Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential

Interpretable molecular encodings and representations for machine learning tasks

Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborhoods (iCAN). Designed to address machine learning models' need for more structured and less flexible input, it captures the neighborhoods of carbon atoms in a counting array and improves the utility of the resulting encodings for machine learning models. The iCAN method provides interpretable molecular encodings and representations, enabling the comparison of molecular neighborhoods, identification of repeating patterns, and visualization of relevance heat maps for a given data set. When reproducing a large biomedical peptide classification study, it outperforms its predecessor encoding. When extended to proteins, it outperforms a lead structure-based encoding on 71% of the data sets. Our method offers interpretable encodings that can be applied to all organic molecules, including exotic amino acids, cyclic peptides, and larger proteins, making it highly versatile across various domains and data sets. This work establishes a promising new direction for machine learning in peptide and protein classification in biomedicine and healthcare, potentially accelerating advances in drug discovery and disease diagnosis.

Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a prevalent airborne infectious disease. Despite the availability of the Bacille Calmette-Guerin vaccine, its global efficacy remains modest, and tuberculosis persists as a significant global public health threat. Addressing this challenge and advancing towards the End MTB Strategy, we developed a multiepitope vaccine (MEV) based on immunoinformatics and computational approaches. Immunoinformatics screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex (MHC) allele binding, immunogenicity, antigenicity, allergenicity, toxicity, and cytokine inducibility. Selected epitopes were integrated into an MEV construct with adjuvant and linkers, forming a fully immunogenic vaccine candidate. Comprehensive analyses encompassed the evaluation of immunological and physicochemical properties, determination of tertiary structure, molecular docking with Toll-Like Receptors (TLR), molecular dynamics (MD) simulations for all atoms, and immune simulations. Our MEV comprises 534 amino acids, featuring 6 cytotoxic T lymphocyte, 8 helper T lymphocyte, and 7 linear B lymphocyte epitopes, demonstrating high antigenicity and stability. Notably, molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to TLR2. In addition, the immune simulation indicated the capacity to effectively induce elevated levels of antibodies and cytokines, emphasizing the vaccine's robust immunogenic response. This study presents a promising MEV against TB, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for TB vaccine development. Our study aligns with the global initiative of the End MTB Strategy, emphasizing its potential impact on addressing persistent challenges in TB control.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Designing a smallpox B-cell and T-cell multi-epitope subunit vaccine using a comprehensive immunoinformatics approach

Smallpox is a highly contagious human disease caused by the variola virus. Although the disease was eliminated in 1979 due to its highly contagious nature and historical pathogenicity, with a mortality rate of up to 30%, this virus is an important candidate for biological weapons. Currently, vaccines are the critical measures to prevent this virus infection and spread. In this study, we designed a peptide vaccine using immunoinformatics tools, which have the potential to activate human immunity against variola virus infection efficiently. The design of peptides derives from vaccine-candidate proteins showing protective potential in vaccinia WR strains. Potential non-toxic and nonallergenic T-cell and B-cell binding and cytokine-inducing epitopes were then screened through a priority prediction using special linkers to connect B-cell epitopes and T-cell epitopes, and an appropriate adjuvant was added to the vaccine construction to enhance the immunogenicity of the peptide vaccine. The 3D structure display, docking, and free energy calculation analysis indicate that the binding affinity between the vaccine peptide and Toll-like receptor 3 is high, and the vaccine receptor complex is highly stable. Notably, the vaccine we designed is obtained from the protective protein of the vaccinia and combined with preventive measures to avoid side effects. This vaccine is highly likely to produce an effective and safe immune response against the variola virus infection in the body.

IMPORTANCE

In this work, we designed a vaccine with a cluster of multiple T-cell/B-cell epitopes, which should be effective in inducing systematic immune responses against variola virus infection. Besides, this work also provides a reference in vaccine design for preventing monkeypox virus infection, which is currently prevalent.

Computer-aided designing of a novel multi‑epitope DNA vaccine against severe fever with thrombocytopenia syndrome virus

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease caused by the SFTS virus (Dabie bandavirus), which has become a substantial risk to public health. No specific treatment is available now, that calls for an effective vaccine. Given this, we aimed to develop a multi-epitope DNA vaccine through the help of bioinformatics. The final DNA vaccine was inserted into a special plasmid vector pVAX1, consisting of CD8+ T cell epitopes, CD4+ T cell epitopes and B cell epitopes (six epitopes each) screened from four genome-encoded proteins--nuclear protein (NP), glycoprotein (GP), RNA-dependent RNA polymerase (RdRp), as well as nonstructural protein (NSs). To ascertain if the predicted structure would be stable and successful in preventing infection, an immunological simulation was run on it. In conclusion, we designed a multi-epitope DNA vaccine that is expected to be effective against Dabie bandavirus, but in vivo trials are needed to verify this claim.

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.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum

Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.

Identification of potential vaccine targets for elicitation of host immune cells against SARS-CoV-2 by reverse vaccinology approach

The COVID-19 pandemic has emerged as a critical global health crisis, demanding urgent and effective strategies for containment. While some knowledge exists about epitope sequences recognized by human immune cells and their activation of CD8+ T cells within the HLA context, comprehensive information remains limited. This study employs reverse vaccinology to explore antigenic HLA-restricted T-cell epitopes capable of eliciting durable immunity. Screening reveals 187 consensus epitopes, with 23 offering broad population coverage worldwide, spanning over 5000 HLA alleles. Sequence alignment analysis highlights the genetic distinctiveness of these peptides from Homo sapiens and their intermediate to high TAP binding efficiency. Notably, these epitopes share 100 % sequence identity across strains from nine countries, indicating potential for a uniform protective immune response among diverse ethnic populations. Docking simulations further confirm their binding capacity with the HLA allele, validating them as promising targets for SARS-CoV-2 immune recognition. The anticipated epitopes are connected with suitable linkers and adjuvant, and then assessed for its translational efficacy within a bacterial expression vector through computational cloning. Through docking, it is observed that the chimeric vaccine construct forms lasting hydrogen bonds with Toll-like receptor (TLR4), while immune simulation illustrates an increased cytotoxic response aimed at CD8+ T cells. This comprehensive computational analysis suggests the chimeric vaccine construct's potential to provoke a robust immune response against SARS-CoV-2. By delineating these antigenic fragments, our study offers valuable insights into effective vaccine and immunotherapy development against COVID-19, contributing significantly to global efforts in combating this infectious threat.

Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus

Powassan virus (POWV) is an arthropod-borne virus (arbovirus) capable of causing severe illness in humans for severe neurological complications, and its incidence has been on the rise in recent years due to climate change, posing a growing public health concern. Currently, no vaccines to prevent or medicines to treat POWV disease, emphasizing the urgent need for effective countermeasures. In this study, we utilize bioinformatics approaches to target proteins of POWV, including the capsid, envelope, and membrane proteins, to predict diverse B-cell and T-cell epitopes. These epitopes underwent screening for critical properties such as antigenicity, allergenicity, toxicity, and cytokine induction potential. Eight selected epitopes were then conjugated with adjuvants using various linkers, resulting in designing of a potentially stable and immunogenic vaccine candidate against POWV. Moreover, molecular docking, molecular dynamics simulations, and immune simulations revealed a stable interaction pattern with the immune receptor, suggesting the vaccine's potential to induce robust immune responses. In conclusion, our study provided a set of derived epitopes from POWV's proteins, demonstrating the potential for a novel vaccine candidate against POWV. Further in vitro and in vivo studies are warranted to advance our efforts and move closer to the goal of combatting POWV and related arbovirus infections.

Understanding SARS-CoV-2 spike glycoprotein clusters and their impact on immunity of the population from Rio Grande do Norte, Brazil

SARS-CoV-2 genome underwent mutations since it started circulating within the human population. The aim of this study was to understand the fluctuation of the spike clusters concomitant to the population immunity either due to natural infection and/or vaccination in a state of Brazil that had both high rate of natural infection and vaccination coverage. A total of 1725 SARS-CoV-2 sequences from the state of Rio Grande do Norte, Brazil, were retrieved from GISAID and subjected to cluster analysis. Immunoinformatics were used to predict T- and B-cell epitopes, followed by simulation to estimate either pro- or anti-inflammatory responses and to correlate with circulating variants. From March 2020 to June 2022, the state of Rio Grande do Norte reported 579,931 COVID-19 cases with a 1.4% fatality rate across the three major waves: May-Sept 2020, Feb-Aug 2021, and Jan-Mar 2022. Cluster 0 variants (wild type strain, Zeta) were prevalent in the first wave and Delta (AY.*), which circulated in Brazil in the latter half of 2021, featuring fewer unique epitopes. Cluster 1 (Gamma (P.1 + P.1.*)) dominated the first half of 2021. Late 2021 had two new clusters, Cluster 2 (Omicron, (B.1.1.529 + BA.*)), and Cluster 3 (BA.*) with the most unique epitopes, in addition to Cluster 4 (Delta sub lineages) which emerged in the second half of 2021 with fewer unique epitopes. Cluster 1 epitopes showed a high pro-inflammatory propensity, while others exhibited a balanced cytokine induction. The clustering method effectively identified Spike groups that may contribute to immune evasion and clinical presentation, and explain in part the clinical outcome.

Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection

Acquired Immunodeficiency Syndrome is caused by the Human Immunodeficiency Virus (HIV), and a significant number of fatalities occur annually. There is a dire need to develop an effective vaccine against HIV-1. Understanding the structural proteins of viruses helps in designing a vaccine based on immunogenic peptides. In the current experiment, we identified gp120 epitopes using bioinformatic epitope prediction tools, molecular docking, and MD simulations. The Gb-1 peptide was considered an adjuvant. Consecutive sequences of GTG, GSG, GGTGG, and GGGGS linkers were used to bind the B cell, Cytotoxic T Lymphocytes (CTL), and Helper T Lymphocytes (HTL) epitopes. The final vaccine construct consisted of 315 amino acids and is expected to be a recombinant protein of approximately 35.49 kDa. Based on docking experiments, molecular dynamics simulations, and tertiary structure validation, the analysis of the modeled protein indicates that it possesses a stable structure and can interact with Toll-like receptors. The analysis demonstrates that the proposed vaccine can provoke an immunological response by activating T and B cells, as well as stimulating the release of IgA and IgG antibodies. This vaccine shows potential for HIV-1 prophylaxis. The in-silico design suggests that multiple-epitope constructs can be used as potentially effective immunogens for HIV-1 vaccine development.

Genome-based solutions for managing mucormycosis

An uncommon opportunistic fungal infection known as mucormycosis is caused by a class of molds called mucoromycetes. Currently, antifungal therapy and surgical debridement are the primary treatment options for mucormycosis. Despite the importance of comprehensive knowledge on mucormycosis, there is a lack of well-annotated databases that provide all relevant information. In this study, we have gathered and organized all available information related to mucormycosis that include disease's genome, proteins, diagnostic methods. Furthermore, using the AlphaFold2.0 prediction tool, we have predicted the tertiary structures of potential drug targets. We have categorized the information into three major sections: "genomics/proteomics," "immunotherapy," and "drugs." The genomics/proteomics module contains information on different strains responsible for mucormycosis. The immunotherapy module includes putative sequence-based therapeutics predicted using established tools. Drugs module provides information on available drugs for treating the disease. Additionally, the drugs module also offers prerequisite information for designing computationally aided drugs, such as putative targets and predicted structures. In order to provide comprehensive information over internet, we developed a web-based platform MucormyDB (https://webs.iiitd.edu.in/raghava/mucormydb/).

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

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

Immunoinformatics-guided approach for designing a pan-proteome multi-epitope subunit vaccine against African swine fever virus

Despite being identified over a hundred years ago, there is still no commercially available vaccine for the highly contagious and deadly African swine fever virus (ASFV). This study used immunoinformatics for the rapid and inexpensive designing of a safe and effective multi-epitope subunit vaccine for ASFV. A total of 18,858 proteins from 100 well-annotated ASFV proteomes were screened using various computational tools to identify potential epitopes, or peptides capable of triggering an immune response in swine. Proteins from genotypes I and II were prioritized for their involvement in the recent global ASFV outbreaks. The screened epitopes exhibited promising qualities that positioned them as effective components of the ASFV vaccine. They demonstrated antigenicity, immunogenicity, and cytokine-inducing properties indicating their ability to induce potent immune responses. They have strong binding affinities to multiple swine allele receptors suggesting a high likelihood of yielding more amplified responses. Moreover, they were non-allergenic and non-toxic, a crucial prerequisite for ensuring safety and minimizing any potential adverse effects when the vaccine is processed within the host. Integrated with an immunogenic 50S ribosomal protein adjuvant and linkers, the epitopes formed a 364-amino acid multi-epitope subunit vaccine. The ASFV vaccine construct exhibited notable immunogenicity in immune simulation and molecular docking analyses, and stable profiles in secondary and tertiary structure assessments. Moreover, this study designed an optimized codon for efficient translation of the ASFV vaccine construct into the Escherichia coli K-12 expression system using the pET28a(+) vector. Overall, both sequence and structural evaluations suggested the potential of the ASFV vaccine construct as a candidate for controlling and eradicating outbreaks caused by the pathogen.

Pharmacogenomic and epigenomic approaches to untangle the enigma of IL-10 blockade in oncology

The host immune system status remains an unresolved mystery among several malignancies. An immune-compromised state or smart immune-surveillance tactics orchestrated by cancer cells are the primary cause of cancer invasion and metastasis. Taking a closer look at the tumour-immune microenvironment, a complex network and crosstalk between infiltrating immune cells and cancer cells mediated by cytokines, chemokines, exosomal mediators and shed ligands are present. Cytokines such as interleukins can influence all components of the tumour microenvironment (TME), consequently promoting or suppressing tumour invasion based on their secreting source. Interleukin-10 (IL-10) is an interlocked cytokine that has been associated with several types of malignancies and proved to have paradoxical effects. IL-10 has multiple functions on cellular and non-cellular components within the TME. In this review, the authors shed the light on the regulatory role of IL-10 in the TME of several malignant contexts. Moreover, detailed epigenomic and pharmacogenomic approaches for the regulation of IL-10 were presented and discussed.

In-silico development of multi-epitope subunit vaccine against lymphatic filariasis

The World Health Organization in 2022 reported that more than 863 million people in 50 countries are at risk of developing lymphatic filariasis (LF), a disease caused by parasitic infection. Immune responses to parasites suggest that the development of a prophylactic vaccine against LF is possible. Using a reverse vaccinology approach, the current study identified Trehalose-6-phosphatase (TPP) as a potential vaccine candidate among 15 reported vaccine antigens for B. malayi. High-ranking B and T-cell epitopes in the Trehalose-6-phosphatase (TPP) were shortlisted using online servers for subsequent analysis. We selected these peptides to construct a vaccine model using I-TASSER and GalaxyRefine server. The vaccine construct showed favorable physicochemical properties, high antigenicity, no allergenicity, no toxicity, and high stability. Structural validation using the Ramachandran plot showed that 98% of the residues were in favorable or mostly allowed regions. Molecular docking and simulation showed a strong binding affinity and stability of the subunit vaccine with toll-like receptor 4 (TLR4). Furthermore, the subunit vaccine showed a strong IgG/IgM response, with the disappearance of the antigen. We propose that our vaccine construct should be further evaluated using cellular and animal models to develop a vaccine that is safe and effective against LF.Communicated by Ramaswamy H. Sarma.

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.

Design of a multi-epitope vaccine (vme-VAC/MST-1) against cholera and vibriosis based on reverse vaccinology and immunoinformatics approaches

Vibriosis and cholera are serious diseases distributed worldwide and caused by six marine bacteria of the Vibrio genus. Thousands of deaths occur each year due to these illnesses, necessitating the development of new preventive measures. Presently, the existing cholera vaccine demonstrates an effectiveness of approximately 60%. Here we describe a new multi-epitope vaccine, 'vme-VAC/MST-1' based on vaccine targets identified by reverse vaccinology and epitopes predicted by immunoinformatics, two currently effective tools for predicting new vaccines for bacterial pathogens. The vaccine was designed to combat vibriosis and cholera by incorporating epitopes predicted for CTL, HTL, and B cells. These epitopes were identified from six vaccine targets revealed through subtractive genomics, combined with reverse vaccinology, and were further filtered using immunoinformatics approaches based on their predicted immunogenicity. To construct the vaccine, 28 epitopes (24 CTL/B and 4 HTL/B) were linked to the sequence of the cholera toxin B subunit adjuvant. In silico analyses indicate that the resulting immunogen is stable, soluble, non-toxic, and non-allergenic. Furthermore, it exhibits no homology to the host and demonstrates a strong capacity to elicit innate, B-cell, and T-cell immune responses. Our analysis suggests that it is likely to elicit immune reactions mediated through the TLR5 pathway, as evidenced by the molecular docking of the vaccine with the receptor, which revealed high affinity and a favorable reaction. Thus, vme-VAC/MST-1 is predicted to be a safe and effective solution against pathogenic Vibrio spp. However, further experimental analyses are required to measure the vaccine's effects In vivo.Communicated by Ramaswamy H. Sarma.

A bioinformatic analysis: Previous allergen exposure may support anti- SARS-CoV-2 immune response

COVID-19, caused by infection with the SARS-CoV-2 has become a global health problem due to significant mortality rates; the exact pathophysiological mechanism remains uncertain. Articles reporting patient data are quite heterogeneous and have several limitations. Surviving patients develop a CD4 and CD8 T-cell response to the virus SARS-CoV-2 during COVID-19. Interestingly, pre-existing virus-reactive T-cells have been found in patients that were not infected before, suggesting some form of cross-reactivity or immunological mimicry. To better understand this phenomenon, we performed a bioinformatic study, which was aimed to identify antigenic structures that may explain the presence of such "reactive" T-cells, which may support or modulate the immune response to SARS-CoV-2 infections. Seven different common environmental allergen epitopes identical to the SARS-CoV-2 S-protein were identified that share affinity to 8 MHCI-specific epitope regions. Pollen showed the greatest similarity with the S protein epitope. In the epitope similarity analysis between the S protein and MHC-II / T helper epitopes, the highest similarity was determined for mites. When S-protein that stimulates B cells and identical epitope antigens are examined, the most common allergens were hornbeam and wheat. The high epitope similarity observed for the allergens examined and S protein epitopes suggest that these allergens may be a reason for pre-existing SARS-CoV-2 - reactive T-cells in previously non-infected subjects and such a previous exposure may affect the course of the disease in COVID-19 infection. It remains to be determined whether such a previous existence of SARS-CoV-2 reactive cells can support the clearance of the virus or if they, in contrast, may even aggravate the disease course. (Table 4, Ref 54).

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

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

EnILs: A General Ensemble Computational Approach for Predicting Inducing Peptides of Multiple Interleukins

Interleukins (ILs) are a group of multifunctional cytokines, which play important roles in immune regulations and inflammatory responses. Recently, IL-6 has been found to affect the development of COVID-19, and significantly elevated levels of IL-6 cytokines have been reported in patients with severe COVID-19. IL-10 and IL-17 are anti-inflammatory and proinflammatory cytokines, respectively, which play multiple protective roles in host defense against pathogens. At present, a number of machine learning methods have been proposed to predict ILs inducing peptides, but their predictive performance needs to be further improved, and the inducing peptides of different ILs are predicted separately, rather than using a general approach. In our work, we combine the statistical features of peptide sequence with word embedding to design a general ensemble model named EnILs to predict inducing peptides of different ILs, in which the predictive probabilities of random forest, eXtreme Gradient Boosting and neural network are integrated in an average way. Compared with the state-of-the-art machine learning methods, EnILs shows considerable performance in the prediction of IL-6, IL-10, and IL-17 inducing peptides. In addition, we predict the most promising IL-6 inducing peptides in Severe Acute Respiratory Syndrome Coronavirus 2 spike protein in the case study for further experimental verification.

In Vivo Validation of Novel Synthetic tbp1 Peptide-Based Vaccine Candidates against Haemophilus influenzae Strains in BALB/c Mice

Haemophilus influenzae is a Gram-negative bacterium characterized as a small, nonmotile, facultative anaerobic coccobacillus. It is a common cause of a variety of invasive and non-invasive infections. Among six serotypes (a-f), H. influenzae type b (Hib) is the most familiar and predominant mostly in children and immunocompromised individuals. Following Hib vaccination, infections due to other serotypes have increased in number, and currently, there is no suitable effective vaccine to induce cross-strain protective antibody responses. The current study was aimed to validate the capability of two 20-mer highly conserved synthetic tbp1 (transferrin-binding protein 1) peptide-based vaccine candidates (tbp1-E1 and tbp1-E2) predicted using in silico approaches to induce immune responses against H. influenzae strains. Cytokine induction ability, immune simulations, and molecular dynamics (MD) simulations were performed to confirm the candidacy of epitopic docked complexes. Synthetic peptide vaccine formulations in combination with two different adjuvants, BGs (Bacterial Ghosts) and CFA/IFA (complete/incomplete Freund's adjuvant), were used in BALB/c mouse groups in three booster shots at two-week intervals. An indirect ELISA was performed to determine endpoint antibody titers using the Student's t-distribution method. The results revealed that the synergistic use of both peptides in combination with BG adjuvants produced better results. Significant differences in absorbance values were observed in comparison to the rest of the peptide-adjuvant combinations. The findings of this study indicate that these tbp1 peptide-based vaccine candidates may present a preliminary set of peptides for the development of an effective cross-strain vaccine against H. influenzae in the future due to their highly conserved nature.

Immunoinformatic-guided designing and evaluating protein and mRNA-based vaccines against Cryptococcus neoformans for immunocompromised patients

BACKGROUND

Cryptococcus neoformans is a fungal pathogen that can cause serious meningoencephalitis in individuals with compromised immune systems due to HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome), liver cirrhosis, and transplantation. Mannoproteins (MPs), glycoproteins in the C. neoformans capsule, crucially impact virulence by mediating adhesion to lung cells and modulating immune response via cytokine induction and phagocytosis influence. Therefore, creating a vaccine that can generate targeted antibodies to fight infection and prevent fungal illnesses is essential.

RESULTS

This research aims to create a unique, stable, and safe vaccine through bioinformatics methodologies, aiming at epitopes of T and B cells found in the MP of C. neoformans. Based on toxicity, immunogenicity, and antigenicity, this research predicted novel T cells (GNPVGGNVT, NPVGGNVTT, QTSYARLLS, TSVGNGIAS, WVMPGDYTN, AAATGSSSSGSTGSG, GSTGSGSGSAAAGST, SGSTGSGSGSAAAGS, SSGSTGSGSGSAAAG, and SSSGSTGSGSGSAAA) and B cell (ANGSTSTFQQRYTGTYTNGDGSLGTWTQGETVTPQTAYSTPATSNCKTYTSVGNGIASLALSNAGSNSTAAATNSSSGGASAAATGSSSSGSTGSGSGSAAAGSTAAASSSGDSSSSTSAAMSNGI, HGATGLGNPVGGNVTT, TMGPTNPSEPTLGTAI, GNPVGGNVTTNATGSD, and NSTAAATNSSSGGASA) epitopes for a multiple-epitope vaccine and constructed a vaccine subunit with potential immunogenic properties. The present study used four linkers (AAY, GPGPG, KK, and EAAAK linkers) to connect the epitopes and adjuvant. After constructing the vaccine, it was confronted with receptor docking and simulation analysis. Subsequently, the vaccine was cloned into the vector of Escherichia coli pET-28a ( +) by ligation process for the expression using the SnapGene tool, which confirmed a significant immune response. To assess the constructed vaccine's properties, multiple computational tools were employed. Based on the MP sequence, the tools evaluated the antigenicity, immunogenicity, cytokine-inducing capacity, allergenicity, toxicity, population coverage, and solubility.

CONCLUSION

Eventually, the results revealed a promising multi-epitope vaccine as a potential candidate for addressing global C. neoformans infection, particularly in immunocompromised patients. Yet, additional in vitro and in vivo investigations are necessary to validate its safety and effectiveness.

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

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

Immunoreactive proteins of Capsicum-based spices as a threat to human health: mass spectrometry analysis and in silico mapping

Dietary patterns are changing severely, especially the consumption of highly processed foods with lots of spices is increasing, carrying an increased risk of immediate hypersensitivity (type I), in sensitised individuals, due to the possible presence of allergens, especially the hidden ones. Paprika is a fruit of the Capsicum genus, which belongs to the Solanaceae family and is commonly consumed fresh or as a spice. Despite recorded cases of anaphylaxis, its allergenicity has yet to be clearly investigated. In this study, we research to identify proteins that could trigger a severe allergic reaction in patients with an equivocal clinical picture. Two types of protein extracts extracted from 3 different paprika spices were immunoblotted with sera from patients with severe allergic symptoms, presumably to paprika. Proteins from the IgE reactive bands obtained were subjected to LC-MS/MS identification and then in silico analysis to assess their possible sensitising capacity and proinflammatory potential using online tools. The spices were shown to contain a number of incompletely investigated highly immunoreactive allergenic proteins, including proteins of foreign origin (contaminants), the presence of which can stimulate inflammatory mechanisms and cross-reactivity with other food allergens, which can threaten life and health and should be investigated in detail.

Initial exploration of a novel fusion protein, IL-4/IL-34/IL-10, which promotes cardiac allograft survival mice through alloregulation

Immune mediated graft loss still represents a major risk to transplant recipients. Creative approaches to immunosuppression that exploit the recipient's own alloregulatory mechanisms could reduce the need for pharmacologic immunosuppression and potentially induce immune tolerance. In the process of studying recipient derived myeloid derived suppressor cells (MDSCs), we identified key alloregulatory MDSC mechanisms, mediated by isolatable proteins IL-4, IL-34, and IL-10. We sought to purify these proteins and fuse them for subsequent infusion into transplant recipients as a means of inducing an alloregulatory response. In this introductory investigation, we leveraged molecular engineering technology to create a fusion protein (FP) of three cytokine coding sequences of IL-4, IL-34, and IL-10 and demonstrated their expressions by Western Blot analysis. Following purification, we tested whether FP IL-4/IL-34/IL-10 (FP1) can protect heart transplant allografts. Injection of FP1 significantly prolonged allogeneic cardiac graft survival in a dose-dependent fashion and the increase of graft survival time exceeded survival attributable to IL-34 alone. In vitro, MDSCs cells were expanded by FP1 treatment. However, FP1 did not directly inhibit T cell proliferation in vitro. In conclusion, newly developed FP1 improves the graft survival in cardiac transplantation mouse model. Significant additional work to optimize FP1 or include other novel proteins could supplement current treatment options for transplant patients.

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.

Design and characterization of a multi-epitope vaccine targeting Chlamydia abortus using immunoinformatics approach

Chlamydiosis is a widespread ailment affecting humans, livestock, and wildlife, caused by C. abortus, a member of the Chlamydia genus. This disease leads to reproductive disorders in bovines and poses a zoonotic risk, resulting in adverse outcomes such as abortion, stillbirths, weak offspring, endometritis, repeat breeding, and perinatal mortality. However, current chlamydiosis vaccines have limitations in terms of safety, efficacy, and stability, necessitating the development of effective and safe alternatives. In this study, our objective was to design a multi-epitope vaccine (MEV) targeting all strains of C. abortus using bioinformatics and immunoinformatics approaches. We identified highly antigenic and non-allergic proteins (yidC, yajC, secY, CAB503, and CAB746) using VaxiJen and AlgPred tools. Physicochemical analyses and secondary structure predictions confirmed protein stability through ProtParam and SOPMA methods. Furthermore, we employed IEDB-AR, NETMHCpan, and ToxinPred2 tools to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, resulting in the identification of conserved epitopes for further analysis. The MEV construct, consisting of 545 amino acids, incorporated the adjuvant Beta defensin-3, along with 9 CTL epitopes and 21 HTL epitopes linked by EAAAK, KK, and AAY linkers. We assessed the safety and immunogenicity of the vaccine through comprehensive evaluations of antigenicity, toxicity, allergenicity, and physicochemical properties. Structural stability and quality were examined using 3D modeling via the ab initio approach with the Robetta platform. Molecular docking analysis explored the compatibility of the MEV with Toll-like receptor 9 (TLR9) using ClusPro, while molecular dynamics simulation with the DESMOND Maestro software predicted the stability and flexibility of the docked complex. Despite promising in silico findings, further wet lab investigations are crucial to validate the safety and efficacy of the MEV. Successful development and validation of this MEV hold significant potential in combatting chlamydiosis in both animal and human populations.Communicated by Ramaswamy H. Sarma.

A comprehensive immunoinformatic analysis of chitin deacetylase's and MP88 for designing multi-epitope vaccines against Cryptococcus neoformans

Cryptococcus neoformans causes life-threatening pneumonia and meningitis and is regarded as one of the leading killers of immunocompromised individuals. There is currently no vaccine against this pathogen. Recently, WHO placed it at the top among the critical priority groups in the fungal priority pathogens to accelerate the development of effective treatments. Numerous studies suggested the potential of subunit vaccines to overcome the challenges associated with live and inactivated whole-cell vaccines. Therefore, this study exploited integrated reverse vaccinology and immunoinformatic approach to construct and characterize multi-epitope vaccines targeting chitin deacetylases (Cda1, Cda2, Cda3) and MP88 of C. neoformans. 4 CTL, 8 HTL and 6 B cell epitopes were fused with different adjuvants and appropriate linkers to design two multi-epitope vaccines (VC1 and VC2). Both chimeric constructs were predicted to be highly antigenic, non-allergenic, non-toxic, soluble and had satisfactory physicochemical properties. Molecular docking and binding free energy calculation revealed strong binding interactions between vaccine constructs and human TLRs (TLR-2 and TLR-4). Classical MD Simulation and Normal mode analysis verified the stability of the vaccine-TLR complex in the biological environment. Codon adaptation, cloning and in silico expression suggested the efficient expression of recombinant vaccine proteins in E. coli. Both candidates also generated robust immune profiles comprising innate, adaptive and humoral immune responses. Taken together, experimental validations of our findings through extensive in vitro and in vivo testing might provide an effective vaccine for prophylactic control of C. neoformans.Communicated by Ramaswamy H. Sarma.

An immunoinformatics and extended molecular dynamics approach for designing a polyvalent vaccine against multiple strains of Human T-lymphotropic virus (HTLV)

Human T-lymphotropic virus (HTLV), a group of retroviruses belonging to the oncovirus family, has long been associated with various inflammatory and immunosuppressive disorders. At present, there is no approved vaccine capable of effectively combating all the highly pathogenic strains of HTLV that makes this group of viruses a potential threat to human health. To combat the devastating impact of any potential future outbreak caused by this virus group, our study employed a reverse vaccinology approach to design a novel polyvalent vaccine targeting the highly virulent subtypes of HTLV. Moreover, we comprehensively analyzed the molecular interactions between the designed vaccine and corresponding Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccine's stability, antigenicity, and immunogenicity. Initially, two vaccine constructs were formulated, and among them, vaccine construct-2 exhibited superior solubility and structural stability. Molecular docking analyses also revealed strong binding affinity between the vaccine construct-2 and both targeted TLR2 and TLR4. Molecular dynamics simulations demonstrated enhanced stability, compactness, and consistent hydrogen bonding within TLR-vaccine complexes, suggesting a strong binding affinity. The stability of the complexes was further corroborated by contact, free energy, structure, and MM-PBSA analyses. Consequently, our research proposes a vaccine targeting multiple HTLV subtypes, offering valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to developing effective preventive and treatment approaches against HTLV-related diseases and preventing possible outbreaks. However, future research should focus on in-depth validation through experimental studies to confirm the interactions identified in silico and to evaluate the vaccine's efficacy in relevant animal models and, eventually, in clinical trials.

Immunoinformatics studies of heat shock proteins 27 and 70: Development of potent therapeutic vaccine constructs against human papillomavirus-related cancers

Heat shock proteins (HSPs) improve cross-presentation of linked tumor antigens, thus they can be exploited in therapeutic vaccine design. Herein, in silico analyses of different vaccine constructs were performed based on human papillomavirus (HPV)-16 E7 protein linked to Homo sapiens/Mus musculus Hsp27 or Hsp70 in multiepitope and whole sequence forms. Then, computational comparison between different orientations of Hsp/E7 was carried out in both forms. Finally, molecular docking was performed between the designed constructs and signaling (TLRs) or endocytic (CD14, LOX-1 and SREC-1) receptors. Our data represented the high-ranked T-cell epitopes and the potential B-cell epitopes of Homo sapiens/Mus musculus Hsp27 and Hsp70. Moreover, molecular docking showed that whole sequence of Hsp27 had better interaction with all receptors than whole sequence of Hsp70 suggesting likely stronger stimulation of innate and adaptive immunity. All designed Homo sapiens/Mus musculus Hsp27/E7 constructs had better docking scores with the endocytic receptors especially SREC-1 than all designed Homo sapiens/Mus musculus Hsp70/E7 constructs in both orientations. Generally, the multiepitope-/whole sequence-based Homo sapiens/Mus musculus Hsp27-E7 fusion constructs showed more conservancy and immunogenicity than other designed constructs. These fusion constructs were non-allergenic, non-toxic and stable suggesting them as promising vaccine candidates against HPV-related cancers.

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.

Modeling of MT. P495, an mRNA-based vaccine against the phosphate-binding protein PstS1 of Mycobacterium tuberculosis

Tuberculosis (TB) is a contagious disease that predominantly affects the lungs, but can also spread to other organs via the bloodstream. TB affects about one-fourth population of the world. With age, the effectiveness of Bacillus Calmette-Guérin (BCG), the only authorized TB vaccine, decreases. In the quest for a prophylactic and immunotherapeutic vaccine, in this study, a hypothetical mRNA vaccine is delineated, named MT. P495, implementing in silico and immunoinformatics approaches to evaluate key aspects and immunogenic epitopes across the PstS1, a highly conserved periplasmic protein of Mycobacterium tuberculosis (Mtb). PstS1 elicited the potential to generate 99.9% population coverage worldwide. The presence of T- and B-cell epitopes across the PstS1 protein were validated using several computational prediction tools. Molecular docking and dynamics simulation confirmed stable epitope-allele interaction. Immune cell response to the antigen clearance rate was verified by the in silico analysis of immune simulation. Codon optimization confirmed the efficient translation of the mRNA in the host cell. With Toll-like receptors, the vaccine exhibited stable and strong interactions. Findings suggest that the MT. P495 vaccine probably will elicit specific immune responses against Mtb. This mRNA vaccine model is a ready source for further wet-lab validation to confirm the efficacy of this proposed vaccine candidate.

Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics

In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.

Meta-IL4: An Ensemble Learning Approach for IL-4-Inducing Peptide Prediction

The cytokine interleukin-4 (IL-4) plays an important role in our immune system. IL-4 leads the way in the differentiation of naïve T-helper 0 cells (Th0) to T-helper 2 cells (Th2). The Th2 responses are characterized by the release of IL-4. CD4+ T cells produce the cytokine IL-4 in response to exogenous parasites. IL-4 has a critical role in the growth of CD8+ cells, inflammation, and responses of T-cells. We propose an ensemble model for the prediction of IL-4 inducing peptides. Four feature encodings were extracted to build an efficient predictor: pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, quasi-sequence-order, and Shannon entropy. We developed an ensemble learning model fusion of random forest, extreme gradient boost, light gradient boosting machine, and extra tree classifier in the first layer, and a Gaussian process classifier as a meta classifier in the second layer. The outcome of the benchmarking testing dataset, with a Matthews correlation coefficient of 0.793, showed that the meta-model (Meta-IL4) outperformed individual classifiers. The highest accuracy achieved by the Meta-IL4 model is 90.70%. These findings suggest that peptides that induce IL-4 can be predicted with reasonable accuracy. These models could aid in the development of peptides that trigger the appropriate Th2 response.

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

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

A computational approach to design a polyvalent vaccine against human respiratory syncytial virus

Human Respiratory Syncytial Virus (RSV) is one of the leading causes of lower respiratory tract infections (LRTI), responsible for infecting people from all age groups-a majority of which comprises infants and children. Primarily, severe RSV infections are accountable for multitudes of deaths worldwide, predominantly of children, every year. Despite several efforts to develop a vaccine against RSV as a potential countermeasure, there has been no approved or licensed vaccine available yet, to control the RSV infection effectively. Therefore, through the utilization of immunoinformatics tools, a computational approach was taken in this study, to design a multi-epitope polyvalent vaccine against two major antigenic subtypes of RSV, RSV-A and RSV-B. Potential predictions of the T-cell and B-cell epitopes were followed by extensive tests of antigenicity, allergenicity, toxicity, conservancy, homology to human proteome, transmembrane topology, and cytokine-inducing ability. The peptide vaccine was modeled, refined, and validated. Molecular docking analysis with specific Toll-like receptors (TLRs) revealed excellent interactions with suitable global binding energies. Additionally, molecular dynamics (MD) simulation ensured the stability of the docking interactions between the vaccine and TLRs. Mechanistic approaches to imitate and predict the potential immune response generated by the administration of vaccines were determined through immune simulations. Subsequent mass production of the vaccine peptide was evaluated; however, there remains a necessity for further in vitro and in vivo experiments to validate its efficacy against RSV infections.

Development of a Universal Multi-Epitope Vaccine Candidate against Streptococcus suis Infections Using Immunoinformatics Approaches

Streptococcus suis is a significant zoonotic pathogen that is a great threat not only to the swine industry but also to human health, causing arthritis, meningitis, and even streptococcal toxic shock-like syndrome. Owing to its many serotypes and high geographic variability, an efficacious cross-protective S. suis vaccine is not readily available. Therefore, this study aimed to design a universal multi-epitope vaccine (MVHP6) that involved three highly immunogenic proteins of S. suis, namely, the surface antigen containing a glycosaminoglycan binding domain (HP0197), endopeptidase (PepO), and 6-phosphogluconate dehydrogenase (6PGD). Forecasted T-cell and B-cell epitopes with high antigenic properties and a suitable adjuvant were linked to construct a multi-epitope vaccine. In silico analysis showed that the selected epitopes were conserved in highly susceptible serotypes for humans. Thereafter, we evaluated the different parameters of MVHP6 and showed that MVHP6 was highly antigenic, non-toxic, and non-allergenic. To verify whether the vaccine could display appropriate epitopes and maintain high stability, the MVHP6 tertiary structure was modeled, refined, and validated. Molecular docking studies revealed a strong binding interaction between the vaccine and the toll-like receptor (TLR4), whereas molecular dynamics simulations demonstrated the vaccine's compatibility, binding stability, and structural compactness. Moreover, the in silico analysis showed that MVHP6 could evoke strong immune responses and enable worldwide population coverage. Moreover, MVHP6 was cloned into the pET28a (+) vector in silico to ensure the credibility, validation, and proper expression of the vaccine construct. The findings suggested that the proposed multi-epitope vaccine can provide cross-protection against S. suis infections.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports that immunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomics discipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.

A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

Interleukin-5 (IL-5) can act as an enticing therapeutic target due to its pivotal role in several eosinophil-mediated diseases. The aim of this study is to develop a model for predicting IL-5 inducing antigenic regions in a protein with high precision. All models in this study have been trained, tested and validated on experimentally validated 1907 IL-5 inducing and 7759 non-IL-5 inducing peptides obtained from IEDB. Our primary analysis indicates that IL-5 inducing peptides are dominated by certain residues like Ile, Asn, and Tyr. It was also observed that binders of a wide range of HLA alleles can induce IL-5. Initially, alignment-based methods have been developed using similarity and motif search. These alignment-based methods provide high precision but poor coverage. In order to overcome this limitation, we explore alignment-free methods which are mainly machine learning-based models. Firstly, models have been developed using binary profiles and eXtreme Gradient Boosting-based model achieved a maximum AUC of 0.59. Secondly, composition-based models have been developed and our dipeptide-based random forest model achieved a maximum AUC of 0.74. Thirdly, random forest model developed using selected 250 dipeptides and achieved AUC 0.75 and MCC 0.29 on validation dataset; best among alignment-free models. In order to improve the performance, we developed an ensemble or hybrid method that combined alignment-based and alignment-free methods. Our hybrid method achieved AUC 0.94 with MCC 0.60 on a validation/independent dataset. The best hybrid model developed in this study has been incorporated into the user-friendly web server and a standalone package named 'IL5pred' (https://webs.iiitd.edu.in/raghava/il5pred/).

Construction and validation of a multi-epitope in silico vaccine model for lymphatic filariasis by targeting Brugia malayi: a reverse vaccinology approach

BACKGROUND

Lymphatic filariasis (LF), often referred to as elephantiasis, has been identified as one of the 17 neglected tropical diseases by the World Health Organization. Currently, there are no vaccines available to treat this infection in humans. Therefore, with the objective of devising a novel preventive measure, we exploited an immunoinformatics approach to design a multi-epitope-based subunit vaccine for LF, that can elicit a variety of immune responses within the host. In this study, different B cell, T_C cell, and T_H cell-binding epitopes were screened from the antigenic proteins of Brugia malayi and they were passed through several immunological filters to determine the optimal epitopes.

RESULTS

As a result, 15 CD8+, 3 CD4+, and 3 B cell epitopes were found to be prominent, antigenic, non-toxic, immunogenic and non-allergenic. The presence of conformational B cell epitopes and cytokine-inducing epitopes confirmed the humoral and cell-mediated immune response that would be triggered by the constructed vaccine model. Following that, the selected epitopes and TLR-4-specific adjuvant were ligated by appropriate peptide linkers to finalize the vaccine construct. Protein-protein docking of the vaccine structure with the TLR4 receptor predicted strong binding affinity and hence putatively confirms its ability to elicit an immune response. Further, the efficiency of the vaccine candidate to provide a long-lasting protective immunity was assessed by in silico immune simulation. The reverse translated vaccine sequence was also virtually cloned in the pET28a (+) plasmid after the optimization of the gene sequence.

CONCLUSION

So taken together, by monitoring the overall in silico assessment, we hypothesize that our engineered peptide vaccine could be a viable prophylactic approach in the development of vaccines against the threat of human lymphatic filariasis.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s42269-023-01013-0.

In silico designed mRNA vaccines targeting CA-125 neoantigen in breast and ovarian cancer

Somatic mutation-derived neoantigens are associated with patient survival in breast and ovarian cancer. These neoantigens are targets for cancer, as shown by the implementation of neoepitope peptides as cancer vaccines. The success of cost-effective multi-epitope mRNA vaccines against SARS-Cov-2 in the pandemic established a model for reverse vaccinology. In this study, we aimed to develop an in silico pipeline designing an mRNA vaccine of the CA-125 neoantigen against breast and ovarian cancer, respectively. Using immuno-bioinformatics tools, we predicted cytotoxic CD8+ T cell epitopes based on somatic mutation-driven neoantigens of CA-125 in breast or ovarian cancer, constructed a self-adjuvant mRNA vaccine with CD40L and MHC-I -targeting domain to enhance cross-presentation of neoepitopes by dendritic cells. With an in silico ImmSim algorithm, we estimated the immune responses post-immunization, showing IFN-γ and CD8+ T cell response. The strategy described in this study may be scaled up and implemented to design precision multi-epitope mRNA vaccines by targeting multiple neoantigens.

In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: an immunoinformatics-based approach

CONTEXT

Leishmaniasis is a group of vector-borne infectious diseases caused by over 20 pathogenic Leishmania species that are endemic in many tropical and subtropical countries. The emergence of drug-resistant strains, the adverse side effects of anti-Leishmania drugs, and the absence of a preventative vaccination strategy threaten the sensitive population. Recently, many groups of researchers have exploited the field of reverse vaccinology to develop vaccines, focusing chiefly on inducing immunity against either visceral or cutaneous leishmaniasis.

METHODS

This present work involves retrieving twelve experimentally validated leishmanial antigenic protein sequences from the UniProt database, followed by their antigenicity profiling employing ANTIGENpro and Vaxijen 2.0 servers. MHC-binding epitopes for the same were predicted using both NetCTL 1.2 and SYFPEITHI servers, while epitopes for B cell were computed using ABCpred and BepiPred 2.0 servers. The screened epitopes with significantly higher scores were utilized for designing the vaccine construct with appropriate linkers and natural adjuvant. The secondary and tertiary structures of the synthetic peptide were determined by conditional random fields, shallow neural networks, and profile-profile threading alignment with iterative structure assembly simulations, respectively. The 3-D vaccine model was validated through CASP10-tested refinement and the MolProbity web server. Molecular docking and multi-scale normal mode analysis simulation were performed to analyze the best vaccine-TLR complex. Finally, computational immune simulation findings revealed promising cellular and humoral immune responses, suggesting that the engineered chimeric peptide is a potential broad-spectrum vaccine against visceral and cutaneous leishmaniasis.

Potential of Onchocerca ochengi inosine-5'-monophosphate dehydrogenase (IMPDH) and guanosine-5'-monophosphate oxidoreductase (GMPR) as druggable and vaccine candidates: immunoinformatics screening

Onchocerciasis is a vector-borne disease caused by the filarial nematode Onchocerca volvulus, which is responsible for most of the visual impairments recorded in Africa, Asia and the Americas. It is known that O. volvulus has similar molecular and biological characteristics as Onchocerca ochengi in cattle. This study was designed to screen for immunogenic epitopes and binding pockets of O. ochengi IMPDH and GMPR ligands using immunoinformatic approaches. In this study, a total of 23 B cell epitopes for IMPDH and 7 B cell epitopes for GMPR were predicted using ABCpred tool, Bepipred 2.0 and Kolaskar and Tongaonkar methods. The CD4+ Th computational results showed 16 antigenic epitopes from IMPDH with strong binding affinity for DRB1_0301, DRB3_0101, DRB1_0103 and DRB1_1501 MHC II alleles while 8 antigenic epitopes from GMPR were predicted to bind DRB1_0101 and DRB1_0401 MHC II alleles, respectively. For the CD8+ CTLs analysis, 8 antigenic epitopes from IMPDH showed strong binding affinity to human leukocyte antigen HLA-A*26:01, HLA-A*03:01, HLA-A*24:02 and HLA-A*01:01 MHC I alleles while 2 antigenic epitopes from GMPR showed strong binding affinity to HLA-A*01:01 allele, respectively. The immunogenic B cell and T cell epitopes were further evaluated for antigenicity, non-alllergernicity, toxicity, IFN-gamma, IL4 and IL10. The docking score revealed favorable binding free energy with IMP and MYD scoring the highest binding affinity at -6.6 kcal/mol with IMPDH and -8.3 kcal/mol with GMPR. This study provides valuable insight on IMPDH and GMPR as potential drug targets and for the development of multiple epitope vaccine candidates.Communicated by Ramaswamy H. Sarma.

In silico design of a polypeptide as a vaccine candidate against ascariasis

Ascariasis is the most prevalent zoonotic helminthic disease worldwide, and is responsible for nutritional deficiencies, particularly hindering the physical and neurological development of children. The appearance of anthelmintic resistance in Ascaris is a risk for the target of eliminating ascariasis as a public health problem by 2030 set by the World Health Organisation. The development of a vaccine could be key to achieving this target. Here we have applied an in silico approach to design a multi-epitope polypeptide that contains T-cell and B-cell epitopes of reported novel potential vaccination targets, alongside epitopes from established vaccination candidates. An artificial toll-like receptor-4 (TLR4) adjuvant (RS09) was added to improve immunogenicity. The constructed peptide was found to be non-allergic, non-toxic, with adequate antigenic and physicochemical characteristics, such as solubility and potential expression in Escherichia coli. A tertiary structure of the polypeptide was used to predict the presence of discontinuous B-cell epitopes and to confirm the molecular binding stability with TLR2 and TLR4 molecules. Immune simulations predicted an increase in B-cell and T-cell immune response after injection. This polypeptide can now be validated experimentally and compared to other vaccine candidates to assess its possible impact in human health.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Bioinformatics and immunoinformatics approach to develop potent multi-peptide vaccine for coxsackievirus B3 capable of eliciting cellular and humoral immune response

Coxsackievirus B3 (CVB3) is a viral pathogen of various human disorders with no effective preventative interventions. Herein, we aimed to design a chimeric vaccine construct for CVB3 using reverse vaccinology and immunoinformatics approaches by screening the whole viral polyprotein sequence. Firstly, screening and mapping of viral polyprotein to predict 21 immunodominant epitopes (B-cell, CD8+ and CD4+ T-cell epitopes), fused with an adjuvant (Resuscitation-promoting factor), appropriate linkers, HIV-TAT peptide, Pan DR epitope, and 6His-tag to assemble a multi-epitope vaccine construct. The chimeric construct is predicted as probable antigen, non-allergen, stable, possess encouraging physicochemical features, and indicates a broader population coverage (98 %). The tertiary structure of the constructed vaccine was predicted and refined, and its interaction with the Toll-like receptor 4 (TLR4) was investigated through molecular docking and dynamics simulation. Computational cloning of the construct was carried out in pET28a (+) plasmid to guarantee the higher expression of the vaccine protein. Lastly, in silico immune simulation foreseen that humoral and cellular immune responses would be elicited in response to the administration of such a potent chimeric construct. Thus, the design constructed could vaccinate against CVB3 infection and various CVB serotypes. However, further in vitro/in vivo research must assess its safety and effectiveness.

Employing an immunoinformatics approach revealed potent multi-epitope based subunit vaccine for lymphocytic choriomeningitis virus

BACKGROUND

Lymphocytic choriomeningitis virus (LCMV) infects many individuals worldwide and causes severe infection in the immunosuppressant recipient, spontaneous abortion, and congenital disabilities in infants.

OBJECTIVES

There is no specific vaccine or therapeutics available to protect against LCMV infection; thus, there is a need to design a potential vaccine to combat the virus by developing immunity in the population. Herein, we attempted to design a potent multi-epitope vaccine for LCMV using immunoinformatics methods.

METHODS

The whole proteome of the virus was screened and mapped to extract immunodominant B-cell and T-cell epitopes which were fused with appropriate linkers (EAAAK, GGGS, AAY, GPGPG, and AAY), PADRE sequence (13aa) and an adjuvant (50 S ribosomal protein L7/L12) to formulate a multi-epitope vaccine ensemble. Codon adaptation and in silico cloning of the constructed vaccine were carried out using bioinformatics tools. The secondary and tertiary structure of the vaccine construct was predicted and refined. The physicochemical profile of the designed vaccine was analyzed, and the multi-epitope vaccine's potential to bind Toll-like receptors (TLR2 and TLR4) was evaluated through molecular docking and molecular dynamics simulations. Computational immune simulation of the designed vaccine antigen was performed using the C-ImmSim server.

RESULTS

The designed multi-epitope-based vaccine (613 aa) comprised 26 immunodominant (six B-cell, nine cytotoxic T lymphocytes, and 11 helper T lymphocytes) epitopes and is predicted antigenic, non-toxic, non-allergen, soluble, and topographically accessible with a suitable physicochemical profile. The designed vaccine is expected to cover a broad worldwide population (96.35 %) and stimulate a robust adaptive immune response against the virus upon administration. In silico cloning of the constructed vaccine in PET28a (+) vector ensured its optimal expression in the Escherichia coli system. Molecular docking, molecular dynamics simulation, and binding free energy estimation collectively support the stability and energetically favourable interaction of the modeled vaccine-TLR2/4 complexes.

CONCLUSION

The designed multi-epitope vaccine in the present study could serve as a potential vaccine candidate to protect against LMCV infection; however, the experimental validation and safety testing of the vaccine is warranted to validate the study's outcomes.