Improved methods for predicting peptide binding affinity to MHC class II molecules

Design of a Chimeric Multi-Epitope Vaccine (CMEV) against Both Leishmania martiniquensis and Leishmania orientalis Parasites Using Immunoinformatic Approaches

Leishmaniasis is a parasitic disease caused by protozoan flagellates of the genus Leishmania. Recently, Leishmania martiniquensis and Leishmania orientalis, emerging species of Leishmania, were isolated from patients in Thailand. Development of the vaccine is demanded; however, genetic differences between the two species make it difficult to design a vaccine that is effective for both species. In this study, we applied immuno-informatic approaches to design a chimeric multi-epitope vaccine (CMEV) against both L. martiniquensis and L. orientalis. We identified seven helper T lymphocyte (HTL) epitopes, sixteen cytotoxic T lymphocyte (CTL) epitopes, and eleven B-cell epitopes from sixteen conserved antigenic proteins found in both species. All these epitopes were joined together, and to further enhance immunogenicity, protein and peptides adjuvant were also added at the N-terminal of the molecule by using specific linkers. The candidate CMEV was subsequently analyzed from the perspectives of the antigenicity, allergenicity, and physiochemical properties. The interaction of the designed multi-epitope vaccine and immune receptor (TLR4) of the host were evaluated based on molecular dockings of the predicted 3D structures. Finally, in silico cloning was performed to construct the expression vaccine vector. Docking analysis showed that the vaccine/TLR4 complex took a stable form. Based on the predicted immunogenicity, physicochemical, and structural properties in silico, the vaccine candidate was expected to be appropriately expressed in bacterial expression systems and show the potential to induce a host immune response. This study proposes the experimental validation of the efficacy of the candidate vaccine construct against the two Leishmania.

Rare variants and HLA haplotypes associated in patients with neuromyelitis optica spectrum disorders

Neuromyelitis optica spectrum disorders (NMOSD) are rare, debilitating autoimmune diseases of the central nervous system. Many NMOSD patients have antibodies to Aquaporin-4 (AQP4). Prior studies show associations of NMOSD with individual Human Leukocyte Antigen (HLA) alleles and with mutations in the complement pathway and potassium channels. HLA allele associations with NMOSD are inconsistent between populations, suggesting complex relationships between the identified alleles and risk of disease. We used a retrospective case-control approach to identify contributing genetic variants in patients who met the diagnostic criteria for NMOSD and their unaffected family members. Potentially deleterious variants identified in NMOSD patients were compared to members of their families who do not have the disease and to existing databases of human genetic variation. HLA sequences from patients from Belgrade, Serbia, were compared to the frequency of HLA haplotypes in the general population in Belgrade. We analyzed exome sequencing on 40 NMOSD patients and identified rare inherited variants in the complement pathway and potassium channel genes. Haplotype analysis further detected two haplotypes, HLA-A*01, B*08, DRB1*03 and HLA-A*01, B*08, C*07, DRB1*03, DQB1*02, which were more prevalent in NMOSD patients than in unaffected individuals. In silico modeling indicates that HLA molecules within these haplotypes are predicted to bind AQP4 at several sites, potentially contributing to the development of autoimmunity. Our results point to possible autoimmune and neurodegenerative mechanisms that cause NMOSD, and can be used to investigate potential NMOSD drug targets.

Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach

Background

Candida tropicalis causes tropical invasive fungal infections, with a high mortality. This fungus has been found to be resistant to antifungal classes such as azoles, echinocandins, and polyenes in several studies. As a result, it is vital to identify novel approaches to prevent and treat C. tropicalis infections. In this study, an in silico technique was utilized to deduce and evaluate a powerful multivalent epitope-based vaccine against C. tropicalis, which targets the secreted aspartic protease 2 (SAP2) protein. This protein is implicated in virulence and host invasion.

Results

By focusing on the Sap2 protein, 11 highly antigenic, non-allergic, non-toxic, and conserved epitopes were identified. These were subsequently paired with RS09 and flagellin adjuvants, as well as a pan HLA DR-binding epitope (PADRE) sequence to create a vaccine candidate that elicited both cell-mediated and humoral immune responses. It was projected that the vaccine design would be soluble, stable, antigenic, and non-allergic. Ramachandran plot analysis was applied to validate the vaccine construct’s 3-dimensional model. The vaccine construct was tested (at 100 ns) using molecular docking and molecular dynamics simulations, which demonstrated that it can stably connect with MHC-I and Toll-like receptor molecules. Based on in silico studies, we have shown that the vaccine construct can be expressed in E. coli. We surmise that the vaccine design is unrelated to any human proteins, indicating that it is safe to use.

Conclusions

The vaccine design looks to be an effective option for preventing C. tropicalis infections, based on the outcomes of the studies. A fungal vaccine can be proposed as prophylactic medicine and could provide initial protection as sometimes diagnosis of infection could be challenging. However, more in vitro and in vivo research is needed to prove the efficacy and safety of the proposed vaccine design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00415-3.

Designing mRNA- and Peptide-Based Vaccine Construct against Emerging Multidrug-Resistant Citrobacter freundii: A Computational-Based Subtractive Proteomics Approach

Background and Objectives: Citrobacter freundii (C. freundii) is an emerging and opportunistic Gram-negative bacteria of the human gastrointestinal tract associated with nosocomial and severe respiratory tract infections. It has also been associated with pneumonia, bloodstream, and urinary tract infections. Intrinsic and adaptive virulence characteristics of C. freundii have become a significant source of diarrheal infections and food poisoning among immune-compromised patients and newborns. Impulsive usage of antibiotics and these adaptive virulence characteristics has modulated the C. freundii into multidrug-resistant (MDR) bacteria. Conventional approaches are futile against MDR C. freundii. Materials and Methods: The current study exploits the modern computational-based vaccine design approach to treat infections related to MDR C. freundii. A whole proteome of C. freundii (strain: CWH001) was retrieved to screen pathogenic and nonhomologous proteins. Six proteins were shortlisted for the selection of putative epitopes for vaccine construct. Highly antigenic, nonallergen, and nontoxic eleven B-cell, HTL, and TCL epitopes were selected for mRNA- and peptide-based multi-epitope vaccine construct. Secondary and tertiary structures of the multi-epitope vaccine (MEVC) were designed, refined, and validated. Results: Evaluation of population coverage of MHC-I and MHC-II alleles were 72% and 90%, respectively. Docking MEVC with TLR-3 receptor with the binding affinity of 21.46 (kcal/mol) occurred through the mmGBSA process. Further validations include codon optimization with an enhanced CAI value of 0.95 and GC content of about 51%. Immune stimulation and molecular dynamic simulation ensure the antibody production upon antigen interaction with the host and stability of the MEVC construct, respectively. Conclusions: These interpretations propose a new strategy to combat MDR C. freundii. Further, in vivo and in vitro trials of this vaccine will be valuable in combating MDR pathogens.

Multi-Epitope-Based Vaccine Candidate for Monkeypox: An In Silico Approach

Currently, there are limited treatment options available for the monkeypox disease. We used a computational strategy to design a specific antigenic vaccine against pathogens. After using various immunoinformatic tools and filters, cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon gamma (IFN-γ)-inducing epitopes, which comprised the vaccine, in addition to other parameters, such as antigenic and allergic profiles, were assessed to confirm the safety of the vaccine. However, vaccine interaction and stability with Toll-like receptors (TLRs) were assessed by dynamic simulation methods, and it was found that the constructed vaccine was stable. In addition, C-IMMSIM tools were used to determine the immune-response-triggering capabilities of the vaccine. These immunoinformatic findings reveal that constructed vaccine candidates may be capable of triggering an efficient immune response for monkeypox viral infections. However, experimental evaluation is required to verify the safety and immunogenic profile of constructed vaccines.

HLA-II-Associated HIV-1 Adaptation Decreases CD4+ T-Cell Responses in HIV-1 Vaccine Recipients

Epitopes with evidence of HLA-II-associated adaptation induce poorly immunogenic CD4+ T-cell responses in HIV-positive (HIV+) individuals. Many such escaped CD4+ T-cell epitopes are encoded by HIV-1 vaccines being evaluated in clinical trials. Here, we assessed whether this viral adaptation adversely impacts CD4+ T-cell responses following HIV-1 vaccination, thereby representing escaped epitopes. When evaluated in separate peptide pools, vaccine-encoded adapted epitopes (AE) induced CD4+ T-cell responses less frequently than nonadapted epitopes (NAE). We also demonstrated that in a polyvalent vaccine, where both forms of the same epitope were encoded, AE were less immunogenic. NAE-specific CD4+ T cells had increased, albeit low, levels of interferon gamma (IFN-γ) cytokine production. Single-cell transcriptomic analyses showed that NAE-specific CD4+ T cells expressed interferon-related genes, while AE-specific CD4+ T cells resembled a Th2 phenotype. Importantly, the magnitude of NAE-specific CD4+ T-cell responses, but not that of AE-specific responses, was found to positively correlate with Env-specific antibodies in a vaccine efficacy trial. Together, these findings show that HLA-II-associated viral adaptation reduces CD4+ T-cell responses in HIV-1 vaccine recipients and suggest that vaccines encoding a significant number of AE may not provide optimal B-cell help for HIV-specific antibody production. IMPORTANCE Despite decades of research, an effective HIV-1 vaccine remains elusive. Vaccine strategies leading to the generation of broadly neutralizing antibodies are likely needed to provide the best opportunity of generating a protective immune response against HIV-1. Numerous studies have demonstrated that T-cell help is necessary for effective antibody generation. However, immunogen sequences from recent HIV-1 vaccine efficacy trials include CD4+ T-cell epitopes that have evidence of immune escape. Our study shows that these epitopes, termed adapted epitopes, elicit lower frequencies of CD4+ T-cell responses in recipients from multiple HIV-1 vaccine trials. Additionally, the counterparts to these epitopes, termed nonadapted epitopes, elicited CD4+ T-cell responses that correlated with Env-specific antibodies in one efficacy trial. These results suggest that vaccine-encoded adapted epitopes dampen CD4+ T-cell responses, potentially impacting both HIV-specific antibody production and efficacious vaccine efforts.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

In silico designing of a novel epitope-based candidate vaccine against Streptococcus pneumoniae with introduction of a new domain of PepO as adjuvant

BACKGROUND

Streptococcus pneumoniae is the leading reason for invasive diseases including pneumonia and meningitis, and also secondary infections following viral respiratory diseases such as flu and COVID-19. Currently, serotype-dependent vaccines, which have several insufficiency and limitations, are the only way to prevent pneumococcal infections. Hence, it is plain to need an alternative effective strategy for prevention of this organism. Protein-based vaccine involving conserved pneumococcal protein antigens with different roles in virulence could provide an eligible alternative to existing vaccines.

METHODS

In this study, PspC, PhtD and PsaA antigens from pneumococcus were taken to account to predict B-cell and helper T-cell epitopes, and epitope-rich regions were chosen to build the construct. To enhance the immunogenicity of the epitope-based vaccine, a truncated N-terminal fragment of pneumococcal endopeptidase O (PepO) was used as a potential TLR2/4 agonist which was identified by molecular docking studies. The ultimate construct was consisted of the chosen epitope-rich regions, along with the adjuvant role (truncated N-PepO) and suitable linkers.

RESULTS

The epitope-based vaccine was assessed as regards physicochemical properties, allergenicity, antigenicity, and toxicity. The 3D structure of the engineered construct was modeled, refined, and validated. Molecular docking and simulation of molecular dynamics (MD) indicated the proper and stable interactions between the vaccine and TLR2/4 throughout the simulation periods.

CONCLUSIONS

For the first time this work presents a novel vaccine consisting of epitopes of PspC, PhtD, and PsaA antigens which is adjuvanted with a new truncated domain of PepO. The computational outcomes revealed that the suggested vaccine could be deemed an efficient therapeutic vaccine for S. pneumoniae; nevertheless, in vitro and in vivo examinations should be performed to prove the potency of the candidate vaccine.

In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis

BACKGROUND

Leprosy is caused by Mycobacterium leprae and Mycobacterium lepromatosis. Most of the affected population lives in low-income countries and may take up to 10 years to show any clinical signs, which is how physicians diagnose it. However, due to progressive cell damage, early diagnosis is very important. The best way to confirm leprosy is through bacilloscopic, which only confirms the diagnosis and has low accuracy or PCR, that requires specialized operators and is expensive. Since the bacteria are fastidious and do not grow in any culture media, therefore, diagnosing leprosy in the lab is still a challenge. In this concern, a recombinant multi-epitope protein can be a beneficial strategy in the management of the diagnosis, as diverse immunogenic epitopes are precisely selected to detect specific antibodies. Therefore, the purposes of the present study were to select immunogenic epitopes from different relevant proteins, with immunogenic properties, and then to construct a recombinant multi-epitope protein that accuses the presence of the antibodies in the early stages of the disease, making it more than appropriate to be applied as a diagnostic tool.

RESULTS

We selected 22 common proteins from both species and, using bioinformatics tools, predicted B and T cell epitopes. After multiple filtering and analyzing, we ended up with 29 epitopes {MHC-I (total 18) and MHC-II (total 11)} from 10 proteins, which were then merged into one construct. Its secondary and tertiary structures were also predicted and refined to comprise the amino acid residues in the best conformation possible. The multi-epitope protein construct was stable, non-host homologous, non-allergic, non-toxic, and elicit humoral and cellular responses. It has conformational B cell epitopes and potential to elicit IFN-γ, IL-4, and IL-10 secretion.

CONCLUSIONS

This novel recombinant multi-epitope protein constructed using the common epitopes from M. leprae and M. lepromatosis has a huge immunological potential, is stable, and can be lyophilized to be used in ELISA plates or even in biosensors, which are user-friendly diagnosis tools, facilitating translation into human sample tests.

Epitope-Evaluator: An interactive web application to study predicted T-cell epitopes

Multiple immunoinformatic tools have been developed to predict T-cell epitopes from protein amino acid sequences for different major histocompatibility complex (MHC) alleles. These prediction tools output hundreds of potential peptide candidates which require further processing; however, these tools are either not graphical or not friendly for non-programming users. We present Epitope-Evaluator, a web tool developed in the Shiny/R framework to interactively analyze predicted T-cell epitopes. Epitope-Evaluator contains six tools providing the distribution of epitopes across a selected set of MHC alleles, the promiscuity and conservation of epitopes, and their density and location within antigens. Epitope-Evaluator requires as input the fasta file of protein sequences and the output prediction file coming out from any predictor. By choosing different cutoffs and parameters, users can produce several interactive plots and tables that can be downloaded as JPG and text files, respectively. Using Epitope-Evaluator, we found the HLA-B*40, HLA-B*27:05 and HLA-B*07:02 recognized fewer epitopes from the SARS-CoV-2 proteome than other MHC Class I alleles. We also identified shared epitopes between Delta, Omicron, and Wuhan Spike variants as well as variant-specific epitopes. In summary, Epitope-Evaluator removes the programming barrier and provides intuitive tools, allowing a straightforward interpretation and graphical representations that facilitate the selection of candidate epitopes for experimental evaluation. The web server Epitope-Evaluator is available at https://fuxmanlab.shinyapps.io/Epitope-Evaluator/.

Immunoinformatics-Aided Analysis of RSV Fusion and Attachment Glycoproteins to Design a Potent Multi-Epitope Vaccine

Respiratory syncytial virus (RSV) usually causes respiratory tract infections of upper airways in infants and young children. Despite recent medical advances, no approved vaccine is available to control RSV infections. Therefore, we conducted an immunoinformatics study to design and evaluate a potential multi-epitope vaccine against RSV. Sequence-based analyses of the glycoproteins F and G revealed a total of eight CD8 T-cell and three CD4 T-cell epitopes after considering antigenicity, binding affinity and other parameters. Molecular docking analysis confirmed that these T-cell epitopes developed strong structural associations with HLA allele(s). By integrating these prioritized epitopes with linkers and a cholera toxin-derived adjuvant, a multi-epitope vaccine was designed. The developed vaccine was found to be stable, non-allergenic, flexible and antigenic. Molecular docking analysis revealed a striking mean HADDOCK score (-143.3) of top-ranked vaccine-TLR cluster and a Gibbs free energy change (ΔG) value of -11.3 kcal mol-1. As per computational immune simulation results, the vaccine generated a high titer of antibodies (especially IgM) and effector T-cells. Also, codon optimization and in silico cloning ensured the increased expression of vaccine in Escherichia coli. Altogether, we anticipate that the multi-epitope vaccine reported in this study will stimulate humoral and cellular responses against RSV infection, subject to follow-up experimental validation.

Promotion of neutralizing antibody-independent immunity to wild-type and SARS-CoV-2 variants of concern using an RBD-Nucleocapsid fusion protein

Both T cells and B cells have been shown to be generated after infection with SARS-CoV-2 yet protocols or experimental models to study one or the other are less common. Here, we generate a chimeric protein (SpiN) that comprises the receptor binding domain (RBD) from Spike (S) and the nucleocapsid (N) antigens from SARS-CoV-2. Memory CD4⁺ and CD8⁺ T cells specific for SpiN could be detected in the blood of both individuals vaccinated with Coronavac SARS-CoV-2 vaccine and COVID-19 convalescent donors. In mice, SpiN elicited a strong IFN-γ response by T cells and high levels of antibodies to the inactivated virus, but not detectable neutralizing antibodies (nAbs). Importantly, immunization of Syrian hamsters and the human Angiotensin Convertase Enzyme-2-transgenic (K18-ACE-2) mice with Poly ICLC-adjuvanted SpiN promotes robust resistance to the wild type SARS-CoV-2, as indicated by viral load, lung inflammation, clinical outcome and reduction of lethality. The protection induced by SpiN was ablated by depletion of CD4⁺ and CD8⁺ T cells and not transferred by antibodies from vaccinated mice. Finally, vaccination with SpiN also protects the K18-ACE-2 mice against infection with Delta and Omicron SARS-CoV-2 isolates. Hence, vaccine formulations that elicit effector T cells specific for the N and RBD proteins may be used to improve COVID-19 vaccines and potentially circumvent the immune escape by variants of concern.

Protection against SARS-CoV-2 infection involves T cell and B cell responses but only studying one or the other has proved difficult. Here the authors immunise with a fusion protein construct of N and RBD proteins from SARS-CoV-2 and find that this promotes protection in animal models preferentially via T cells.

Refining the DC-targeting vaccination for preventing emerging infectious diseases

The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.

Computer-Aided Tools and Resources for Fungal Pathogens: An Application of Reverse Vaccinology for Mucormycosis

Increasing fungal infections in immunocompromised hosts are a growing concern for global public health. Along with treatments, preventive measures are required. The emergence of reverse vaccinology has opened avenues for using genomic and proteomic data from pathogens in the design of vaccines. In this work, we present a comprehensive collection of various computational tools and databases with potential to aid in vaccine development. The ongoing pandemic has directed attention toward the increasing number of mucormycosis infections in COVID-19 patients. As a case study, we developed a computational pipeline for assisting vaccine development for mucormycosis. We obtained 6 proteins from 29,447 sequences from UniProtKB as potential vaccine candidates against mucormycosis, fulfilling multiple criteria. These criteria included potential characteristics, namely adhesin properties, surface or extracellular localization, antigenicity, no similarity to any human proteins, nonallergenicity, stability in vitro, and expression in fungal cells. These six proteins were predicted to have B cell and T cell epitopes, proinflammatory inducing peptides, and orthologs in several mucormycosis-causing species. These data could aid in vaccine development against mucormycosis for at-risk individuals.

In-silico design of an immunoinformatics based multi-epitope vaccine against Leishmania donovani

BACKGROUND

Visceral Leishmaniasis (VL) is a fatal vector-borne parasitic disorder occurring mainly in tropical and subtropical regions. VL falls under the category of neglected tropical diseases with growing drug resistance and lacking a licensed vaccine. Conventional vaccine synthesis techniques are often very laborious and challenging. With the advancement of bioinformatics and its application in immunology, it is now more convenient to design multi-epitope vaccines comprising predicted immuno-dominant epitopes of multiple antigenic proteins. We have chosen four antigenic proteins of Leishmania donovani and identified their T-cell and B-cell epitopes, utilizing those for in-silico chimeric vaccine designing. The various physicochemical characteristics of the vaccine have been explored and the tertiary structure of the chimeric construct is predicted to perform docking studies and molecular dynamics simulations.

RESULTS

The vaccine construct is generated by joining the epitopes with specific linkers. The predicted tertiary structure of the vaccine has been found to be valid and docking studies reveal the construct shows a high affinity towards the TLR-4 receptor. Population coverage analysis shows the vaccine can be effective on the majority of the world population. In-silico immune simulation studies confirms the vaccine to raise a pro-inflammatory response with the proliferation of activated T and B cells. In-silico codon optimization and cloning of the vaccine nucleic acid sequence have also been achieved in the pET28a vector.

CONCLUSION

The above bioinformatics data support that the construct may act as a potential vaccine. Further wet lab synthesis of the vaccine and in vivo works has to be undertaken in animal model to confirm vaccine potency.

Exploring whole proteome to contrive multi-epitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach

Neo-Coronavirus (NeoCoV) is a novel Betacoronavirus (β-CoVs or Beta-CoVs) discovered in bat specimens in South Africa during 2011. The viral sequence is highly similar to Middle East Respiratory Syndrome, particularly that of structural proteins. Thus, scientists have emphasized the threat posed by NeoCoV associated with human angiotensin-converting enzyme 2 (ACE2) usage, which could lead to a high death rate and faster transmission rate in humans. The development of a NeoCoV vaccine could provide a promising option for the future control of the virus in case of human infection. In silico predictions can decrease the number of experiments required, making the immunoinformatics approaches cost-effective and convenient. Herein, with the aid of immunoinformatics and reverse vaccinology, we aimed to formulate a multi-epitope vaccine that may be used to prevent and treat NeoCoV infection. Based on the NeoCoV proteins, B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were shortlisted. Four vaccines (Neo-1-4) were devised by fusing shortlisted epitopes with appropriate adjuvants and linkers. The secondary and three-dimensional structures of final vaccines were then predicted. The binding interactions of these potential vaccines with toll-like immune receptors (TLR-2, TLR-3, and TLR-4) and major histocompatibility complex molecules (MHC-I and II) reveal that they properly fit into the receptors' binding domains. Besides, Neo-1 and Neo-4 vaccines exhibited better docking energies of -101.08 kcal/mol and -114.47 kcal/mol, respectively, with TLR-3 as compared to other vaccine constructs. The constructed vaccines are highly antigenic, non-allergenic, soluble, non-toxic, and topologically assessable with good physiochemical characteristics. Codon optimization and in-silico cloning confirmed efficient expression of the designed vaccines in Escherichia coli strain K12. In-silico immune simulation indicated that Neo-1 and Neo-4 vaccines could induce a strong immune response against NeoCoV. Lastly, the binding stability and strong binding affinity of Neo-1 and Neo-4 with TLR-3 receptor were validated using molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). The final vaccines require experimental validation to establish their safety and effectiveness in preventing NeoCoV infections.

Germline IgM predicts T-cell immunity to Pneumocystis

Pneumocystis is the most common fungal pulmonary infection in children under 5. In children with primary immunodeficiency, Pneumocystis often presents at 3-6 months that coincides with the nadir of maternal IgG and where IgM is the dominant immunoglobulin isotype. Since B cells are the dominant antigen-presenting cells for Pneumocystis, we hypothesized the presence of fungal specific IgMs in human and mice and that these IgM specificities would predict T cell antigens. We detected fungal specific IgMs in human and mouse serum and utilized immunoprecipitation to determine if any antigens were similar across donors. We then assessed T cell responses to these antigens. We found anti-Pneumocystis IgM in wild-type mice as well as Aicda-/- mice and in human cord blood. Immunoprecipitation of Pneumocystis murina with human cord blood identified shared antigens among these donors. Using class II MHC binding prediction, we designed peptides with these antigens and identified robust peptide specific lung T cell responses after P. murina infection. After mice were immunized with two of the antigens, adoptive transfer of vaccine elicited CD4+ T cells showed effector activity suggesting that these antigens contain protective Pneumocystis epitopes. These data support the notion that germline encoded IgM B-cell receptors are critical in antigen presentation and T cell priming in early Pneumocystis infection.

CAF08 adjuvant enables single dose protection against respiratory syncytial virus infection in murine newborns

Respiratory syncytial virus is a leading cause of morbidity and mortality in children, due in part to their distinct immune system, characterized by impaired induction of Th 1 immunity. Here we show application of cationic adjuvant formulation CAF08, a liposomal vaccine formulation tailored to induce Th 1 immunity in early life via synergistic engagement of Toll-like Receptor 7/8 and the C-type lectin receptor Mincle. We apply quantitative phosphoproteomics to human dendritic cells and reveal a role for Protein Kinase C-δ for enhanced Th1 cytokine production in neonatal dendritic cells and identify signaling events resulting in antigen cross-presentation. In a murine in vivo model a single immunization at birth with CAF08-adjuvanted RSV pre-fusion antigen protects newborn mice from RSV infection by induction of antigen-specific CD8+ T-cells and Th1 cells. Overall, we describe a pediatric adjuvant formulation and characterize its mechanism of action providing a promising avenue for development of early life vaccines against RSV and other respiratory viral pathogens.

Immunoinformatics-Based Proteome Mining to Develop a Next-Generation Vaccine Design against Borrelia burgdorferi: The Cause of Lyme Borreliosis

The tick-borne bacterium, Borrelia burgdorferi has been implicated in Lyme disease-a deadly infection, formerly confined to North America, but currently widespread across Europe and Asia. Despite the severity of this disease, there is still no human Lyme disease vaccine available. A reliable immunoinformatic approach is urgently needed for designing a therapeutic vaccine against this Gram-negative pathogen. Through this research, we explored the immunodominant proteins of B. burgdorferi and developed a novel and reliable vaccine design with great immunological predictability as well as low contamination and autoimmunity risks. Our initial analysis involved proteome-wide analysis to filter out proteins on the basis of their redundancy, homology to humans, virulence, immunogenicity, and size. Following the selection of proteins, immunoinformatic tools were employed to identify MHC class I & II epitopes and B-cell epitopes, which were subsequently subjected to a rigorous screening procedure. In the final formulation, ten common MHC-I and II epitopes were used together with a suitable adjuvant. We predicted that the final chimeric multi-epitope vaccine could invoke B-cell responses and IFN-gamma-mediated immunity as well as being stable and non-allergenic. The dynamics simulations predicted the stable folding of the designed molecule, after which the molecular docking predicted the stability of the interaction between the potential antigenic epitopes and human immune receptors. Our studies have shown that the designed next-generation vaccine stimulates desirable immune responses, thus potentially providing a viable way to prevent Lyme disease. Nevertheless, further experimental studies in a wet lab are needed in order to validate the results.

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4⁺ and CD8⁺ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

Immunoinformatic screening of Marburgvirus epitopes and computational investigations of epitope-allele complexes

Marburgvirus (MARV), a member of the Filovirus family, causes severe hemorrhagic fever in humans. Currently, there are no approved vaccines or post exposure treatment methods available against MARV. With the aim of identifying vaccine candidates against MARV, we employ different sequence-based computational methods to predict the MHC-I and MHC-II T-cell epitopes as well as B-cell epitopes for the complete MARV genome. We analyzed the variations in the predicted epitopes among four MARV variants, the Lake Victoria, Angola, Musoke, and Ravn. We used a consensus approach to identify several epitopes, including novel epitopes, and narrowed down the selection based on different parameters such as antigenicity and IC₅₀ values. The selected epitopes can be used in various vaccine constructs that give effective antibody responses. The MHC-I epitope-allele complexes for GP and NP with favorably low IC₅₀ values were investigated using molecular dynamics computations to determine the molecular details of the epitope-allele complexes. This study provides information for further experimental validation of the potential epitopes and the design and development of MARV vaccines.

Multi Epitope-Based Vaccine Design for Protection Against Mycobacterium tuberculosis and SARS-CoV-2 Coinfection

Background

A prophylactic and immunotherapeutic vaccine for Mycobacterium tuberculosis (MTB) and SARS-CoV-2 coinfection needs to be developed for a proactive and effective therapeutic approach. Therefore, this study aims to use immunoinformatics to design a multi-epitope vaccine for protection against MTB and SARS-CoV-2 coinfection.

Methods

The bioinformatic techniques were used to screen and construct potential epitopes from outer membrane protein A Rv0899 of MTB and spike glycoprotein of SARS-CoV-2 for B and T cells. The antigenicity, allergenicity, and several physiochemical properties of the developed multi-epitope vaccination were then evaluated. Additionally, molecular docking and normal mode analysis (NMA) were utilized in evaluating the vaccine’s immunogenicity and complex stability.

Results

Selected proteins and predicted epitopes suggest that the vaccine prediction can be helpful in the protection against both SARS-CoV-2 and MTB coinfection. Through docking molecular and NMA, the vaccine-TLR4 protein interaction was predicted to be efficient with a high level of IgG, T-helper cells, T-cytotoxic cells, andIFN-γ.

Conclusion

This epitope-based vaccine is a potentially attractive tool for SARS-CoV-2 and MTB coinfection vaccine development.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

CD4+ T-Cell Epitope Prediction by Combined Analysis of Antigen Conformational Flexibility and Peptide-MHCII Binding Affinity

Antigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition among antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy for F1-V without considering peptide-MHCII binding affinity. We also show that APL achieves excellent performance over two benchmark antigen sets. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, MHCII binding, or both. For a sample of 13 diverse antigens, the accuracy of epitope prediction by the combination of APL and I-A^b-MHCII-peptide affinity reached 36%. When MHCII allele specificity was also diverse, such as in human immunity, prediction of dominant epitopes by APL alone reached 42% when using a stringent scoring threshold. Because dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL, and thus, the requirement for a crystal structure is not a severe limitation.

A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach

Malaria is a complex disease caused by parasites of the genus Plasmodium and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. Thus, a combination of different approaches is needed to control malaria. Resistance to first-line drugs and insecticides, on the other hand, makes the need for an effective vaccination more urgent than ever. Because erythrocyte parasites cause the most clinical symptoms, developing a vaccination for this stage of infection might be highly beneficial. In this research, we employed various bioinformatics methods to create an efficient multi-epitope vaccine that induces antibodies against the blood stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The B, HTL epitopes, and epitope conservation were predicted. The predicted epitopes (including 5 B and 5 HTL epitopes) were connected using suitable linkers, and the flagellin molecule was used as an adjuvant to improve its immunogenicity. The final construct vaccine with 414 amino acids long was designed. The vaccine's allergenicity, antigenicity, solubility, physicochemical characteristics, 2D and 3D structure modeling, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation were tested. In silico immune simulation results showed significantly elevated IgG1 and IgM and T helper cells, INF γ, IL 2, and B-cell populations after the injection of the designed vaccine. These significant computational analyses indicated that our proposed vaccine candidate might activate suitable immune responses against malaria. However, in vitro and in vivo studies are essential for further validation.

Neoantigens and NK Cells: “Trick or Treat” the Cancers?

Immunotherapy has become an important treatment strategy for cancer patients nowadays. Targeting cancer neoantigens presented by major histocompatibility complex (MHC) molecules, which emerge as a result of non-synonymous somatic mutations with high immunogenicity, is one of the most promising cancer immunotherapy strategies. Currently, several therapeutic options based on the personalized or shared neoantigens have been developed, including neoantigen vaccine and adoptive T-cell therapy, both of which are now being tested in clinical trials for various malignancies. The goal of this review is to outline the use of neoantigens as cancer therapy targets, with an emphasis on neoantigen identification, clinical usage of personalized neoantigen-based cancer therapy agents, and the development of off-the-shelf products based on shared neoantigens. In addition, we introduce and discuss the potential impact of the neoantigen–MHC complex on natural killer (NK) cell antitumor function, which could be a novel way to boost immune response-induced cytotoxicity against malignancies.

In silico design of multi-epitope-based peptide vaccine against SARS-CoV-2 using its spike protein

SARS-CoV-2 has been efficient in ensuring that many countries are brought to a standstill. With repercussions ranging from rampant mortality, fear, paranoia, and economic recession, the virus has brought together countries to look at possible therapeutic countermeasures. With prophylactic interventions possibly months away from being particularly effective, a slew of measures and possibilities concerning the design of vaccines are being worked upon. We attempted a structure-based approach utilizing a combination of epitope prediction servers and Molecular dynamic (MD) simulations to develop a multi-epitope-based subunit vaccine that involves the two subunits of the spike glycoprotein of SARS-CoV-2 (S1 and S2) coupled with a substantially effective chimeric adjuvant to create stable vaccine constructs. The designed constructs were evaluated based on their docking with Toll-Like Receptor (TLR) 4. Our findings provide an epitope-based peptide fragment that can be a potential candidate for the development of a vaccine against SARS-CoV-2. Recent experimental studies based on determining immunodominant regions across the spike glycoprotein of SARS-CoV-2 indicate the presence of the predicted epitopes included in this study.Communicated by Ramaswamy H. Sarma.

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction

Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.

In Silico Designed Multi-Epitope Immunogen “Tpme-VAC/LGCM-2022” May Induce Both Cellular and Humoral Immunity against Treponema pallidum Infection

Syphilis, a sexually transmitted infection caused by the spirochete Treponema pallidum, has seen a resurgence over the past years. T. pallidum is capable of early dissemination and immune evasion, and the disease continues to be a global healthcare burden. The purpose of this study was to design a multi-epitope immunogen through an immunoinformatics-based approach. Multi-epitope immunogens constitute carefully selected epitopes belonging to conserved and essential bacterial proteins. Several physico-chemical characteristics, such as antigenicity, allergenicity, and stability, were determined. Further, molecular docking and dynamics simulations were performed, ensuring binding affinity and stability between the immunogen and TLR-2. An in silico cloning was performed using the pET-28a(+) vector and codon adaptation for E. coli. Finally, an in silico immune simulation was performed. The in silico predictions obtained in this work indicate that this construct would be capable of inducing the requisite immune response to elicit protection against T. pallidum. Through this methodology we have designed a promising potential vaccine candidate for syphilis, namely Tpme-VAC/LGCM-2022. However, it is necessary to validate these findings in in vitro and in vivo assays.

The influence of HLA genotype on the development of metal hypersensitivity following joint replacement

Background

Over five million joint replacements are performed across the world each year. Cobalt chrome (CoCr) components are used in most of these procedures. Some patients develop delayed-type hypersensitivity (DTH) responses to CoCr implants, resulting in tissue damage and revision surgery. DTH is unpredictable and genetic links have yet to be definitively established.

Methods

At a single site, we carried out an initial investigation to identify HLA alleles associated with development of DTH following metal-on-metal hip arthroplasty. We then recruited patients from other centres to train and validate an algorithm incorporating patient age, gender, HLA genotype, and blood metal concentrations to predict the development of DTH. Accuracy of the modelling was assessed using performance metrics including time-dependent receiver operator curves.

Results

Using next-generation sequencing, here we determine the HLA genotypes of 606 patients. 176 of these patients had experienced failure of their prostheses; the remaining 430 remain asymptomatic at a mean follow up of twelve years. We demonstrate that the development of DTH is associated with patient age, gender, the magnitude of metal exposure, and the presence of certain HLA class II alleles. We show that the predictive algorithm developed from this investigation performs to an accuracy suitable for clinical use, with weighted mean survival probability errors of 1.8% and 3.1% for pre-operative and post-operative models respectively.

Conclusions

The development of DTH following joint replacement appears to be determined by the interaction between implant wear and a patient's genotype. The algorithm described in this paper may improve implant selection and help direct patient surveillance following surgery. Further consideration should be given towards understanding patient-specific responses to different biomaterials.

Calreticulin mutant myeloproliferative neoplasms induce MHC-I skewing, which can be overcome by an optimized peptide cancer vaccine

The majority of JAK2V617F-negative myeloproliferative neoplasms (MPNs) have disease-initiating frameshift mutations in calreticulin (CALR), resulting in a common carboxyl-terminal mutant fragment (CALRMUT), representing an attractive source of neoantigens for cancer vaccines. However, studies have shown that CALRMUT-specific T cells are rare in patients with CALRMUT MPN for unknown reasons. We examined class I major histocompatibility complex (MHC-I) allele frequencies in patients with CALRMUT MPN from two independent cohorts. We observed that MHC-I alleles that present CALRMUT neoepitopes with high affinity are underrepresented in patients with CALRMUT MPN. We speculated that this was due to an increased chance of immune-mediated tumor rejection by individuals expressing one of these MHC-I alleles such that the disease never clinically manifested. As a consequence of this MHC-I allele restriction, we reasoned that patients with CALRMUT MPN would not efficiently respond to a CALRMUT fragment cancer vaccine but would when immunized with a modified CALRMUT heteroclitic peptide vaccine approach. We found that heteroclitic CALRMUT peptides specifically designed for the MHC-I alleles of patients with CALRMUT MPN efficiently elicited a CALRMUT cross-reactive CD8+ T cell response in human peripheral blood samples but not to the matched weakly immunogenic CALRMUT native peptides. We corroborated this effect in vivo in mice and observed that C57BL/6J mice can mount a CD8+ T cell response to the CALRMUT fragment upon immunization with a CALRMUT heteroclitic, but not native, peptide. Together, our data emphasize the therapeutic potential of heteroclitic peptide-based cancer vaccines in patients with CALRMUT MPN.

Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks

Simple Summary

Major histocompatibility complex molecules are of significant biological and clinical importance due to their utility in immunotherapy. The prediction of potential MHC binding peptides can estimate a T-cell immune response. The variable length of existing MHC binding peptides creates difficulty for MHC binding prediction algorithms. Thus, we utilized a bilateral and variable long-short term memory neural network to address this specific problem and developed a novel MHC binding prediction tool.

Abstract

As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use.

Design, immunogenicity, and efficacy of a pan-sarbecovirus dendritic-cell targeting vaccine

BACKGROUND

There is an urgent need of a new generation of vaccine that are able to enhance protection against SARS-CoV-2 and related variants of concern (VOC) and emerging coronaviruses.

METHODS

We identified conserved T- and B-cell epitopes from Spike (S) and Nucleocapsid (N) highly homologous to 38 sarbecoviruses, including SARS-CoV-2 VOCs, to design a protein subunit vaccine targeting antigens to Dendritic Cells (DC) via CD40 surface receptor (CD40.CoV2).

FINDINGS

CD40.CoV2 immunization elicited high levels of cross-neutralizing antibodies against SARS-CoV-2, VOCs, and SARS-CoV-1 in K18-hACE2 transgenic mice, associated with viral control and survival after SARS-CoV-2 challenge. A direct comparison of CD40.CoV2 with the mRNA BNT162b2 vaccine showed that the two vaccines were equally immunogenic in mice. We demonstrated the potency of CD40.CoV2 to recall in vitro human multi-epitope, functional, and cytotoxic SARS-CoV-2 S- and N-specific T-cell responses that are unaffected by VOC mutations and cross-reactive with SARS-CoV-1 and, to a lesser extent, MERS epitopes.

INTERPRETATION

We report the immunogenicity and antiviral efficacy of the CD40.CoV2 vaccine in a preclinical model providing a framework for a pan-sarbecovirus vaccine.

FUNDINGS

This work was supported by INSERM and the Investissements d'Avenir program, Vaccine Research Institute (VRI), managed by the ANR and the CARE project funded from the Innovative Medicines Initiative 2 Joint Undertaking (JU).

SM-COLSARSPROT: Highly Immunogenic Supramutational Synthetic Peptides Covering the World's Population

Fifty ~20-amino acid (aa)-long peptides were selected from functionally relevant SARS-CoV-2 S, M, and E proteins for trial B-21 and another 53 common ones, plus some new ones derived from the virus' main genetic variants for complementary trial C-21. Peptide selection was based on tremendous SARS-CoV-2 genetic variability for analysing them concerning vast human immunogenetic polymorphism for developing the first supramutational, Colombian SARS-protection (SM-COLSARSPROT), peptide mixture. Specific physicochemical rules were followed, i.e., aa predilection for polyproline type II left-handed (PPIIL) formation, replacing β-branched, aromatic aa, short-chain backbone H-bond-forming residues, π-π interactions (n→π* and π-CH), aa interaction with π systems, and molecular fragments able to interact with them, disrupting PPIIL propensity formation. All these modified structures had PPIIL formation propensity to enable target peptide interaction with human leukocyte antigen-DRβ1* (HLA-DRβ1*) molecules to mediate antigen presentation and induce an appropriate immune response. Such modified peptides were designed for human use; however, they induced high antibody titres against S, M, and E parental mutant peptides and neutralising antibodies when suitably modified and chemically synthesised for immunising 61 major histocompatibility complex class II (MHCII) DNA genotyped Aotus monkeys (matched with their corresponding HLA-DRβ1* molecules), predicted to cover 77.5% to 83.1% of the world's population. Such chemically synthesised peptide mixture represents an extremely pure, stable, reliable, and cheap vaccine for COVID-19 pandemic control, providing a new approach for a logical, rational, and soundly established methodology for other vaccine development.

Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol

Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.

Elucidating the Therapeutic Potential of Cell-Penetrating Peptides in Human Tenon Fibroblast Cells

Cell-penetrating peptides (CPPs) have been widely used as vehicles for delivering therapeutic molecules to the site of action. Apart from their delivering potential, the biological effects of CPPs have not been explored in detail. JTS-1 is a CPP that has been reported to have gene delivery functions, although its biological role is yet to be determined. Hence, in this study, we revealed the biological mechanism such as its uptake mechanism and immunogenic potential and function using primary human tenon fibroblast (TF) cells collected from patients undergoing glaucoma trabeculectomy surgery. Our results showed that the JTS-1 peptide has an α-helical structure and is nontoxic up to 1 μM concentration. It was found to be colocalized with early endosome (Rab5), recycling endosome (Rab7), and Rab11 and interacted with major histocompatibility complex (MHC) class I and II. The peptide also affected actin polymerization, which is regulated by cofilin phosphorylation and ROCK1 localization. It also inhibited TF cell proliferation. Therefore, the JTS-1 peptide could be used as a possible therapeutic agent for modifying the fibrosis process, where TF proliferation is a key cause of surgery failure.

T-cell epitope-based vaccine prediction against Aspergillus fumigatus: a harmful causative agent of aspergillosis

BACKGROUND

Among the most common causes of invasive aspergillosis and acute bronchopulmonary aspergillosis is Aspergillus fumigatus. Transmission with A. fumigatus produces aggressive aspergillosis in allogeneic haematopoietic stem cell transplant recipients, HIV patients, and cancer patients. Asthmatics and cystic fibrosis patients are allergic to A. fumigatus. MHC class-II binding epitopes can initiate immunogenic responses in patients. In this study, we deployed immunoinformatic study to reveal epitopes from fungal proteins.

RESULTS

In modern research, we found multiple epitopes ITLKLLHRYSYKLAG, KLVLRAFPNHFRGDS, RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD from crucial proteins of A. fumigatus 5,8-linoleate diol synthase (ACO55067.2) and ChainB-chitinase A1 (2XVN_B). RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD epitopes interact with HLA-DRB01_0101, while ITLKLLHRYSYKLAG and KLVLRAFPNHFRGDS epitopes interact with HLA-DRB01_1501. Molecular docking analysis reveals atomic contact energy (ACE) value for these five epitopes shown below -5 Kcal/mol in docked state.

CONCLUSIONS

The invasive aspergillosis and acute bronchopulmonary aspergillosis are caused by harmful fungal pathogen Aspergillus fumigatus. Our modern immunoinformatic research shows ITLKLLHRYSYKLAG, KLVLRAFPNHFRGDS, RYSYKLAGVNQVDVV, GKSFELNQAARAVTQ, and LHRYSYKLAGVNQVD epitopes could bind to MHC-II HLA allelic determinants and can initiate immunogenic response in patients affected by Aspergillus fumigatus.

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

Designing a novel multi‑epitope vaccine against Ebola virus using reverse vaccinology approach

Ebola virus (EBOV) is a dangerous zoonotic infectious disease. To date, more than 25 EBOV outbreaks have been documented, the majority of which have occurred in Central Africa. The rVSVG-ZEBOV-GP vaccine (ERVEBO), a live attenuated vaccine, has been approved by the US Food and Drug Administration (FDA) to combat EBOV. Because of the several drawbacks of live attenuated vaccines, multi-epitope vaccines probably appear to be safer than live attenuated vaccines. In this work, we employed immunoinformatics tools to design a multi-epitope vaccine against EBOV. We collected sequences of VP35, VP24, VP30, VP40, GP, and NP proteins from the NCBI database. T-cell and linear B-cell epitopes from target proteins were identified and tested for antigenicity, toxicity, allergenicity, and conservancy. The selected epitopes were then linked together in the vaccine's primary structure using appropriate linkers, and the 50S ribosomal L7/L12 (Locus RL7 MYCTU) sequence was added as an adjuvant to the vaccine construct's N-terminal. The physicochemical, antigenicity, and allergenicity parameters of the vaccine were all found to be satisfactory. The 3D model of the vaccine was predicted, refined, and validated. The vaccine construct had a stable and strong interaction with toll-like receptor 4 (TLR4) based on molecular docking and molecular dynamic simulation (MD) analysis. The results of codon optimization and in silico cloning revealed that the proposed vaccine was highly expressed in Escherichia coli (E. coli). The findings of this study are promising; however, experimental validations should be carried out to confirm these findings.

Computational design of immunogenic peptide constructs comprising multiple HLA restricted Dengue virus envelope epitopes

Dengue virus (DENV) is endemic in 100 countries with ability to impact nearly 50% of world population. DENV envelope (E) protein is responsible for viral attachment to host cells and has been target of various countermeasure development efforts. The current study focuses on a consensus computational approach to identify cross-reactive, immunogenic DENV-2 E peptides displaying promiscuity with a wide array of HLA molecules. Four conserved peptides (FP-1, FP-2, FP-3 and FP-4) containing multiple CD8⁺ and CD4⁺ T cell epitopes were identified by employment of various immunoinformatics tools. FP-1, FP-2, FP-3 and FP-4 were estimated to bind with 227, 1787, 1008 and 834 HLA alleles respectively. RMSD values obtained by Molecular docking (CABS-Dock) with 20 HLA alleles (10 each of HLA class I and II) resulted into comparable RMSD values of identified epitopes with native peptides which represents the natural presentation of epitopes to HLA molecules. These peptides were also found to be part of previous experimentally validated immunogenic peptides. Further, a dengue immunogenic peptide construct was generated by linking the four peptides, an adjuvant and a 6x histidine tag. The construct showed strong binding and stability with Toll-like receptor (TLR4). Collectively, these results provide strong evidence in the support of the immunogenic potential of the dengue immunogenic peptide construct. This article is protected by copyright. All rights reserved.

High-resolution profiling of MHC II peptide presentation capacity reveals SARS-CoV-2 CD4 T cell targets and mechanisms of immune escape

Understanding peptide presentation by specific MHC alleles is fundamental for controlling physiological functions of T cells and harnessing them for therapeutic use. However, commonly used in silico predictions and mass spectroscopy have their limitations in precision, sensitivity, and throughput, particularly for MHC class II. Here, we present MEDi, a novel mammalian epitope display that allows an unbiased, affordable, high-resolution mapping of MHC peptide presentation capacity. Our platform provides a detailed picture by testing every antigen-derived peptide and is scalable to all the MHC II alleles. Given the urgent need to understand immune evasion for formulating effective responses to threats such as SARS-CoV-2, we provide a comprehensive analysis of the presentability of all SARS-CoV-2 peptides in the context of several HLA class II alleles. We show that several mutations arising in viral strains expanding globally resulted in reduced peptide presentability by multiple HLA class II alleles, while some increased it, suggesting alteration of MHC II presentation landscapes as a possible immune escape mechanism.

Mining the Proteome of Toxoplasma Parasites Seeking Vaccine and Diagnostic Candidates

Simple Summary

The One Health concept to toxoplasmosis highlights that the health of humans is closely related to the health of animals and our common environment. Toxoplasmosis outcomes might be severe and fatal in patients with immunodeficiency, diabetes, and pregnant women and infants. Consequently, the development of effective vaccine and diagnostic strategies is urgent for the elimination of this disease. Proteomics analysis has allowed the identification of key proteins that can be utilized in the development of novel disease diagnostics and vaccines. This work presents relevant proteins found in the proteome of the life cycle-specific stages of Toxoplasma parasites. In fact, it brings together the main functionality key proteins from Toxoplasma parasites coming from proteomic approaches that are most likely to be useful in improving the disease management, and critically proposes innovative directions to finally develop promising vaccines and diagnostics tools.

Abstract

Toxoplasma gondii is a pathogenic protozoan parasite that infects the nucleated cells of warm-blooded hosts leading to an infectious zoonotic disease known as toxoplasmosis. The infection outcomes might be severe and fatal in patients with immunodeficiency, diabetes, and pregnant women and infants. The One Health approach to toxoplasmosis highlights that the health of humans is closely related to the health of animals and our common environment. The presence of drug resistance and side effects, the further improvement of sensitivity and specificity of serodiagnostic tools and the potentiality of vaccine candidates to induce the host immune response are considered as justifiable reasons for the identification of novel targets for the better management of toxoplasmosis. Thus, the identification of new critical proteins in the proteome of Toxoplasma parasites can also be helpful in designing and test more effective drugs, vaccines, and diagnostic tools. Accordingly, in this study we present important proteins found in the proteome of the life cycle-specific stages of Toxoplasma parasites that are potential diagnostic or vaccine candidates. The current study might help to understand the complexity of these parasites and provide a possible source of strategies and biomolecules that can be further evaluated in the pathobiology of Toxoplasma parasites and for diagnostics and vaccine trials against this disease.

Designing a novel mRNA vaccine against Vibrio harveyi infection in fish: an immunoinformatics approach

Vibrio harveyi belongs to the family Vibrionaceae of class Gammaproteobacteria. Around 12 Vibrio species can cause gastroenteritis (gastrointestinal illness) in humans. A large number of bacterial particles can be found in the infected cells, which may cause death. Despite these devastating complications, there is still no cure or vaccine for the bacteria. As a result, we used an immunoinformatics approach to develop a multi-epitope vaccine against the most pathogenic hemolysin gene of V. harveyi. The immunodominant T- and B-cell epitopes were identified using the hemolysin protein. We developed a vaccine employing three possible epitopes: cytotoxic T-lymphocytes, helper T-lymphocytes, and linear B-lymphocyte epitopes, after thorough testing. The vaccine was developed to be antigenic, immunogenic, and non-allergenic, as well as have a better solubility. Molecular dynamics simulation revealed significant structural stiffness and binding stability. In addition, the immunological simulation generated by computers revealed that the vaccination might elicit immune reactions Escherichia coli K12 as a model, codon optimization yielded ideal GC content and a higher codon adaptation index value, which was then included in the cloning vector pET2+ (a). Altogether, our experiment implies that the proposed peptide vaccine might be a good option for vibriosis prophylaxis.

Functional Profile of CD8+ T-Cells in Response to HLA-A*02:01-Restricted Mutated Epitopes Derived from the Gag Protein of Circulating HIV-1 Strains from Medellín, Colombia

CD8⁺ T-cells play a crucial role in the control of HIV replication. HIV-specific CD8⁺ T-cell responses rapidly expand since the acute phase of the infection, and it has been observed that HIV controllers harbor CD8⁺ T-cells with potent anti-HIV capacity. The development of CD8⁺ T-cell-based vaccine against HIV-1 has focused on searching for immunodominant epitopes. However, the strong immune pressure of CD8⁺ T-cells causes the selection of viral variants with mutations in immunodominant epitopes. Since HIV-1 mutations are selected under the context of a specific HLA-I, the circulation of viral variants with these mutations is highly predictable based on the most prevalent HLA-I within a population. We previously demonstrated the adaptation of circulating strains of HIV-1 to the HLA-A*02 molecule by identifying mutations under positive selection located in GC9 and SL9 epitopes derived from the Gag protein. Also, we used an in silico prediction approach and evaluated whether the mutations found had a higher or lower affinity to the HLA-A*02. Although this strategy allowed predicting the interaction between mutated peptides and HLA-I, the functional response of CD8⁺ T-cells that these peptides induce is unknown. In the present work, peripheral blood mononuclear cells from 12 HIV-1⁺ HLA-A*02:01⁺ individuals were stimulated with the mutated and wild-type peptides derived from the GC9 and SL9 epitopes. The functional profile of CD8⁺ T-cells was evaluated using flow cytometry, and the frequency of subpopulations was determined according to their number of functions and the polyfunctionality index. The results suggest that the quality of the response (polyfunctionality) could be associated with the binding affinity of the peptide to the HLA molecule, and the functional profile of specific CD8⁺ T-cells to mutated epitopes in individuals under cART is maintained.

Immunogenic epitope panel for accurate detection of non-cross-reactive T cell response to SARS-CoV-2

The ongoing COVID-19 pandemic calls for more effective diagnostic tools. T cell response assessment serves as an independent indicator of prior COVID-19 exposure while also contributing to a more comprehensive characterization of SARS-CoV-2 immunity. In this study, we systematically assessed the immunogenicity of 118 epitopes with immune cells collected from multiple cohorts of vaccinated, convalescent, healthy unexposed, and SARS-CoV-2–exposed donors. We identified 75 immunogenic epitopes, 24 of which were immunodominant. We further confirmed HLA restriction for 49 epitopes and described association with more than 1 HLA allele for 14 of these. Exclusion of 2 cross-reactive epitopes that generated a response in prepandemic samples left us with a 73-epitope set that offered excellent diagnostic specificity without losing sensitivity compared with full-length antigens, and this evoked a robust cross-reactive response. We subsequently incorporated this set of epitopes into an in vitro diagnostic Corona-T-test, which achieved a diagnostic accuracy of 95% in a clinical trial. In a cohort of asymptomatic seronegative individuals with a history of prolonged SARS-CoV-2 exposure, we observed a complete absence of T cell response to our epitope panel. In combination with strong reactivity to full-length antigens, this suggests that a cross-reactive response might protect these individuals.

Identification of neoantigens for individualized therapeutic cancer vaccines

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.