MHCPred 2.0

Harnessing microbial antigens as cancer antigens: a promising avenue for cancer immunotherapy

Immunotherapy has revolutionized cancer treatment by leveraging the immune system's innate capabilities to combat malignancies. Despite the promise of tumor antigens in stimulating anti-tumor immune responses, their clinical utility is hampered by limitations in eliciting robust and durable immune reactions, exacerbated by tumor heterogeneity and immune evasion mechanisms. Recent insights into the immunogenic properties of host homologous microbial antigens have sparked interest in their potential for augmenting anti-tumor immunity while minimizing off-target effects. This review explores the therapeutic potential of microbial antigen peptides in tumor immunotherapy, beginning with an overview of tumor antigens and their challenges in clinical translation. We further explore the intricate relationship between microorganisms and tumor development, elucidating the concept of molecular mimicry and its implications for immune recognition of tumor-associated antigens. Finally, we discuss methodologies for identifying and characterizing microbial antigen peptides, highlighting their immunogenicity and prospects for therapeutic application.

Evaluation of humoral and cellular immune responses against Vibrio cholerae using oral immunization by multi-epitope-phage-based vaccine

INTRODUCTION

Cholera is a severe gastrointestinal disease that manifests with rapid onset of diarrhea, vomiting, and high mortality rates. Due to its widespread occurrence in impoverished communities with poor water sanitation, there is an urgent demand for a cost-effective and highly efficient vaccine. Multi-epitope vaccines containing dominant immunological epitopes and adjuvant compounds have demonstrated potential in boosting the immune response.

MATERIAL AND METHODS

B and T epitopes of OMPU, OMPW, TCPA, CTXA, and CTXB proteins were predicted using bioinformatics methods. Subsequently, highly antigenic multi-epitopes that are non-allergenic and non-toxic were synthesized. These multi-epitopes were then cloned into the pCOMB phagemid. A plasmid M13KO7ΔpIII containing all helper phage proteins except pIII was created to produce the recombinant phage. Female Balb/c mice were divided into three groups and immunized accordingly. The mice received the helper phage, recombinant phage or PBS via gavage feeding thrice within two weeks. Serum samples were collected before and after immunization for the ELISA test as well as evaluating immune system induction through ELISpot testing of spleen lymphocytes.

RESULTS

The titer of the recombinant phage was determined to be 1011 PFU/ml. The presence of the recombinant phage was confirmed through differences in optical density between sample and control groups in the ELISA phage technique, as well as by observing transduction activity, which demonstrated successful production of a recombinant phage displaying the Vibrio multi-epitope on M13 phage pIII. ELISA results revealed significant differences in phage antibodies before and after inoculation, particularly notable in the negative control mice. Mice treated with multi-epitope phages exhibited antibodies against Vibrio cholerae lysate. Additionally, ELISpot results indicated activation of cellular immunity in mice receiving both Vibrio and helper phage.

CONCLUSION

This study emphasizes the potential of multi-epitope on phage to enhance both cellular and humoral immunity in mice, demonstrating how phages can be used as adjuvants to stimulate mucosal immunity and act as promising candidates for oral vaccination.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Computational exploration and design of a multi-epitopes vaccine construct against Chlamydia psittaci

Chlamydia psittaci is an intracellular pathogen and causes variety of deadly infections in humans. Antibiotics are effective against C. psittaci however high percentage of resistant strains have been reported in recent times. As there is no licensed vaccine, we used in-silico techniques to design a multi-epitopes vaccine against C. psittaci. Following a step-wise protocol, the proteome of available 26 strains was retrieved and filtered for subcellular localized proteins. Five proteins were selected (2 extracellular and 3 outer membrane) and were further analyzed for B-cell and T-cell epitopes prediction. Epitopes were further checked for antigenicity, solubility, stability, toxigenicity, allergenicity, and adhesive properties. Filtered epitopes were linked via linkers and the 3D structure of the designed vaccine construct was predicted. Binding of the designed vaccine with immune receptors: MHC-I, MHC-II, and TLR-4 was analyzed, which resulted in docking energy scores of -4.37 kcal/mol, -0.20 kcal/mol and -22.38 kcal/mol, respectively. Further, the docked complexes showed stable dynamics with a maximum value of vaccine-MHC-I complex (7.8 Å), vaccine-MHC-II complex (6.2 Å) and vaccine-TLR4 complex (5.2 Å). As per the results, the designed vaccine construct reported robust immune responses to protect the host against C. psittaci infections. In the study, the C. psittaci proteomes were considered in pan-genome analysis to extract core proteins. The pan-genome analysis was conducted using bacterial pan-genome analysis (BPGA) software. The core proteins were checked further for non-redundant proteins using a CD-Hit server. Surface localized proteins were investigated using PSORTb v 3.0. The surface proteins were BLASTp against Virulence Factor Data Base (VFDB) to predict virulent factors. Antigenicity prediction of the shortlisted proteins was further done using VAXIGEN v 2.0. The epitope mapping was done using the immune epitope database (IEDB). A multi-epitopes vaccine was built and a 3D structure was generated using 3Dprot online server. The docking analysis of the designed vaccine with immune receptors was carried out using PATCHDOCK. Molecular dynamics and post-simulation analyses were carried out using AMBER v20 to decipher the dynamics stability and intermolecular binding energies of the docked complexes.Communicated by Ramaswamy H. Sarma.

COVID Variants, Villain and Victory: A Bioinformatics Perspective

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

In silico design, production and immunization evaluation of a recombinant bivalent fusion protein candidate vaccine against E. coli O157:H7

In silico techniques are highly suited for both the discovery of new and development of available vaccines. Escherichia coli O157: H7, a main cause of food poisoning can infect humans through the consumption of contaminated water or food. Vaccination is a choice strategy to combat the bacterium. In the present study, we designed, expressed and purified a chimeric protein comprising two antigens of Escherichia coli O157: H7, including intimin and flagellin proteins, as a vaccine candidate and evaluated its immunization ability in mice. Thein silicoresults showed that the proposed antigen has a high antigenicity and conformation to be used as a potent vaccine candidate. The protein was successfully expressed in E. coli expression system with a proper level of expression (0/8g/L). Immunization evaluation showed that the protein is able to evoke the mice's humoral immunity and can confer a protective immunity against E. coli O157:H7, so that 80 % of the immunized animals were survived following the intraperitoneal injection of 100 LD₅₀ of the live bacteria. Shedding analysis also showed the protectivity power of the protein. Bacterial excretion in control animals remained stable at about 10⁸ CFU after 15 days, while the excreted bacteria in the feces of immunized mice's decreased to about 10² after the same time. According to the results, the proposed protein is able to stimulate the immune responses of mice and protect them against E. coli O157:H7.

Design of a Multi-Epitopes Based Chimeric Vaccine against Enterobacter cloacae Using Pan-Genome and Reverse Vaccinology Approaches

Enterobacter cloacae (EC) is a significant emerging pathogen that is occasionally associated with lung infection, surgical site infection, urinary infection, sepsis, and outbreaks in neonatal intensive care units. In light of the fact that there is currently no approved vaccine or therapeutic option for the treatment of EC, the current study was developed to concentrate on applications based on modern computational approaches to design a multi-epitope-based E. cloacae peptide vaccine (MEBEPV) expressing the antigenic determinants prioritized from the EC genome. Integrated computational analyses identified two potential protein targets (phosphoporin protein-PhoE and putative outer-membrane porin protein) for further exploration on the basis of pangenome subtractive proteomics and immunoinformatic in-depth examination of the core proteomes. Then, a multi-epitope peptide vaccine was designed, which comprised shortlisted epitopes that were capable of eliciting both innate and adaptive immunity, as well as the cholera toxin’s B-subunit, which was used as an adjuvant in the vaccine formulation. To ensure maximum expression, the vaccine’s 3D structure was developed and the loop was refined, improving the stability by disulfide engineering, and the physicochemical characteristics of the recombinant vaccine sequence were found to be ideal for both in vitro and in vivo experimentation. Blind docking was then used for the prediction of the MEBEPV predominant blinding mode with MHCI, MHCII, and TLR3 innate immune receptors, with lowest global energy of −18.64 kJ/mol, −48.25 kJ/mol, and −5.20 kJ/mol for MHC-I, MHC-II, and TLR-4, respectively, with docked complexes considered for simulation. In MD and MMGBSA investigations, the docked models of MEBEPV-TLR3, MEBEPV-MHCI, and MEBEPV-MHCII were found to be stable during the course of the simulation. MM-GBSA analysis calculated −122.17 total net binding free energies for the TLR3-vaccine complex, −125.4 for the MHC I-vaccine complex, and −187.94 for the MHC II-vaccine complex. Next, MM-PBSA analysis calculated −115.63 binding free energy for the TLR3-vaccine complex, −118.19 for the MHC I-vaccine complex, and −184.61 for the MHC II-vaccine complex. When the vaccine was tested in silico, researchers discovered that it was capable of inducing both types of immune responses (cell mediated and humoral) at the same time. Even though the suggested MEBEPV has the potential to be a powerful contender against E. cloacae-associated illnesses, further testing in the laboratory will be required before it can be declared safe and immunogenic.

Promising Acinetobacter baumannii Vaccine Candidates and Drug Targets in Recent Years

In parallel to the uncontrolled use of antibiotics, the emergence of multidrug-resistant bacteria, like Acinetobacter baumannii, has posed a severe threat. A. baumannii predominates in the nosocomial setting due to its ability to persist in hospitals and survive antibiotic treatment, thereby eventually leading to an increasing prevalence and mortality due to its infection. With the increasing spectra of drug resistance and the incessant collapse of newly discovered antibiotics, new therapeutic countermeasures have been in high demand. Hence, recent research has shown favouritism towards the long-term solution of designing vaccines. Therefore, being a realistic alternative strategy to combat this pathogen, anti-A. Baumannii vaccines research has continued unearthing various antigens with variable results over the last decade. Again, other approaches, including pan-genomics, subtractive proteomics, and reverse vaccination strategies, have shown promise for identifying promiscuous core vaccine candidates that resulted in chimeric vaccine constructs. In addition, the integration of basic knowledge of the pathobiology of this drug-resistant bacteria has also facilitated the development of effective multiantigen vaccines. As opposed to the conventional trial-and-error approach, incorporating the in silico methods in recent studies, particularly network analysis, has manifested a great promise in unearthing novel vaccine candidates from the A. baumannii proteome. Some studies have used multiple A. baumannii data sources to build the co-functional networks and analyze them by k-shell decomposition. Additionally, Whole Genomic Protein Interactome (GPIN) analysis has utilized a rational approach for identifying essential proteins and presenting them as vaccines effective enough to combat the deadly pathogenic threats posed by A. baumannii. Others have identified multiple immune nodes using network-based centrality measurements for synergistic antigen combinations for different vaccination strategies. Protein-protein interactions have also been inferenced utilizing structural approaches, such as molecular docking and molecular dynamics simulation. Similar workflows and technologies were employed to unveil novel A. baumannii drug targets, with a similar trend in the increasing influx of in silico techniques. This review integrates the latest knowledge on the development of A. baumannii vaccines while highlighting the in silico methods as the future of such exploratory research. In parallel, we also briefly summarize recent advancements in A. baumannii drug target research.

Peptide-Based Vaccines for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.

Chikungunya Virus Vaccine Development: Through Computational Proteome Exploration for Finding of HLA and cTAP Binding Novel Epitopes as Vaccine Candidates

Chikungunya virus is a major arbovirus of great public health concern in the whole world, but no vaccine is yet available, still advance therapeutic treatment and effective vaccines are in progress. The present multistep screening and structural binding analysis with CHIKV proteome exploration can be crucial in the development phase of CHIKV epitope based vaccine. The approach employed in two phases (i) Sequence based screening of peptides through propred and IEDB Server (ii) Structure based study through autodocking and NAMD VMD simulation analysis. Among all 29 extracted peptides, only two peptides ²LLANTTFPC¹⁰ of protein E3 and ⁹⁸VNSVAIPLL¹⁰⁶ of protein nsP3 were observed most prominent over all consider parameters such as peptide conserve nature, supertype population coverage, TAP binding, docking and simulation study. During docking interaction study, the best peptide and allele docked complexes such as ²LLANTTFPC¹⁰–B*0702 allele and ⁹⁸VNSVAIPLL¹⁰⁶–A*0301 allele exhibited best binding energy of − 3.13 kcal/mol and − 3.19 kcal/mol, respectively, with stable bonding patterns and their motion during NAMD simulation which confirm conserve peptide and allele stable interaction. The current study also exhibited the good docking interaction of both peptides ²LLANTTFPC¹⁰ and ⁹⁸VNSVAIPLL¹⁰⁶ with c TAP1 protein (1jj7 -PDB ID) cavity which confirm as a channel passageway to peptide transport through the cytoplasm to lumen of ER during antigen processing and presentation. Overall, this multistep screening and crosscheck structural binding analysis with an exploration of the complete proteome of CHIKV can be a novel step in the development of CHIKV epitope based vaccine as well as diagnostic development with aspect of time, cost and side effects.

Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics

The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.

Immunoinformatics and molecular docking studies reveal a novel Multi-Epitope peptide vaccine against pneumonia infection

Pneumonia is a major endemic disease around the world, and an effective vaccine is the need of the hour to fight against the disease. When there are no appropriate antiviral and associated therapies available, vaccine development becomes even more essential. Therefore, in the present study, a variety of immunoinformatics techniques was utilized to develop a novel multi-epitope vaccine that targets the highly immunodominant type 3 fimbrial protein of Klebsiella pneumoniae, the causal organism for pneumonia. The putative B and T cell epitopes were predicted from the protein and screened for antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. Subsequently, the selected epitopes were joined with the help of linkers to form a robust vaccine construct. In addition, an adjuvant was applied to the N-terminal of the construct to improve the immunogenicity of the vaccine. The physicochemical properties, solubility, the secondary and tertiary structure of the final vaccine were also established. MD simulations for 100 ns were employed to assess the stability of the vaccine-TLR-2 docked complex. The final vaccine was optimized and cloned in pET28a (+) vector with His-tag to achieve maximum vaccine protein expression for ease of purification. Immune simulation results indicated the potency of this vaccine candidate as a probable therapeutic agent. In conclusion, the overall results of various immunoinformatics tools and methods employed revealed that the constructed multi-epitope vaccine exhibits a high potential for stimulating both B and T-cells immune responses against pneumonia infection. However, experimental immunological studies are required to corroborate the viability of the novel multi-epitope construct as a commercial vaccine.

Pan-vaccinomics approach towards a universal vaccine candidate against WHO priority pathogens to address growing global antibiotic resistance

Antimicrobial resistance (AMR) in bacterial pathogens is a major global distress. Due to the slow progress of antibiotics development and the fast pace of resistance acquisition, there is an urgent need for effective vaccines against such bacterial pathogens. In-silico approaches including pan-genomics, subtractive proteomics, reverse vaccinology, immunoinformatics, molecular docking, and dynamics simulation studies were applied in the current study to identify a universal potential vaccine candidate against the 18 multi-drug resistance (MDRs) bacterial pathogenic species from a WHO priority list. Ten non-redundant, non-homologous, virulent, and antigenic vaccine candidates were filtered against all targeted species. Nine B-cell-derived T-cell antigen epitopes which show a great affinity to the dominant HLA allele (DRB1*0101) in the human population were screened from selected vaccine candidates using immunoinformatics approaches. Screened epitopes were then used to design a multi-epitope peptide vaccine construct (MEPVC) along with β-defensin adjuvant to improve the immunogenic properties of the proposed vaccine construct. Molecular docking and MD simulation were carried out to study the binding affinity and molecular interaction of MEPVC with human immune receptors (TLR2, TLR3, TLR4, and TLR6). The final MEPVC construct was reverse translated and in-silico cloned in the pET28a(+) vector to ensure its effectiveness. This in silico construct is expected to be helpful for vaccinologists to assess its immune protection effectiveness in vivo and in vitro to counter rising antibiotic resistance worldwide.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches

COVID-19 caused by SARS-CoV-2 was declared a global pandemic by WHO (World Health Organization) in March, 2020. Within 6 months, nearly 750,000 deaths are claimed by COVID-19 across the globe. This called for immediate social, scientific, technological, public and community interventions. Considering the severity of infection and the associated mortalities, global efforts are underway to develop preventive measures against SARS-CoV-2. Among the SARS-CoV-2 target proteins, Spike (S) glycoprotein (a.k.a S Protein) is the most studied target known to trigger strong host immune response. A detailed analysis of S protein-based epitopes enabled us to design a novel B-cell-derived T-cell Multi-epitope-based peptide (MEBP) vaccine candidate. This involved a systematic and comprehensive computational protocol consisting of prediction of dual-purpose epitopes and designing an MEBP vaccine construct. This was followed by 3D structure validation, MEBP complex interaction studies, in silico cloning and vaccine dose-based immune response simulation to evaluate the immunogenic potency of the vaccine construct. The dual-purpose epitope prediction protocol was designed such that the same epitope elicits both humoral and cellular immune response unlike the earlier designs. Further, the epitopes predicted were screened against stringent criteria to ensure selection of a potent candidate with maximum antigen coverage and best immune response. The vaccine dose-based immune response simulation studies revealed a rapid antigen clearance through antibody generation and elevated levels of cell-mediated immunity during repeated exposure of the vaccine. The favourable results of the analysis strongly indicate that the vaccine construct is indeed a potent vaccine candidate and ready to proceed to the next steps of experimental validation and efficacy studies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-020-02574-x.

MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides

Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we describe a complementary approach called MHCII3D, which is based on structural scaffolds of MHC II-peptide complexes and statistical scoring functions (SSFs). The MHC II alleles reported in the Immuno Polymorphism Database are processed in a dedicated 3D-modeling pipeline providing a set of scaffold complexes for each distinct allotype sequence. Antigen protein sequences are threaded through the scaffolds and evaluated by optimized SSFs. We compared the predictive power of MHCII3D with different sequence-based machine learning methods. The Pearson correlation to experimentally determine IC₅₀ values for MHC II Automated Server Benchmarks data sets from IEDB (Immune Epitope Database) is 0.42, which is in the competitor methods range. We show that MHCII3D is quite robust in leaving one molecule out tests and is therefore not prone to overfitting. Finally, we provide evidence that MHCII3D can complement the current sequence-based methods and help to identify problematic entries in IEDB. Scaffolds and MHCII3D executables can be freely downloaded from our web pages.

Immunoinformatics design of multivalent chimeric vaccine for modulation of the immune system in Pseudomonas aeruginosa infection

Increasing in drug-resistant Pseudomonas aeruginosa and high mortality and morbidity rate have become a health challenge worldwide; therefore, developing the novel therapeutic strategies such as immunogenic vaccine candidate are required. Despite a substantial research effort, the future of immunization against P. aeruginosa due to failure in covering two separate stages of infection, and furthermore, inducing ineffective type of immune response, still remains controversial. In this study, immunoinformatics approach was utilized to design multivalent chimeric vaccine from both stages of infection containing Lectin, HIV TAT peptide, N-terminal fragment of exotoxin A and Epi8 of outer membrane protein F (OprF) with hydrophobic linkers which have a high density of B-cell, T Lymphocytes (HTL), T Lymphocytes (CTL), and IFN-γ epitopes. The physicochemical properties, antigenicity, and allergenicity for designed vaccine were analyzed. 3D model generation and refinement further validation of the final vaccine were followed by computational docking with molecular dynamics analyses that demonstrated high- affinity interaction between vaccine and TLR-4. Finally, designed vaccine was in silico cloned in pET22b. We have expected that the designed vaccine able to elucidate innate, humoral and cellular innate immune responses and control the interaction of P. aeruginosa with host and maybe overcome to P. aeruginosa vaccines drawback.

Identification of CD4+ T cell epitopes on glyceraldehyde-3-phosphate dehydrogenase-C of Staphylococcus aureus in Babl/c mice

Glyceraldehyde-3-phosphate dehydrogenase-C (GapC) is a highly conserved surface protein of Staphylococcus aureus, with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activity, which represents an excellent vaccine candidate antigen. It can induce protective immune responses to S. aureus infections. However, CD4⁺ T cell epitopes of GapC that induce CD4⁺ T cell immune responses are currently unclear. In this study, we used bioinformatics prediction algorithms to predict CD4⁺ T cell epitopes of GapC. Ten peptides were synthesized to investigate the candidate epitopes. Our results showed that the peptides, G4 (GapC _104-123) and G10 (GapC _314-333) were able to induce proliferation of CD4⁺ T cells and secrete high levels of interferon (IFN)-γ, respectively. In addition, they significantly reduced bacterial loads in tissue and induced immunoprotective effects. It is suggested that G4 and G10 are Th1-type epitopes of S. aureus GapC. This study provides the potential development of the design of epitope-based vaccine against S. aureus.

Neoantigens in Hematologic Malignancies

T cell cancer neoantigens are created from peptides derived from cancer-specific aberrant proteins, such as mutated and fusion proteins, presented in complex with human leukocyte antigens on the cancer cell surface. Because expression of the aberrant target protein is exclusive to malignant cells, immunotherapy directed against neoantigens should avoid “on-target, off-tumor” toxicity. The efficacy of neoantigen vaccines in melanoma and glioblastoma and of adoptive transfer of neoantigen-specific T cells in epithelial tumors indicates that neoantigens are valid therapeutic targets. Improvements in sequencing technology and innovations in antigen discovery approaches have facilitated the identification of neoantigens. In comparison to many solid tumors, hematologic malignancies have few mutations and thus fewer potential neoantigens. Despite this, neoantigens have been identified in a wide variety of hematologic malignancies. These include mutated nucleophosmin1 and PML-RARA in acute myeloid leukemia, ETV6-RUNX1 fusions and other mutated proteins in acute lymphoblastic leukemia, BCR-ABL1 fusions in chronic myeloid leukemia, driver mutations in myeloproliferative neoplasms, immunoglobulins in lymphomas, and proteins derived from patient-specific mutations in chronic lymphoid leukemias. We will review advances in the field of neoantigen discovery, describe the spectrum of identified neoantigens in hematologic malignancies, and discuss the potential of these neoantigens for clinical translation.

Immunogenicity of Protein Pharmaceuticals

Protein therapeutics have drastically changed the landscape of treatment for many diseases by providing a regimen that is highly specific and lacks many off-target toxicities. The clinical utility of many therapeutic proteins has been undermined by the potential development of unwanted immune responses against the protein, limiting their efficacy and negatively impacting its safety profile. This review attempts to provide an overview of immunogenicity of therapeutic proteins, including immune mechanisms and factors influencing immunogenicity, impact of immunogenicity, preclinical screening methods, and strategies to mitigate immunogenicity.

Immuno-informatics driven proteome-wide investigation revealed novel peptide-based vaccine targets against emerging multiple drug resistant Providencia stuartii

The bacterium Providencia stuartii, is associated with urinary tract infections and is the most common cause of purple urine bag syndrome. The increasing multi-drug resistance pattern shown by the pathogen and lack of licensed vaccines make treatment of infections caused by P. stuartii challenging. As vaccinology data against the pathogen is scarce, an in silico proteome based Reverse Vaccinology (RV) protocol, in combination with subtractive proteomics is introduced in this work to screen potential vaccine candidates against P. stuartii. The analysis identified three potential vaccine candidates for designing broad-spectrum and strain-specific peptide vaccines: FimD4, FimD6, and FimD8. These proteins are essential for pathogen survival, localized in the outer membrane, virulent, and antigenic in nature. Immunoproteomic tools mapped surface exposed and non-allergenic 9mer B-cell derived T-cell antigenic epitopes for the proteins. The epitopes also show stable and rich interactions with the most predominant HLA allele (DRB1*0101) in the human population. Metabolic pathway annotation of the proteins indicated that fimbrial biogenesis outer membrane usher protein (FimD6) is the most suitable candidate for vaccine design, due to its involvement in several significant pathways. These pathways include: the bacterial secretion system, two-component system, β-lactam resistance, and cationic antimicrobial peptide pathways. The predicted epitopes may provide a basis for designing a peptide-based vaccine against P. stuartii.

Identification and characterization of CD4+ T cell epitopes on manganese transport protein C of Staphylococcus aureus

Manganese transport protein C (MntC) of Staphylococcus aureus represents an excellent vaccine-candidate antigen. The important role of CD4⁺ T cells in effective immunity against S. aureus infection was shown; however, CD4⁺ T cell-specific epitopes on S. aureus MntC have not been well identified. Here, we used bioinformatics prediction algorithms to evaluate and identify nine candidate epitopes within MntC. Our results showed that peptide M8 emulsified in Freund's adjuvant induced a much higher cell-proliferation rate as compared with controls. Additionally, CD4⁺ T cells stimulated with peptide M8 secreted significantly higher levels of interferon-γ and interleukin-17A. These results suggested that peptide M8 represented an H-2d (I-E)-restricted Th17-specific epitope.

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants.

An Overview of Bioinformatics Tools and Resources in Allergy

The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

Specific cytotoxic T-cell immune responses against autoantigens recognized by chronic lymphocytic leukaemia cells

Mounting evidence suggests that autoreactivity and inflammatory processes are involved in the pathogenesis of chronic lymphocytic leukaemia (CLL). Cytoskeletal proteins, including non-muscle myosin heavy chain IIA (MYHIIA), vimentin (VIM) and cofilin-1 (CFL1), exposed on the surface of apoptotic cells have been identified as autoantigens that are recognized by the specific B-cell receptors of the CLL cells. In 212 CLL patients analysed with quantitative reverse transcriptase-polymerase chain reaction we found CFL1 overexpression and low expression of MYH9 in comparison with healthy volunteers. We detected specific cytotoxic immune responses for peptides derived from MYHIIA in 66·7%, VIM in 87·5% and CFL1 in 62·5% CLL patients in an Enzyme-Linked ImmunoSpot assay. Low frequencies of autoreactive peptide-specific T cells were detected against MYHIIA, VIM and CFL1 in CLL patients ex vivo; most of the detected cells had an effector-memory phenotype. Our findings support the existence of cytotoxic immune responses against three autoantigens that have been identified as targets of CLL clonotypic B-cell receptors. The presence of autoreactive CD8(+) T cells against MYHIIA, VIM and CFL1 in CLL patients indicates the involvement of antigen-specific autoreactive T cells in the pathogenesis of CLL.

Developments in cancer vaccines for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) accounts for about 6 % of all new cancers diagnosed worldwide and represents one of the leading causes of cancer-related death globally in men and women, respectively. The overall prognosis for HCC patients is poor, especially in the majority of patients with more advanced stage of disease. Indeed, in such cases immunotherapeutic strategies may represent a novel and effective tool. A few immunotherapy trials conducted for HCC have provided divergent results, urging the scientific community to explore additional paths to improve efficacy of immunotherapeutic approaches. The "Cancer Vaccine development for Hepatocellular Carcinoma"-HEPAVAC Consortium has been funded by the EU within the FP7 with the goal of developing a novel therapeutic peptide-based cancer vaccine strategy for HCC including both "off-the-shelf" and personalized antigens. This will be one of the very few vaccine trials for HCC and the first multi-epitope, multi-target and multi-HLA allele therapeutic cancer vaccine for such a frequent and aggressive disease with a hitherto high unmet medical need. Feasibility, safety and biological efficacy will be evaluated in a randomized, controlled European multicenter phase I/II clinical trial.

Antigen-specific vaccines for cancer treatment

Vaccines targeting pathogens are generally effective and protective because based on foreign non-self antigens which are extremely potent in eliciting an immune response. On the contrary, efficacy of therapeutic cancer vaccines is still disappointing. One of the major reasons for such poor outcome, among others, is the difficulty of identifying tumor-specific target antigens which should be unique to the tumors or, at least, overexpressed on the tumors as compared to normal cells. Indeed, this is the only option to overcome the peripheral immune tolerance and elicit a non toxic immune response. New and more potent strategies are now available to identify specific tumor-associated antigens for development of cancer vaccine approaches aiming at eliciting targeted anti-tumor cellular responses. In the last years this aspect has been addressed and many therapeutic vaccination strategies based on either whole tumor cells or specific antigens have been and are being currently evaluated in clinical trials. This review summarizes the current state of cancer vaccines, mainly focusing on antigen-specific approaches.

Identification of CD4+ T-cell epitopes on iron-regulated surface determinant B of Staphylococcus aureus

Iron-regulated surface determinant B (IsdB) of Staphylococcus aureus (S. aureus) is a highly conserved surface protein that can induce protective CD4(+) T-cell immune response. A pivotal role of CD4(+) T-cells in effective immunity against S. aureus infection has been proved, but CD4(+) T-cell epitopes on the S. aureus IsdB have not been well identified. In this study, MHC binding assay was firstly used to predict CD4(+) T-cell epitopes on S. aureus IsdB protein, and six peptides were synthesized to validate the probable epitopes. Two novel IsdB CD4(+) T-cell epitopes, P1 (residues 159-178) and P4 (residues 287-306), were for the first time identified using CD4(+) T-cells obtained from IsdB-immunized C57BL/6 (H-2(b)) and BALB/c (H-2(d)) mice spleen based on cell proliferation and cytokines response. The results showed that P1 and P4 emulsified in Freund's adjuvant (FA) induced much higher cell proliferation compared with PBS emulsified in FA. CD4(+) T-cells stimulated with peptides P1 and P4 secreted significantly higher levels of IFN-γ and IL-17A. However, the level of the cytokine IL-4 almost remained unchanged, suggesting that P1 and P4 preferentially elicited polarized Th1-type responses. In addition, BALB/c mice just respond to P4 not P1, while C57BL/6 mice respond to P1 not P4, implying that epitope P1 and P4 were determined as H-2(b) and H-2(d) restricted epitope, respectively. Taken together, our data may provide an explanation of the IsdB-induced protection against S. aureus and highlight the possibility of developing the epitope-based vaccine against the S. aureus.

Systems vaccinology for cancer vaccine development

Results of therapeutic vaccines for established chronic infections or cancers are still unsatisfactory. The only therapeutic cancer vaccine approved for clinical use is the sipuleucel-T, for the treatment of metastatic prostate cancer, which induces a limited 4-month improvement in the overall survival of vaccinated patients compared to controls. This represents a remarkable advancement in the cancer immunotherapy field, although the clinical outcome of cancer vaccines needs to be substantially improved. To this aim, a multipronged strategy is required, including the evaluation of mechanisms underlying the effective elicitation of immune responses by cancer vaccines. The recent development of new technologies and computational tools allows the comprehensive and quantitative analysis of the interactions between all of the components of innate and adaptive immunity over time. Here we review the potentiality of systems biology in providing novel insights in the mechanisms of action of vaccines to improve their design and effectiveness.

Functional characterization of ocular-derived human alphaherpesvirus cross-reactive CD4 T cells

Intraocular varicella-zoster virus (VZV) and HSV type 1 (HSV-1) infections cause sight-threatening uveitis. The disease is characterized by an intraocular inflammatory response involving herpesvirus-specific T cells. T cell reactivity to the noncausative human alphaherpesvirus (αHHV) is commonly detected in the affected eyes of herpetic uveitis patients, suggesting the role of cross-reactive T cells in the disease. This study aimed to identify and functionally characterize intraocular human alphaherpesvirus cross-reactive T cells. VZV protein immediate early 62 (IE62), which shares extensive homology with HSV ICP4, is a previously identified T cell target in VZV uveitis. Two VZV-specific CD4 T cell clones (TCC), recovered from the eye of a VZV uveitis patient, recognized the same IE62918-927 peptide using different TCR and HLA-DR alleles. The IE62918-927 peptide bound with high affinity to multiple HLA-DR alleles and was recognized by blood-derived T cells of 5 of 17 HSV-1/VZV-seropositive healthy adults but not in cord blood donors (n = 5). Despite complete conservation of the IE62 epitope in the orthologous protein ICP4 of HSV-1 and HSV-2, the TCC recognized VZV and HSV-1- but not HSV-2-infected B cells. This was not attributed to proximal epitope-flanking amino acid polymorphisms in HSV-2 ICP4. Notably, VZV/HSV-1 cross-reactive CD4 T cells controlled VZV but not HSV-1 infection of human primary retinal pigment epithelium (RPE) cells. In conclusion, we report on the first VZV/HSV-1 cross-reactive CD4 T cell epitope, which is HLA-DR promiscuous and immunoprevalent in coinfected individuals. Moreover, ocular-derived peptide-specific CD4 TCC controlled VZV but not HSV-1 infection of RPE cells, suggesting that HSV-1 actively inhibits CD4 T cell activation by infected human RPE cells.

Challenges in cancer vaccine development for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common liver malignancy, representing the third and fifth leading cause of death from cancer worldwide in men and women, respectively. The main risk factor for the development of HCC is the hepatitis B and C virus (HBV and HCV) infection; non-viral causes (e.g., alcoholism and aflatoxin) are additional risk factors. HCC prognosis is generally poor because of the low effectiveness of available treatments and the overall 5-year survival rate is approximately 5-6%. In this framework, immunotherapeutic interventions, including cancer vaccines, may represent a novel and effective therapeutic tool. However, only few immunotherapy trials for HCC have been conducted so far with contrasting results, suggesting that improvements in several aspects of the immunotherapy approaches need to be implemented. In particular, identification of novel specific tumor antigens and evaluation of most advanced combinatorial strategies could result in unprecedented clinical outcomes with great beneficial effect for HCC patients. The state of the art in immunotherapy strategies for HCC and future perspectives are reported in the present review.

Therapeutic cancer vaccines and translating vaccinomics science to the global health clinic: emerging applications toward proof of concept

As vaccines evolve to be a more common treatment for some cancers, further research is needed to improve the process of developing vaccines and assessing response to treatment. Vaccinomics involves a wide-ranging integration of multiple high throughput technologies including transcriptional, translational, and posttranslational population-based assessments of the human genome, transcriptome, proteome, and immunome. Vaccinomics combines the fields of immunogenetics, immunogenomics, immunoproteomics, and basic immunology to create vaccines that are tailor made to an individual or groups of individuals. This broad range of omics applications to tumor immunology includes antigen discovery, diagnostic biomarkers, cancer vaccine development, predictors of immune response, and clinical response biomarkers. These technologies have aided in the advancement of cancer vaccine development, as illustrated in examples including NY-ESO-1 originally defined by SEREX, and HER2/neu peptides analyzed via high-throughput epitope prediction methods. As technology improves, it presents an opportunity to improve cancer immunotherapy on a global scale, and attention must also be given to utilize these high-throughput methods for the understanding of cancer and immune signatures across populations.

Identification of epitopes in Indian human papilloma virus 16 E6: a bioinformatics approach

HPV-16 is reported as the cause of cervical and other related carcinomas. The early expressed protein E6 in cancer cells is found to be the target for immune therapeutic methods. The sequence of HPV-16 E6 (Accession No: ABK32509) from NCBI databank has been taken for this study. Hydrophilicity, flexibility, accessibility, turns, exposed surface, polarity and antigenic propensity scales were used for the B cell epitope prediction. MHC Class I and Class II alleles for the accession were predicted by the MHCPred 2.0 Program. The epitope sequences were also found out. Computer-based prediction program results show, A0203 and DRB0101 lower IC50 than other alleles. The best peptide binding affinity was 21HLCTELQTT30 of A0203 allele. In DRB0101 allele the peptide found was 39YCKQQLLRR48. Different structural features of the protein have also been predicted including glycosylation, kinase C phosphorylation, casein kinase II phosphorylation and N-myristylation sites. These computational prediction programs show four glycosylation, five kinase C phosphorylation, two casein kinase II phosphorylation, zero N-myristylation sites and seven disulphide sites. Development and approval of new vaccines are the keys for control of cancer. Epitopes and other structural features of protein prediction could be the best source of information and can help in molecular and medical studies of viral infection and development of HPV associated cancer drugs.

Immunodominant T cell determinants of aquaporin-4, the autoantigen associated with neuromyelitis optica

Autoantibodies that target the water channel aquaporin-4 (AQP4) in neuromyelitis optica (NMO) are IgG1, a T cell-dependent Ig subclass. However, a role for AQP4-specific T cells in this CNS inflammatory disease is not known. To evaluate their potential role in CNS autoimmunity, we have identified and characterized T cells that respond to AQP4 in C57BL/6 and SJL/J mice, two strains that are commonly studied in models of CNS inflammatory diseases. Mice were immunized with either overlapping peptides or intact hAQP4 protein encompassing the entire 323 amino acid sequence. T cell determinants identified from examination of the AQP4 peptide (p) library were located within AQP4 p21-40, p91-110, p101-120, p166-180, p231-250 and p261-280 in C57BL/6 mice, and within p11-30, p21-40, p101-120, p126-140 and p261-280 in SJL/J mice. AQP4-specific T cells were CD4⁺ and MHC II-restricted. In recall responses to immunization with intact AQP4, T cells responded primarily to p21-40, indicating this region contains the immunodominant T cell epitope(s) for both strains. AQP4 p21-40-primed T cells secreted both IFN-γ and IL-17. The core immunodominant AQP4 21-40 T cell determinant was mapped to residues 24-35 in C57BL/6 mice and 23-35 in SJL/J mice. Our identification of the AQP4 T cell determinants and characterization of its immunodominant determinant should permit investigators to evaluate the role of AQP4-specific T cells in vivo and to develop AQP4-targeted murine NMO models.

Prediction and characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitidis serogroup B

Neisseria meningitidis serogroup B (MC58) is a leading cause of meningitis and septicaemia, principally infects the infants and adolescents. No vaccine is available for the prevention of these infections because the serogroup B capsular polysaccharide is unable to stimulate an immune response, due to its similarity with polysialic acid. To overcome these obstacles, we proposed to develop a peptide based epitope vaccine from outer membrane protein contained in outer membrane vesicles (OMV) based on our computational analysis. In OMV a total of 236 proteins were identified, only 15 (6.4%) of which were predicted to be located in outer membrane. The major requirement is the identification and selection of T-cell epitopes that act as a vaccine target. We have selected 13 out of 15 outer membrane proteins from OMV proteins. Due to similarity of the fkpA and omp85 with the human FKBP2 and SAMM50 protein, we removed these two sequences from the analysis as their presence in the vaccine is likely to elicit an autoimmune response. ProPred and ProPred1 were used to predict promiscuous helper T Lymphocytes (HTL) and cytotoxic T Lymphocytes (CTL) epitopes and MHCPred for their binding affinity in N. meningitidis serogroup B (MC58), respectively. Binding peptides (epitopes) are distinguished from nonbinding peptides in properties such as amino acid preference on the basis of amino acid composition. By using this dataset, we compared physico-chemical and structural properties at amino acid level through amino acid composition, computed from ProtParam server. Results indicate that porA, porB, opc, rmpM, mtrE and nspA are more suitable vaccine candidates. The predicted peptides are expected to be useful in the design of multi-epitope vaccines without compromising the human population coverage.

Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Cancer immunotherapy is fast gaining global attention with its unique position as a potential therapy showing promise in cancer prevention and cure. It utilizes the natural system of immunity as opposed to chemotherapy and radiotherapy that utilize chemical drugs and radiation, respectively. Cancer immunotherapy essentially involves treatment and/or prevention with vaccines in the form of peptide vaccines (T and B cell epitopes), DNA vaccines and vaccination using whole tumor cells, dendritic cells, viral vectors, antibodies and adoptive transfer of T cells to harness the body's own immune system towards the targeting of cancer cells for destruction. Given the time, cost and labor involved in the vaccine discovery and development, researchers have evinced interest in the novel field of immunoinformatics to cut down the escalation of these critical resources. Immunoinformatics is a relatively new buzzword in the scientific circuit that is showing its potential and delivering on its promise in expediting the development of effective cancer immunotherapeutic agents. This review attempts to present a holistic picture of our race against cancer and time using the science and technology of immunoinformatics and molecular modeling in T cell epitope-based cancer immunotherapy. It also attempts to showcase some problem areas as well as novel ones waiting to be explored where development of novel immunoinformatics tools and simulations in the context of cancer immunotherapy would be highly welcome.

Towards in silico design of epitope-based vaccines

BACKGROUND

Epitope-based vaccines (EVs) make use of immunogenic peptides (epitopes) to trigger an immune response. Due to their manifold advantages, EVs have recently been attracting growing interest. The success of an EV is determined by the choice of epitopes used as a basis. However, the experimental discovery of candidate epitopes is expensive in terms of time and money. Furthermore, for the final choice of epitopes various immunological requirements have to be considered.

METHODS

Numerous in silico approaches exist that can guide the design of EVs. In particular, computational methods for MHC binding prediction have already become standard tools in immunology. Apart from binding prediction and prediction of antigen processing, methods for epitope design and selection have been suggested. We review these in silico approaches for epitope discovery and selection along with their strengths and weaknesses. Finally, we discuss some of the obvious problems in the design of EVs.

CONCLUSION

State-of-the-art in silico approaches to MHC binding prediction yield high accuracies. However, a more thorough understanding of the underlying biological processes and significant amounts of experimental data will be required for the validation and improvement of in silico approaches to the remaining aspects of EV design.

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research

BACKGROUND

Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.

RESULTS

Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds.

CONCLUSION

The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.

Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research

Background

Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.

Results

Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).

Conclusion

Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.

T-cell responses over time in a mild hemophilia A inhibitor subject: epitope identification and transient immunogenicity of the corresponding self-peptide

BACKGROUND

Antibodies that neutralize factor (F) VIII activity, clinically referred to as 'inhibitors', complicate the treatment of hemophilia A patients; current tolerance and bypass strategies are extremely costly and sometimes ineffective. The development of inhibitors requires T-cell help.

OBJECTIVES

We characterized T-cell responses of a subject with mild hemophilia A with missense genotype A2201P for one year following his initial inhibitor response, with the goals of defining the primary epitope(s) and its (their) MHC Class II restriction. We investigated the possible involvement of regulatory T cells in modulating immune responses.

PATIENTS/METHODS

The subject developed high-titer FVIII-neutralizing antibodies (250 BU mL(-1)) that declined over time to 8 BU ml(-1). His clotting activity was initially impaired (3%) but returned to baseline (8-10%) within four weeks. MHC Class II tetramers were used to analyze his CD4 T cells, which were stimulated with peptides spanning the C2 domain. Responses of total and CD25-depleted CD4 cells to sequences containing A2201 (native), P2201 (hemophilic), and other predicted T-cell epitopes were evaluated.

RESULTS AND CONCLUSIONS

An HLA-DRA-DRB1*0101 restricted T-cell epitope containing the wild-type A2201 sequence was identified. Interestingly, peptides containing A2201 were recognized by CD4 T cells at all time points, whereas a P2201 peptide was recognized only near the initial peak response. The responsiveness of CD25-depleted CD4 cells to an A2201 peptide was enhanced 11 and 19 weeks following inhibitor detection, suggesting the possible involvement of CD4+CD25+ regulatory T cells in modulating immune responses. Patient-derived T-cell clones proliferated in response to C2 protein and to peptides containing A2201 but not P2201.

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.