A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus

Pan-vaccinomics strategy for developing a universal multi-epitope vaccine against endocarditis-related pathogens

Introduction

Endocarditis is a life-threatening infection of the heart valves, frequently caused by pathogenic bacteria. The growth of multidrug-resistant bacteria necessitates the development of innovative therapeutic techniques, such as vaccines.

Methods

The current study employed core proteome analysis and a computational-based reverse vaccinology approach across multiple bacterial pathogens associated with endocarditis to identify prospective universal vaccine candidates. The core proteome analysis contained 121 highly pathogenic strains from ten distinct pathogens (Streptococcus mutans, Streptococcus viridans, Streptococcus pyogenes, Staphylococcus aureus, Enterococcus faecalis, Streptococcus agalactiae, Gemella morbillorum, Streptococcus pneumonia, Enterococcus faecium, and Streptococcus gallolyticus). The core proteome was subjected to a subtractive proteomics methodology.

Results

Three proteins that were virulent, non-homologous, antigenic, and non-allergenic have been identified as prospective candidates for vaccine development: 30S ribosomal protein S13, 50S ribosomal protein L6, and UMP Kinase. B and T cell epitopes were predicted from vaccine candidate proteins using a range of immune-informatics methods. An in silico vaccine was created by using meticulously chosen epitopes-seven Cytotoxic T lymphocyte (CTL), seven Linear B lymphocyte (LBL), and three Helper T lymphocyte (HTL) epitopes-and subsequently aligning them with the major histocompatibility complex (MHC) molecules (MHC I & MHC II) and human TLR4. A Cholera toxin subunit B (CTB) adjuvant was added to the vaccine to enhance the immunological response. The molecular interactions and binding affinity of the vaccine with TLR4 and MHC molecules were analyzed using molecular dynamics (MD) simulations and molecular docking. To ensure optimal vaccine protein expression, the vaccine was cloned and reverse-translated in E. coli.

Discussion

This methodology tackles the difficulties presented by the diversity of pathogens and antibiotic resistance, providing a strategic option for developing efficient and durable vaccines against infections associated with endocarditis.

Epidermal Resident Memory T Cell Fitness Requires Antigen Encounter in the Skin

CD8 + tissue resident memory T cells (T RM ) develop from effectors that seed peripheral tissues where they persist providing defense against subsequent challenges. T RM persistence requires autocrine TGFβ transactivated by integrins expressed on keratinocytes. T RM precursors that encounter antigen in the epidermis during development outcompete bystander T RM for TGFβ resulting in enhanced persistence. ScRNA-seq analysis of epidermal T RM revealed that local antigen experience in the skin resulted in an enhanced differentiation signature in comparison with bystanders. Upon recall, T RM displayed greater proliferation dictated by affinity of antigen experienced during epidermal development. Finally, local antigen experienced T RM differentially expressed TGFβRIII, which increases avidity of the TGFβRI/II receptor complex for TGFβ. Selective ablation of Tgfbr3 reduced local antigen experienced T RM capacity to persist, rendering them phenotypically like bystander T RM . Thus, antigen driven TCR signaling in the epidermis during T RM differentiation results in a lower TGFβ requirement for persistence and increased proliferative capacity that together enhance epidermal T RM fitness.

Revolutionizing Chikungunya Vaccines: mRNA Breakthroughs With Molecular and Immune Simulations

With the ability to cause massive epidemics that have consequences on millions of individuals globally, the Chikungunya virus (CHIKV) emerges as a severe menace. Developing an effective vaccine is urgent as no effective therapeutics are available for such viral infections. Therefore, we designed a novel mRNA vaccine against CHIKV with a combination of highly antigenic and potential MHC-I, MHC-II, and B-cell epitopes from the structural polyprotein. The vaccine demonstrated well-characterized physicochemical properties, indicating its solubility and potential functional stability within the body (GRAVY score of -0.639). Structural analyses of the vaccine revealed a well-stabilized secondary and tertiary structure (Ramachandran score of 82.8% and a Z-score of -4.17). Docking studies of the vaccine with TLR-2 (-1027.7 KJ/mol) and TLR-4 (-1212.4 KJ/mol) exhibited significant affinity with detailed hydrogen bond interactions. Molecular dynamics simulations highlighted distinct conformational dynamics among the vaccine, "vaccine-TLR-2" and "vaccine-TLR-4" complexes. The vaccine's ability to elicit both innate and adaptive immune responses, including the presence of memory B-cells and T-cells, persistent B-cell immunity for a year, and the activation of TH cells leading to the release of IFN-γ and IL-2, has significant implications for its potential effectiveness. The CHIKV vaccine developed in this study shows promise as a potential candidate for future vaccine production against CHIKV, suggesting its suitability for further clinical advancement, including in vitro and in vivo experiments.

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.

An mRNA vaccine against monkeypox virus inhibits infection by co-activation of humoral and cellular immune responses

The persistent monkeypox outbreaks intensify the demand for monkeypox vaccines. Based on the mRNA vaccine platform, we conduct a systematic screening of monkeypox virus (MPXV) surface proteins from two types of viral particles, extracellular enveloped viruses (EVs) and intracellular mature viruses (MVs). This screening unveils 12 important antigens with diverse levels of neutralizing immunogenicity. Further assessment reveals that the combinations of 4, 8, and 12 of these antigens, namely Mix-4, Mix-8, and Mix-12, induce varying degrees of immune protection, with Mix-12 being the most potent. This finding demonstrates the significance of not only the level but also the diversity of the neutralizing antibodies in providing potent immune protection. Additionally, we utilize a T cell-epitope enrichment strategy, analyzing the complete proteome sequence of the MPXV to predict antigenic epitope-rich regions. Integration of these epitope-rich regions into a cellular immune-targeting antigen, named MPX-EPs, showcases that a cellular immune-targeting mRNA vaccine can independently confer immune protection. Furthermore, co-immunization with Mix-12 and MPX-EPs achieves complete protection against MPXV challenge. Overall, these results suggest an effective approach to enhance the immune protection of mRNA vaccines through the specific coordination of humoral and cellular immune responses.

Computationally designed multi-epitope vaccine construct targeting the SARS-CoV-2 spike protein elicits robust immune responses in silico

Our research is driven by the need to design an advanced multi-epitope vaccine construct (MEVC) using the S-protein of SARS-CoV-2 to combat the emergence of new variants. Through rigorous computational screening, we have identified linear and discontinuous B-cell epitopes, CD8 + and CD4 + T-cell epitopes, ensuring extensive MEVC coverage across 90.03% of the global population. The MEVC, featuring four CD4 + and four CD8 + T-cell epitopes connected linearly with two adjuvant proteins on both ends, has been carefully designed to elicit robust immune response. Our in-silico analysis has confirmed the construct's antigenicity, non-allergenicity, and non-toxicity with optimized codon sequences for enhanced expression in E. coli K12. Furthermore, molecular docking and dynamics analyses have demonstrated its strong binding affinity with TLR-3 and TLR 4, and in-silico immune simulation yielded promising results on heightened B-cell and T-cell-mediated immunity. However, wet lab experiments are essential to validate computational findings to revolutionize the development of vaccines against SARS-CoV-2.

A computational approach for MHC-restricted multi-epitope vaccine design targeting Oropouche virus structural proteins

In recent years, Brazil has recorded approximately 500,000 Oropouche virus (OROV) cases in the Amazon region, underscoring the growing global threat posed by emerging and reemerging viruses. Symptoms of OROV closely resemble those of Dengue virus and Zika virus, contributing to underreporting and underestimation of its true impact. In the absence of specific treatments, the development of vaccines becomes essential. This study aimed to identify immunogenic epitopes in three structural proteins of OROV and develop a multi-epitope vaccine candidate. RefSeq sequences of the nucleocapsid protein and the Gn and Gc glycoproteins were obtained from the National Center for Biotechnology Information Virus and submitted to epitope search in Immune Epitope Database. Antigenicity, allergenicity, stability, and toxicity analyses were conducted, and the approved epitopes were aligned to the global protein to remove transmembrane regions and N-glycosylation sites. Thirteen epitopes were selected and used to construct a multi-epitope vaccine candidate, with β-defensin and PADRE adjuvants. The protein demonstrated optimal antigenicity, low allergenicity, and satisfactory stability and solubility. Predictions of humoral and cellular immune responses were performed, indicating satisfactory results for three doses of the vaccine candidate. 3D modeling of the protein was performed, evaluating the molecular docking of the multi-epitope protein with TLR-2, TLR-3, TLR-6, and TLR-8 receptors. Our findings present a promising vaccine candidate against OROV, potentially protecting immunocompromised individuals and high-risk populations, and establishing a foundation for both in vitro and in vivo testing. The identified epitopes could also aid in immunodiagnostic test development, advancing surveillance and control strategies.

Computational identification of B and T-cell epitopes for designing a multi-epitope vaccine against SARS-CoV-2 spike glycoprotein

Although the peak of the COVID-19 pandemic has passed, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a significant global threat and remains a public health concern. Given the ongoing risk and the substantial loss of life caused by the virus, continuous research into vaccine development is essential. This study employs immunoinformatics approaches to identify T-cell and B-cell epitopes for designing a multi-epitope peptide vaccine candidate targeting the Omicron variant. The proposed vaccine construct comprises 1435 amino acids, including eight linear B lymphocyte, seven cytotoxic T lymphocyte, and five helper T lymphocyte epitopes, along with appropriate adjuvants and linkers. The evaluation of the vaccine revealed high antigenicity, non-allergenicity, non-toxicity, and favorable physicochemical properties. To further assess its efficacy, molecular docking studies were performed to investigate interactions between the vaccine and key immune components, including Toll-like receptors and major histocompatibility complex molecules. Stability of these interactions was confirmed using molecular dynamics simulations in triplicate, conducted over 100 ns using GROMACS 2023 to compute key metrics, such as root mean square deviation, root mean square fluctuation, solvent-accessible surface area, and radius of gyration. The results demonstrate that the multi-epitope vaccine has the potential to elicit strong immune responses against the Omicron variant, providing a promising foundation for further experimental validation and clinical development in COVID-19 vaccine research.

A Mutant of Africa Swine Fever Virus Protein p72 Enhances Antibody Production and Regulates the Production of Cytokines

African swine fever virus (ASFV) is a severe threat to the global pig industry, and domestic pigs mostly develop severe clinical manifestations upon viral invasion. Currently, there is no available vaccine against ASFV. Its capsid structural protein p72 is one of the immuno-dominant proteins. In this study, we unexpectedly obtained a p72 mutant protein (p72∆377-428) which deleted the aa 377-428 within p72 and had stable and high expression in E. coli. Using SWISS-MODEL 1.0 software, the prediction showed that p72∆377-428 was quite distinct from the wild-type p72 protein in structure. p72∆377-428 induced stronger antibody production in mice on day 42 and 56 post immunization and could recognize ASFV-infected swine sera. p72∆377-428 reduced IFN-γ production in the splenocytes from p72∆377-428-immunized mice and p72∆377-428-treated swine macrophages compared to p72. p72∆377-428 also decreased the production of pro-inflammatory cytokine genes, including IL-1β, IL-6, and IL-12, compared to p72 in mice. Further, we found that p72∆377-428 reduced the induction of pro-inflammatory cytokine genes by inhibiting AKT phosphorylation and HIF1α expression. Taken together, these findings have implications for immunological function and the corresponding mechanism of ASFV p72, and our study indicates that p72∆377-428 could serve as a novel candidate for ASFV vaccines and diagnostic reagents.

In silico screening and immunogenic features of putative tick cement protein PA107 from Ixodes ricinus tick

Tick salivary proteins are crucial for efficient and successful tick feeding. Most of them are still uncharacterized, especially those involved in the formation of tick cement. Tick salivary protein PA107 is a putative cement protein, which is transcribed in salivary glands during the initial phase of tick feeding. It is a tick-unique protein, with homologs described in several tick genera. In this study, a detailed in silico analysis of its primary and tertiary structure was performed, along with the immunogenicity assessment for the PA107 protein from Ixodes ricinus species. The screening of the primary structure placed it to the glycine-rich protein family, revealing in parallel an overlapping 15mer at the C-terminus and borderline homology to non-tick proteins with antimicrobial activity. The analysis of tertiary structure revealed a high degree of intrinsic disorder for monomeric PA107, in contrast to highly ordered structures for different oligomeric states that might correlate with the putative role in the tick cement formation process. Regarding in silico PA107 immunogenicity inference, obtained results were inconclusive, which aligns with the in vitro findings showing definitely the lack of humoral response induction in experimentally infested rats and persons bitten by the I. ricinus ticks. The results represent new pieces of a huge puzzle depicting a complex tick-host relationship, but also identify PA107 as a possible compound of novel formulations to be used in biomedicine as bioadhesives, and as a target for new anti-tick strategies, by interfering with the cement cone formation and stability, i.e. tick attachment and feeding.

Designing of an mRNA vaccine against high-risk human papillomavirus targeting the E6 and E7 oncoproteins exploiting immunoinformatics and dynamic simulation

Human papillomavirus 16 and human papillomavirus 18 have been associated with different life-threatening cancers, including cervical, lung, penal, vulval, vaginal, anal, and oropharyngeal cancers, while cervical cancer is the most prominent one. Several research studies have suggested that the oncoproteins E6 and E7 are the leading cause of cancers associated with the human papillomavirus infection. Therefore, we developed two mRNA vaccines (V1 and V2) targeting these oncoproteins. We used several bioinformatics tools to predict helper T lymphocyte, cytotoxic T lymphocyte, and B-cell epitopes derived from the proteins and assessed their antigenicity, allergenicity, and toxicity. Both vaccines were constructed using selected epitopes, linkers, and adjuvants. After that, the vaccines were applied for physicochemical properties, secondary and tertiary structure predictions, and subsequent docking and simulation analyses. Accordingly, vaccine 1 (V1) and vaccine 2 (V2) showed better hydrophilicity with the grand average hydropathicity score of -0.811 and -0.648, respectively. The secondary and tertiary structures of the vaccines were also deemed satisfactory, with high stability indicated by the Ramachandran plot (V1:94.5% and V2:87.1%) and Z scores (V1: -5.15 and V2: -4.1). Docking analysis revealed substantial affinity of the vaccines towards the toll-like receptor-2 (V1: -1159.3, V2: -1246.3) and toll-like receptor-4 (V1: -1109.3, V2: -1244.8) receptors. Molecular dynamic simulation validated structural integrity and indicated varying stability throughout the simulation. Codon optimization showed significant expression of the vaccines (V1:51.88% and V2:51.63%) in E. coli vectors. Furthermore, regarding immune stimulation, the vaccines elicited significant B-cell and T-cell responses, including sustained adaptive and innate immune responses. Finally, thermodynamic predictions indicated stable mRNA structures of the vaccines (V1: -502.60 kcal/mol and V2: -450.90 kcal/mol). The proposed vaccines designed effectively targeting human papillomavirus oncoproteins have demonstrated promising results via robust immune responses, suggesting their suitability for further clinical advancement, including in vitro and in vivo experiments.

Immunoinformatics-driven design and computational analysis of a multiepitope vaccine targeting uropathogenic Escherichia coli

Urinary tract infections (UTIs), largely caused by uropathogenic Escherichia coli (UPEC), are increasingly resistant to antibiotics and frequently recur. Using immunoinformatics, we designed a multiepitope peptide vaccine targeting UPEC virulence factors, including iron acquisition systems and adhesins. The construct features 12 cytotoxic T lymphocyte epitopes, six helper T lymphocyte epitopes, and six B-cell epitopes,and isoptimized for high antigenicity, immunogenicity, nontoxic, and low allergenic potential. Molecular docking and 0.4-µs molecular dynamics simulations revealed the molecular mechanism of theinteraction of the vaccine with Toll-like receptor 4 and a favorable binding energy of - 41.83 kcal/mol using an implicit solvation model. These promising in silico results suggest the potential efficacy of the vaccine in preventing UPEC infections and underscore immunoinformatics as a powerful tool for addressing antibiotic-resistant UTI pathogens.

Graphical Abstract

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-024-00288-z.

Developing a chimeric multiepitope vaccine against Nipah virus (NiV) through immunoinformatics, molecular docking and dynamic simulation approaches

Nipah virus (NiV) is a highly lethal zoonotic pathogen that poses a significant threat to human and animal health. Unfortunately, no effective treatments have been developed for this deadly zoonotic disease. Therefore, we designed a chimeric multiepitope vaccine targeting the Nipah virus (NiV) glycoprotein and fusion protein through immunoinformatic approaches. Therefore, the vaccine was developed by combining promising and potential antigenic MHC-I, MHC-II, and B-cell epitopes obtained from the selected proteins. When combined, the MHC-I and MHC-II epitopes offered 100 % global population coverage. The physicochemical characterization also exhibited favorable properties, including solubility and potential functional stability of the vaccine within the body (GRAVY score of -0.308). Structural analyses unveiled a well-stabilized secondary and tertiary structure with a Ramachandran score of 84.4 % and a Z score of -5.02. Findings from docking experiments with TLR-2 (-1089.3 kJ/mol) and TLR-4 (-1016.7 kJ/mol) showed a strong affinity of the vaccine towards the receptor. Molecular dynamics simulations revealed unique conformational dynamics among the "vaccine-apo," "vaccine-TLR-2," and "vaccine-TLR-4″ complexes. Consequently, the complexes exhibited significant compactness, flexibility, and exposure to solvents. The results of the codon optimization were remarkable, as the vaccine showed a significant amount of expression in the E. coli vector (GC content of 45.36 % and a CAI score of 1.0). The results of immune simulations, however, showed evidence of both adaptive and innate immune responses induced by the vaccine. Therefore, we highly recommend further research on this chimeric multiepitope vaccine to establish its efficacy and safety against the Nipah virus (NiV).

Rational design of a multi-epitope vaccine against heartland virus (HRTV) using immune-informatics, molecular docking and dynamics approaches

Heartland virus (HRTV) is a single-stranded negative-sense RNA virus that infects human beings. Because there are no antiviral medications available to treat HRTV infection, supportive care management is used in cases of severe disease. Therefore, it has spurred research into developing a multi-epitope vaccine capable of providing effective protection against HRTV infection. A multi-epitope vaccine was created using a combination of immuno-informatics, molecular docking and molecular dynamics simulation in this investigation. The HRTV proteome was utilized to predict B-cell, T-cell (HTL and CTL), and IFN-epitopes. Following prediction, highly antigenic, non-allergenic and immunogenic epitopes were chosen, including 6 CTL, 8 HTL, and 5 LBL epitopes that were connected to the final peptide by AAY, GPGPG, and KK linkers, respectively. An adjuvant was introduced to the vaccine's N-terminal through the EAAAK linker to increase its immunogenicity. Following the inclusion of linkers and adjuvant, the final construct has 359 amino acids. The presence of B-cell and IFN-γ-epitopes validates the construct's acquired humoral and cell-mediated immune responses. To ensure the vaccine's safety and immunogenicity profile, its allergenicity, antigenicity, and various physicochemical characteristics were assessed. Docking was used to assess the binding affinity and molecular interaction between the vaccination and TLR-3. In silico cloning was used to confirm the construct's validity and expression efficiency. The results of these computer assays demonstrated that the designed vaccine is highly promising in terms of developing protective immunity against HRTV; nevertheless, additional in vivo and in vitro investigations are required to validate its true immune-protective efficiency.

Advancements in monkeypox vaccines development: a critical review of emerging technologies

Monkeypox (mpox) is a zoonotic illness caused by the monkeypox virus (MPXV), with higher health concerns among people who are pregnant, children, and persons who are immunocompromised, including people with untreated and advanced HIV disease. Significant progress has been made in developing vaccines against mpox, yet critical challenges and limitations persist in ensuring their effectiveness, safety, and accessibility. The pertinence of this review is highlighted by the World Health Organization's declaration of a global health emergency on August 14, 2024, due to the recent mpox outbreak, underscoring the critical necessity for effective vaccine solutions in the face of a rapidly evolving virus. Here, we comprehensively analyze various vaccine platforms utilized in mpox prevention, including attenuated and non-replicating virus vaccines, viral vector-based vaccines, recombinant protein vaccines, and DNA and mRNA vaccines. We evaluate the advantages and limitations of each platform, highlighting the urgent need for ongoing research and innovation to enhance vaccine efficacy and safety. Recent advancements, such as incorporating immunostimulatory sequences, improved delivery systems, and developing polyvalent vaccines, are explored for their potential to offer broader protection against diverse orthopoxvirus strains. This work underscores the need to optimize currently available vaccines and investigate novel vaccination strategies to address future public health emergencies effectively. By focusing on these advanced methodologies, we aim to contribute to the development of robust and adaptable vaccine solutions for mpox and other related viral threats.

HLAPepBinder: An Ensemble Model for The Prediction Of HLA-Peptide Binding

Background

Human leukocyte antigens (HLAs) play a pivotal role in orchestrating the host's immune response, offering a promising avenue with reduced adverse effects compared to conventional treatments. Cancer immunotherapies use HLA class I molecules for T cells to recognize tumor antigens, emphasizing the importance of identifying peptides that bind effectively to HLAs. Computer modeling of HLA-peptide binding speeds up the search for immunogenic epitopes, which enhances the prospect of personalized medicine and targeted therapies. The Immune Epitope Database (IEDB) is a vital repository, housing curated immune epitope data and prediction tools for HLA-peptide binding. It can be challenging for immunologists to choose the best tool from the IEDB for predicting HLA-peptide binding. This has led to the creation of consensus-based methods that combine the results of several predictors. One of the major challenges in these methods is how to effectively integrate the results from multiple predictors.

Objectives

Previous consensus-based methods integrate at most three tools by relying on simple strategies, such as selecting prediction methods based on their proximity to HLA in training data. In this study, we introduce HLAPepBinder, a novel consensus approach using ensemble machine learning methods to predict HLA-peptide binding, addressing the challenges biologists face in model selection.

Materials and Methods

The key contribution is the development of an automatic pipeline named HLAPepBinder that integrates the predictions of multiple models using a random forest approach. Unlike previous approaches, HLAPepBinder seamlessly integrates results from all nine predictors, providing a comprehensive and accurate predictive framework. By combining the strengths of these models, HLAPepBinder eliminates the need for manual model selection, providing a streamlined and reliable solution for biologists.

Results

HLAPepBinder offers a practical and high-performing alternative for HLA-peptide binding predictions, outperforming both traditional methods and complex deep learning models. Compared to the recently introduced transformer-based model, TranspHLA, which requires substantial computational resources, HLAPepBinder demonstrates superior performance in both prediction accuracy and resource efficiency. Notably, it operates effectively in limited computational environments, making it accessible to researchers with minimal resources. The codes are available online at https://github.com/CBRC-lab/HLAPepBinder.

Conclusion

Our study introduces a novel ensemble-learning model designed to enhance the accuracy and efficiency of HLA-peptide binding predictions. Due to the lack of reliable negative data and the typical assumption of unknown interactions being negative, we focus on analyzing the unknown HLA-peptide bindings in the test set that our model predicts with 100% certainty as positive bindings. Using HLAPepBinder, we identify 26 HLA-peptide pairs with absolute prediction confidence. These predictions are validated through a multi-step pipeline involving literature review, BLAST sequence similarity analysis, and molecular docking studies. This comprehensive validation process highlights HLAPepBinder's ability to make accurate and reliable predictions, contributing significantly to advancements in immunotherapy and vaccine development.

3D genome contributes to MHC-II neoantigen prediction

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

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

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

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

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.

Bioinformatic elucidation of conserved epitopes to design a potential vaccine candidate against existing and emerging SARS-CoV-2 variants of concern

The COVID-19 pandemic caused by SARS-CoV-2 poses a significant adverse effects on health and economy globally. Due to mutations in genome, COVID-19 vaccine efficacy decreases. We used immuno-informatics to design a Multi epitope vaccine (MEV) candidate for SARS-CoV-2 variants of concern (VOCs). Hence, we predicted binders/epitopes MHC-I, CD8+, MHC-II, CD4+, and CTLs from spike, membrane and envelope proteins of VOCs. In addition, we assessed the conservation of these binders and epitopes across different VOCs. Subsequently, we designed MEV by combining the predicted CTL and CD4+ epitopes from spike protein, peptide linkers, and an adjuvant. Further, we evaluated the binding of MEV candidate against immune receptors namely HLA class I histocompatibility antigen, HLA class II histocompatibility antigen, and TLR4, achieving binding scores of -1265.3, -1330.7, and -1337.9. Molecular dynamics and normal mode analysis revealed stable docking complexes. Moreover, immune simulation suggested MEV candidate elicits both innate and adaptive immune response. We anticipate that this conserved MEV candidate will provide protection from VOCs and emerging strains.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction

Accurately predicting the binding affinities between Human Leukocyte Antigen (HLA) molecules and peptides is a crucial step in understanding the adaptive immune response. This knowledge can have important implications for the development of effective vaccines and the design of targeted immunotherapies. Existing sequence-based methods are insufficient to capture the structure information. Besides, the current methods lack model interpretability, which hinder revealing the key binding amino acids between the two molecules. To address these limitations, we proposed an interpretable graph convolutional neural network (GCNN) based prediction method named GIHP. Considering the size differences between HLA and short peptides, GIHP represent HLA structure as amino acid-level graph while represent peptide SMILE string as atom-level graph. For interpretation, we design a novel visual explanation method, gradient weighted activation mapping (Grad-WAM), for identifying key binding residues. GIHP achieved better prediction accuracy than state-of-the-art methods across various datasets. According to current research findings, key HLA-peptide binding residues mutations directly impact immunotherapy efficacy. Therefore, we verified those highlighted key residues to see whether they can significantly distinguish immunotherapy patient groups. We have verified that the identified functional residues can successfully separate patient survival groups across breast, bladder, and pan-cancer datasets. Results demonstrate that GIHP improves the accuracy and interpretation capabilities of HLA-peptide prediction, and the findings of this study can be used to guide personalized cancer immunotherapy treatment. Codes and datasets are publicly accessible at: https://github.com/sdustSu/GIHP.

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.

Subtractive proteomics-based vaccine targets annotation and reverse vaccinology approaches to identify multiepitope vaccine against Plesiomonas shigelloides

Plesiomonas shigelloides, an aquatic bacterium belonging to the Enterobacteriaceae family, is a frequent cause of gastroenteritis with diarrhea and gastrointestinal severe disease. Despite decades of research, discovering a licensed and globally accessible vaccine is still years away. Developing a putative vaccine that can combat the Plesiomonas shigelloides infection by boosting population immunity against P. shigelloides is direly needed. In the framework of the current study, the entire proteome of P. shigelloides was explored using subtractive genomics integrated with the immunoinformatics approach for designing an effective vaccine construct against P. shigelloides. The overall stability of the vaccine construct was evaluated using molecular docking, which demonstrated that MEV showed higher binding affinities with toll-like receptors (TLR4: 51.5 ± 10.3, TLR2: 60.5 ± 9.2) and MHC receptors(MHCI: 79.7 ± 11.2 kcal/mol, MHCII: 70.4 ± 23.7). Further, the therapeutic efficacy of the vaccine construct for generating an efficient immune response was evaluated by computational immunological simulation. Finally, computer-based cloning and improvement in codon composition without altering amino acid sequence led to the development of a proposed vaccine. In a nutshell, the findings of this study add to the existing knowledge about the pathogenesis of this infection. The schemed MEV can be a possible prophylactic agent for individuals infected with P. shigelloides. Nevertheless, further authentication is required to guarantee its safeness and immunogenic potential.

Ancient genomes reveal insights into ritual life at Chichén Itzá

The ancient city of Chichén Itzá in Yucatán, Mexico, was one of the largest and most influential Maya settlements during the Late and Terminal Classic periods (AD 600-1000) and it remains one of the most intensively studied archaeological sites in Mesoamerica1-4. However, many questions about the social and cultural use of its ceremonial spaces, as well as its population's genetic ties to other Mesoamerican groups, remain unanswered2. Here we present genome-wide data obtained from 64 subadult individuals dating to around AD 500-900 that were found in a subterranean mass burial near the Sacred Cenote (sinkhole) in the ceremonial centre of Chichén Itzá. Genetic analyses showed that all analysed individuals were male and several individuals were closely related, including two pairs of monozygotic twins. Twins feature prominently in Mayan and broader Mesoamerican mythology, where they embody qualities of duality among deities and heroes5, but until now they had not been identified in ancient Mayan mortuary contexts. Genetic comparison to present-day people in the region shows genetic continuity with the ancient inhabitants of Chichén Itzá, except at certain genetic loci related to human immunity, including the human leukocyte antigen complex, suggesting signals of adaptation due to infectious diseases introduced to the region during the colonial period.

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.

A Multi-epitope Vaccine Candidate Against Bolivian Hemorrhagic fever Caused by Machupo Virus

Bolivian hemorrhagic fever (BHF) caused by Machupo virus (MACV) is a New World arenavirus having a reported mortality rate of 25-35%. The BHF starts with fever, followed by headache, and nausea which rapidly progresses to severe hemorrhagic phase within 7 days of disease onset. One of the key promoters for MACV viral entry into the cell followed by viral propagation is performed by the viral glycoprotein (GPC). GPC is post-transcriptionally cleaved into GP1, GP2 and a signal peptide. These proteins all take part in the viral infection in host body. Therefore, GPC protein is an ideal target for developing therapeutics against MACV infection. In this study, GPC protein was considered to design a multi-epitope, multivalent vaccine containing antigenic and immunogenic CTL and HTL epitopes. Different structural validations and physicochemical properties were analysed to validate the vaccine. Docking and molecular dynamics simulations were conducted to understand the interactions of the vaccine with various immune receptors. Finally, the vaccine was codon optimised in silico and along with which immune simulation studies was performed in order to evaluate the vaccine's effectiveness in triggering an efficacious immune response against MACV.

In Silico Analysis of a Candidate Multi-epitope Peptide Vaccine Against Human Brucellosis

Brucellosis is one of the neglected endemic zoonoses in the world. Vaccination appears to be a promising health strategy to prevent it. This study used advanced computational techniques to develop a potent multi-epitope vaccine for human brucellosis. Seven epitopes from four main brucella species that infect humans were selected. They had significant potential to induce cellular and humoral responses. They showed high antigenic ability without the allergenic characteristic. In order to improve its immunogenicity, suitable adjuvants were also added to the structure of the vaccine. The physicochemical and immunological properties of the vaccine were evaluated. Then its two and three-dimensional structure was predicted. The vaccine was docked with toll-like receptor4 to assess its ability to stimulate innate immune responses. For successful expression of the vaccine protein in Escherichia coli, in silico cloning, codon optimization, and mRNA stability were evaluated. The immune simulation was performed to reveal the immune response profile of the vaccine after injection. The designed vaccine showed the high ability to induce immune response, especially cellular responses to human brucellosis. It showed the appropriate physicochemical properties, a high-quality structure, and a high potential for expression in a prokaryotic system.

Immunoinformatic Approach for Rational Identification of Immunogenic Peptides Against Host Entry and/or Exit Mpox Proteins and Potential Multiepitope Vaccine Construction

COVID-19 has intensified humanity's concern about the emergence of new pandemics. Since 2018, epidemic outbreaks of the mpox virus have become worrisome. In June 2022, the World Health Organization declared the disease a global health emergency, with 14 500 cases reported by the Centers for Disease Control and Prevention in 60 countries. Therefore, the development of a vaccine based on the current virus genome is paramount in combating new cases. In view of this, we hypothesized the obtainment of rational immunogenic peptides predicted from proteins responsible for entry of the mpox virus into the host (A17L, A26L/A30L, A33R, H2R, L1R), exit (A27L, A35R, A36R, C19L), and both (B5R). To achieve this, we aligned the genome sequencing data of mpox virus isolated from an infected individual in the United States in June 2022 (ON674051.1) with the reference genome dated 2001 (NC_003310.1) for conservation analysis. The Immune Epitope Database server was used for the identification and characterization of the epitopes of each protein related to major histocompatibility complex I or II interaction and recognition by B-cell receptors, resulting in 138 epitopes for A17L, 233 for A28L, 48 for A33R, 77 for H2R, 77 for L1R, 270 for A27L, 72 for A35R, A36R, 148 for C19L, and 276 for B5R. These epitopes were tested in silico for antigenicity, physicochemical properties, and allergenicity, resulting in 51, 40, 10, 34, 38, 57, 25, 7, 47, and 53 epitopes, respectively. Additionally, to select an epitope with the highest promiscuity of binding to major histocompatibility complexes and B-cell receptor simultaneously, all epitopes of each protein were aligned, and the most repetitive and antigenic regions were identified. By classifying the results, we obtained 23 epitopes from the entry proteins, 16 from the exit proteins, and 7 from both. Subsequently, 1 epitope from each protein was selected, and all 3 were fused to construct a chimeric protein that has potential as a multiepitope vaccine. The constructed vaccine was then analyzed for its physicochemical, antigenic, and allergenic properties. Protein modeling, molecular dynamics, and molecular docking were performed on Toll-like receptors 2, 4, and 8, followed by in silico immune simulation of the vaccine. Finally, the results indicate an effective, stable, and safe vaccine that can be further tested, especially in vitro and in vivo, to validate the findings demonstrated in silico.

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Streptococcus gallolyticus is a non-motile, gram-positive bacterium that causes infective endocarditis. S. gallolyticus has developed resistance to existing antibiotics, and no vaccine is currently available. Therefore, it is essential to develop an effective S. gallolyticus vaccine. Core proteomics was used in this study together with subtractive proteomics and reverse vaccinology approach to find antigenic proteins that could be utilized for the design of the S. gallolyticus multi-epitope vaccine. The pipeline identified two antigenic proteins as potential vaccine targets: penicillin-binding protein and the ATP synthase subunit. T and B cell epitopes from the specific proteins were forecasted employing several immunoinformatics and bioinformatics resources. A vaccine (360 amino acids) was created using a combination of seven cytotoxic T cell lymphocyte (CTL), three helper T cell lymphocyte (HTL), and five linear B cell lymphocyte (LBL) epitopes. To increase immune responses, the vaccine was paired with a cholera enterotoxin subunit B (CTB) adjuvant. The developed vaccine was highly antigenic, non-allergenic, and stable for human use. The vaccine's binding affinity and molecular interactions with the human immunological receptor TLR4 were studied using molecular mechanics/generalized Born surface area (MMGBSA), molecular docking, and molecular dynamic (MD) simulation analyses. Escherichia coli (strain K12) plasmid vector pET-28a ( +) was used to examine the ability of the vaccine to be expressed. According to the outcomes of these computer experiments, the vaccine is quite promising in terms of developing a protective immunity against diseases. However, in vitro and animal research are required to validate our findings.

Revolutionizing and identifying novel drug targets in Citrobacter koseri via subtractive proteomics and development of a multi-epitope vaccine using reverse vaccinology and immuno-informatics

Citrobacter koseri is a gram-negative rod that has been linked to infections in people with significant comorbidities and immunocompromised immune systems. It is most commonly known to cause urinary tract infections. Thus, the development of an efficacious C. koseri vaccine is imperative, as the pathogen has acquired resistance to current antibiotics. Subtractive proteomics was employed during this research to identify potential antigenic proteins to design an effective vaccine against C. koseri. The pipeline identified two antigenic proteins as potential vaccine targets: DP-3-O-acyl-N-acetylglucosamine deacetylase and Arabinose 5-phosphate isomerase. B and T cell epitopes from the specific proteins were forecasted employing several immunoinformatic and bioinformatics resources. A vaccine was created using a combination of seven cytotoxic T cell lymphocytes (CTL), five helper T cell lymphocyte (HTL), and seven linear B cell lymphocyte (LBL) epitopes. An adjuvant (β-defensin) was added to the vaccine to enhance immunological responses. The created vaccine was stable for use in humans, highly antigenic, and non-allergenic. The vaccine's molecular and interactions binding affinity with the human immunological receptor TLR3 were studied using MMGBSA, molecular dynamics (MD) simulations, and molecular docking analyses. E. coli (strain-K12) plasmid vector pET-28a (+) was used to examine the ability of the vaccine to be expressed. The vaccine shows great promise in terms of developing protective immunity against diseases, based on the results of these computer experiments. However, in vitro and animal research are required to validate our findings.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Designing a multi-epitope candidate vaccine by employing immunoinformatics approaches to control African swine fever spread

The African swine fever virus has been circulating for decades and is highly infectious, often fatal to farmed and wild pigs. There is currently no approved vaccine or treatment for the disease, making prevention even more difficult. Therefore, vaccine development is necessary and urgent to limit the consequences of ASF and ensure the food chain and sustainability of the swine industry. This research study was conducted to design a multi-epitope vaccine for controlling veterinary diseases caused by the African swine fever virus. We employed the immunoinformatics approaches to reveal 37 epitopes from different viral proteins of ASFV. These epitopes were linked to adjuvants and linkers to form a full-fledged immunogenic vaccine construct. The tertiary structure of the final vaccine was predicted using a deep-learning approach. The molecular docking and molecular dynamics predicted stable interactions between the vaccine and immune receptor TLR5 of Sus scrofa (Pig). The MD simulation studies reflect that the calculated parameters like RMSD, RMSF, number of hydrogen bonds, and finally, the buried interface surface area for the complex remained stable throughout the simulation time. This analysis suggests the stability of interface interactions between the TLR5 and the multi-epitope vaccine construct. Further, the physiochemical analysis demonstrated that our designed vaccine construct was expected to have high stability and prolonged half-life time in mammalian cells. Traditional vaccine design experiments require significant time and financial input from the development stage to the final product. Studies like this can assist in accelerating vaccine development while minimizing the cost.Communicated by Ramaswamy H. Sarma.

Identification and In Silico Characterization of a Conserved Peptide on Influenza Hemagglutinin Protein: A New Potential Antigen for Universal Influenza Vaccine Development

Antigenic changes in surface proteins of the influenza virus may cause the emergence of new variants that necessitate the reformulation of influenza vaccines every year. Universal influenza vaccine that relies on conserved regions can potentially be effective against all strains regardless of any antigenic changes and as a result, it can bring enormous public health impact and economic benefit worldwide. Here, a conserved peptide (HA288-107) on the stalk domain of hemagglutinin glycoprotein is identified among highly pathogenic influenza viruses. Five top-ranked B-cell and twelve T-cell epitopes were recognized by epitope mapping approaches and the corresponding Human Leukocyte Antigen alleles to T-cell epitopes showed high population coverage (>99%) worldwide. Moreover, molecular docking analysis indicated that VLMENERTL and WTYNAELLV epitopes have high binding affinity to the antigen-binding groove of the HLA-A*02:01 and HLA-A*68:02 molecules, respectively. Theoretical physicochemical properties of the peptide were assessed to ensure its thermostability and hydrophilicity. The results suggest that the HA288-107 peptide can be a promising antigen for universal influenza vaccine design. However, in vitro and in vivo analyses are needed to support and evaluate the effectiveness of the peptide as an immunogen for vaccine development.

Using a novel structure/function approach to select diverse swine major histocompatibility complex 1 alleles to predict epitopes for vaccine development

MOTIVATION

Swine leukocyte antigens (SLAs) (i.e. swine major histocompatibility complex proteins) conduct a fundamental role in swine immunity. To generate a protective vaccine across an outbred species, such as pigs, it is critical that epitopes that bind to diverse SLA alleles are used in the vaccine development process. We introduced a new strategy for epitope prediction.

RESULTS

We employed molecular dynamics simulation to identify key amino acids for interactions with epitopes. We developed an algorithm wherein each SLA-1 is compared to a crystalized reference allele with unique weighting for non-conserved amino acids based on R group and position. We then performed homology modeling and electrostatic contact mapping to visualize how relatively small changes in sequences impacted the charge distribution in the binding site. We selected eight diverse SLA-1 alleles and performed homology modeling followed, by protein-peptide docking and binding affinity analyses, to identify porcine reproductive and respiratory syndrome virus matrix protein epitopes that bind with high affinity to these alleles. We also performed docking analysis on the epitopes identified as strong binders using NetMHCpan 4.1. Epitopes predicted to bind to our eight SLA-1 alleles had equivalent or higher energetic interactions than those predicted to bind to the NetMHCpan 4.1 allele repertoire. This approach of selecting diverse SLA-1 alleles, followed by homology modeling, and docking simulations, can be used as a novel strategy for epitope prediction that complements other available tools and is especially useful when available tools do not offer a prediction for SLAs/major histocompatibility complex.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online Supplementary Material.

Cross-reactive MHC class I T cell epitopes may dictate heterologous immune responses between respiratory viruses and food allergens

Respiratory virus infections play a major role in asthma, while there is a close correlation between asthma and food allergy. We hypothesized that T cell-mediated heterologous immunity may induce asthma symptoms among sensitized individuals and used two independent in silico pipelines for the identification of cross-reactive virus- and food allergen- derived T cell epitopes, considering individual peptide sequence similarity, MHC binding affinity and immunogenicity. We assessed the proteomes of human rhinovirus (RV1b), respiratory syncytial virus (RSVA2) and influenza-strains contained in the seasonal quadrivalent influenza vaccine 2019/2020 (QIV 2019/2020), as well as SARS-CoV-2 for human HLA alleles, in addition to more than 200 most common food allergen protein sequences. All resulting allergen-derived peptide candidates were subjected to an elaborate scoring system considering multiple criteria, including clinical relevance. In both bioinformatics approaches, we found that shortlisted peptide pairs that are potentially binding to MHC class II molecules scored up to 10 × lower compared to MHC class I candidate epitopes. For MHC class I food allergen epitopes, several potentially cross-reactive peptides from shrimp, kiwi, apple, soybean and chicken were identified. The shortlisted set of peptide pairs may be implicated in heterologous immune responses and translated to peptide immunization strategies with immunomodulatory properties.

BMX-A and BMX-S: Accessible cell-free methods to estimate peptide-MHC-I affinity and stability

The affinity and stability of peptide binding to Major Histocompatibility Complex Class I (MHC-I) molecules are fundamental parameters that underpin the specificity and magnitude of CD8+ T cell responses. These parameters can be estimated in some cases by computational tools, but experimental validation remains valuable, especially for stability. Methods to measure peptide binding can be broadly categorised into either cell-based assays using TAP-deficient cell lines such as RMA/S, or cell-free strategies, such as peptide competition-binding assays and surface plasmon resonance. Cell-based assays are subject to confounding biological activity, including peptide trimming by peptidases and dilution of peptide-loaded MHC-I on the surface of cells through cell division. Current cell-free methods require in-house production and purification of MHC-I. In this study, we present the development of new cell-free assays to estimate the relative affinity and dissociation kinetics of peptide binding to MHC-I. These assays, which we have called BMX-A (relative affinity) and BMX-S (kinetic stability), are reliable, scalable and accessible, in that they use off-the-shelf commercial reagents and standard flow cytometry techniques.

Computational analysis of immunogenic epitopes in the p30 and p54 proteins of African swine fever virus

African swine fever (ASF) is a highly infectious viral disease of pigs, which causes acute fatal haemorrhage and is a severe concern to the global pork industry. The present study followed computational approaches to identify B- and T-cell epitopes for the p30 and p54 proteins of the African swine fever virus (ASFV) by interacting with the swine leukocyte antigen (SLA) alleles. The amino acid sequences of p30 and p54 were analysed for variability and relative solvent accessibility, and their three-dimensional structures were predicted and validated. Molecular dynamics simulation was performed to study the structural and dynamic properties of the protein. Six and five linear B-cell epitopes have been predicted for p30 and p54, respectively. Four and three discontinuous B-cell epitopes have been predicted for p30 and p54, respectively. Further, the top five T-cell epitopes for SLA-1, 2, and 3 have been listed for both proteins. These results can help us to understand the immunodominant regions in the p30 and p54 proteins of ASFV and potentially assist in designing peptide-based diagnostics and vaccines. Also, the identified T-cell epitopes may be considered for peptide-based vaccine design against ASFV.Communicated by Ramaswamy H. Sarma.

Designing a multi-epitope chimeric protein from different potential targets: A potential vaccine candidate against Plasmodium

Malaria is an infectious disease that has been a continuous threat to mankind since the time immemorial. Owing to the complex multi-staged life cycle of the plasmodium parasite, an effective malaria vaccine which is fully protective against the parasite infection is urgently needed to deal with the challenges. In the present study, essential parasite proteins were identified and a chimeric protein with multivalent epitopes was generated. The designed chimeric protein consists of best potential B and T cell epitopes from five different essential parasite proteins. Physiochemical studies of the chimeric protein showed that the modeled vaccine construct was thermo-stable, hydrophilic and antigenic in nature. And the binding of the vaccine construct with Toll-like receptor-4 (TLR-4) as revealed by the molecular docking suggests the possible interaction and role of the vaccine construct in activating the innate immune response. The constructed vaccine being a chimeric protein containing epitopes from different potential candidates could target different stages or pathways of the parasite. Moreover, the approach used in this study is time and cost effective, and can be applied in the discoveries of new potential vaccine targets for other pathogens.

Next-Generation TB Vaccines: Progress, Challenges, and Prospects

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette-Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

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.

Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.

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.

Immunogenicity of CRISPR therapeutics-Critical considerations for clinical translation

CRISPR offers new hope for many patients and promises to transform the way we think of future therapies. Ensuring safety of CRISPR therapeutics is a top priority for clinical translation and specific recommendations have been recently released by the FDA. Rapid progress in the preclinical and clinical development of CRISPR therapeutics leverages years of experience with gene therapy successes and failures. Adverse events due to immunogenicity have been a major setback that has impacted the field of gene therapy. As several in vivo CRISPR clinical trials make progress, the challenge of immunogenicity remains a significant roadblock to the clinical availability and utility of CRISPR therapeutics. In this review, we examine what is currently known about the immunogenicity of CRISPR therapeutics and discuss several considerations to mitigate immunogenicity for the design of safe and clinically translatable CRISPR therapeutics.

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

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

Design of a multi-epitope vaccine against six Nocardia species based on reverse vaccinology combined with immunoinformatics

Background

Nocardia genus, a complex group of species classified to be aerobic actinomycete, can lead to severe concurrent infection as well as disseminated infection, typically in immunocompromised patients. With the expansion of the susceptible population, the incidence of Nocardia has been gradually growing, accompanied by increased resistance of the pathogen to existing therapeutics. However, there is no effective vaccine against this pathogen yet. In this study, a multi-epitope vaccine was designed against the Nocardia infection using reverse vaccinology combined with immunoinformatics approaches.

Methods

First, the proteomes of 6 Nocardia subspecies Nocardia subspecies (Nocardia farcinica, Nocardia cyriacigeorgica, Nocardia abscessus, Nocardia otitidiscaviarum, Nocardia brasiliensis and Nocardia nova) were download NCBI (National Center for Biotechnology Information) database on May 1st, 2022 for the target proteins selection. The essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome proteins were selected for epitope identification. The shortlisted T-cell and B-cell epitopes were fused with appropriate adjuvants and linkers to construct vaccines. The physicochemical properties of the designed vaccine were predicted using multiple online servers. The Molecular docking and molecular dynamics (MD) simulation were performed to understand the binding pattern and binding stability between the vaccine candidate and Toll-like receptors (TLRs). The immunogenicity of the designed vaccines was evaluated via immune simulation.

Results

3 proteins that are essential, virulent-associated or resistant-associated, surface-exposed, antigenic, non-toxic, and non-homologous with the human proteome were selected from 218 complete proteome sequences of the 6 Nocardia subspecies epitope identification. After screening, only 4 cytotoxic T lymphocyte (CTL) epitopes, 6 helper T lymphocyte (HTL) epitopes, and 8 B cell epitopes that were antigenic, non-allergenic, and non-toxic were included in the final vaccine construct. The results of molecular docking and MD simulation showed that the vaccine candidate has a strong affinity for TLR2 and TLR4 of the host and the vaccine-TLR complexes were dynamically stable in the natural environment. The results of the immune simulation indicated that the designed vaccine had the potential to induce strong protective immune responses in the host. The codon optimization and cloned analysis showed that the vaccine was available for mass production.

Conclusion

The designed vaccine has the potential to stimulate long-lasting immunity in the host, but further studies are required to validate its safety and efficacy.

A Structural View at Vaccine Development against M. tuberculosis

Tuberculosis (TB) is still the leading global cause of death from an infectious bacterial agent. Limiting tuberculosis epidemic spread is therefore an urgent global public health priority. As stated by the WHO, to stop the spread of the disease we need a new vaccine, with better coverage than the current Mycobacterium bovis BCG vaccine. This vaccine was first used in 1921 and, since then, there are still no new licensed tuberculosis vaccines. However, there is extremely active research in the field, with a steep acceleration in the past decades, due to the advance of technologies and more rational vaccine design strategies. This review aims to gather latest updates in vaccine development in the various clinical phases and to underline the contribution of Structural Vaccinology (SV) to the development of safer and effective antigens. In particular, SV and the development of vaccine adjuvants is making the use of subunit vaccines, which are the safest albeit the less antigenic ones, an achievable goal. Indeed, subunit vaccines overcome safety concerns but need to be rationally re-engineered to enhance their immunostimulating effects. The larger availability of antigen structural information as well as a better understanding of the complex host immune response to TB infection is a strong premise for a further acceleration of TB vaccine development.