Immunoinformatics and epitope prediction in the age of genomic medicine

Deep learning of antibody epitopes using positional permutation vectors

Background

The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.

Results

Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training.

Conclusions

With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Design of multivalent-epitope vaccine models directed toward the world's population against HIV-Gag polyprotein: Reverse vaccinology and immunoinformatics

Significant progress has been made in HIV-1 research; however, researchers have not yet achieved the objective of eradicating HIV-1 infection. Accordingly, in this study, eucaryotic and procaryotic in silico vaccines were developed for HIV-Gag polyproteins from 100 major HIV subtypes and CRFs using immunoinformatic techniques to simulate immune responses in mice and humans. The epitopes located in the conserved domains of the Gag polyprotein were evaluated for allergenicity, antigenicity, immunogenicity, toxicity, homology, topology, and IFN-γ induction. Adjuvants, linkers, CTLs, HTLs, and BCL epitopes were incorporated into the vaccine models. Strong binding affinities were detected between HLA/MHC alleles, TLR-2, TLR-3, TLR-4, TLR-7, and TLR-9, and vaccine models. Immunological simulation showed that innate and adaptive immune cells elicited active and consistent responses. The human vaccine model was matched with approximately 93.91% of the human population. The strong binding of the vaccine to MHC/HLA and TLR molecules was confirmed through molecular dynamic stimulation. Codon optimization ensured the successful translation of the designed constructs into human cells and E. coli hosts. We believe that the HIV-1 Gag vaccine formulated in our research can reduce the challenges faced in developing an HIV-1 vaccine. Nevertheless, experimental verification is necessary to confirm the effectiveness of these vaccines in these models.

An in vitro CD8 T-cell priming assay enables epitope selection for hepatitis C virus vaccines

For the rational design of epitope-specific vaccines, identifying epitopes that can be processed and presented is essential. As algorithm-based epitope prediction is frequently discordant with actually recognized CD8+ T-cell epitopes, we developed an in vitro CD8 T-cell priming protocol to enable the identification of truly and functionally expressed HLA class I epitopes. The assay was established and validated to identify epitopes presented by hepatitis C virus (HCV)-infected cells. In vitro priming of naïve CD8 T cells was achieved by culturing unfractionated PBMCs in the presence of a specific cocktail of growth factors and cytokines, and next exposing the cells to hepatic cells expressing the NS3 protein of HCV. After a 10-day co-culture, HCV-specific T-cell responses were identified based on IFN-γ ELISpot analysis. For this, the T cells were restimulated with long synthetic peptides (SLPs) spanning the whole NS3 protein sequence allowing the identification of HCV-specificity. We demonstrated that this protocol resulted in the in vitro priming of naïve precursors to antigen-experienced T-cells specific for 11 out of 98 SLPs tested. These 11 SLPs contain 12 different HLA-A*02:01-restricted epitopes, as predicted by a combination of three epitope prediction algorithms. Furthermore, we identified responses against 3 peptides that were not predicted to contain any immunogenic HLA class I epitopes, yet showed HCV-specific responses in vitro. Separation of CD8+ and CD8- T cells from PBMCs primed in vitro showed responses only upon restimulation with short peptides. We established an in vitro method that enables the identification of HLA class I epitopes resulting from cross-presented antigens and that can cross-prime T cells and allows the effective selection of functional immunogenic epitopes, but also less immunogenic ones, for the design of tailored therapeutic vaccines against persistent viral infections and tumor antigens.

A novel Candida albicans Als3, Hwp1 and Met6 derived complex peptide protects mice against hematogenously induced candidiasis

Candida albicans can cause superficial or systemic infections in humans, particularly in immunocompromised individuals. Vaccination strategies targeting specific antigens of C. albicans have shown promise in providing protection against invasive candidiasis. This study aimed to evaluate the immuno-protective capacity of a KLH conjugated complex peptide, 3P-KLH, containing epitopes from C. albicans antigens Als3, Hwp1, and Met6 in a murine model of hematogenously induced candidiasis. Mice immunized with 3P-KLH raised a specific antibody response, and protection against C. albicans infection was assessed. Immunized mice exhibited significantly lower fungal load in their kidneys compared to the control group. Moreover, 37.5 % of immunized mice survived 21 days after the infection, while all control animals died within the first nine days. These findings suggest that the 3P-KLH complex peptide, targeting C. albicans key antigens, elicits a protective immune response and reduces the severity of systemic Candida infection. In addition, the high binding affinity of the selected epitopes with MHC II alleles further supports the potential immunogenicity of this peptide in humans. This research provides insights into the development of novel immunotherapeutic approaches against invasive candidiasis.

Allogeneic HLA Humoral Immunogenicity and the Prediction of Donor-Specific HLA Antibody Development

The development of de novo donor-specific HLA antibodies (dnDSAs) following solid organ transplantation is considered a major risk factor for poor long-term allograft outcomes. The prediction of dnDSA development is a boon to transplant recipients, yet the assessment of allo-HLA immunogenicity remains imprecise. Despite the recent technological advances, a comprehensive evaluation of allo-HLA immunogenicity, which includes both B and T cell allorecognition, is still warranted. Recent studies have proposed using mismatched HLA epitopes (antibody and T cell) as a prognostic biomarker for humoral alloimmunity. However, the identification of immunogenic HLA mismatches has not progressed despite significant improvements in the identification of permissible mismatches. Certainly, the prediction of dnDSA development may benefit permissible HLA mismatched organ transplantations, personalized immunosuppression, and clinical trial design. However, characteristics that go beyond the listing of mismatched HLA antibody epitopes and T cell epitopes, such as the generation of HLA T cell epitope repertoires, recipient's HLA class II phenotype, and immunosuppressive regiments, are required for the precise assessment of allo-HLA immunogenicity.

Immunoinformatics-Based Design of Multi-epitope DNA and mRNA Vaccines Against Zika Virus

In this study, we used an immunoinformatics approach to predict antigenic epitopes of Zika virus (ZIKV) proteins to assist in designing a vaccine antigen against ZIKV. We performed the prediction of CD8+ T-lymphocyte and antigenic B-cell epitopes of ZIKV proteins. The binding interactions of T-cell epitopes with major histocompatibility complex class I (MHC-I) proteins were assessed. We selected the antigenic, conserved, nontoxic, and immunogenic epitopes, which indicated significant interactions with the human leucocyte antigen (HLA-A and HLA-B) alleles and worldwide population coverage of 76.35%. The predicted epitopes were joined with the help of linkers and an adjuvant. The vaccine antigen was then analyzed through molecular docking with TLR3 and TLR8, and it was in silico cloned in the pVAX1 vector to be used as a DNA vaccine and designed as a mRNA vaccine.

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

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

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Identification and evaluation in-vitro of conserved peptides with high affinity to MHC-I as potential protective epitopes for Newcastle disease virus vaccines

BACKGROUND

Newcastle disease (ND) is a major threat to the poultry industry, leading to significant economic losses. The current ND vaccines, usually based on active or attenuated strains, are only partially effective and can cause adverse effects post-vaccination. Therefore, the development of safer and more efficient vaccines is necessary. Epitopes represent the antigenic portion of the pathogen and their identification and use for immunization could lead to safer and more effective vaccines. However, the prediction of protective epitopes for a pathogen is a major challenge, especially taking into account the immune system of the target species.

RESULTS

In this study, we utilized an artificial intelligence algorithm to predict ND virus (NDV) peptides that exhibit high affinity to the chicken MHC-I complex. We selected the peptides that are conserved across different NDV genotypes and absent in the chicken proteome. From the filtered peptides, we synthesized the five peptides with the highest affinities for the L, HN, and F proteins of NDV. We evaluated these peptides in-vitro for their ability to elicit cell-mediated immunity, which was measured by the lymphocyte proliferation in spleen cells of chickens previously immunized with NDV.

CONCLUSIONS

Our study identified five peptides with high affinity to MHC-I that have the potential to serve as protective epitopes and could be utilized for the development of multi-epitope NDV vaccines. This approach can provide a safer and more efficient method for NDV immunization.

Computational prediction of B and T-cell epitopes of Kyasanur Forest Disease virus marker proteins towards the development of precise diagnosis and potent subunit vaccine

Kyasanur Forest Disease (KFD), also known as 'monkey fever', caused by KFD Virus (KFDV), is a highly neglected tropical disease endemic to Western Ghat region of Karnataka, India. Recently, KFD, which is fatal for both monkeys and humans with a mortality rate of 2-10% has been found to spread from its epicenter to neighboring districts and states also. The current ELISA based KFD detection method is very non-specific due to cross-reactivity with other flaviviruses. Further, presently available formalin-inactivated vaccine has been found to be less effective leading to disease susceptibility and severity. To address these, the present study was aimed at predicting the potent specific B and T-cell epitopes of KFDV immunogenic marker proteins using diverse computational tools aiming at developing precise diagnostic method and an effective subunit vaccine. Here, we have chosen E, NS1 and NS5 proteins as markers of KFDV by taking into account of their differential and non-overlapping sequences with selected arboviruses. Based on the linear and nonlinear epitope prediction tools and important biophysical parameters, we identified three potential linear and ten nonlinear B-cell epitopes. We also predicted T-cell epitope peptides which binds to MHC class-I and class-II receptors for the effective T-cell activation. Thus, our molecular docking and molecular dynamics simulation analysis has identified six different TH-cell epitopes based on the distribution frequency of MHC-II haplotypes in the human population and one TC-cell epitope from NS5 protein that has maximum interaction with class-I MHC. Overall, we have successfully identified potential B and T-cell epitope marker peptides present in the envelope and two non-structural proteins.Communicated by Ramaswamy H. Sarma.

Identification of two novel T cell epitopes on the E2 protein of classical swine fever virus C-strain

C-strain, also known as the HCLV strain, is a well-known live attenuated vaccine against classical swine fever (CSF), a devastating disease caused by classical swine fever virus (CSFV). Vaccination with C-strain induces a rapid onset of protection, which is associated with virus-specific gamma interferon (IFN-γ)-secreting CD8+ T cell responses. The E2 protein of CSFV is a major protective antigen. However, the T cell epitopes on the E2 protein remain largely unknown. In this study, eight overlapping nonapeptides of the E2 protein were predicted and synthesized to screen for potential T cell epitopes on the CSFV C-strain E2 protein. Molecular docking was performed on the candidate epitopes with the swine leukocyte antigen-1*0401. The analysis obtained two highly conserved T cell epitopes, 90STEEMGDDF98 and 331ATDRHSDYF339, which were further identified by enzyme-linked immunospot assay. Interestingly, the mutants deleting or substituting the epitopes are nonviable. Further analysis demonstrated that 90STEEMGDDF98 is crucial for the E2 homodimerization, while CSFV infection is significantly inhibited by the 331ATDRHSDYF339 peptide treatment. The two novel T cell epitopes can be used to design new vaccines that are able to provide rapid-onset protection.

Preclinical proof of concept of a tetravalent lentiviral T-cell vaccine against dengue viruses

Dengue virus (DENV) is responsible for approximately 100 million cases of dengue fever annually, including severe forms such as hemorrhagic dengue and dengue shock syndrome. Despite intensive vaccine research and development spanning several decades, a universally accepted and approved vaccine against dengue fever has not yet been developed. The major challenge associated with the development of such a vaccine is that it should induce simultaneous and equal protection against the four DENV serotypes, because past infection with one serotype may greatly increase the severity of secondary infection with a distinct serotype, a phenomenon known as antibody-dependent enhancement (ADE). Using a lentiviral vector platform that is particularly suitable for the induction of cellular immune responses, we designed a tetravalent T-cell vaccine candidate against DENV ("LV-DEN"). This vaccine candidate has a strong CD8+ T-cell immunogenicity against the targeted non-structural DENV proteins, without inducing antibody response against surface antigens. Evaluation of its protective potential in the preclinical flavivirus infection model, i.e., mice knockout for the receptor to the type I IFN, demonstrated its significant protective effect against four distinct DENV serotypes, based on reduced weight loss, viremia, and viral loads in peripheral organs of the challenged mice. These results provide proof of concept for the use of lentiviral vectors for the development of efficient polyvalent T-cell vaccine candidates against all DENV serotypes.

Peptide Hamming Graphs: A network representation of peptides presented through specific HLAs to identify potential epitope clusters

BACKGROUND

Class I Major Histocompatibility Complex plays a critical role in the adaptive immune response by binding to peptides processed by Proteasome and Transporter associated with antigen processing complex and presenting them on the cell surface to cytotoxic T-cells. Understanding the process of peptide presentation and studying how presented peptides are distributed in the huge space of all potential epitopes could have a dramatic impact in the context of vaccine design, transplantation, autoimmunity, and cancer development.

METHODS

In the present work we propose a graph-driven approach to investigate the landscape of both self (human) and viral (254 organisms) peptides presented on cell surface through class I Major Histocompatibility Complex considering specific HLAs. For each considered HLA (N = 89) we designed a network, namely Peptide Hamming Graph, where nodes are peptides predicted to be presented by a given HLA and an edge is set when the Hamming distance between two peptides is equal or smaller than 2 (i.e. the same amino acid occurs in at least 7 positions of the two sequences).

RESULTS

Through the analysis of Peptide Hamming Graphs we studied how predicted presented peptides are distributed in the whole configurational space for different HLAs, identifying sets of viral peptides that can constitute a potential target for the immune system. In particular we selected connected components of the graph made exclusively of viral peptides and sets of viral peptides with high node degree interacting exclusively with viral neighbours.

CONCLUSIONS

This work constitutes an innovative approach to study potential cytotoxic T-cell epitopes relying on a network approach, overcoming the classical paradigm based on the identification of potential epitopes only considering their features as single peptides. T-cell cross-reactivity plays a focal role for the efficacy of this strategy increasing the probability of recognition, and consequently a stronger immune response, of presented peptides far from self, sharing a common pattern in terms of sequence similarity.

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.

Immunoinformatic analysis of the whole proteome for vaccine design: An application to Clostridium perfringens

Clostridium perfringens is a dangerous bacterium and known biological warfare weapon associated with several diseases, whose lethal toxins can produce necrosis in humans. However, there is no safe and fully effective vaccine against C. perfringens for humans yet. To address this problem, we computationally screened its whole proteome, identifying highly immunogenic proteins, domains, and epitopes. First, we identified that the proteins with the highest epitope density are Collagenase A, Exo-alpha-sialidase, alpha n-acetylglucosaminidase and hyaluronoglucosaminidase, representing potential recombinant vaccine candidates. Second, we further explored the toxins, finding that the non-toxic domain of Perfringolysin O is enriched in CTL and HTL epitopes. This domain could be used as a potential sub-unit vaccine to combat gas gangrene. And third, we designed a multi-epitope protein containing 24 HTL-epitopes and 34 CTL-epitopes from extracellular regions of transmembrane proteins. Also, we analyzed the structural properties of this novel protein using molecular dynamics. Altogether, we are presenting a thorough immunoinformatic exploration of the whole proteome of C. perfringens, as well as promising whole-protein, domain-based and multi-epitope vaccine candidates. These can be evaluated in preclinical trials to assess their immunogenicity and protection against C. perfringens infection.

Vaccines against candidiasis: Status, challenges and emerging opportunity

Candidiasis is a mycosis caused by opportunistic Candida species. The occurrence of fungal infections has considerably increased in the last few years primarily due to an increase in the number of immune-suppressed individuals. Alarming bloodstream infections due to Candida sp. are associated with a higher rate of morbidity and mortality, and are emerged as major healthcare concerns worldwide. Currently, chemotherapy is the sole available option for combating fungal diseases. Moreover, the emergence of resistance to these limited available anti-fungal drugs has further accentuated the concern and highlighted the need for early detection of fungal infections, identification of novel antifungal drug targets, and development of effective therapeutics and prophylactics. Thus, there is an increasing interest in developing safe and potent immune-based therapeutics to tackle fungal diseases. In this context, vaccine design and its development have a priority. Nonetheless, despite significant advances in immune and vaccine biology over time, a viable commercialized vaccine remains awaited against fungal infections. In this minireview, we enumerate various concerted efforts made till date towards the development of anti-Candida vaccines, an option with pan-fugal vaccine, vaccines in the clinical trial, challenges, and future opportunities.

Prediction of B cell epitopes in proteins using a novel sequence similarity-based method

Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast.

Development of epitope-based chimeric protein as a vaccine against Lujo virus by utilizing immunoinformatic tools

Aim: Lujo is a modern zoonotic virus that is potentially fatal and spreads by bodily fluids. In this research, immunoinformatic tools are used to build a vaccine. Methodology: The epitopes of cytotoxic T-lymphocytes, helper T-lymphocytes and linear B-lymphocytes were predicted from the most antigenic protein. The designed vaccine's physiochemical properties and 3D structure have been forecasted. Low free energy and strong binding affinity estimated in molecular docking against toll-like receptor 4 (TLR4) and dynamic simulation. Furthermore, in silico cloning in the Escherichia coli K12 host system was performed for high level of expression. Conclusion: Finally, immune simulation was used to determine immune responses to the vaccine that was formulated confirming the developed vaccine as a good candidate against Lujo virus.

A multi-epitope chimeric protein elicited a strong antibody response and partial protection against Edwardsiella ictaluri in Nile tilapia

Edwardsiella ictaluri infects several fish species and protection of the all the susceptible fish hosts from the pathogen using a monovalent vaccine is impossible because the species is composed of host-based genotypes that are genetic, serological and antigenic heterogenous. Here, immunoinformatic approach was employed to design a cross-immunogenic chimeric EiCh protein containing multi-epitopes. The chimeric EiCh protein is composed of 11 B-cell epitopes and 7 major histocompatibility complex class II epitopes identified from E. ictaluri immunogenic proteins previously reported. The 49.32 kDa recombinant EiCh protein was expressed in vitro in Escherichia coli BL-21 (DE3) after which inclusion bodies were successfully solubilized and refolded. Ab initio protein modelling revealed secondary and tertiary structures. Secondary structure was confirmed by circular dichroism spectroscopy. Antigenicity of the chimeric EiCh protein was exhibited by strong reactivity with serum from striped catfish and Nile tilapia experimentally infected with E. ictaluri. Furthermore, immunogenicity of the chimeric EiCh protein was investigated in vivo in Nile tilapia juveniles and it was found that the protein could strongly induce production of specific antibodies conferring agglutination activity and partially protected Nile tilapia juveniles with a relative survival percentage (RPS) of 42%. This study explored immunoinformatics as reverse vaccinology approach in vaccine design for aquaculture to manage E. ictaluri infections.

Antibodies targeting enzyme inhibition as potential tools for research and drug development

Antibodies have transformed biomedical research and are now being used for different experimental applications. Generally, the interaction of enzymes with their specific antibodies can lead to a reduction in their enzymatic activity. The effect of the antibody is dependent on its narrow i.e. the regions of the enzyme to which it is directed. The mechanism of this inhibition is rarely a direct combination of the antibodies with the catalytic site, but is rather due to steric hindrance, barring the substrate access to the active site. In several systems, however, the interaction with the antibody induces conformational changes on the enzyme that can either inhibit or enhance its catalytic activity. The extent of enzyme inhibition or enhancement is, therefore, a reflection of the nature and distribution of the various antigenic determinants on the enzyme molecule. Currently, the mode of action of many enzymes has been elucidated at the molecular level. We here review the molecular mechanisms and recent trends by which antibodies inhibit the catalytic activity of enzymes and provide examples of how specific antibodies can be useful for the neutralization of biologically active molecules.

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

In silico epitope prediction analyses highlight the potential for distracting antigen immunodominance with allogeneic cancer vaccines

Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types. Results show limited therapeutic benefit in clinical trials and currently there are no FDA approved allogeneic cancer vaccines. We used recently developed bioinformatics tools including the pVAC-seq suite of software tools to analyze DNA/RNA sequencing data from the TCGA to examine the repertoire of antigens presented by a typical allogeneic cancer vaccine, and to simulate allogeneic cancer vaccine clinical trials. Specifically, for each simulated clinical trial we modeled the repertoire of antigens presented by allogeneic cancer vaccines consisting of three hypothetical cancer cell lines to 30 patients with the same cancer type. Simulations were repeated ten times for each cancer type. Each tumor sample in the vaccine and the vaccine recipient was subjected to HLA typing, differential expression analyses for tumor associated antigens (TAAs), germline variant calling, and neoantigen prediction. These analyses provided a robust, quantitative comparison between potentially beneficial TAAs and neoantigens versus distracting antigens present in the allogeneic cancer vaccines. We observe that distracting antigens greatly outnumber shared TAAs and neoantigens, providing one potential explanation for the lack of observed responses to allogeneic cancer vaccines. This analysis provides additional rationale for the redirection of efforts towards a personalized cancer vaccine approach.

Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

In Silico Designing of a Multitope Vaccine against Rhizopus microsporus with Potential Activity against Other Mucormycosis Causing Fungi

During the current era of the COVID-19 pandemic, the dissemination of Mucorales has been reported globally, with elevated rates of infection in India, and because of the high rate of mortality and morbidity, designing an effective vaccine against mucormycosis is a major health priority, especially for immunocompromised patients. In the current study, we studied shared Mucorales proteins, which have been reported as virulence factors, and after analysis of several virulent proteins for their antigenicity and subcellular localization, we selected spore coat (CotH) and serine protease (SP) proteins as the targets of epitope mapping. The current study proposes a vaccine constructed based on top-ranking cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B cell lymphocyte (BCL) epitopes from filtered proteins. In addition to the selected epitopes, β-defensins adjuvant and PADRE peptide were included in the constructed vaccine to improve the stimulated immune response. Computational tools were used to estimate the physicochemical and immunological features of the proposed vaccine and validate its binding with TLR-2, where the output data of these assessments potentiate the probability of the constructed vaccine to stimulate a specific immune response against mucormycosis. Here, we demonstrate the approach of potential vaccine construction and assessment through computational tools, and to the best of our knowledge, this is the first study of a proposed vaccine against mucormycosis based on the immunoinformatics approach.

A simple pan-specific RNN model for predicting HLA-II binding peptides

The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers. The results demonstrated strong discriminant capabilities of the GRU-based RNN model, of which the AUC values were 0.92, 0.88, and 0.88 for the training, validation, and test sets, respectively. Also, the GRU-based model showed state-of-the-art performances in predicting the binding peptides with the length ranging from 8-32 mers, which provides an efficient method for predicting HLA-II-binding peptides of longer lengths in comparison with the available methods. Overall, taking the advantages of the RNN architecture, the established pan-specific GRU model can be used for predicting accurately the HLA-II-binding peptides in a simple and direct manner.

Antigen processing and presentation in cancer immunotherapy

Background

Knowledge about and identification of T cell tumor antigens may inform the development of T cell receptor-engineered adoptive cell transfer or personalized cancer vaccine immunotherapy. Here, we review antigen processing and presentation and discuss limitations in tumor antigen prediction approaches.

Methods

Original articles covering antigen processing and presentation, epitope discovery, and in silico T cell epitope prediction were reviewed.

Results

Natural processing and presentation of antigens is a complex process that involves proteasomal proteolysis of parental proteins, transportation of digested peptides into the endoplasmic reticulum, loading of peptides onto major histocompatibility complex (MHC) class I molecules, and shuttling of peptide:MHC complexes to the cell surface. A number of T cell tumor antigens have been experimentally validated in patients with cancer. Assessment of predicted MHC class I binding and total score for these validated T cell antigens demonstrated a wide range of values, with nearly one-third of validated antigens carrying an IC₅₀ of greater than 500 nM.

Conclusions

Antigen processing and presentation is a complex, multistep process. In silico epitope prediction techniques can be a useful tool, but comprehensive experimental testing and validation on a patient-by-patient basis may be required to reliably identify T cell tumor antigens.

Integrative immunoinformatics paradigm for predicting potential B-cell and T-cell epitopes as viable candidates for subunit vaccine design against COVID-19 virulence

Background

The increase in global mortality rates from SARS-COV2 (COVID-19) infection has been alarming thereby necessitating the continual search for viable therapeutic interventions. Due to minimal microbial components, subunit (peptide-based) vaccines have demonstrated improved efficacies in stimulating immunogenic responses by host B- and T-cells.

Methods

Integrative immunoinformatics algorithms were used to determine linear and discontinuous B-cell epitopes from the S-glycoprotein sequence. End-point selection of the most potential B-cell epitope was based on highly essential physicochemical attributes. NetCTL-I and NetMHC-II algorithms were used to predict probable MHC-I and II T-cell epitopes for globally frequent HLA-A∗O2:01, HLA-B∗35:01, HLA-B∗51:01 and HLA-DRB1∗15:02 molecules. Highly probable T-cell epitopes were selected based on their high propensities for C-terminal cleavage, transport protein (TAP) processing and MHC-I/II binding.

Results

Preferential epitope binding sites were further identified on the HLA molecules using a blind peptide-docking method. Phylogenetic analysis revealed close relativity between SARS-CoV-2 and SARS-CoV S-protein. LALHRSYLTPGDSSSGWTAGAA_242→263 was the most probable B-cell epitope with optimal physicochemical attributes. MHC-I antigenic presentation pathway was highly favourable for YLQPRTFLL_269-277 (HLA-A∗02:01), LPPAYTNSF_24-32 (HLA-B∗35:01) and IPTNFTISV7_14-721 (HLA-B∗51:01). Also, LTDEMIAQYTSALLA_865-881 exhibited the highest binding affinity to HLA-DR B1∗15:01 with core interactions mediated by IAQYTSALL_870-878. COVID-19 YLQPRTFLL_269-277 was preferentially bound to a previously undefined site on HLA-A∗02:01 suggestive of a novel site for MHC-I-mediated T-cell stimulation.

Conclusion

This study implemented combinatorial immunoinformatics methods to model B- and T-cell epitopes with high potentials to trigger immunogenic responses to the S protein of SARS-CoV-2.

Proteome Based Approach Defines Candidates for Designing a Multitope Vaccine against the Nipah Virus

Nipah virus is one of the most harmful emerging viruses with deadly effects on both humans and animals. Because of the severe outbreaks, in 2018, the World Health Organization focused on the urgent need for the development of effective solutions against the virus. However, up to date, there is no effective vaccine against the Nipah virus in the market. In the current study, the complete proteome of the Nipah virus (nine proteins) was analyzed for the antigenicity score and the virulence role of each protein, where we came up with fusion glycoprotein (F), glycoprotein (G), protein (V), and protein (W) as the candidates for epitope prediction. Following that, the multitope vaccine was designed based on top-ranking CTL, HTL, and BCL epitopes from the selected proteins. We used suitable linkers, adjuvant, and PADRE peptides to finalize the constructed vaccine, which was analyzed for its physicochemical features, antigenicity, toxicity, allergenicity, and solubility. The designed vaccine passed these assessments through computational analysis and, as a final step, we ran a docking analysis between the designed vaccine and TLR-³ and validated the docked complex through molecular dynamics simulation, which estimated a strong binding and supported the nomination of the designed vaccine as a putative solution for Nipah virus. Here, we describe the computational approach for design and analysis of this vaccine.

Peptide Platform as a Powerful Tool in the Fight against COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic causing over 195 million infections and more than 4 million fatalities as of July 2021.To date, it has been demonstrated that a number of mutations in the spike glycoprotein (S protein) of SARS-CoV-2 variants of concern abrogate or reduce the neutralization potency of several therapeutic antibodies and vaccine-elicited antibodies. Therefore, the development of additional vaccine platforms with improved supply and logistic profile remains a pressing need. In this work, we have validated the applicability of a peptide-based strategy focused on a preventive as well as a therapeutic purpose. On the basis of the involvement of the dipeptidyl peptidase 4 (DPP4), in addition to the angiotensin converting enzyme 2 (ACE2) receptor in the mechanism of virus entry, we analyzed peptides bearing DPP4 sequences by protein-protein docking and assessed their ability to block pseudovirus infection in vitro. In parallel, we have selected and synthetized peptide sequences located within the highly conserved receptor-binding domain (RBD) of the S protein, and we found that RBD-based vaccines could better promote elicitation of high titers of neutralizing antibodies specific against the regions of interest, as confirmed by immunoinformatic methodologies and in vivo studies. These findings unveil a key antigenic site targeted by broadly neutralizing antibodies and pave the way to the design of pan-coronavirus vaccines.

Epitope-Based Immunoinformatic Approach on Heat Shock 70 kDa Protein Complex of Cryptococcus neoformans var. grubii

INTRODUCTION

Cryptococcosis is a ubiquitous opportunistic fungal disease caused by Cryptococcus neoformans var. grubii. It has high global morbidity and mortality among HIV patients and non-HIV carriers with 99% and 95%, respectively. Furthermore, the increasing prevalence of undesired toxicity profile of antifungal, multidrug-resistant organisms and the scarcity of FDA-authorized vaccines were the hallmark in the present days. This study was undertaken to design a reliable epitope-based peptide vaccine through targeting highly conserved immunodominant heat shock 70 kDa protein of Cryptococcus neoformans var. grubii that covers a considerable digit of the world population through implementing a computational vaccinology approach.

MATERIALS AND METHODS

A total of 38 sequences of Cryptococcus neoformans var. grubii's heat shock 70 kDa protein were retrieved from the NCBI protein database. Different prediction tools were used to analyze the aforementioned protein at the Immune Epitope Database (IEDB) to discriminate the most promising T-cell and B-cell epitopes. The proposed T-cell epitopes were subjected to the population coverage analysis tool to compute the global population's coverage. Finally, the T-cell projected epitopes were ranked based on their binding scores and modes using AutoDock Vina software. Results and Discussion. The epitopes (ANYVQASEK, QSEKPKNVNPVI, SEKPKNVNPVI, and EKPKNVNPVI) had shown very strong binding affinity and immunogenic properties to B-cell. (FTQLVAAYL, YVYDTRGKL) and (FFGGKVLNF, FINAQLVDV, and FDYALVQHF) exhibited a very strong binding affinity to MHC-I and MHC-II, respectively, with high population coverage for each, while FYRQGAFEL has shown promising results in terms of its binding profile to MHC-II and MHC-I alleles and good strength of binding when docked with HLA-C∗12:03. In addition, there is massive global population coverage in the three coverage modes. Accordingly, our in silico vaccine is expected to be the future epitope-based peptide vaccine against Cryptococcus neoformans var. grubii that covers a significant figure of the entire world citizens.

Paper-based ELISA diagnosis technology for human brucellosis based on a multiepitope fusion protein

BACKGROUND

Brucellosis, as a serious zoonotic infectious disease, has been recognized as a re-emerging disease in the developing countries worldwide. In china, the incidence of brucellosis is increasing each year, seriously threatening the health of humans as well as animal populations. Despite a quite number of diagnostic methods currently being used for brucellosis, innovative technologies are still needed for its rapid and accurate diagnosis, especially in area where traditional diagnostic is unavailable.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, a total of 22 B cell linear epitopes were predicted from five Brucella outer membrane proteins (OMPs) using an immunoinformatic approach. These epitopes were then chemically synthesized, and with the method of indirect ELISA (iELISA), each of them displayed a certain degree of capability in identifying human brucellosis positive sera. Subsequently, a fusion protein consisting of the 22 predicted epitopes was prokaryotically expressed and used as diagnostic antigen in a newly established brucellosis testing method, nano-ZnO modified paper-based ELISA (nano-p-ELISA). According to the verifying test using a collection of sera collected from brucellosis and non-brucellosis patients, the sensitivity and specificity of multiepitope based nano-p-ELISA were 92.38% and 98.35% respectively. The positive predictive value was 98.26% and the negative predictive value was 91.67%. The multiepitope based fusion protein also displayed significantly higher specificity than Brucella lipopolysaccharide (LPS) antigen.

CONCLUSIONS

B cell epitopes are important candidates for serologically testing brucellosis. Multiepitope fusion protein based nano-p-ELISA displayed significantly sensitivity and specificity compared to Brucella LPS antigen. The strategy applied in this study will be helpful to develop rapid and accurate diagnostic method for brucellosis in human as well as animal populations.

New Computational Approach for Peptide Vaccine Design Against SARS-COV-2

The design for vaccines using in silico analysis of genomic data of different viruses has taken many different paths, but lack of any precise computational approach has constrained them to alignment methods and some alignment-free techniques. In this work, a precise computational approach has been established wherein two new mathematical parameters have been suggested to identify the highly conserved and surface-exposed regions which are spread over a large region of the surface protein of the virus so that one can determine possible peptide vaccine candidates from those regions. The first parameter, w, is the sum of the normalized values of the measure of surface accessibility and the normalized measure of conservativeness, and the second parameter is the area of a triangle formed by a mathematical model named 2D Polygon Representation. This method has been, therefore, used to determine possible vaccine targets against SARS-CoV-2 by considering its surface-situated spike glycoprotein. The results of this model have been verified by a parallel analysis using the older approach of manually estimating the graphs describing the variation of conservativeness and surface-exposure across the protein sequence. Furthermore, the working of the method has been tested by applying it to find out peptide vaccine candidates for Zika and Hendra viruses respectively. A satisfactory consistency of the model results with pre-established results for both the test cases shows that this in silico alignment-free analysis proposed by the model is suitable not only to determine vaccine targets against SARS-CoV-2 but also ready to extend against other viruses.

Anti-cancer immunoprotective effects of immunization with hydatid cyst wall antigens in a non-immunogenic and metastatic triple-negative murine mammary carcinoma model

Cancer vaccines have gained lots of attention as the future of cancer treatment. However, poor immunogenicity of tumor-associated antigens often fails to induce an efficient immune response against the tumor. Strange anti-tumor immune responses at the parasite-infected patients due to cross-reactivity have been reported in various studies. Therefore, parasite antigens with significant immunogenicity and high epitope homology with cancer antigens may activate a strong immune response against cancer cells. Herein, the sera of immunized rabbits with the hydatid cyst wall (HCW) antigens were incubated with 4 T1 mammary carcinoma cells to investigate cross-reactivity between the HCW antigens antisera and surface antigens of the breast cancer cells. Also, the SDS-PAGE profile of HCW antigens was prepared and incubated with the breast cancer patients' sera and considerable reactivity was observed between their sera and a specific band (~27/28 kDa) according to Western blotting analyzes. Then, the protein bands with cross-reactivity with breast cancer patients' sera were utilized for prophylactic immunizations of Balb/c mice. The immunoprotective effect of immunization with the HCW antigens caused significant inhibition of 4 T1 breast tumor growth, decrease of metastasis, and enlargement of the tumor-bearing mice survival time in comparison with PBS and pure immune adjuvant injected groups. Mass spectrometry analysis showed that the ~ 27/28 kDa band has numbers of proteins/polypeptides with a high degree of homology with cancer cells antigens which can be the reason for this cross-reactivity and anti-tumor immune response. Taking together, immunization with HCW antigens would be a promising approach in cancer immunotherapy after further investigations.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Challenges targeting cancer neoantigens in 2021: a systematic literature review

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.

Bioinformatic HLA Studies in the Context of SARS-CoV-2 Pandemic and Review on Association of HLA Alleles with Preexisting Medical Conditions

After the announcement of a new coronavirus in China in December 2019, which was then called SARS-CoV-2, this virus changed to a global concern and it was then declared as a pandemic by WHO. Human leukocyte antigen (HLA) alleles, which are one of the most polymorphic genes, play a pivotal role in both resistance and vulnerability of the body against viruses and other infections as well as chronic diseases. The association between HLA alleles and preexisting medical conditions such as cardiovascular diseases and diabetes mellitus is reported in various studies. In this review, we focused on the bioinformatic HLA studies to summarize the HLA alleles which responded to SARS-CoV-2 peptides and have been used to design vaccines. We also reviewed HLA alleles that are associated with comorbidities and might be related to the high mortality rate among COVID-19 patients. Since both genes and patients' medical conditions play a key role in both severity of the disease and the mortality rate in COVID-19 patients, a better understanding of the connection between HLA alleles and SARS-CoV-2 can provide a wider perspective on the behavior of the virus. Such understanding can help scientists, especially in terms of protecting healthcare workers and designing effective vaccines.

Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida glabrata: An Immunoinformatics Approach

Background

Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although there are multiple effective vaccines against fungal infections and some of these vaccines are engaged in different stages of clinical trials, none of them have yet been approved by the FDA.

Aim

Using immunoinformatics approach to predict the most conserved and immunogenic B- and T-cell epitopes from the fructose bisphosphate aldolase (Fba1) protein of C. glabrata. Material and Method. 13 C. glabrata fructose bisphosphate aldolase protein sequences (361 amino acids) were retrieved from NCBI and presented in several tools on the IEDB server for prediction of the most promising epitopes. Homology modeling and molecular docking were performed.

Result

The promising B-cell epitopes were AYFKEH, VDKESLYTK, and HVDKESLYTK, while the promising peptides which have high affinity to MHC I binding were AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL, and YFKEHGEPL. Two peptides, LFSSHMLDL and YIRSIAPAY, were noted to have the highest affinity to MHC class II that interact with 9 alleles. The molecular docking revealed that the epitopes QTSNGGAAY and LFSSHMLDL have the lowest binding energy to MHC molecules.

Conclusion

The epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines in that they are more specific, less time consuming, safe, less allergic, and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidate's epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cell epitopes from the Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. glabrata.

Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development

Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and post-administration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous "big data" repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now makes possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.

Novel Potent IFN-γ-Inducing CD8+ T Cell Epitopes Conserved among Diverse Bovine Viral Diarrhea Virus Strains

Studies of immune responses elicited by bovine viral diarrhea virus (BVDV) vaccines have primarily focused on the characterization of neutralizing B cell and CD4⁺ T cell epitopes. Despite the availability of commercial vaccines for decades, BVDV prevalence in cattle has remained largely unaffected. There is limited knowledge regarding the role of BVDV-specific CD8⁺ T cells in immune protection, and indirect evidence suggests that they play a crucial role during BVDV infection. In this study, the presence of BVDV-specific CD8⁺ T cells that are highly cross-reactive in cattle was demonstrated. Most importantly, novel potent IFN-γ-inducing CD8⁺ T cell epitopes were identified from different regions of BVDV polyprotein. Eight CD8⁺ T cell epitopes were identified from the following structural BVDV Ags: E^rns, E1, and E2 glycoproteins. In addition, from nonstructural BVDV Ags N^pro, NS2-3, NS4A-B, and NS5A-B, 20 CD8⁺ T cell epitopes were identified. The majority of these IFN-γ-inducing CD8⁺ T cell epitopes were found to be highly conserved among more than 200 strains from BVDV-1 and -2 genotypes. These conserved epitopes were also validated as cross-reactive because they induced high recall IFN-γ⁺CD8⁺ T cell responses ex vivo in purified bovine CD8⁺ T cells isolated from BVDV-1- and -2-immunized cattle. Altogether, 28 bovine MHC class I-binding epitopes were identified from key BVDV Ags that can elicit broadly reactive CD8⁺ T cells against diverse BVDV strains. The data presented in this study will lay the groundwork for the development of a contemporary CD8⁺ T cell-based BVDV vaccine capable of addressing BVDV heterogeneity more effectively than current vaccines.

epitopepredict: a tool for integrated MHC binding prediction

A key step in the cellular adaptive immune response is the presentation of antigens to T cells. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics, which allows in silico screening of peptides before experiments are performed. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open-source license from https://github.com/dmnfarrell/epitopepredict.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/.

A Critical Review about Different Vaccines against Classical Swine Fever Virus and Their Repercussions in Endemic Regions

Classical swine fever (CSF) is, without any doubt, one of the most devasting viral infectious diseases affecting the members of Suidae family, which causes a severe impact on the global economy. The reemergence of CSF virus (CSFV) in several countries in America, Asia, and sporadic outbreaks in Europe, sheds light about the serious concern that a potential global reemergence of this disease represents. The negative aspects related with the application of mass stamping out policies, including elevated costs and ethical issues, point out vaccination as the main control measure against future outbreaks. Hence, it is imperative for the scientific community to continue with the active investigations for more effective vaccines against CSFV. The current review pursues to gather all the available information about the vaccines in use or under developing stages against CSFV. From the perspective concerning the evolutionary viral process, this review also discusses the current problematic in CSF-endemic countries.

The double-edged sword of cancer mutations: exploiting neoepitopes for the fight against cancer

Defects in DNA repair have been linked to the accumulation of somatic mutations in tumours. These mutations can promote oncogenesis; however, recent developments have indicated that they may also lead to a targeted immune response against the tumour. This response is initiated by the development of new antigenic epitopes (neoepitopes) arising from mutations in protein-coding genes that are processed and then presented on the surface of tumour cells. These neoepitopes are unique to the tumour, thus enabling lymphocytes to launch an immune response against the cancer cells. Immunotherapies, such as checkpoint inhibitors (CPIs) and tumour-derived vaccines, have been shown to enhance the immunogenic response to cancers and have led to complete remission in some cancer patients. There are tumours that are not responsive to immunotherapy or conventional tumour therapeutics; therefore, there is a push for new treatments to combat these unresponsive cancers. Recently, combinatorial treatments have been developed to further utilise the immune system in the fight against cancer. These treatments have the potential to exploit the defects in DNA repair by inducing more DNA damage and mutations. This can potentially lead to the expression of high levels of neoepitopes on the surface of tumour cells that will stimulate an immunological response. Overall, exploiting DNA repair defects in tumours may provide an edge in this long fight against cancer.

Immunoinformatics Approach for the Identification and Characterization of T Cell and B Cell Epitopes towards the Peptide-Based Vaccine against SARS-CoV-2

Presently, immunoinformatics is playing a significant role in epitope identification and vaccine designing for various critical diseases. Using immunoinformatics, several scientists are trying to identify and characterize T cell and B cell epitopes as well as design peptide-based vaccine against SARS-CoV-2. In this review article, we have tried to discuss the importance in adaptive immunity and its significance for designing the SARS-CoV-2 vaccine. Moreover, we have attempted to illustrate several significant key points for utilizing immunoinformatics for vaccine designing, such as the criteria for selection and identification of epitopes, T cell epitope, and B cell epitope prediction and different emerging tools/databases for immunoinformatics. In the current scenario, a few immunoinformatics studies have been performed for various infectious pathogens and related diseases. Thus, we have also summarized and included these current immunoinformatics studies in this review article. Finally, we have discussed about the probable T cell and B cell epitopes and their identification and characterization for vaccine designing against SARS-CoV-2.

Adjuvant-pulsed mRNA vaccine nanoparticle for immunoprophylactic and therapeutic tumor suppression in mice

Synthetic mRNA represents an exciting cancer vaccine technology for the implementation of effective cancer immunotherapy. However, inefficient in vivo mRNA delivery along with a requirement for immune co-stimulation present major hurdles to achieving anti-tumor therapeutic efficacy. Here, we demonstrate a proof-of-concept adjuvant-pulsed mRNA vaccine nanoparticle (NP) that is composed of an ovalbumin-coded mRNA and a palmitic acid-modified TLR7/8 agonist R848 (C16-R848), coated with a lipid-polyethylene glycol (lipid-PEG) shell. This mRNA vaccine NP formulation retained the adjuvant activity of encapsulated C16-R848 and markedly improved the transfection efficacy of the mRNA (>95%) and subsequent MHC class I presentation of OVA mRNA derived antigen in antigen-presenting cells. The C16-R848 adjuvant-pulsed mRNA vaccine NP approach induced an effective adaptive immune response by significantly improving the expansion of OVA-specific CD8+ T cells and infiltration of these cells into the tumor bed in vivo, relative to the mRNA vaccine NP without adjuvant. The approach led to an effective anti-tumor immunity against OVA expressing syngeneic allograft mouse models of lymphoma and prostate cancer, resulting in a significant prevention of tumor growth when the vaccine was given before tumor engraftment (84% reduction vs. control) and suppression of tumor growth when given post engraftment (60% reduction vs. control). Our findings indicate that C16-R848 adjuvant pulsation to mRNA vaccine NP is a rational design strategy to increase the effectiveness of synthetic mRNA vaccines for cancer immunotherapy.

Immunization with Hydatid Cyst Wall Antigens Can Inhibit Breast Cancer through Changes in Serum Levels of Th1/Th2 Cytokines

Background

Hydatid cysts are the larval stage of Echinococcus granulosus, which lead to humoral and cellular immune responses in hosts. Such immune responses play a key role in the inhibition of tumor growth and cancers. To test this hypothesis, it was attempted not only to examine the changes in serum level of some Th1 and Th2 cytokines but also to find relationships between the cytokines and cancer in 4T1 breast cancer-bearing mice immunized with hydatid cyst wall (HCW) antigens.

Methods

Six to eight-week-old Balb/c female mice were immunized with alum, PBS and HCW antigens, including crude extract of HCW (laminated layer) 28 and 27 kDa protein bands (upper and lower bands) and then challenged with 4T1 breast cancer cells. The amounts of IL2, TNF-α, IFN-γ (Th1 cytokines), and IL4 (Th2 cytokine) were estimated using ELISA. Correlations between these cytokines and cancer parameters (tumor growth, metastasis, and survival) were determined by Pearson's correlation coefficients.

Results

Overall, HCW antigens increased the amounts of IL2, TNF-α, IFN-γ, and IL4. Pearson's correlation coefficients indicated reverse relationships between changes in amounts of these cytokines and tumor growth/metastasis. However, except for IL-4, all cytokines had a direct relationship with mouse survival.

Conclusions

The results of this study indicated that the inhibition of breast tumor growth and metastasis and improvement of survival in 4T1 mice immunized with HCW antigens, especially laminated layer and 27 kDa protein band can occur through a rise in the levels of cytokines.