In-silico evaluation of adenoviral COVID-19 vaccination protocols: Assessment of immunological memory up to 6 months after the third dose

Computer Integrated Dominant Epitopes Evoke Protective Immune Response Against Streptococcus pneumoniae

Streptococcus pneumoniae is a gram-positive bacterium responsible for various diseases like pneumonia, acute otitis media, sinusitis, meningitis and bacteraemia. These diseases cause a significant amount of morbidity and mortality. Although polysaccharide vaccines are available, the protection provided by these vaccines is serotype-dependent and not enough in sensitive populations like children and older people. Designing a subunit vaccine by using proteins that are responsible for the pathogenesis of diseases can provide better protection against bacterial infections. In this study, we present the design of a novel multi-epitope vaccine against Streptococcus pneumoniae using an immunoinformatic approach. More than 1170 epitopes were identified against B cells, cytotoxic T lymphocytes and helper T lymphocytes from more than 60 pneumococcal proteins. Epitopes were further screened, and potential epitopes were selected for vaccine development. Seven different vaccine combinations that harbour the 15 dominant B-cell, cytotoxic T cell and helper T cell epitopes were evaluated with linker and β-defensin adjuvant to finalise the best vaccine construct. Bioinformatics tools were used to analyse the construct's physicochemical properties, secondary and tertiary structures, allergenicity, antigenicity and immunogenicity. Docking studies with the TLR-4 receptor and molecular dynamics simulations indicated strong binding affinity and stability. In silico immune response simulations predicted robust IgG immune response generation and observed more than 200 000 IgG1 + IgG2 counts per mL. Similarly, cell-mediated immunity was also enhanced by the designed vaccine construct. The construct was codon-optimised and cloned in silico for expression in Escherichia coli. These findings suggest that the construct is a promising candidate for further experimental validation.

Immmunoinformatics-based design of T and B-cell multi-epitope vaccine to combat Borrelia burgdorferi infection

Lyme disease is one of the most common vector-borne infectious diseases globally, partly due to the absence of a vaccine for humans. Hence, in this study, an immunoinformatics method was used to design a multi-epitope vaccine (MEV) against Borrelia burgdorferi. The optimal B- and T-cell epitopes from Borrelia burgdorferi proteins (BmpA and OspC) were joined with the appropriate linkers to construct a MEV. In addition, β-defensin was included as an adjuvant in the vaccine construct. Secondary and tertiary structures of MEV were predicted, refined and validated. The developed vaccine was high antigenicity, non-allergenicity, solubility and stability. The Ramachandran plot, ProSA-web and ERRAT were employed to ensure the final model's authenticity. The immune simulation confirmed acceptable responses of both cellular and humoral immune. The vaccine's binding stability with Toll-like receptor 2 (TLR2) was confirmed using molecular docking and molecular dynamics (MD) simulation. Furthermore, MEV effectively stimulated high-level antibody production in mice, significantly promoted splenocyte proliferation in immunized mice, and markedly enhanced splenic IFN-γand IL-4 mRNA transcription levels. These results suggest that MEV, as a novel vaccine candidate, holds significant potential for future prevention and control of Borrelia burgdorferi infections.

Whole proteome-integrated and vaccinomics-based next generation mRNA vaccine design against Pseudomonas aeruginosa-A hierarchical subtractive proteomics approach

Pseudomonas aeruginosa (P. aeruginosa) is a multidrug-resistant opportunistic pathogen responsible for chronic obstructive pulmonary disease (COPD), cystic fibrosis, and ventilator-associated pneumonia (VAP), leading to cancer. Developing an efficacious vaccine remains the most promising strategy for combating P. aeruginosa infections. In this study, we employed an advanced in silico strategy to design a highly efficient and stable mRNA vaccine using immunoinformatics tools. Whole proteome data were utilized to identify highly immunogenic vaccine candidates using subtractive proteomics. Three extracellular proteins were prioritized for T- and linear B-cell epitope prediction. Beta-definsin protein sequence was incorporated as an adjuvant at the N-terminus of the construct. A total of 3 CTL, 3 HTL, and 3 linear B cell highly immunogenic epitopes were combined using specific linkers to design this multi-peptide construct. The 5' and 3' UTR sequences, Kozak sequence with a stop codon, and signal peptides followed by a poly-A tail were incorporated into the above vaccine construct to create our final mRNA vaccine. The vaccines exhibited antigenicity scores >0.88, ensuring high antigenicity with no allergenic or toxic. Physiochemical properties analysis revealed high solubility and thermostability. Three-dimensional structural analysis determined high-quality structures. Vaccine-receptor docking and molecular dynamic simulations demonstrated strong molecular interactions, stable binding affinities, dynamic nature, and structural stability of this vaccine, with significant immunogenic responses of the immune system against the vaccine. The immunological simulation indicates successful cellular and humoral immune responses to defend against P. aeruginosa infection. Validation of the study outcomes necessitates both experimental and clinical testing.

Designing a multi-epitope vaccine against Pseudomonas aeruginosa via integrating reverse vaccinology with immunoinformatics approaches

Pseudomonas aeruginosa is a typically opportunistic pathogen responsible for a wide range of nosocomial infections. In this study, we designed two multi-epitope vaccines targeting P. aeruginosa proteins, incorporating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B lymphocyte (LBL) epitopes identified using reverse vaccinology and immunoinformatics approaches. The vaccines exhibited favorable physicochemical properties, including stability, solubility, and optimal molecular weight, suggesting their potential as viable candidates for vaccine development. Molecular docking studies revealed strong binding affinity to Toll-like receptors 1 (TLR1) and 2 (TLR2). Furthermore, molecular dynamics simulations confirmed the stability of the vaccine-TLR complexes over time. Immune simulation analyses indicated that the vaccines could induce robust humoral and cellular immune responses, providing a promising new approach for combating P. aeruginosa infections, particularly in the face of increasing antibiotic resistance.

Tuberculosis vaccines and therapeutic drug: challenges and future directions

Tuberculosis (TB) remains a prominent global health challenge, with the World Health Organization documenting over 1 million annual fatalities. Despite the deployment of the Bacille Calmette-Guérin (BCG) vaccine and available therapeutic agents, the escalation of drug-resistant Mycobacterium tuberculosis strains underscores the pressing need for more efficacious vaccines and treatments. This review meticulously maps out the contemporary landscape of TB vaccine development, with a focus on antigen identification, clinical trial progress, and the obstacles and future trajectories in vaccine research. We spotlight innovative approaches, such as multi-antigen vaccines and mRNA technology platforms. Furthermore, the review delves into current TB therapeutics, particularly for multidrug-resistant tuberculosis (MDR-TB), exploring promising agents like bedaquiline (BDQ) and delamanid (DLM), as well as the potential of host-directed therapies. The hurdles in TB vaccine and therapeutic development encompass overcoming antigen diversity, enhancing vaccine effectiveness across diverse populations, and advancing novel vaccine platforms. Future initiatives emphasize combinatorial strategies, the development of anti-TB compounds targeting novel pathways, and personalized medicine for TB treatment and prevention. Despite notable advances, persistent challenges such as diagnostic failures and protracted treatment regimens continue to impede progress. This work aims to steer future research endeavors toward groundbreaking TB vaccines and therapeutic agents, providing crucial insights for enhancing TB prevention and treatment strategies.

Innovative epitopes in Staphylococcal Protein-A an immuno-informatics approach to combat MDR-MRSA infections

Background

Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant challenge in clinical environments due to its resistance to standard antibiotics. Staphylococcal Protein A (SpA), a crucial virulence factor of MRSA, undermines host immune responses, making it an attractive target for vaccine development. This study aimed to identify potential epitopes within SpA that could elicit robust immune responses, ultimately contributing to the combat against multidrug-resistant (MDR) MRSA.

Methods

The SpA protein sequence was retrieved from the UniProt database, with various bioinformatics tools employed for epitope prediction. T-cell epitopes were identified using the Tepitool server, focusing on high-affinity interactions with prevalent human leukocyte antigens (HLAs). B-cell epitopes were predicted using the BepiPred tool. Predicted epitopes underwent docking studies with HLA molecules to evaluate binding properties. In-silico analyses confirmed the antigenicity, promiscuity, and non-glycosylated nature of the selected epitopes.

Results

Several T and B cell epitopes within SpA were identified, showcasing high binding affinities and extensive population coverage. A multi-epitope vaccine construct, linked by synthetic linkers and an adjuvant, was modelled, refined, and validated through various bioinformatics assessments. The vaccine candidate was subsequently docked with Toll-like receptor 4 (TLR-4) to evaluate its potential for immunogenicity.

Conclusion

This study lays the groundwork for developing epitope-based vaccines targeting SpA in MRSA, identifying promising candidates for experimental validation and contributing to innovative immunotherapeutic strategies against MRSA infections.

Immunoinformatics approach: Developing a multi-epitope vaccine with novel carriers targeting monkeypox virus

Since May 2022, the global spread of monkeypox virus (MPXV) has presented a significant threat to public health. Despite this, there are limited preventive measures available. In this study, different computational tools were employed to design a multi-epitope vaccine targeting MPXV. Three key MPXV proteins, M1R, B6R, and F3L, were chosen for epitope selection, guided by bioinformatic analyses to identify immunodominant epitopes for T- and B-cell activation. To enhance immune stimulation and facilitate targeted delivery of the vaccine to specific cells, the selected epitopes were linked to novel carriers, including the extracellular domain of cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), a 12-mer Clec9a binding peptide (CBP-12), and a Toll-like receptor 2 (TLR2) peptide ligand. The designed vaccine construct exhibited strong antigenicity along with nonallergenic and nontoxic properties, with favorable physicochemical characteristics. The validated vaccine's tertiary structure underwent evaluation for interactions with CD80/86, Clec9a, and TLR2 through molecular docking and molecular dynamics simulation. The results ensured the vaccine's stability and high affinity for the aforementioned receptors. In silico immune simulations studies revealed robust innate and adaptive immune responses, including enhanced mucosal immunity essential for protection against MPXV. Ultimately, the DNA sequence of the vaccine construct was synthesized and successfully cloned into the pET-22b(+) vector. Our study, through integration of computational predictions, suggests the proposed vaccine's potential efficacy in safeguarding against MPXV; however, further in vitro and in vivo validations are imperative to assess real-world effectiveness and safety.

Current Progress in the Development of mRNA Vaccines Against Bacterial Infections

Bacterial infections have accompanied humanity for centuries. The discovery of the first antibiotics and the subsequent golden era of their discovery temporarily shifted the balance in this confrontation to the side of humans. Nevertheless, the excessive and improper use of antibacterial drugs and the evolution of bacteria has gotten the better of humans again. Therefore, today, the search for new antibacterial drugs or the development of alternative approaches to the prevention and treatment of bacterial infections is relevant and topical again. Vaccination is one of the most effective strategies for the prevention of bacterial infections. The success of new-generation vaccines, such as mRNA vaccines, in the fight against viral infections has prompted many researchers to design mRNA vaccines against bacterial infections. Nevertheless, the biology of bacteria and their interactions with the host's immunity are much more complex compared to viruses. In this review, we discuss structural features and key mechanisms of evasion of an immune response for nine species of bacterial pathogens against which mRNA vaccines have been developed and tested in animals. We focus on the results of experiments involving the application of mRNA vaccines against various bacterial pathogens in animal models and discuss possible options for improving the vaccines' effectiveness. This is one of the first comprehensive reviews of the use of mRNA vaccines against bacterial infections in vivo to improve our knowledge.

An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri

Proteus penneri (P. penneri) is a bacillus-shaped, gram-negative, facultative anaerobe bacterium that is primarily an invasive pathogen and the etiological agent of several hospital-associated infections. P. penneri strains are naturally resistant to macrolides, amoxicillin, oxacillin, penicillin G, and cephalosporins; in addition, no vaccines are available against these strains. This warrants efforts to propose a theoretical based multi-epitope vaccine construct to prevent pathogen infections. In this research, reverse vaccinology bioinformatics and immunoinformatics approaches were adopted for vaccine target identification and construction of a multi-epitope vaccine. In the first phase, a core proteome dataset of the targeted pathogen was obtained using the NCBI database and subjected to bacterial pan-genome analysis using bacterial pan-genome analysis (BPGA) to predict core protein sequences which were then used to find good vaccine target candidates. This identified two proteins, Hcp family type VI secretion system effector and superoxide dismutase family protein, as promising vaccine targets. Afterward using the IEDB database, different B-cell and T-cell epitopes were predicted. A set of four epitopes "KGSVNVQDRE, NTGKLTGTR, IIHSDSWNER, and KDGKPVPALK" were chosen for the development of a multi-epitope vaccine construct. A 183 amino acid long vaccine design was built along with "EAAAK" and "GPGPG" linkers and a cholera toxin B-subunit adjuvant. The designed vaccine model comprised immunodominant, non-toxic, non-allergenic, and physicochemical stable epitopes. The model vaccine was docked with MHC-I, MHC-II, and TLR-4 immune cell receptors using the Cluspro2.0 web server. The binding energy score of the vaccine was - 654.7 kcal/mol for MHC-I, - 738.4 kcal/mol for MHC-II, and - 695.0 kcal/mol for TLR-4. A molecular dynamic simulation was done using AMBER v20 package for dynamic behavior in nanoseconds. Additionally, MM-PBSA binding free energy analysis was done to test intermolecular binding interactions between docked molecules. The MM-GBSA net binding energy score was - 148.00 kcal/mol, - 118.00 kcal/mol, and - 127.00 kcal/mol for vaccine with TLR-4, MHC-I, and MHC-II, respectively. Overall, these in silico-based predictions indicated that the vaccine is highly promising in terms of developing protective immunity against P. penneri. However, additional experimental validation is required to unveil the real immune response to the designed vaccine.

Designing a multi-epitope subunit vaccine against Orf virus using molecular docking and molecular dynamics

Orf virus (ORFV) is an acute contact, epitheliotropic, zoonotic, and double-stranded DNA virus that causes significant economic losses in the livestock industry. The objective of this study is to design an immunoinformatics-based multi-epitope subunit vaccine against ORFV. Various immunodominant cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL), and B-cell epitopes from the B2L, F1L, and 080 protein of ORFV were selected and linked by short connectors to construct a multi-epitope subunit vaccine. Immunogenicity was enhanced by adding an adjuvant β-defensin to the N-terminal of the vaccine using the EAAAK linker. The vaccine exhibited a significant degree of antigenicity and solubility, without allergenicity or toxicity. The 3D formation of the vaccine was subsequently anticipated, improved, and verified. The optimized model exhibited a lower Z-score of -4.33, indicating higher quality. Molecular docking results demonstrated that the vaccine strongly binds to TLR2 and TLR4. Molecular dynamics results indicated that the docked vaccine-TLR complexes were stable. Immune simulation analyses further confirmed that the vaccine can induce a marked increase in IgG and IgM antibody titers, and elevated levels of IFN-γ and IL-2. Finally, the optimized DNA sequence of the vaccine was cloned into the vector pET28a (+) for high expression in the E.coli expression system. Overall, the designed multi-epitope subunit vaccine is highly stable and can induce robust humoral and cellular immunity, making it a promising vaccine candidate against ORFV.

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

A pan-genomic analysis based multi-epitope vaccine development by targeting Stenotrophomonas maltophilia using reverse vaccinology method: an in-silico approach

Antibiotic resistance in bacteria leads to high mortality rates and healthcare costs, a significant concern for public health. A colonizer of the human respiratory system, Stenotrophomonas maltophilia is frequently associated with hospital-acquired infections in individuals with cystic fibrosis, cancer, and other chronic illnesses. The importance of this study is underscored by its capacity to meet the critical demand for effective preventive strategies against this pathogen, particularly among susceptible groups of cystic fibrosis and those undergoing cancer treatment. In this study, we engineered a multi-epitope vaccine targeting S. maltophilia through genomic analysis, reverse vaccination strategies, and immunoinformatic techniques by examining a total of 81 complete genomes of S. maltophilia strains. Our investigation revealed 1945 core protein-coding genes alongside their corresponding proteomic sequences, with 191 of these genes predicted to exhibit virulence characteristics. Out of the filtered proteins, three best antigenic proteins were selected for epitope prediction while seven epitopes each from CTL, HTL, and B cell were chosen for vaccine development. The vaccine was refined and validated, showing highly antigenic and desirable physicochemical features. Molecular docking assessments revealed stable binding with TLR-4. Molecular dynamic simulation demonstrated stable dynamics with minor alterations. The originality of this investigation is rooted in the thorough techniques aimed at designing a vaccine that directly targets S. maltophilia, a microorganism of considerable clinical relevance that currently lacks an available vaccine. This study not only responds to a pressing public health crisis but also lays the groundwork for subsequent research endeavors focused on the prevention of S. maltophilia outbreaks. Further evidence from studies in mice models is needed to confirm immune protection against S. maltophilia.

Design of a Helicobacter pylori multi-epitope vaccine based on immunoinformatics

Helicobacter pylori (H. pylori) is an infectious bacterium that colonizes the stomach of approximately half of the global population. It has been classified as a Group I carcinogen by the World Health Organization due to its strong association with an increased incidence of gastric cancer and exacerbation of stomach diseases. The primary treatment for H. pylori infection currently involves triple or quadruple therapy, primarily consisting of antibiotics and proton pump inhibitors. However, the increasing prevalence of antibiotic resistance poses significant challenges to this approach, underscoring the urgent need for an effective vaccine. In this study, a novel multi-epitope H. pylori vaccine was designed using immunoinformatics. The vaccine contains epitopes derived from nine essential proteins. Software tools and online servers were utilized to predict, evaluate, and analyze the physiochemical properties, secondary and tertiary structures, and immunogenicity of the candidate vaccine. These comprehensive assessments ultimately led to the formulation of an optimal design scheme for the vaccine. Through constructing a novel multi-epitope vaccine based on immunoinformatics, this study offers promising prospects and great potential for the prevention of H. pylori infection. This study also provides a reference strategy to develop multi-epitope vaccines for other pathogens.

DNA Vaccines Encoding HTNV GP-Derived Th Epitopes Benefited from a LAMP-Targeting Strategy and Established Cellular Immunoprotection

Vaccines has long been the focus of antiviral immunotherapy research. Viral epitopes are thought to be useful biomarkers for immunotherapy (both antibody-based and cellular). In this study, we designed a novel vaccine molecule, the Hantaan virus (HTNV) glycoprotein (GP) tandem Th epitope molecule (named the Gnc molecule), in silico. Subsequently, computer analysis was used to conduct a comprehensive and in-depth study of the various properties of the molecule and its effects as a vaccine molecule in the body. The Gnc molecule was designed for DNA vaccines and optimized with a lysosomal-targeting membrane protein (LAMP) strategy. The effects of GP-derived Th epitopes and multiepitope vaccines were initially verified in animals. Our research has resulted in the design of two vaccines based on effective antiviral immune targets. The effectiveness of molecular therapies has also been preliminarily demonstrated in silico and in laboratory animals, which lays a foundation for the application of a vaccines strategy in the field of antivirals.

Design of a cryptococcus neoformans vaccine by subtractive proteomics combined with immunoinformatics

The emergence of Cryptococcus neoformans has posed an undeniable burden to many regions worldwide, with its strains mainly entering the lungs through the respiratory tract and spreading throughout the body. Limitations of drug regimens, such as high costs and limited options, have directed our attention toward the promising field of vaccine development. In this study, the subtractive proteomics approach was employed to select target proteins from databases that can accurately cover serotypes A and D of the Cryptococcus neoformans. Further, two multi-epitope vaccines consisting of T and B cell epitopes were demonstrated that they have good structural stability and could bind with immune receptor to induce desired immune responses in silico. After further evaluation, these vaccines show the potential for large-scale production and applicability to the majority of the population of the world. In summary, these two vaccines have been theoretically proven to combat Cryptococcus neoformans infections, awaiting further experimental validation of their actual protective effects.

Decrypting the multi-genome data for chimeric vaccine designing against the antibiotic resistant Yersinia pestis

Yersinia pestis, the causative agent of plague, is a gram-negative bacterium that can be fatal if not treated properly. Three types of plague are currently known: bubonic, septicemic, and pneumonic plague, among which the fatality rate of septicemic and pneumonic plague is very high. Bubonic plague can be treated, but only if antibiotics are used at the initial stage of the infection. But unfortunately, Y. pestis has also shown resistance to certain antibiotics such as kanamycin, minocycline, tetracycline, streptomycin, sulfonamides, spectinomycin, and chloramphenicol. Despite tremendous progress in vaccine development against Y. pestis, there is no proper FDA-approved vaccine available to protect people from its infections. Therefore, effective broad-spectrum vaccine development against Y. pestis is indispensable. In this study, vaccinomics-assisted immunoinformatics techniques were used to find possible vaccine candidates by utilizing the core proteome prepared from 58 complete genomes of Y. pestis. Human non-homologous, pathogen-essential, virulent, and extracellular and membrane proteins are potential vaccine targets. Two antigenic proteins were prioritized for the prediction of lead epitopes by utilizing reverse vaccinology approaches. Four vaccine designs were formulated using the selected B- and T-cell epitopes coupled with appropriate linkers and adjuvant sequences capable of inducing potent immune responses. The HLA allele population coverage of the T-cell epitopes selected for vaccine construction was also analyzed. The V2 constructs were top-ranked and selected for further analysis on the basis of immunological, physicochemical, and immune-receptor docking interactions and scores. Docking and molecular dynamic simulations confirmed the stability of construct V2 interactions with the host immune receptors. Immune simulation analysis anticipated the strong immune profile of the prioritized construct. In silico restriction cloning ensured the feasible cloning ability of the V2 construct in the expression system of E. coli strain K12. It is anticipated that the designed vaccine construct may be safe, effective, and able to elicit strong immune responses against Y. pestis infections and may, therefore, merit investigation using in vitro and in vivo assays.

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.

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

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

Toward mechanistic medical digital twins: some use cases in immunology

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

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

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

Modeling the kinetics of the neutralizing antibody response against SARS-CoV-2 variants after several administrations of Bnt162b2

Because SARS-CoV-2 constantly mutates to escape from the immune response, there is a reduction of neutralizing capacity of antibodies initially targeting the historical strain against emerging Variants of Concern (VoC)s. That is why the measure of the protection conferred by vaccination cannot solely rely on the antibody levels, but also requires to measure their neutralization capacity. Here we used a mathematical model to follow the humoral response in 26 individuals that received up to three vaccination doses of Bnt162b2 vaccine, and for whom both anti-S IgG and neutralization capacity was measured longitudinally against all main VoCs. Our model could identify two independent mechanisms that led to a marked increase in measured humoral response over the successive vaccination doses. In addition to the already known increase in IgG levels after each dose, we identified that the neutralization capacity was significantly increased after the third vaccine administration against all VoCs, despite large inter-individual variability. Consequently, the model projects that the mean duration of detectable neutralizing capacity against non-Omicron VoC is between 348 days (Beta variant, 95% Prediction Intervals PI [307; 389]) and 587 days (Alpha variant, 95% PI [537; 636]). Despite the low neutralization levels after three doses, the mean duration of detectable neutralizing capacity against Omicron variants varies between 173 days (BA.5 variant, 95% PI [142; 200]) and 256 days (BA.1 variant, 95% PI [227; 286]). Our model shows the benefit of incorporating the neutralization capacity in the follow-up of patients to better inform on their level of protection against the different SARS-CoV-2 variants. Trial registration: This clinical trial is registered with ClinicalTrials.gov, Trial IDs NCT04750720 and NCT05315583.

Computational Modeling and Evaluation of Potential mRNA and Peptide-Based Vaccine against Marburg Virus (MARV) to Provide Immune Protection against Hemorrhagic Fever

A hemorrhagic fever caused by the Marburg virus (MARV) belongs to the Filoviridae family and has been classified as a risk group 4 pathogen. To this day, there are no approved effective vaccinations or medications available to prevent or treat MARV infections. Reverse vaccinology-based approach was formulated to prioritize B and T cell epitopes utilizing a numerous immunoinformatics tools. Potential epitopes were systematically screened based on various parameters needed for an ideal vaccine such as allergenicity, solubility, and toxicity. The most suitable epitopes capable of inducing immune response were shortlisted. Epitopes with population coverage of 100% and fulfilling set parameters were selected for docking with human leukocyte antigen molecules, and binding affinity of each peptide was analyzed. Finally, 4 CTL and HTL each while 6 B cell 16-mers were used for designing multiepitope subunit (MSV) and mRNA vaccine joined via suitable linkers. Immune simulations were used to validate the constructed vaccine's capacity to induce a robust immune response whereas molecular dynamics simulations were used to confirm epitope-HLA complex stability. Based on these parameter's studies, both the vaccines constructed in this study offer a promising choice against MARV but require further experimental verification. This study provides a rationale point to begin with the development of an efficient vaccine against Marburg virus; however, the findings need further experimental validation to confirm the computational finding of this study.