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

Unlocking Hope: Paving the Way for a Cutting-Edge Multi-Epitope Dengue Virus Vaccine

Dengue fever is a significant health issue in Pakistan, demanding a vaccine effective against all the viral strains. This study employs reverse vaccinology to develop potential dengue vaccine candidates (DVAX I-III). The study thoroughly examined conserved areas of dengue virus serotypes 1-4's structural and non-structural proteins. Key viral proteins were analyzed to find antigenic peptides, which were incorporated into vaccine candidates and potentiated with adjuvants. Computational methods predicted peptide structures and evaluated their binding to immune receptors TLR 2, TLR 4, HLA *A1101, and DRB*401. A molecular dynamics simulation lasting 100 ns of the DVAX II-TLR4 complex at different time intervals clearly indicated that the ligand is attached to the receptor. Normal mode analysis assessed the stability and flexibility of these interactions. Encouragingly, all three vaccine candidates demonstrated favorable interactions with these immune receptors and the potential to induce a robust immune response. These findings suggest their safety and warrant further in vivo studies to evaluate their efficacy for clinical development.

In Silico Design of a Trans-Amplifying RNA-Based Vaccine against SARS-CoV-2 Structural Proteins

Nucleic acid-based vaccines allow scalable, rapid, and cell-free vaccine production in response to an emerging disease such as the current COVID-19 pandemic. Here, we objected to the design of a multiepitope mRNA vaccine against the structural proteins of SARS-CoV-2. Through an immunoinformatic approach, promising epitopes were predicted for the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. Fragments rich in overlapping epitopes were selected based on binding affinities with HLA classes I and II for the specific presentation to B and T lymphocytes. Two constructs were designed by fusing the fragments in different arrangements via GG linkers. Construct 1 showed better structural properties and interactions with toll-like receptor 2 (TLR-2), TLR-3, and TLR-4 during molecular docking and dynamic simulation. A 50S ribosomal L7/L12 adjuvant was added to its N-terminus to improve stability and immunogenicity. The final RNA sequence was used to design a trans-amplifying RNA (taRNA) vaccine in a split-vector system. It consists of two molecules: a nonreplicating RNA encoding a trans-acting replicase to amplify the second one, a trans-replicon (TR) RNA encoding the vaccine protein. Overall, the immune response simulation detected that activated B and T lymphocytes and increased memory cell formation. Macrophages and dendritic cells proliferated continuously, and IFN-γ and cytokines like IL-2 were released highly.

A novel multi-epitope peptide vaccine targeting immunogenic antigens of Ebola and monkeypox viruses with potential of immune responses provocation in silico

The emergence or reemergence of monkeypox (Mpox) and Ebola virus (EBOV) agents causing zoonotic diseases remains a huge threat to human health. Our study aimed at designing a multi-epitope vaccine (MEV) candidate to target both the Mpox and EBOV agents using immunoinformatics tools. Viral protein sequences were retrieved, and potential nonallergenic, nontoxic, and antigenic epitopes were obtained. Next, cytotoxic and helper T-cell (CTL and HTL, respectively) and B-cell (BCL) epitopes were predicted, and those potential epitopes were fused utilizing proper linkers. The in silico cloning and expression processes were implemented using Escherichia coli K12. The immune responses were prognosticated using the C-ImmSim server. The MEV construct (29.53 kDa) included four BCL, two CTL, and four HTL epitopes and adjuvant. The MEV traits were pertinent in terms of antigenicity, non-allergenicity, nontoxicity, physicochemical characters, and stability. The MEV candidate was also highly expressed in E. coli K12. The strong affinity of MEV-TLR3 was confirmed using molecular docking and molecular dynamics simulation analyses. Immune simulation analyses unraveled durable activation and responses of cellular and humoral arms alongside innate immune responses. The designed MEV candidate demonstrated appropriate traits and was promising in the prediction of immune responses against both Mpox and EBOV agents. Further experimental assessments of the MEV are required to verify its efficacy.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Design of a novel multi-epitope vaccine against Marburg virus using immunoinformatics studies

Marburg virus (MARV) is a highly contagious and virulent agent belonging to Filoviridae family. MARV causes severe hemorrhagic fever in humans and non-human primates. Owing to its highly virulent nature, preventive approaches are promising for its control. There is currently no approved drug or vaccine against MARV, and management mainly involves supportive care to treat symptoms and prevent complications. Our aim was to design a novel multi-epitope vaccine (MEV) against MARV using immunoinformatics studies. In this study, various proteins (VP35, VP40 and glycoprotein precursor) were used and potential epitopes were selected. CTL and HTL epitopes covered 79.44% and 70.55% of the global population, respectively. The designed MEV construct was stable and expressed in Escherichia coli (E. coli) host. The physicochemical properties were also acceptable. MARV MEV candidate could predict comprehensive immune responses such as those of humoral and cellular in silico. Additionally, efficient interaction to toll-like receptor 3 (TLR3) and its agonist (β-defensin) was predicted. There is a need for validation of these results using further in vitro and in vivo studies.

In silico identification of chikungunya virus replication inhibitor validated using biochemical and cell-based approaches

Discovering an alternative therapy with a long-lasting effect on symptoms caused by chikungunya virus (CHIKV) infection is prompted by the lack of a vaccine and the absence of safe, effective and non-toxic medications. One potential strategy is synthesizing or identifying small compounds that can specifically target the active site of an essential enzyme and prevent virus replication. Previous site-directed mutagenesis studies have demonstrated the crucial role of the macrodomain, which is a part of non-structural protein 3 (nsP3), in virus replication. Exploiting this fact, the macrodomain can be targeted to discover a natural substance that can inhibit its function and thereby impede virus replication. With this aim, the present study focused on potential CHIKV nsP3 macrodomain (nsP3MD) inhibitors through in silico, in vitro and cell-based methods. Through virtual screening of the natural compound library, nine nsP3MD inhibitors were initially identified. Molecular dynamics (MD) simulations were employed to evaluate these nine compounds based on the stability of their ligand-receptor complexes and energy parameters. Target analysis and ADMET (i.e. absorption, distribution, metabolism, excretion and toxicity) prediction of the selected compounds revealed their drug-like characteristics. Subsequent in vitro investigation allowed us to narrow the selection down to one compound, N-[2-(5-methoxy-1H-indol-3-yl) ethyl]-2-oxo-1,2-dihydroquinoline-4-carboxamide, which exhibited potent inhibition of CHIKV growth. This molecule effectively inhibited CHIKV replication in the stable embryonal rhabdomyosarcoma cell line capable of producing CHIKV. Our findings demonstrate that the selected compound possesses substantial anti-CHIKV nsP3MD activity both in vitro and in vivo. This work provides a promising molecule for further preclinical studies to develop a potential drug against the CHIKV.

Designing a multi-epitope vaccine against Shigella dysenteriae using immuno-informatics approach

Shigella dysenteriae has been recognized as the second most prevalent pathogen associated with diarrhea that contains blood, contributing to 12.9% of reported cases, and it is additionally responsible for approximately 200,000 deaths each year. Currently, there is no S. dysenteriae licensed vaccine. Multidrug resistance in all Shigella spp. is a growing concern. Current vaccines, such as O-polysaccharide (OPS) conjugates, are in clinical trials but are ineffective in children but protective in adults. Thus, innovative treatments and vaccines are needed to combat antibiotic resistance. In this study, we used immuno-informatics to design a new multiepitope vaccine and identified S. dysenteriae strain SD197's membrane protein targets using in-silico methods. The target protein was prioritized using membrane protein topology analysis to find membrane proteins. B and T-cell epitopes were predicted for vaccine formulation. The epitopes were shortlisted based on an IC50 value <50, antigenicity, allergenicity, and a toxicity analysis. In the final vaccine construct, a total of 8 B-cell epitopes, 12 MHC Class I epitopes, and 7 MHC Class II epitopes were identified for the Lipopolysaccharide export system permease protein LptF. Additionally, 17 MHC Class I epitopes and 14 MHC Class II epitopes were predicted for the Lipoprotein-releasing ABC transporter permease subunit LolE. These epitopes were selected and linked via KK, AAY, and GGGS linkers, respectively. To enhance the immunogenic response, RGD (arginine-glycine-aspartate) adjuvant was incorporated into the final vaccine construct. The refined vaccine structure exhibits a Ramachandran score of 91.5% and demonstrates stable interaction with TLR4. Normal Mode Analysis (NMA) reveals low eigenvalues (3.925996e-07), indicating steady and flexible molecular mobility of docked complexes. Codon optimization was carried out in an effective microbial expression system of the Escherichia coli K12 strain using the recombinant plasmid pET-28a (+). Finally, the entire in-silico analysis suggests that the suggested vaccine may induce a significant immune response against S. dysenteriae, making it a promising option for additional experimental trials.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

Designing a Novel Multiepitope Vaccine from the Human Papilloma Virus E1 and E2 Proteins for Indonesia with Immunoinformatics and Molecular Dynamics Approaches

One of the deadliest malignant cancer in women globally is cervical cancer. Specifically, cervical cancer is the second most common type of cancer in Indonesia. The main infectious agent of cervical cancer is the human papilloma virus (HPV). Although licensed prophylactic vaccines are available, cervical cancer cases are on the rise. Therapy using multiepitope-based vaccines is a very promising therapy for cervical cancer. This study aimed to develop a multiepitope vaccine based on the E1 and E2 proteins of HPV 16, 18, 45, and 52 using in silico. In this study, we develop a novel multiepitope vaccine candidate using an immunoinformatic approach. We predicted the epitopes of the cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) and evaluated their immunogenic properties. Population coverage analysis of qualified epitopes was conducted to determine the successful use of the vaccine worldwide. The epitopes were constructed into a multiepitope vaccine by using AAY linkers between the CTL epitopes and GPGPG linkers between the HTL epitopes. The tertiary structure of the multiepitope vaccine was modeled with AlphaFold and was evaluated by Prosa-web. The results of vaccine construction were analyzed for B-cell epitope prediction, molecular docking with Toll like receptor-4 (TLR4), and molecular dynamics simulation. The results of epitope prediction obtained 4 CTL epitopes and 7 HTL epitopes that are eligible for construction of multiepitope vaccines. Prediction of the physicochemical properties of multiepitope vaccines obtained good results for recombinant protein production. The interaction showed that the interaction of the multiepitope vaccine-TLR4 complex is stable based on the binding free energy value -106.5 kcal/mol. The results of the immune response simulation show that multiepitope vaccine candidates could activate the adaptive and humoral immune systems and generate long-term B-cell memory. According to these results, the development of a multiepitope vaccine with a reverse vaccinology approach is a breakthrough to develop potential cervical cancer therapeutic vaccines.

In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model

Background

In-silico analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.

Objective

To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online in-silico (IS) tools with AURKA gene as a model.

Materials and Methods

We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using AURKA gene as model. We predicted a protein model and analyzed 209 out of 64,369 AURKA variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.

Results

Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.

Conclusion

This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.

Protective Efficacy Induced by the Common Eimeria Antigen Elongation Factor 2 against Challenge with Three Eimeria Species in Chickens

Avian coccidiosis arises from co-infection involving multiple Eimeria species, which could give rise to substantial economic losses in the global poultry industry. As a result, multivalent anticoccidial vaccines containing common Eimeria antigens offer considerable promise for controlling co-infection in clinical practice. In our previous study, Elongation factor 2 (EF2) was deemed as an immunogenic common antigen across various Eimeria species. This current investigation aimed to further assess the immunogenicity and protective efficacy of EF2 in recombinant subunit vaccine format against three Eimeria species. The EF2 gene cloned from Eimeria maxima (E. maxima) cDNA was designated as EF2 of E. maxima (EmEF2). The immunogenicity of the recombinant protein EmEF2 (rEmEF2) was assessed through Western blot analysis. The evaluation of the vaccine-induced immune response encompassed the determination of T lymphocyte subset proportions, cytokine mRNA transcription levels, and specific IgY concentrations in rEmEF2-vaccinated chickens using flow cytometry, quantitative real-time PCR (qPCR), and indirect enzyme-linked immunosorbent assay (ELISA). Subsequently, the protective efficacy of rEmEF2 was evaluated through vaccination and challenge experiments. The findings demonstrated that rEmEF2 was effectively recognized by the His-tag monoclonal antibody and E. maxima chicken antiserum. Vaccination with rEmEF2 increased the proportions of CD4+ and CD8+ T lymphocytes, elevated IL-4 and IFN-γ mRNA transcription levels, and enhanced IgY antibody levels compared to the control groups. Moreover, compared to the control groups, vaccination with rEmEF2 led to decreased weight loss, reduced oocyst outputs, and alleviated enteric lesions. Furthermore, in the rEmEF2-immunized groups, challenges with E. maxima and E. acervulina resulted in anticoccidial index (ACI) scores of 166.35 and 185.08, showing moderate-to-excellent protective efficacy. Nevertheless, challenges with E. tenella and mixed Eimeria resulted in ACI scores of 144.01 and 127.94, showing low protective efficacy. In conclusion, EmEF2, a common antigen across Eimeria species, demonstrated the capacity to induce a significant cellular and humoral immune response, as well as partial protection against E. maxima, E. acervulina, and E. tenella. These results highlight EmEF2 as a promising candidate antigen for the development of multivalent vaccines targeting mixed infections by Eimeria species.

An updated review on oral protein-based antigen vaccines efficiency and delivery approaches: a special attention to infectious diseases

Various infectious agents affect human health via the oral entrance. The majority of pathogens lack approved vaccines. Oral vaccination is a convenient, safe and cost-effective approach with the potential of provoking mucosal and systemic immunity and maintaining individual satisfaction. However, vaccines should overcome the intricate environment of the gastrointestinal tract (GIT). Oral protein-based antigen vaccines (OPAVs) are easier to administer than injectable vaccines and do not require trained healthcare professionals. Additionally, the risk of needle-related injuries, pain, and discomfort is eliminated. However, OPAVs stability at environmental and GIT conditions should be considered to enhance their stability and facilitate their transport and storage. These vaccines elicit the local immunity, protecting GIT, genital tract and respiratory epithelial surfaces, where numerous pathogens penetrate the body. OPAVs can also be manipulated (such as using specific incorporated ligand and receptors) to elicit targeted immune response. However, low bioavailability of OPAVs necessitates development of proper protein carriers and formulations to enhance their stability and efficacy. There are several strategies to improve their efficacy or protective effects, such as incorporation of adjuvants, enzyme inhibitors, mucoadhesive or penetrating devices and permeation enhancers. Hence, efficient delivery of OPAVs into GIT require proper delivery systems mainly including smart target systems, probiotics, muco-adhesive carriers, lipid- and plant-based delivery systems and nano- and microparticles.