Development Of Multi-epitope Vaccine Research Articles

Multi-epitope vaccine against SARS-CoV-2 targeting the spike RBD: an immunoinformatics approach

Aim: We designed a SARS-CoV-2 epitope vaccine based on the receptor-binding domain (RBD) in virus spike protein. Methods: RT-PCR performed on nasopharyngeal swab COVID-19 patients. After registering RBD region in the GenBank, physicochemical parameters, secondary structure, homology modeling, 3D structure of RBD region and antigenicity were determined using ProtParam ExPASy, PSIPRED, MolProbity, IEDB and Vaxijen online tools, respectively. Results: B and T cell epitopes were predicted in terms of non-allergenicity and antigenicity. MolProbity analysis provided a qualitative model for RBD. The homology model showed that most of the residues are in optimal district of energy. Conclusion: High immunogenicity score of epitopes indicates promising candidates for the development of multi-epitope vaccines. It may help to develop an effective vaccine.

Development of multi-epitope vaccines against the monkeypox virus based on envelope proteins using immunoinformatics approaches.

Since May 2022, cases of monkeypox, a zoonotic disease caused by the monkeypox virus (MPXV), have been increasingly reported worldwide. There are, however, no proven therapies or vaccines available for monkeypox. In this study, several multi-epitope vaccines were designed against the MPXV using immunoinformatics approaches. Three target proteins, A35R and B6R, enveloped virion (EV) form-derived antigens, and H3L, expressed on the mature virion (MV) form, were selected for epitope identification. The shortlisted epitopes were fused with appropriate adjuvants and linkers to vaccine candidates. The biophysical andbiochemical features of vaccine candidates were evaluated. The Molecular docking and molecular dynamics(MD) simulation were run to understand the binding mode and binding stability between the vaccines and Toll-like receptors (TLRs) and major histocompatibility complexes (MHCs). The immunogenicity of the designed vaccines was evaluated via immune simulation. Five vaccine constructs (MPXV-1-5) were formed. After the evaluation of various immunological and physicochemical parameters, MPXV-2 and MPXV-5 were selected for further analysis. The results of molecular docking showed that the MPXV-2 and MPXV-5 had a stronger affinity to TLRs (TLR2 and TLR4) and MHC (HLA-A*02:01 and HLA-DRB1*02:01) molecules, and the analyses of molecular dynamics (MD) simulation have further confirmed the strong binding stability of MPXV-2 and MPXV-5 with TLRs and MHC molecules. The results of the immune simulation indicated that both MPXV-2 and MPXV-5 could effectively induce robust protective immune responses in the human body. The MPXV-2 and MPXV-5 have good efficacy against the MPXV in theory, but further studies are required to validate their safety and efficacy.

Sph2(176-191) and Sph2(446-459): Identification of B-Cell Linear Epitopes in Sphingomyelinase 2 (Sph2), Naturally Recognized by Patients Infected by Pathogenic Leptospires.

Sphingomyelin is a major constituent of eukaryotic cell membranes, and if degraded by bacteria sphingomyelinases may contribute to the pathogenesis of infection. Among Leptospira spp., there are five sphingomyelinases exclusively expressed by pathogenic leptospires, in which Sph2 is expressed during natural infections, cytotoxic, and implicated in the leptospirosis hemorrhagic complications. Considering this and the lack of information about associations between Sph2 and leptospirosis severity, we use a combination of immunoinformatics approaches to identify its B-cell epitopes, evaluate their reactivity against samples from leptospirosis patients, and investigate the role of antibodies anti-Sph2 in protection against severe leptospirosis. Two B-cell epitopes, Sph2(176-191) and Sph2(446-459), were predicted in Sph2 from L. interrogans serovar Lai, presenting different levels of identity when compared with other pathogenic leptospires. These epitopes were recognized by about 40% of studied patients with a prevalence of IgG antibodies against both Sph2(176-191) and Sph2(446-459). Remarkably, just individuals with low reactivity to Sph2(176-191) presented clinical complications, while high responders had only mild symptoms. Therefore, we identified two B-cell linear epitopes, recognized by antibodies of patients with leptospirosis, that could be further explored in the development of multi-epitope vaccines against leptospirosis.

Designing Multi‐Epitope Subunit Vaccine Candidate for Zika Virus Utilizing In silico Tools

Background: The arboviruses Zika virus (ZIKV) is a pathogen that threatens human health. Scientists have warned that a single mutation in the mosquito-borne ZIKV could spark another major outbreak of the disease in humans. Therefore, designing a suitable vaccine for this virus seems necessary. This study aimed to predict the protective epitopes of envelope protein from the Zika virus with bioinformatics methods for multi-epitope vaccine development. Materials and Methods: Computational studies including the identification of potential B-cell and T-cell epitopes were used. For generating a multi-epitopic vaccine construct (MEVC), selected epitopes are connected by suitable linkers. To enhance protein immunogenicity, Maltosebound protein was added to the MEVC after the prediction and refinement of the 3D structure of the designed vaccine. The binding mode of the MEVC with toll-like receptor was investigated by molecular docking technique. Finally, molecular dynamics and in silico cloning were performed for the designed vaccine. Results: This study showed that this recombinant vaccine is nontoxic, nonallergenic, and thermostable and elicits immune responses against the Zika virus. Conclusion: The computational data suggest that the MEVC has appropriate characteristics and a high-quality structure.

Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches

Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99–880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases. Communicated by Ramaswamy H. Sarma

Bioinformatics characterization of Plasmodium knowlesi apical membrane antigen 1 (PkAMA1) for multi-epitope vaccine design.

Malaria caused by Plasmodium knowlesi species has become a public health concern, especially in Malaysia. Plasmodium knowlesi parasite which originates from the macaque species, infects human through the bite of the Anopheles mosquitoes. Research on malaria vaccine has been a continuous effort to eradicate the malaria infection, yet there is no vaccine against P. knowlesi malaria to date. Apical membrane antigen 1 (AMA1) is a unique surface protein of all apicomplexan parasites that plays a crucial role in parasite-host cell invasion and thus has been a long-standing malaria vaccine candidate. The selection of protective epitopes in silico has led to significant advances in the design of the vaccine. The present study aimed to employ bioinformatics tools to predict the potential immunogenic B- and T-cell epitopes in designing malaria vaccine targeting P. knowlesi AMA1 (PkAMA1). B-cell epitopes were predicted using four bioinformatics tools, i.e., BepiPred, ABCpred, BcePred, and IEDB servers whereas T-cell epitopes were predicted using two bioinformatics servers, i.e., NetMHCpan4.1 and NetMHCIIpan-4.0 targeting human major histocompatibility complex (MHC) class I and class II molecules, respectively. The antigenicity of the selected epitopes computed by both B- and T-cell predictors were further analyzed using the VaxiJen server. The results demonstrated that PkAMA1 protein encompasses multi antigenic regions that have the potential for the development of multi-epitope vaccine. Two B- and T-cell epitopes consensus regions, i.e., NSGIRIDLGEDAEVGNSKYRIPAGKCP (codons 28-54) and KTHAASFVIAEDQNTSY RHPAVYDEKNKT (codons 122-150) at domain I (DI) of PkAMA1 were reported. Advancement of bioinformatics in characterization of the target protein may facilitate vaccine development especially in vaccine design which is costly and cumbersome process. Thus, comprehensive B-cell and T-cell epitope prediction of PkAMA1 offers a promising pipeline for the development and design of multi-epitope vaccine against P. knowlesi.

Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches

Staphylococcus aureus is a deadly human bacterial pathogen that causes a wide variety of clinical manifestations. Invasive S. aureus infections in hospitals and the community are one of the main causes of mortality and morbidity, as virulent and multi-drug-resistant strains have evolved. There is an unmet and urgent clinical need for immune-based non-antibiotic approaches to treat these infections as the growing antibiotic resistance poses a significant public health danger. Subtractive proteomics assisted reverse vaccinology-based immunoinformatics pipeline was used in this study to target the suitable antigenic proteins for the development of multi-epitope vaccine (MEV). Three essential virulent and antigenic proteins were identified including Glycosyltransferase, Elastin Binding Protein, and Staphylococcal secretory antigen. A variety of immunoinformatics tools have been used to forecast T-cell and B-cell epitopes from target proteins. Seven CTL, five HTL, and eight LBL epitopes, connected through suitable linkers and adjuvant, were employed to design 444 amino acids long MEV construct. The vaccine was paired with the TLR4 agonist 50S ribosomal protein L7/L12 adjuvant to enhance the immune response towards the vaccine. The predicted MEV structure was assessed to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. Molecular docking simulation of the MEV with the human TLR4 (toll-like receptor 4) and major histocompatibility complex molecules (MHCI and MHCII) was performed to validate the interactions with the receptors. Molecular dynamics (MD) simulation and MMGBSA binding free energy analyses were carried out for the stability evaluation and binding of the MEV docked complexes with TLR4, MHCI and MHCII. To achieve maximal vaccine protein expression with optimal post-translational modifications, MEV was reverse translated, its mRNA structure was analyzed, and finally in silico cloning was performed into E. coli expression host. These rigorous computational analyses supported the effectivity of proposed MEV in protection against infections associated with S. aureus. However, further experimental validations are required to fully evaluate the potential of proposed vaccine candidate.

Reverse vaccinology approach towards the in-silico multiepitope vaccine development against SARS-CoV-2

Background: The novel severe acute respiratory syndrome related corona virus-2 (SARS-CoV-2) belongs to the “Coronaviridae” family and order “Nidovirales”, which has caused the pandemic coronavirus disease 2019 (COVID-19). SARS-CoV-2 has been spread in more than a 100 countries, and more than a million have lost their lives. Vaccination and immunization could be an effective strategy to combat fatal COVID-19. Methods: For identification of effective vaccine candidate against COVID-19, various immunoinformatics online tools and softwares were used to predict epitopes. Cytotoxic T cell epitopes, helper T cell epitopes, and B cell epitopes from three structural polyproteins (Spike, Membrane, and Nucleocapsid (SMN) based on the binding affinity towards MHC, antigenicity, non-allergenicity, and non-toxicity) were identified for vaccine development. The multiepitope based vaccine was constructed linking two additional adjuvants human beta-defensin-3 and human beta-defensin-2 at N and C terminal, respectively. Results: The constructed vaccine sequence was found to be a good antigen and non-allergen for the human body. The constructed vaccine was docked with the TLR-3 receptor. The docked complex was further taken for molecular dynamics simulations and RMSD was calculated, which showed stable binding of the complex. The codon adaptation index (CAI) of 0.92 and GC content of 55.5% for E. coli (K12 strain) suggested efficient expression of the predicted vaccine. Conclusion: The current study can be helpful in the reduction of time and cost for further experimental validations and could give a valuable contribution against this pandemic.

Reverse vaccinology approach towards the in-silico multiepitope vaccine development against SARS-CoV-2.

Background: The novel severe acute respiratory syndrome related corona virus-2 (SARS-CoV-2) belongs to the "Coronaviridae" family and order "Nidovirales", which has caused the pandemic coronavirus disease 2019 (COVID-19). SARS-CoV-2 has been spread in more than a 100 countries, and more than a million have lost their lives. Vaccination and immunization could be an effective strategy to combat fatal COVID-19. Methods: For identification of effective vaccine candidate against COVID-19, various immunoinformatics online tools and softwares were used to predict epitopes. Cytotoxic T cell epitopes, helper T cell epitopes, and B cell epitopes from three structural polyproteins (Spike, Membrane, and Nucleocapsid (SMN) based on the binding affinity towards MHC, antigenicity, non-allergenicity, and non-toxicity) were identified for vaccine development. The multiepitope based vaccine was constructed linking two additional adjuvants human beta-defensin-3 and human beta-defensin-2 at N and C terminal, respectively. Results: The constructed vaccine sequence was found to be a good antigen and non-allergen for the human body. The constructed vaccine was docked with the TLR-3 receptor. The docked complex was further taken for molecular dynamics simulations and RMSD was calculated, which showed stable binding of the complex. The codon adaptation index (CAI) of 0.92 and GC content of 55.5% for E. coli (K12 strain) suggested efficient expression of the predicted vaccine. Conclusion: The current study can be helpful in the reduction of time and cost for further experimental validations and could give a valuable contribution against this pandemic.

Predicted peptide-based MHC-I, MHC-II, CTL and B-cell epitopes of MUC-1 by immunoinformatics methods: a clue for novel multi-epitope vaccine development against breast cancer

article: Predicted peptide-based MHC-I, MHC-II, CTL and B-cell epitopes of MUC-1 by immunoinformatics methods: a clue for novel multi-epitope vaccine development against breast cancer - Minerva Biotecnologica 2020 December;32(4):139-42 - Minerva Medica - Journals

Using in-silica Analysis and Reverse Vaccinology Approach for COVID-19 Vaccine Development

Background: The recent pandemic of COVID19 that has struck the world is yet to be battled by a potential cure. Countless lives have been claimed due to the existing pandemic and the societal normalcy has been damaged permanently. As a result, it becomes crucial for academic researchers in the field of bioinformatics to combat the existing pandemic. Materials and Methods: The study involved collecting the virulent strain sequence of SARS-nCoV19 for the country USA against human host through publically available bioinformatics databases. Using in-silica analysis, reverse vaccinology, and 3-D modelling, two leader proteins were identified to be potential vaccine candidates for development of a multi-epitope drug. Results: It was revealed that the two leader proteins ORF1ab MT326102 and MT326715 had the highest extinction coefficient and the lowest score on the GRAVY. Along with the given parameters, these leader proteins were highly stable and were also antigenic in nature. The two selected epitopes were then docked against their respective alleles to obtain the global energy scores, which was the lowest of all possible pairs. Conclusion: The epitopes which displayed the lowest global energy score on docking with the alleles were selected and proposed as successful and potential vaccine candidates for multi-epitope vaccine development. Doi: 10.28991/SciMedJ-2020-02-SI-9 Full Text: PDF

Exploring the SARS-CoV-2 structural proteins for multi-epitope vaccine development: an in-silico approach

ABSTRACT Introduction The ongoing life-threatening pandemic of coronavirus disease 2019 (COVID-19) has extensively affected the world. During this global health crisis, it is fundamentally crucial to find strategies to combat SARS-CoV-2. Despite several efforts in this direction and continuing clinical trials, no vaccine has been approved for it yet. Methods To find a preventive measure, we have computationally designed a multi-epitopic subunit vaccine using immuno-informatic approaches. Results The structural proteins of SARS-CoV-2 involved in its survival and pathogenicity were used to predict antigenic epitopes. The antigenic epitopes were capable of eliciting a strong humoral as well as cell-mediated immune response, our predictions suggest. The final vaccine was constructed by joining the all epitopes with specific linkers and to enhance their stability and immunogenicity. The physicochemical property of the vaccine was assessed. The vaccine 3D structure prediction and validation were done and docked with the human TLR-3 receptor. Furthermore, molecular dynamics simulations of the vaccine-TLR-3 receptor complex are employed to assess its dynamic motions and binding stability in-silico. Conclusion Based on this study, we strongly suggest synthesizing this vaccine, which further can be tested in-vitro and in-vivo to check its potency in a cure for COVID-19.

Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach.

BackgroundMiddle East respiratory syndrome (MERS) is caused by MERS coronavirus (MERS-CoV). Thus far, MERS outbreaks have been reported from Saudi Arabia (2013 and 2014) and South Korea (2015). No specific vaccine has yet been reported against MERS.PurposeTo address the urgent need for an MERS vaccine, in the present study, we have designed two multi-epitope vaccines (MEVs) against MERS utilizing several in silico methods and tools.MethodsThe design of both the multi-epitope vaccines (MEVs) are composed of cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes, screened form thirteen different proteins of MERS-CoV. Both the MEVs also carry potential B-cell linear epitope regions, B-cell discontinuous epitopes as well as interferon-γ-inducing epitopes. Human β-defensin-2 and β-defensin-3 were used as adjuvants to enhance the immune response of MEVs. To design the MEVs, short peptide molecular linkers were utilized to link screened most potential CTL epitopes, HTL epitopes and the adjuvants. Tertiary models for both the MEVs were generated, refined, and further studied for their molecular interaction with toll-like receptor 3. The cDNAs of both MEVs were generated and analyzed in silico for their expression in a mammalian host cell line (human).ResultsScreened CTL and HTL epitopes were found to have high propensity for stable molecular interaction with HLA alleles molecules. CTL epitopes were also found to have favorable molecular interaction within the cavity of transporter associated with antigen processing. The selected CTL and HTL epitopes jointly cover upto 94.0% of worldwide human population. Both the CTL and HTL MEVs molecular models have shown to have stable binding and complex formation propensity with toll-like receptor 3. The cDNA analysis of both the MEVs have shown high expression tendency in mammalian host cell line (human).ConclusionAfter multistage in silico analysis, both the MEVs are predicted to elicit humoral as well as cell mediated immune response. Epitopes of the designed MEVs are predicted to cover large human population worldwide. Hence both the designed MEVs could be tried in vivo as potential vaccine candidates against MERS.

Abstract 753: In vitro veritas: Successful prediction of clinical immunogenicity of two cancer vaccines by a high-throughput in vitro priming platform

Abstract Identification of tumor-associated peptides (TUMAPs) by HLA ligandome exploration or by means of reverse immunology allows the identification of high numbers of candidate substances for therapeutic cancer vaccines. However, the immunogenicity of such peptide antigens has to be validated by a separate step. Animal models are generally not appropriate for this task due to the high species-specificity of a) the HLA system, b) the T-cell repertoire and c) the expression of tumor antigens. Testing the immunogenicity of TUMAPs in a preclinical setting therefore requires the use of human cells. We assume that central tolerance (i.e. the deletion of T cells with distinct specificity to self-antigens during T-cell maturation) will be a major mechanism shaping the immunogenicity of TUMAPs and that “holes” in the T-cell repertoire may be identified by comparing in vitro priming efficiency of TUMAPs. Unfortunately, classical approaches for in vitro priming of human T cells are often difficult to standardize and scale. Here, we show that highly parallel and standardized in vitro priming using fixed numbers of isolated T cells and artificial antigen presenting cells (aAPCs) is scalable to determine the in vitro immunogenicity (% positive wells or derived estimated T-cell precursor frequencies) for hundreds of different TUMAPs from multiple donors. The method has been adopted to include the HLA alleles A*0201, A*2402, B*0702 and A*0301. We have validated the platform by two steps: First, the in vitro immunogenicity for literature-known natural ligand / altered ligands pairs (from gp100, tyrosinase and CEA) was determined and found to confirm previously reported results, with the altered ligand being the more immunogenic form in all cases. Second, the in vitro immunogenicity for individual class I binding TUMAPs as determined with the method described herein was used for the selection of two multi-peptide cancer vaccines, IMA901 for renal cell cancer and IMA910 for colorectal cancer. The drug products contained N=9 and N=10 class I binding TUMAPs, respectively. After clinical testing in two first-in-man studies (IMA901-101: N=27 and IMA910-101: N=65 evaluable patients), the in vivo immunogenicity measured in both studies (% of patients with vaccine-induced responses) was found to strongly correlate with the previously determined in vitro immunogenicity (Spearman correlation, r=0.82, p<0.05 and r=0.95, p<0.001). Recently, the in vivo immunogenicity of the peptides contained in IMA901 was confirmed by a further phase II study (IMA901-202: N=61 evaluable patients). To conclude, we believe that this method will be useful for the development of multi-epitope vaccines with high immunogenicity that can elicit broad immune responses in cancer patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 753. doi:10.1158/1538-7445.AM2011-753

Development of multi-epitope vaccines targeting wild-typesequence p53 peptides

Loss of p53 tumor-suppressor function is the most common abnormality in human cancer, which can result in enhanced presentation to immune cells of wild-type (wt)-sequence peptides from tumor p53 molecules, thus providing the rationale for wt p53 peptide-based cancer vaccines. We review evidence from preclinical murine tumor models and preclinical studies that led to the clinical introduction of wt p53 peptide-based vaccines for cancer immunotherapy. Overall, this review illustrates the complex process of wt p53 epitope selection and the issues and concerns involved in the application of p53-based vaccines for patients with cancer.

PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands

Prediction of peptide binding to major histocompatibility complex (MHC) molecules is a basis for anticipating T-cell epitopes, as well as epitope discovery-driven vaccine development. In the human, MHC molecules are known as human leukocyte antigens (HLAs) and are extremely polymorphic. HLA polymorphism is the basis of differential peptide binding, until now limiting the practical use of current epitope-prediction tools for vaccine development. Here, we describe a web server, PEPVAC (Promiscuous EPitope-based VACcine), optimized for the formulation of multi-epitope vaccines with broad population coverage. This optimization is accomplished through the prediction of peptides that bind to several HLA molecules with similar peptide-binding specificity (supertypes). Specifically, we offer the possibility of identifying promiscuous peptide binders to five distinct HLA class I supertypes (A2, A3, B7, A24 and B15). We estimated the phenotypic population frequency of these supertypes to be 95%, regardless of ethnicity. Targeting these supertypes for promiscuous peptide-binding predictions results in a limited number of potential epitopes without compromising the population coverage required for practical vaccine design considerations. PEPVAC can also identify conserved MHC ligands, as well as those with a C-terminus resulting from proteasomal cleavage. The combination of these features with the prediction of promiscuous HLA class I ligands further limits the number of potential epitopes. The PEPVAC server is hosted by the Dana-Farber Cancer Institute at the site .

Naturally occurring peptides associated with HLA-A2 in ovarian cancer cell lines identified by mass spectrometry are targets of HLA-A2-restricted cytotoxic T cells.

Identifying naturally occurring peptides bound to HLA class I molecules recognized by HLA-restricted cytotoxic T lymphocytes (CTL) is both relevant and central to the development of effective immunotherapeutic strategies against cancer. Several cancer-related genes have been reported for ovarian cancer, but very few are known to be naturally processed T cell epitopes. In the present study we used mass spectrometry to identify 16 novel HLA-A2-bound peptides from HLA-A2(+) ovarian cancer cell lines. All 16 peptides are derived from source proteins with diverse functions and marked homology to known proteins found in public databases. Synthetic peptide analogues of identified sequences were found to stabilize HLA-A2.1, albeit with varying affinities. The peptides were found to be antigenic in that a primary CD8(+) CTL response could be elicited from normal donor blood. The CTL generated were not only peptide specific, but failed to recognize targets pulsed with control peptides. In addition, recognition of shared HLA-A2-restricted epitopes by these CTL is suggested by their reactivity with a subset of HLA-A2(+) tumor lines and freshly isolated cancer cells or cell lines established from peritoneal ascites. These results were further corroborated by competitive inhibition of lysis of an otherwise susceptible cell line in the presence of cold peptide-pulsed targets. Furthermore, lack of recognition of several HLA-A2(+) control cell lines or cells isolated from normal ovaries suggests that these peptides are cancer related. These findings broaden the list of CTL-defined antigens that could lead to the development of multi-epitope vaccines for the treatment of ovarian cancer.

The Development of Multi-epitope Vaccines: Epitope Identification, Vaccine Design and Clinical Evaluation

We have developed efficient methods for epitope identification and vaccine design. Our process for epitope selection based on the combined use of motif analyses, binding assays and immunogenicity evaluations is described. We also describe how the projected population coverage and vaccine design can be optimized. Finally, it is discussed how vaccine potency is evaluated by immunogenicity and antigenicity assays.