the antigenic regions of the T cruzi. Our study utilized computational techniques to identify B- and T-cell epitopes in the T. cruzi core proteome. In current study the developed vaccine candidate exhibits immunodominant features. Our findings suggest that formulating a vaccine targeting the causative agent of Chagas disease should be the initial step in its development.

Systems approach to design multi-epitopic peptide vaccine candidate against fowl adenovirus structural proteins for Gallus gallus domesticus

Introduction

Fowl adenovirus (FAdV) is a significant pathogen in poultry, causing various diseases such as hepatitis-hydropericardium, inclusion body hepatitis, and gizzard erosion. Different serotypes of FAdV are associated with specific conditions, highlighting the need for targeted prevention strategies. Given the rising prevalence of FAdV-related diseases globally, effective vaccination and biosecurity measures are crucial. In this study, we explore the potential of structural proteins to design a multi-epitope vaccine targeting FAdV.

Methods

We employed an in silico approach to design the multi-epitope vaccine. Essential viral structural proteins, including hexon, penton, and fiber protein, were selected as vaccine targets. T-cell and B-cell epitopes binding to MHC-I and MHC-II molecules were predicted using computational methods. Molecular docking studies were conducted to validate the interaction of the multi-epitope vaccine candidate with chicken Toll-like receptors 2 and 5.

Results

Our in silico methodology successfully identified potential T-cell and B-cell epitopes within the selected viral structural proteins. Molecular docking studies revealed strong interactions between the multi-epitope vaccine candidate and chicken Toll-like receptors 2 and 5, indicating the structural integrity and immunogenic potential of the designed vaccine.

Discussion

The designed multi-epitope vaccine presents a promising approach for combating FAdV infections in chickens. By targeting essential viral structural proteins, the vaccine is expected to induce a robust immunological response. The in silico methodology utilized in this study provides a rapid and cost-effective means of vaccine design, offering insights into potential vaccine candidates before experimental validation. Future studies should focus on in vitro and in vivo evaluations to further assess the efficacy and safety of the proposed vaccine.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Lactobacilli-derived adjuvants combined with immunoinformatics-driven multi-epitope antigens based approach protects against Clostridium perfringens in a mouse model

Clostridium perfringens is ubiquitously distributed and capable of secreting toxins, posing a significant threat to animal health. Infections caused by Clostridium perfringens, such as Necrotic Enteritis (NE), result in substantial economic losses to the livestock industry annually. However, there is no effective commercial vaccine available. Hence, we set out to propose an effective approach for multi-epitope subunit vaccine construction utilizing biomolecules. We utilized immunoinformatics to design a novel multi-epitope antigen against C. perfringens (CPMEA). Furthermore, we innovated novel bacterium-like particles (BLPs) through thermal acid treatment of various Lactobacillus strains and selected BLP23017 among them. Then, we detailed the structure of CPMEA and BLPs and utilized them to prepare a multi-epitope vaccine. Here, we showed that our vaccine provided full protection against C. perfringens infection after a single dose in a mouse model. Additionally, BLP23017 notably augmented the secretion of secretory immunoglobulin A (sIgA) and enhanced antibody production. We conclude that our vaccine possess safety and high efficacy, making it an excellent candidate for preventing C. perfringens infection. Moreover, we demonstrate our approach to vaccine construction and the preparation of BLP23017 with distinct advantages may contribute to the prevention of a wider array of diseases and the novel vaccine development.

Development of membrane protein-based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

INTRODUCTION

Lumpy skin disease, an economically significant bovine illness, is now found in previously unheard-of geographic regions. Vaccination is one of the most important ways to stop its further spread.

AIM

Therefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.

METHODS

The membrane glycoprotein was selected for prediction of the different B- and T-cell epitopes by using the immune epitope database. The selected B- and T-cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll-like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.

RESULTS

The in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide-based LSDV vaccine.

CONCLUSION

Thus, the present findings may indicate that the engineered multi-epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

A computational quest for identifying potential vaccine candidates against Moraxella lacunata: a multi-pronged approach

Moraxella lacunata is an emerging gram-negative bacterium that is responsible for multiple nosocomial infections. The bacterium is evolving resistance to several antibiotics, and currently, no effective licensed vaccines are available, which warrants the search for new therapeutics. A multi-epitope-based vaccine has been designed for M. lacunata. The complete proteome of M. lacunata contains 10,110 core proteins. Subcellular localization analysis revealed the presence of five proteins in the extracellular matrix, while 19 proteins were predicted to be located in the outer membrane, and 21 proteins were predicted to be located in the periplasmic region. Only two proteins, the type VI secretion system tube protein (Hcp) and the transporter substrate-binding domain-containing protein, were selected for epitope prediction as they fulfilled all the criteria for being potential vaccine candidates. Shortlisted epitopes from the selected proteins were fused together using "GPGPG" linkers to overcome the limitations of single-epitope vaccines. Next, the cholera toxin-B adjuvant was attached to the peptide epitope using an EAAAK linker. Docking analysis was performed to examine the interaction between the vaccine and immune cell receptors, revealing robust intermolecular interactions and a stable binding conformation. Molecular dynamics simulation findings revealed no drastic changes in the binding conformation of complexes during the simulation period. The net binding free energy of vaccine-receptor complexes was estimated using the molecular mechanics energies combined with the Poisson-Boltzmann and surface area continuum solvation (MM-PBSA) method. The reported values were -586.38 kcal/mol, -283.74 kcal/mol, and -296.88 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. Furthermore, the molecular mechanics energies combined with the generalized Born and surface area continuum solvation (MM-GBSA) analysis predicted binding free energies of -596.69 kcal/mol, -287.39 kcal/mol, and -298.28 kcal/mol for the TLR-4-vaccine complex, MHC-I-vaccine complex, and MHC-II-vaccine complex, respectively. The theoretical vaccine design proposed in the study could potentially serve as a powerful therapeutic against targeted pathogens, subject to validation through experimental studies.Communicated by Ramaswamy H. Sarma.

Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets

Pancreatic cancer is a highly aggressive and often lethal malignancy with limited treatment options. Its late-stage diagnosis and resistance to conventional therapies make it a significant challenge in oncology. Immunotherapy, particularly cancer vaccines, has emerged as a promising avenue for treating pancreatic cancer. Multi-epitope vaccines, designed to target multiple epitopes derived from various antigens associated with pancreatic cancer, have gained attention as potential candidates for improving therapeutic outcomes. In this study, we have explored transcriptomics and protein expression databases to identify potential upregulated proteins in pancreatic cancer cells. After examining a total of 21,054 proteins from various databases, it was discovered that 143 proteins expressed differently in malignant and healthy cells. The CTL, HTL and BCE epitopes were predicted for the shortlisted proteins. 51,840 vaccine constructs were created by concatenating CTL, HTL, and B-cell epitopes in the respective sequences. The best 86 structures were selected from a set of 51,840 designs after they were analyzed for vaxijenicity, allergenicity, toxicity, and antigenicity scores. In further simulation of the immune response using constructs, it was found that 41417, 37961, and 40841 constructs could produce a strong immune response when injected. Further, it was found that construct 37961 showed stronger interaction and stability with TLR-9 as determined from the large-scale molecular dynamics simulations. Moreover, the 37961 construct has shown interactions with TLR-9 suggests its potential in inducing immune response. In addition, construct 37961 has shown 100% predicted solubility in the E. coli expression system. Overall, the study indicates the designed construct 37961 has the potential to induce an anti-tumor immune response and long-standing protection pending further studies.

Leveraging the immunoinformatics approach for designing the SARS-CoV-2 omicron-specific antigenic cassette of mRNA vaccine

Emergence of SARS-CoV-2 Omicron variant has presented a significant challenge to global health, demanding rapid development of mRNA-based vaccines. The mRNA-guided vaccine platforms offer various advantages over traditional vaccine platforms. The mRNA by nature is a short-lived molecule that guides the cells to manufacture antigenic proteins. In the present work, we have created an omicron spike antigenic protein sequence characterized by base composition analysis, modeling, and docking with the ACE-2 receptor. Further, we predicted the B-cell and T-cell epitopes followed by antigenicity, toxicity, and allergenicity. Finally, the protein was reverse translated, codon-optimized, and encoding mRNA sequence was checked for its stability by predicting the secondary structures. A comprehensive examination of in-silico data revealed 628.2 as a potent antigenic candidate that was finally used in Gemcovac®-OM, a heterologous booster mRNA vaccine for COVID-19.

Designing, cloning and simulation studies of cancer/testis antigens based multi-epitope vaccine candidates against cutaneous melanoma: An immunoinformatics approach

Background

Melanoma is the most fatal kind of skin cancer. Among its various types, cutaneous melanoma is the most prevalent one. Melanoma cells are thought to be highly immunogenic due to the presence of distinct tumor-associated antigens (TAAs), which includes carcinoembryonic antigen (CEA), cancer/testis antigens (CTAs) and neo-antigens. The CTA family is a group of antigens that are only expressed in malignancies and testicular germ cells.

Methods

We used integrative framework and systems-level analysis to predict potential vaccine candidates for cutaneous melanoma involving epitopes prediction, molecular modeling and molecular docking to cross-validate the binding affinity and interaction between potential vaccine agents and major histocompatibility molecules (MHCs) followed by molecular dynamics simulation, immune simulation and in silico cloning.

Results

In this study, three cancer/testis antigens were targeted for immunotherapy of cutaneous melanoma. Among many CTAs that were studied for their expression in primary and malignant melanoma, NY-ESO-1, MAGE1 and SSX2 antigens are most prevalent in cutaneous melanoma. Cytotoxic and Helper epitopes were predicted, and the finest epitopes were shortlisted based on binding score. The vaccine construct was composed of the four epitope-rich domains of antigenic proteins, an appropriate adjuvant, His tag and linkers. This potential multi-epitope vaccine was further evaluated in terms of antigenicity, allergencity, toxicity and other physicochemical properties. Molecular interaction estimated through protein-protein docking unveiled good interactions characterized by favorable binding energies. Molecular dynamics simulation ensured the stability of docked complex and the predicted immune response through immune simulation revealed elevated levels of antibodies titer, cytokines, interleukins and immune cells (NK, DC and MA) population.

Conclusion

The findings indicate that the potential vaccine candidates could be effective immunotherapeutic agents that modify the treatment strategies of cutaneous melanoma.

Peptide-ligand conjugate based immunotherapeutic approach for targeted dismissal of non-structural protein 1 of dengue virus: A novel therapeutic solution for mild and severe dengue infections

Dengue virus infection has significantly increased, with reported cases soaring from 505,430 in 2000 to 2,809,818 in 2022, emphasizing the need for effective treatments. Among the eleven structural and non-structural proteins of DENV, Non-structural protein 1 (NS1) has emerged as a promising target due to its diverse role in modulating the immune response, inducing vascular leakage, and facilitating viral replication and assembly. Monoclonal antibodies are the sole therapeutics to target NS1, but concerns about their cross-reactivity persist. Given these concerns, our study focuses on designing a novel Peptide Ligand Conjugate (PLC) as a potential alternative immunotherapeutic agent against NS1. This PLC aims to mediate the immune elimination of soluble NS1 and NS1-presenting DENV-infected host cells by pre-existing vaccine-induced immunity. By employing the High Throughput Virtual Screening (HTVS) method, QikProp analysis, and Molecular Dynamics studies, we identified three hits from Asinex Biodesigned Ligands out of 220,177 compounds that show strong binding affinity towards the monoclonal binding site of NS1 protein. After a rigorous analysis of physicochemical characteristics, antigenicity, allergenicity, and toxicity using various servers, we selected two peptides: the minimum epitopic region of the Diphtheria and Tetanus toxins as the peptide components of the PLCs. A non-cleavable, non-reactive oxime linker connected the ligand with the peptide through oxime and amide bonds. DPT vaccine is widely used in dengue-endemic countries, and it has been reported that antibodies titer against MER of Diphtheria toxin and Tetanus toxins persist lifelong in DPT-vaccinated people. Therefore, once the rationally designed PLCs bind to NS1 through the ligands, the peptide will induce an immune response against NS1 by triggering pre-existing DPT antibodies and activating memory cells. This orchestrated immune response will destroy soluble NS1 and NS1-expressing DENV-infected cells, thereby reducing the illness of severe dengue hemorrhagic fever and the DENV infection, respectively. Given the increasing demand for new therapeutics for DENV treatment, further investigation into this novel immune-therapeutic strategy may offer a new avenue for treating mild and severe dengue infections.

Development of a novel multi‑epitope vaccine against the pathogenic human polyomavirus V6/7 using reverse vaccinology

BACKGROUND

Human polyomaviruses contribute to human oncogenesis through persistent infections, but currently there is no effective preventive measure against the malignancies caused by this virus. Therefore, the development of a safe and effective vaccine against HPyV is of high priority.

METHODS

First, the proteomes of 2 polyomavirus species (HPyV6 and HPyV7) were downloaded from the NCBI database for the selection of the target proteins. The epitope identification process focused on selecting proteins that were crucial, associated with virulence, present on the surface, antigenic, non-toxic, and non-homologous with the human proteome. Then, the immunoinformatic methods were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes from the target antigens, which could be used to create epitope-based vaccine. The physicochemical features of the designed vaccine were predicted through various online servers. The binding pattern and stability between the vaccine candidate and Toll-like receptors were analyzed through molecular docking and molecular dynamics (MD) simulation, while the immunogenicity of the designed vaccines was assessed using immune simulation.

RESULTS

Online tools were utilized to forecast the most optimal epitope from the immunogenic targets, including LTAg, VP1, and VP1 antigens of HPyV6 and HPyV7. A multi-epitope vaccine was developed by combining 10 CTL, 7 HTL, and 6 LBL epitopes with suitable linkers and adjuvant. The vaccine displayed 98.35% of the world's population coverage. The 3D model of the vaccine structure revealed that the majority of residues (87.7%) were located in favored regions of the Ramachandran plot. The evaluation of molecular docking and MD simulation revealed that the constructed vaccine exhibits a strong binding (-1414.0 kcal/mol) towards the host's TLR4. Moreover, the vaccine-TLR complexes remained stable throughout the dynamic conditions present in the natural environment. The immune simulation results demonstrated that the vaccine design had the capacity to elicit robust immune responses in the host.

CONCLUSION

The multi-parametric analysis revealed that the designed vaccine is capable of inducing sustained immunity against the selected polyomaviruses, although further in-vivo investigations are needed to verify its effectiveness.

In silico designing a novel TLR4-mediating multiepitope vaccine against monkeypox via advanced immunoinformatics and bioinformatics approaches

Monkeypox virus is a member of the Poxviridae family, which causes monkeypox zoonotic disease. Since July 2022, the prevention of monkeypox have become more considerable due to the new outbreak, making it a global concern. Therefore, we used an in silico-based method, including immunoinformatics, bioinformatics, molecular docking, and gene cloning approaches to design a novel multiepitope vaccine against monkeypox. Three immunogenic envelope proteins of monkeypox virus, including G10R, E8L, and A30L, were selected to predict appropriate immune system stimulator epitopes. The A30L is common between smallpox and monkeypox virus, so the proposed vaccine may be effective against smallpox too. There is no evidence of allergenicity and toxicity of the vaccine epitopes. To boost the immunogenicity of the designed vaccine, we used the helper epitope of PADRE and RS01as adjuvants. Furthermore, some linkers are used to link epitopes and adjuvants together. The physicochemical futures of the designed vaccine were assessed. The tertiary structure of the vaccine was modeled and then refined to improve its structure and physicochemical properties. To analyze the vaccine construct and TLR4 complex affinity, they were docked to gather. Besides, the vaccine was cloned into E.coli. pET-21b(+) plasmid to reveal that it can be expressed and stimulate the immune system. Immune stimulation evaluation showed that the candidate vaccine could induce the production of IgM, IgG1, and IgG2 antibodies. Overall, we suggested an effective vaccine candidate against monkeypox. However, Future studies and clinical trials should be done to ensure the efficacy and safety of this vaccine.Communicated by Ramaswamy H. Sarma.

Self-assembled peptide/polymer hybrid nanoplatform for cancer immunostimulating therapies

Integrating peptide epitopes in self-assembling materials is a successful strategy to obtain nanovaccines with high antigen density and improved efficacy. In this study, self-assembling peptides containing MAGE-A3/PADRE epitopes were designed to generate functional therapeutic nanovaccines. To achieve higher stability, peptide/polymer hybrid nanoparticles were formulated by controlled self-assembly of the engineered peptides. The nanoparticles showed good biocompatibility to both human red blood- and dendritic cells. Incubation of the nanoparticles with immature dendritic cells triggered immune effects that ultimately activated CD8 + cells. The antigen-specific and IgG antibody responses of healthy C57BL/6 mice vaccinated with the nanoparticles were analyzed. The in vivo results indicate a specific response to the nanovaccines, mainly mediated through a cellular pathway. This research indicates that the immunogenicity of peptide epitope vaccines can be effectively enhanced by developing self-assembled peptide-polymer hybrid nanostructures.

Identification and prioritisation of potential vaccine candidates using subtractive proteomics and designing of a multi-epitope vaccine against Wuchereria bancrofti

This study employed subtractive proteomics and immunoinformatics to analyze the Wuchereria bancrofti proteome and identify potential therapeutic targets, with a focus on designing a vaccine against the parasite species. A comprehensive bioinformatics analysis of the parasite's proteome identified 51 probable therapeutic targets, among which "Kunitz/bovine pancreatic trypsin inhibitor domain-containing protein" was identified as the most promising vaccine candidate. The candidate protein was used to design a multi-epitope vaccine, incorporating B-cell and T-cell epitopes identified through various tools. The vaccine construct underwent extensive analysis of its antigenic, physical, and chemical features, including the determination of secondary and tertiary structures. Docking and molecular dynamics simulations were performed with HLA alleles, Toll-like receptor 4 (TLR4), and TLR3 to assess its potential to elicit the human immune response. Immune simulation analysis confirmed the predicted vaccine's strong binding affinity with immunoglobulins, indicating its potential efficacy in generating an immune response. However, experimental validation and testing of this multi-epitope vaccine construct would be needed to assess its potential against W. bancrofti and even for a broader range of lymphatic filarial infections given the similarities between W. bancrofti and Brugia.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Design of a Multi-Epitope Vaccine against Histoplasma capsulatum through Immunoinformatics Approaches

Histoplasmosis is a widespread systemic disease caused by Histoplasma capsulatum, prevalent in the Americas. Despite its significant morbidity and mortality rates, no vaccines are currently available. Previously, five vaccine targets and specific epitopes for H. capsulatum were identified. Immunoinformatics has emerged as a novel approach for determining the main immunogenic components of antigens through in silico methods. Therefore, we predicted the main helper and cytotoxic T lymphocytes and B-cell epitopes for these targets to create a potential multi-epitope vaccine known as HistoVAC-TSFM. A total of 38 epitopes were found: 23 common to CTL and B-cell responses, 11 linked to HTL and B cells, and 4 previously validated epitopes associated with the B subunit of cholera toxin, a potent adjuvant. In silico evaluations confirmed the stability, non-toxicity, non-allergenicity, and non-homology of these vaccines with the host. Notably, the vaccine exhibited the potential to trigger both innate and adaptive immune responses, likely involving the TLR4 pathway, as supported by 3D modeling and molecular docking. The designed HistoVAC-TSFM appears promising against Histoplasma, with the ability to induce important cytokines, such as IFN-γ, TNF-α, IL17, and IL6. Future studies could be carried out to test the vaccine's efficacy in in vivo models.

Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine's design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of -1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.Communicated by Ramaswamy H. Sarma.

Immunoinformatics Prediction and Protective Efficacy of Vaccine Candidate PiuA-PlyD4 Against Streptococcus Pneumoniae

Purpose

This study was designed to evaluate the immune protective efficacy of the novel Streptococcus pneumoniae (S. pneumoniae) protein vaccine PiuA-PlyD4 through immunoinformatics prediction and in vitro and in vivo experiments.

Methods

In this study, we conducted immunoinformatics prediction and protection analysis on the fusion protein PiuA-PlyD4. The epitope composition of the vaccine was analyzed based on the prediction of B-cell and helper T-cell epitopes. Meanwhile, the molecular docking of PiuA and TLR2/4 was simulated. After immunizing C57BL/6 mice with the prepared vaccine, the biological safety, immunogenicity and conservation were evaluated. By constructing different infection models and from the aspects of adhesion inhibition and cytokines, the protective effect of the fusion protein vaccine PiuA-PlyD4 on S. pneumoniae infection was explored.

Results

PiuA-PlyD4 has abundant B-cell and helper T-cell epitopes and shows a high antigenicity score and structural stability. Molecular docking analysis suggested the potential interaction between PiuA and TLR2/4. The specific antibody titer of fusion protein antiserum was as high as (7.81±2.32) ×105. The protective effect of the immunized mice on nasal and lung colonization was significantly better than that of the control group, and the survival rate against S. pneumoniae infection of serotype 3 reached 50%. Cytokine detection showed that the humoral immune response, Th1, Th2 and Th17 cellular immune pathways were all involved in the process.

Conclusion

The study indicates that PiuA-PlyD4, whether the results are predicted by immunoinformatics or experimentally validated in vivo and in vitro, has good immunogenicity and immunoreactivity and can provide effective protection against S. pneumoniae infection. Therefore, it can be considered a promising prophylactic vaccine candidate for S. pneumoniae.

Leveraging immunoinformatics for developing a multi-epitope subunit vaccine against Helicobacter pylori and Fusobacterium nucleatum

Gastric ulcers caused by Helicobacter pylori and Fusobacterium nucleatum remain a significant global health concern without an established vaccine. In this study, we utilized immunoinformatics methods to design a multi-epitope vaccine targeting these pathogens. Outer membrane proteins from H. pylori and F. nucleatum were scrutinized to identify high antigenic T-cell and B-cell epitopes. The resulting vaccine comprised carefully analyzed and evaluated epitopes, including cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocytes epitopes. This vaccine exhibited notable antigenicity, suitable immunogenicity, and demonstrated non-allergenicity and non-toxicity. It displayed favorable physiochemical characteristics and high solubility. In interaction studies, the vaccine exhibited robust binding to toll-like receptor 4 (TLR4). Molecular dynamic simulations revealed cohesive structural integrity and stable attachment. Codon adaptation utilizing Escherichia coli K12 host yielded a vaccine with elevated Codon Adaptation Index (CAI) and optimal GC content. In silico cloning into the pET28+(a) vector demonstrated efficient expression. Immune simulations indicated the vaccine's ability to initiate immune responses in humans, mirroring real-life scenarios. Based on these comprehensive findings, we propose that our developed vaccine has the potential to confer robust immunity against H. pylori and F. nucleatum infections.Communicated by Ramaswamy H. Sarma.

Glycine-rich peptides from fermented Chenopodium formosanum sprout as an antioxidant to modulate the oxidative stress

Rhizopus oligosporus was utilized in the solid-state fermentation of Chenopodiumformosanumsprouts (FCS) in a bioreactor. Subsequently, the antioxidant activity of food proteins derived from FCS was investigated. Results showed that glycine-rich peptide (GGGGGKP, G-rich peptide), identified from the <2 kDa FCS proteins, had antioxidant values. According to SwissADME, AllerTOP, ToxinPred, and BIOPEP-UWM analyses, G-rich peptide was identified as safe, non-toxic, and non-allergenic. Afterward, the peptide was examined using in silico and in vitro studies to evaluate its potential alleviating oxidative stress caused by particulate matter. This study proposed plausible mechanisms that involve the binding of G-rich peptide which inhibited phosphorylation of the v-rel avian reticuloendotheliosis viral oncogene homologA(RELA) subunit onNF-κB pathway. The inhibition then resulted in down regulation of NF-κB transcription and genetic expression of inflammatory responses. These findings suggested that G-rich peptide from FCS proteins can potentially alleviate oxidative stress.

Al-Abbasi F, M. Alghamdi A, Al-Regaiey K, Ul-Haq A, Kaleem I, Bashir S, Waheed Y. Computational design of experimentally validated multi-epitopes vaccine against hepatitis E virus: An immunological approach

Hepatitis E virus (HEV) is one of the leading acute liver infections triggered by viral hepatitis. Patients infected with HEV usually recover and the annual death rate is negligible. Currently, there is no HEV licensed vaccine available globally. This study was carried out to design a multi-epitope HEV peptide-based vaccine by retrieving already experimentally validated epitopes from ViPR database leading to epitope prioritization. Epitopes selected as potential vaccine candidates were non-allergen, immunogenic, soluble, non-toxic and IFN gamma positive. The epitopes were linked together by AAY linkers and the linker EAAAK was used to join adjuvant with epitopes. Toll-like receptor (TLR)-4 agonist was used as an adjuvant to boost efficacy of the vaccine. Furthermore, codon optimization followed by disulfide engineering was performed to analyse the designed vaccine's structural stability. Computational modeling of the immune simulation was done to examine the immune response against the vaccine. The designed vaccine construct was docked with TLR-3 receptor for their interactions and then subjected to molecular dynamic simulations. The vaccine model was examined computationally towards the capability of inducing immune responses which showed the induction of both humoral and cell mediated immunity. Taken together, our study suggests an In-silico designed HEV based multi-epitope peptide-based vaccine (MEPV) that needs to be examined in the wet lab-based data that can help to develop a potential vaccine against HEV.

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.

Characterization of exclusively non-commensal Neisseria gonorrhoeae pangenome to prioritize globally conserved and thermodynamically stable vaccine candidates using immune-molecular dynamic simulations

Neisseria gonorrhoeae (Ngo) has emerged as a global threat leading to one of the most common sexually transmitted diseases in the world. It has also become one of the leading antimicrobial resistant organisms, resulting in fewer treatment options and an increased morbidity. Therefore, in recent years, there has been an increased focus on the development of new treatments and preventive strategies to combat its infection. In this study, we have combined the most conserved epitopes from the completely assembled strains of Ngo to develop a universal and a thermodynamically stable vaccine candidate. For our vaccine design, the epitopes were selected for their high immunogenicity, non-allergenicity and non-cytotoxicity, making them the ideal candidates for vaccine development. For the screening process, several reverse vaccinology tools were employed to rigorously extract non-homologous and immunogenic epitopes from the selected proteins. Consequently, a total number of 3 B-cell epitopes and 6 T-cell epitopes were selected and joined by multiple immune-modulating adjuvants and linkers to generate a promiscuous immune response. Additionally, the stability and flexible nature of the vaccine construct was confirmed using various molecular dynamic simulation tools. Overall, the vaccine candidate showed promising binding affinity to various HLA alleles and TLR receptors; however, further studies are needed to assess its efficacy in-vivo. In this way, we have designed a multi-subunit vaccine candidate to potentially combat and control the spread of N. gonorrhoeae.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

Genomic annotation for vaccine target identification and immunoinformatics-guided multi-epitope-based vaccine design against Songling virus through screening its whole genome encoded proteins

Songling virus (SGLV), a newly discovered tick-borne orthonairovirus, was recently identified in human spleen tissue. It exhibits cytopathic effects in human hepatoma cells and is associated with clinical symptoms including headache, fever, depression, fatigue, and dizziness, but no treatments or vaccines exist for this pathogenic virus. In the current study, immunoinformatics techniques were employed to identify potential vaccine targets within SGLV by comprehensively analyzing SGLV proteins. Four proteins were chosen based on specific thresholds to identify B-cell and T-cell epitopes, validated through IFN-γ epitopes. Six overlap MHC-I, MHC-II, and B cell epitopes were chosen to design a comprehensive vaccine candidate, ensuring 100% global coverage. These structures were paired with different adjuvants for broader protection against international strains. Vaccine constructions' 3D models were high-quality and validated by structural analysis. After molecular docking, SGLV-V4 was selected for further research due to its lowest binding energy (-66.26 kcal/mol) and its suitable immunological and physiochemical properties. The vaccine gene is expressed significantly in E. coli bacteria through in silico cloning. Immunological research and MD simulations supported its molecular stability and robust immune response within the host cell. These findings can potentially be used in designing safer and more effective experimental SGLV-V4 vaccines.

In silico designing of an epitope-based peptide vaccine cocktail against Nipah virus: an Indian population-based epidemiological study

Nipah virus, a zoonotic virus from the family Paramyxoviridae has led to significant loss of lives till date with the most recent outbreak in India reported in Kerala. The virus has a considerably high mortality rate along with lack of characteristic symptoms which results in the delay of the virus detection. No specific vaccine is available for the virus although monoclonal antibody treatment has been seen to be effective along with favipiravir. The high mortality and complications caused by the virus underscores the necessity to develop alternative modes of vaccination. One such method has been designed in this study using peptide cocktail consisting of the immunologically important epitopes for use as vaccine. The human leucocytic antigens that are used for the study were analyzed for their presence in various ethnic Indian populations. This study may serve as a new avenue for development of more efficient peptide cocktail vaccines in recent future based on the population genetics and ethnicity.

In silico discovery of epitopes of gag and env proteins for the development of a multi-epitope vaccine candidate against Maedi Visna Virus using reverse vaccinology approach

Maedi Visna Virus (MVV) causes a chronic viral disease in sheep. Since there is no specific therapeutic drug that targets MVV, development of a vaccine against the MVV is inevitable. This study aimed to analyze the gag and env proteins as vaccine candidate proteins and to identify epitopes in these proteins. In addition, it was aimed to construct a multi-epitope vaccine candidate. According to the obtained results, the gag protein was detected to be more conserved and had a higher antigenicity value. Also, the number of alpha helix in the secondary structure was higher and transmembrane helices were not detected. Although many B cell and MHC-I/II epitopes were predicted, only 19 of them were detected to have the properties of antigenic, non-allergenic, non-toxic, soluble, and non-hemolytic. Of these epitopes, five were remarkable due to having the highest antigenicity value. However, the final multi-epitope vaccine was constructed with 19 epitopes. A strong affinity was shown between the final multi-epitope vaccine and TLR-2/4. In conclusion, the gag protein was a better antigen. However, both proteins had epitopes with high antigenicity value. Also, the final multi-epitope vaccine construct had a potential to be used as a peptide vaccine due to its immuno-informatics results.

Computational prediction of allergenic proteins based on multi-feature fusion

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

Immunoinformatic exploration of a multi-epitope-based peptide vaccine candidate targeting emerging variants of SARS-CoV-2

Many countries around the world are facing severe challenges due to the recently emerging variants of SARS-CoV-2. Over the last few months, scientists have been developing treatments, drugs, and vaccines to subdue the virus and prevent its transmission. In this context, a peptide-based vaccine construct containing pathogenic proteins of the virus known to elicit an immune response was constructed. An analysis of the spike protein-based epitopes allowed us to design an "epitope-based subunit vaccine" against coronavirus using the approaches of "reverse vaccinology" and "immunoinformatics." Computational experimentation and a systematic, comprehensive protocol were followed with an aim to develop and design a multi-epitope-based peptide (MEBP) vaccine candidate. Our study attempted to predict an MEBP vaccine by introducing mutations of SARS-CoV-2 (Delta, Lambda, Iota, Omicron, and Kappa) in Spike glycoprotein and predicting dual-purpose epitopes (B-cell and T-cell). This was followed by screening the selected epitopes based on antigenicity, allergenicity, and population coverage and constructing them into a vaccine by using linkers and adjuvants. The vaccine construct was analyzed for its physicochemical properties and secondary structure prediction, and a 3D structure was built, refined, and validated. Furthermore, the peptide-protein interaction of the vaccine construct with Toll-like receptor (TLR) molecules was performed. Immune profiling was performed to check the immune response. Codon optimization of the vaccine construct was performed to obtain the GC content before cloning it into the E. coli genome, facilitating its progression it into a vector. Finally, an in-silico simulation of the vaccine-protein complex was performed to comprehend its stability and conformational behavior.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

Motivation

Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes.

Results

To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.

Availability and implementation

The web service NetAllergen and the datasets are available at https://services.healthtech.dtu.dk/services/NetAllergen-1.0/.

A deep learning based ensemble approach for protein allergen classification

In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions. Bioinformatics and computational methods can be used to analyze the information contained in amino-acid sequences to detect possible allergens. The article presents a deep learning based ensemble approach to identify protein allergens using Extra Tree, Deep Belief Network (DBN), and CatBoost models. The proposed ensemble model achieves higher detection accuracy by combining the prediction results of the three models using majority voting. The evaluation of the proposed model was carried out on the benchmark protein allergen dataset, and the performance analysis revealed that the proposed model outperforms the other state-of-the-art literature techniques with a protein allergen detection accuracy of 89.16%.

Immunoinformatics-based multi-epitope vaccine design for the re-emerging monkeypox virus

BACKGROUND

On May 7, 2022, WHO reported a new monkeypox case. By May 2023 over 80,000 cases had been reported worldwide outside previously endemic nations. (This primarily affected the men who have sex with men (MSM) community in rich nations). The present research aims to develop a multi-epitope vaccine for the monkeypox virus (MPXV) using structural and cell surface proteins.

METHODS

The first part of the research involved retrieving protein sequences. The Immune Epitope Database (IEDB) was then used to analyze the B and T lymphocyte epitopes. After analyzing the sensitizing properties, toxicity, antigenicity, and molecular binding, appropriate linkers were utilizedto connect selected epitopes to adjuvants, and the structure of the vaccine was formulated. Algorithms from the field of immunoinformatics predicted the secondary and tertiary structures of vaccines. The physical, chemical, and structural properties were refined and validated to achieve maximum stability. Molecular docking and molecular dynamic simulations were subsequently employed to assess the vaccine's efficacy. Afterward, the ability of the vaccine to interact with toll-like receptors 3 and 4 (TLR3 and TLR4) was evaluated. Finally, the optimized sequence was then introduced into the Escherichia coli (E. coli) PET30A + vector.

RESULTS

An immunoinformatics evaluation suggested that such a vaccine might be safe revealed that this vaccine is safe, hydrophilic, temperature- and condition-stable, and can stimulate innate immunity by binding to TLR3 and TLR4.

CONCLUSION

Our findings suggest that the first step in MPXV pathogenesis is structural and cell surface epitopes. In this study, the most effective and promising epitopes were selected and designed throughprecision servers. Furthermore,through the utilization of multi-epitope structures and a combination of two established adjuvants, this research has the potential to be a landmarkin developing an antiviralvaccine against MPXV. However, additional in vitro and in vivo tests are required to confirm these results.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

Multi-epitope vaccine against drug-resistant strains of Mycobacterium tuberculosis: a proteome-wide subtraction and immunoinformatics approach

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Toll-like receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

Isolation and Identification of a Novel Anti-Dry Eye Peptide from Tilapia Skin Peptides Based on In Silico, In Vitro, and In Vivo Approaches

Tilapia skin is a great source of collagen. Here, we aimed to isolate and identify the peptides responsible for combating dry eye disease (DED) in tilapia skin peptides (TSP). In vitro cell DED model was used to screen anti-DED peptides from TSP via Sephadex G-25 chromatography, LC/MS/MS, and in silico methods. The anti-DED activity of the screened peptide was further verified in the mice DED model. TSP was divided into five fractions (TSP-I, TSP-II, TSP-III, TSP-IV, and TSP-V), and TSP-II exerted an effective effect for anti-DED. A total of 131 peptides were identified using LC/MS/MS in TSP-II, and NGGPSGPR (NGG) was screened as a potential anti-DED fragment in TSP-II via in silico methods. In vitro, NGG restored cell viability and inhibited the expression level of Cyclooxygenase-2 (COX-2) protein in Human corneal epithelial cells (HCECs) induced by NaCl. In vivo, NGG increased tear production, decreased tear ferning score, prevented corneal epithelial thinning, alleviated conjunctival goblet cell loss, and inhibited the apoptosis of corneal epithelial cells in DED mice. Overall, NGG, as an anti-DED peptide, was successfully identified from TSP, and it may be devoted to functional food ingredients or medicine for DED.

Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches

Acinetobacter baumannii is one of the major pathogenic ESKAPE bacterium, which is responsible for about more than 722,000 cases in a year, globally. Despite the alarming increase in multidrug resistance, a safe and effective vaccine for Acinetobacter infections is still not available. Hence in the current study, a multiepitope vaccine construct was developed using linear B cell, cytotoxic T cell, and helper T cell epitopes from the antigenic and well-conserved lipopolysaccharide assembly proteins employing systematic immunoinformatics and structural vaccinology strategies. The multi-peptide vaccine was predicted to be highly antigenic, non-allergenic, non-toxic, and cover maximum population coverage worldwide. Further, the vaccine construct was modeled along with adjuvant and peptide linkers and validated to achieve a high-quality three-dimensional structure which was subsequently utilized for cytokine prediction, disulfide engineering, and docking analyses with Toll-like receptor (TLR4). Ramachandran plot showed 98.3% of the residues were located in the most favorable and permitted regions, thereby corroborating the feasibility of the modeled vaccine construct. Molecular dynamics simulation for a 100 ns timeframe further confirmed the stability of the binding vaccine-receptor complex. Finally, in silico cloning and codon adaptation were also performed with the pET28a (+) plasmid vector to determine the efficiency of expression and translation of the vaccine. Immune simulation studies demonstrated that the vaccine could trigger both B and T cell responses and can elicit strong primary, secondary, and tertiary immune responses. The designed multi-peptide subunit vaccine would certainly expedite the experimental approach for the development of a vaccine against A. baumannii infection.

Screening of apical membrane antigen-1 (AMA1), dense granule protein-7 (GRA7) and rhoptry protein-16 (ROP16) antigens for a potential vaccine candidate against Toxoplasma gondii for chickens

Toxoplasmosis is a zoonotic disease caused by the protozoan parasite, Toxoplasma gondii known to infect almost all animals, including birds and humans globally. This disease has impacted the livestock industry and public health, where infection of domestic animals increases the zoonotic risk of transmission of infection to humans, threatening public health. Hence the need to discover novel and safe vaccines to fight against toxoplasmosis. In the current study, a novel multiepitope vaccine was designed using immunoinformatics techniques targeting T. gondii AMA1, GRA7 and ROP16 antigens, consisting of antigenic, immunogenic, non-allergenic and cytokine inducing T-cell (9 CD8⁺ and 15 CD4⁺) epitopes and four (4) B-cell epitopes fused together using AAY, KK and GPGPG linkers. The tertiary model of the proposed vaccine was predicted and validated to confirm the structural quality of the vaccine. The designed vaccine was highly antigenic (antigenicity = 0.6645), immunogenic (score = 2.89998), with molecular weight of 73.35 kDa, instability and aliphatic index of 28.70 and 64.10, respectively; and GRAVY of -0.363. The binding interaction, stability and flexibility were assessed with molecular docking and dynamics simulation, which revealed the proposed vaccine to have good structural interaction (binding affinity = -106.882 kcal/mol) and stability when docked with Toll like receptor-4 (TLR4). The results revealed that the Profilin-adjuvanted vaccine is promising, as it predicted induction of enhanced immune responses through the production of cytokines and antibodies critical in blocking host invasion.

Development of conserved multi-epitopes based hybrid vaccine against SARS-CoV-2 variants: an immunoinformatic approach

The world has faced unprecedented disruptions like global quarantine and the COVID-19 pandemic due to SARS-CoV-2. To combat these unsettling situations, several effective vaccines have been developed and are currently being used. However, the emergence of new variants due to the high mutation rate of SARS-CoV-2 challenges the efficacy of existing vaccines and has highlighted the need for novel vaccines that will be effective against various SARS-CoV-2 variants. In this study, we exploited the four structural proteins of SARS-CoV-2 to execute a potential multi-epitope vaccine against SARS-CoV-2 and its variants. The vaccine was designed by utilizing the antigenic, non-toxic, and non-allergenic B-cell and T-cell epitopes, which were selected from conserved regions of viral proteins. To build a vaccine construct, epitopes were connected through different linkers and an adjuvant was also attached at the start of the construct to enhance the immunogenicity and specificity of the epitopes. The vaccine construct was then screened through the aforementioned filters and it scored 0.6019 against the threshold of 0.4 on VexiJen 2.0 which validates its antigenicity. Toll-like receptors (i.e., TLR2, TLR3, TLR4, TLR5, and TLR8) and vaccine construct were docked by Cluspro 2.0, and TLR8 showed strong interaction with construct having a maximum negative binding energy of - 1577.1 kCal/mole. C-IMMSIM's immune simulations over three doses of the vaccine and iMODS' molecular dynamic simulations were executed to assess the reliability of the docked complexes. The stability of the vaccine construct was evaluated through the physicochemical analyses and the findings suggested that the manufactured vaccine is stable under a wide range of circumstances and can trigger immune responses against various SARS-CoV-2 variants (due to conserved epitopes). However, to strengthen the formulation of the vaccine and assess its safety and effectiveness, additional investigations and studies are required to support the computational data of this research at in-vitro and in-vivo levels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00156-2.

Bioinformatics-based prediction and screening of immunogenic epitopes of Toxoplasma gondii rhoptry proteins 7, 21 and 22 as candidate vaccine target

Introduction

Toxoplasmosis is a well-known zoonotic disease caused by Toxoplasma gondii. The main causes of the disease range from eating undercooked or contaminated meat and shellfish to cleaning litter trays into which cats that excreted toxoplasma via faeces. This pathogen can live for a very long time, possibly a lifetime, within the bodies of humans and other animals.

Aims and objectives

This study aimed to predict and analyse candidate immunogenic epitopes for vaccine development by evaluating the physio-chemical properties, multiple sequence alignment, secondary and tertiary structures, phosphorylation sites, transmembrane domains, and signal peptides, of T. gondii rhoptry proteins ROP7, ROP21, and ROP22 using bioinformatics tools.

Methods

To find immunogenic epitopes of rhoptry proteins, numerous bioinformatics web servers were used containing multiple sequence alignment, physiochemical properties, antigenicity and allergenicity, post-translational modification sites (PTMs), signal peptides, transmembrane domains, secondary and tertiary structures, and screening of predicted epitopes. We evaluated immunogenic linear B-cell epitopes as candidate proteins for vaccine development.

Results

Nine epitopes were identified for each protein, and analysis of immunogenicity, revealed three candidate epitopes for ROP7, one for ROP21, and four for ROP22. Among all candidate epitopes, ROP22 contained the most immunogenic epitopes with immunogenicity score of 0.50575.

Conclusion

We acquired detailed information on predicted immunogenic epitopes using in-silico methods. The results provide a foundation for further experimental analysis of toxoplasmosis, and potential vaccine development.