Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: a biotech's challenge

DNA vaccination has been widely explored to develop new, alternative and efficient vaccines for cancer immunotherapy. DNA vaccines offer several benefits such as specific targeting, use of multiple genes to enhance immunity and reduced risk compared to conventional vaccines. Rapid developments in molecular biology and immunoinformatics enable rational design approaches. These technologies allow construction of DNA vaccines encoding selected tumor antigens together with molecules to direct and amplify the desired effector pathways, as well as highly targeted vaccines aimed at specific epitopes. Reliable predictions of immunogenic T cell epitope peptides are crucial for rational vaccine design and represent a key problem in immunoinformatics. Computational approaches have been developed to facilitate the process of epitope detection and show potential applications to the immunotherapeutic treatment of cancer. In this review a number of different epitope prediction methods are briefly illustrated and effective use of these resources to support experimental studies is described. Epitope-driven vaccine design employs these bioinformatics algorithms to identify potential targets of vaccines against cancer. In this paper the selection of T cell epitopes to develop epitope-based vaccines, the need for CD4(+) T cell help for improved vaccines and the assessment of vaccine performance against tumor are reviewed. We focused on two applications, namely prediction of novel T cell epitopes and epitope enhancement by sequence modification, and combined rationale design with bioinformatics for creation of new synthetic mini-genes. This review describes the development of epitope-based DNA vaccines and their antitumor effects in preclinical research against B-cell lymphoma, corroborating the usefulness of this platform as a potential tool for cancer therapy. Achievements in the field of DNA vaccines allow to overcome hurdles to clinical translation. In a scenario where the vaccine industry is rapidly changing from a mostly empirical approach to a rational design approach, these new technologies promise to discover and develop high-value vaccines, creating a new opportunity for future markets.     

A structural-energetic basis for B-cell epitope prediction

Structural-energetic analysis of peptide and protein antigens in the context of binding to antibody reveals fundamental differences between the cross-reactions of antipeptide antibody with protein and antiprotein antibody with peptide, providing a physicochemical basis for B-cell epitope prediction as applied to the development of peptide-based vaccines and immunodiagnostics.     

Mining for the antibody-antigen interacting associations that predict the B cell epitopes

Background

Predicting B-cell epitopes is very important for designing vaccines and drugs to fight against the infectious agents. However, due to the high complexity of this problem, previous prediction methods that focus on linear and conformational epitope prediction are both unsatisfactory. In addition, antigen interacting with antibody is context dependent and the coarse binary classification of antigen residues into epitope and non-epitope without the corresponding antibody may not reveal the biological reality. Therefore, we take a novel way to identify epitopes by using associations between antibodies and antigens.

Results

Given a pair of antibody-antigen sequences, the epitope residues can be identified by two types of associations: paratope-epitope interacting biclique and cooccurrent pattern of interacting residue pairs. As the association itself does not include the neighborhood information on the primary sequence, residues' cooperativity and relative composition are then used to enhance our method. Evaluation carried out on a benchmark data set shows that the proposed method produces very good performance in terms of accuracy. After compared with other two structure-based B-cell epitope prediction methods, results show that the proposed method is competitive to, sometimes even better than, the structure-based methods which have much smaller applicability scope.

Conclusions

The proposed method leads to a new way of identifying B-cell epitopes. Besides, this antibody-specified epitope prediction can provide more precise and helpful information for wet-lab experiments.

     

Peptides as tools and drugs for immunotherapies.

Peptides are essential tools for discovery and pre-clinical and pharmaceutical development of viral and cancer vaccines ('active immunotherapies') as well as for therapeutic antibodies ('passive immunotherapies'). They help to trigger and analyze immune responses at a molecular level (B-cell, T-helper and CTL epitopes). They contribute largely to the design of new vaccine candidates and to the generation of monoclonal antibodies. They are also valuable analytical reference compounds for the structural characterisation by liquid chromatography and mass spectrometry of recombinant proteins used as biopharmaceuticals. As for other therapeutic applications, formulation, solubilisation, batch consistency and stability, issues have to be addressed to allow the pre-clinical and clinical development of this class of compounds as immunotherapeutic drugs. In the present review, three case studies dealing with (i) the design and the characterisation of Respiratory Syntycial Virus subunit vaccines, (ii) peptide-based melanoma vaccines, and (iii) therapeutic monoclonal antibodies, all investigated in clinical trials, are reported and discussed.     

A novel strategy of epitope design in Neisseria gonorrhoeae

In spite of genome sequences of both human and N. gonorrhoeae in hand, vaccine for gonorrhea is yet not available. Due to availability of several host and pathogen genomes and numerous tools for in silico prediction of effective B-cell and T-cell epitopes; recent trend of vaccine designing has been shifted to peptide or epitope based vaccines that are more specific, safe, and easy to produce. In order to design and develop such a peptide vaccine against the pathogen, we adopted a novel computational approache based on sequence, structure, QSAR, and simulation methods along with fold level analysis to predict potential antigenic B-cell epitope derived T-cell epitopes from four vaccine targets of N. gonorrhoeae previously identified by us [Barh and Kumar (2009) In Silico Biology 9, 1-7]. Four epitopes, one from each protein, have been designed in such a way that each epitope is highly likely to bind maximum number of HLA molecules (comprising of both the MHC-I and II) and interacts with most frequent HLA alleles (A*0201, A*0204, B*2705, DRB1*0101, and DRB1*0401) in human population. Therefore our selected epitopes are highly potential to induce both the B-cell and T-cell mediated immune responses. Of course, these selected epitopes require further experimental validation.     

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

Background  

The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.     

HER-2/neu Cancer Vaccines: Present Status and Future Prospects

Immunotherapeutic approaches to cancer should focus on novel undertakings that modulate immune responses by synergistic enhancement of anti-tumor immunological parameters. Cancer vaccines should preferably be composed of multiple defined tumor antigen specific B- and T-cell epitopes. The main focus of this article is to briefly review the present status of Her-2/neu vaccine strategies and to describe the innovative strategies developed in my laboratory for a vaccine against HER-2/neu (ErbB-2) with emphasis on the humoral arm of the immune response. Elucidating the underlining mechanisms of anti-tumor effects elicited by peptide vaccines against a self-protein is a requirement for developing an immunotherapeutic strategy that might be effective in human cancer vaccines. Our approach entails the identification of biologically relevant epitopes, establishing relevant in vitro assays for monitoring vaccine efficacy, devising strategies to engineer conformationally dependent sequences, developing highly immunogenic vaccines for an outbred population and delivering the immunogen/vaccine in a safe and efficacious vehicle, utilizing transgenic animal models for assessing tumor development, and developing challenge models using transplantable tumors to study efficacy of vaccine constructs. We have developed a multi-HER-2/neu B-cell epitope approach and shown in preclinical studies that immunization with a combination of two B-cell epitope was more effective in preventing mammary tumors than a single epitope. We have translated that work to the clinic (OSU 0105) in an FDA approved, NCI sponsored “Phase 1 Active Immunotherapy trial with Chimeric and Multi-epitope based peptide vaccine targeting HER-2 oncoprotein and nor-MDP adjuvant in patients with metastatic and/or recurrent solid tumors” at the James Cancer Hospital at the Ohio State University. The correlation between overexpression of HER-2/neu and up-regulation of VEGF has been demonstrated in breast cancer patients. Thus, blocking angiogenesis is an attractive strategy to inhibit tumor growth, invasion, and metastasis. The hypothesis that combination of anti-angiogenic therapy and tumor immunotherapy of cancer may be synergistic is an important future goal. In this review, I will discuss insights into our preclinical studies that might aid in the design of the next generation of cancer vaccines and become an integrated component of prophylactic/preventive and therapeutic approach.     

HIV-1 Vaccine-Induced T-Cell Reponses Cluster in Epitope Hotspots that Differ from Those Induced in Natural Infection with HIV-1

Several recent large clinical trials evaluated HIV vaccine candidates that were based on recombinant adenovirus serotype 5 (rAd-5) vectors expressing HIV-derived antigens. These vaccines primarily elicited T-cell responses, which are known to be critical for controlling HIV infection. In the current study, we present a meta-analysis of epitope mapping data from 177 participants in three clinical trials that tested two different HIV vaccines: MRKAd-5 HIV and VRC-HIVAD014-00VP. We characterized the population-level epitope responses in these trials by generating population-based epitope maps, and also designed such maps using a large cohort of 372 naturally infected individuals. We used these maps to address several questions: (1) Are vaccine-induced responses randomly distributed across vaccine inserts, or do they cluster into immunodominant epitope hotspots? (2) Are the immunodominance patterns observed for these two vaccines in three vaccine trials different from one another? (3) Do vaccine-induced hotspots overlap with epitope hotspots induced by chronic natural infection with HIV-1? (4) Do immunodominant hotspots target evolutionarily conserved regions of the HIV genome? (5) Can epitope prediction methods be used to identify these hotspots? We found that vaccine responses clustered into epitope hotspots in all three vaccine trials and some of these hotspots were not observed in chronic natural infection. We also found significant differences between the immunodominance patterns generated in each trial, even comparing two trials that tested the same vaccine in different populations. Some of the vaccine-induced immunodominant hotspots were located in highly variable regions of the HIV genome, and this was more evident for the MRKAd-5 HIV vaccine. Finally, we found that epitope prediction methods can partially predict the location of vaccine-induced epitope hotspots. Our findings have implications for vaccine design and suggest a framework by which different vaccine candidates can be compared in early phases of evaluation.     

Optimisation of peptide-based cytotoxic T-cell determinants using

In this brief review we describe the methods that our group and others have developed in incorporating non-natural amino acids into peptide antigens, principally to increase protease resistance, for potential use in peptide-based vaccines. Peptide-based vaccination has the potential to generate protective immunity without the need for in situ antigen synthesis or further proteolytic processing of the antigen. The ability to deliver minimal T cell epitopes to the effector cells of the immune system also minimises unwanted side effects and simplifies clinical monitoring. The major hurdle in designing successful peptide-based vaccines resides in issues surrounding the delivery and stability of the peptide immunogen, as it is the form in which the epitope is delivered that will determine how it will be processed by the immune system and ultimately whether it will be capable of inducing an appropriate immune response. Thus, one of the confounding issues with peptide-based vaccines is their poor bioavailability, which is predominantly due to proteolysis and oxidative damage of the `naked' peptide. Strategies that stabilise peptide epitopes promise to overcome the current problems and make peptide-based immunogens more applicable in vaccine design.     

Induction of PrPSc-specific systemic and mucosal immune responses in white-tailed deer with an oral vaccine for chronic wasting disease

The ongoing epidemic of chronic wasting disease (CWD) within cervid populations indicates the need for novel approaches for disease management. A vaccine that either reduces susceptibility to infection or reduces shedding of prions by infected animals, or a combination of both, could be of benefit for disease control. The development of such a vaccine is challenged by the unique nature of prion diseases and the requirement for formulation and delivery in an oral format for application in wildlife settings. To address the unique nature of prions, our group targets epitopes, termed disease specific epitopes (DSEs), whose exposure for antibody binding depends on disease-associated misfolding of PrP^C into PrP^Sc. Here, a DSE corresponding to the rigid loop (RL) region, which was immunogenic following parenteral vaccination, was translated into an oral vaccine. This vaccine consists of a replication-incompetent human adenovirus expressing a truncated rabies glycoprotein G recombinant fusion with the RL epitope (hAd5:tgG-RL). Oral immunization of white-tailed deer with hAd5:tgG-RL induced PrP^Sc-specific systemic and mucosal antibody responses with an encouraging safety profile in terms of no adverse health effects nor prolonged vector shedding. By building upon proven strategies of formulation for wildlife vaccines, these efforts generate a particular PrP^Sc-specific oral vaccine for CWD as well as providing a versatile platform, in terms of carrier protein and biological vector, for generation of other oral, peptide-based CWD vaccines.     

T cell determinants incorporating beta-amino acid residues are protease resistant and remain immunogenic in vivo

A major hurdle in designing successful epitope-based vaccines resides in the delivery, stability, and immunogenicity of the peptide immunogen. The short-lived nature of unmodified peptide-based vaccines in vivo limits their therapeutic application in the immunotherapy of cancers and chronic viral infections as well as their use in generating prophylactic immunity. The incorporation of beta-amino acids into peptides decreases proteolysis, yet its potential application in the rational design of T cell mimotopes is poorly understood. To address this, we have replaced each residue of the SIINFEKL epitope individually with the corresponding beta-amino acid and examined the resultant efficacy of these mimotopes. Some analogs displayed similar MHC binding and superior protease stability compared with the native epitope. Importantly, these analogs were able to generate cross-reactive CTLs in vivo that were capable of lysing tumor cells that expressed the unmodified epitope as a surrogate tumor Ag. Structural analysis of peptides in which anchor residues were substituted with beta-amino acids revealed the basis for enhanced MHC binding and retention of immunogenicity observed for these analogs and paves the way for future vaccine design using beta-amino acids. We conclude that the rational incorporation of beta-amino acids into T cell determinants is a powerful alternative to the traditional homologous substitution of randomly chosen naturally occurring alpha-amino acids, and these mimotopes may prove particularly useful for inclusion in epitope-based vaccines.     

Prediction of Potential Epitopes for Peptide Vaccine Formulation Against Teschovirus A Using Immunoinformatics

Teschovirus A belongs to the family Picornaviridae and is a causal agent of the disease Teschovirus encephalomyelitis and other infections that remain asymptomatic. The present study was performed to design epitope-based peptide vaccine against Teschovirus A by identifying the potential T cell and B-cell epitopes from capsid proteins (VP1, VP3 and VP2) of the virus using reverse vaccinology and immunoinformatics approaches. In the current study, hexapeptide T-cell and octapeptide B-cell epitopes were analyzed for immunogenicity, antigenicity and hydrophilicity scores of each epitope. Each potential epitope was further characterized using ExPASy-ProtParam and Antimicrobial Peptide Database (APD3) tools for determining various physical and chemical parameters of the epitope. One linear hexapeptide T-cell epitope, i.e., RPVNDE (epitope position 77–82) and one linear octapeptide B-cell epitope, i.e., AYSRSHPQ (236–243) were identified from the viral capsid protein as they possess the capability to raise effective immunogenic reaction in the host organism against the virus. Pharmaceutical industries could harness the results of this investigation to develop epitope-based peptide vaccines by loading the identified epitopes in combination with targeting signal peptides of T-cells and B-cells and then inserting the combination into virus like particle (vlp) or constructing subunit vaccines for further trial.

     

Peptide vaccine models using statistical data mining

Design and synthesis of peptide vaccines is of significant pharmaceutical importance. A knowledge based statistical model is fitted here for prediction of binding of an antigenic site of a protein or a B-cell epitope on a CDR (complementarity determining region) of an immunoglobulin. Linear analogues of the 3D structure of the epitopes are computed using this model. Extension for prediction of peptide epitopes from the protein sequence alone is also presented. Validation results show promising potential of this approach in computer-aided peptide vaccine production. The computed probabilities of binding also provide a pioneering approach for ab-initio prediction of 'potency' of protein or peptide vaccines modeled by this method.     

FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vaccinology

The flavivirus genus is unusually large, comprising more than 70 species, of which more than half are known human pathogens. It includes a set of clinically relevant infectious agents such as dengue, West Nile, yellow fever, and Japanese encephalitis viruses. Although these pathogens have been studied exten-sively, safe and efficient vaccines lack for the majority of the flaviviruses. We have assembled a database that combines antigenic data of flaviviruses, specialized analysis tools, and workflows for automated complex analyses focusing on applications in immunology and vaccinology. FLAVIdB contains 12,858 entries of flavivirus antigen sequences, 184 verified T-cell epitopes, 201 verified B-cell epitopes, and 4 representative molecular structures of the dengue virus envelope protein. FLAVIdB was assembled by collection, annotation, and integration of data from GenBank, GenPept, UniProt, IEDB, and PDB. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). Further annotation of selected functionally relevant features was performed by organizing information extracted from the literature. The database was incorporated into a web-accessible data mining system, combining specialized data analysis tools for integrated analysis of relevant data categories (protein sequences, macromolecular structures, and immune epitopes). The data mining system includes tools for variability and conservation analysis, T-cell epitope prediction, and characterization of neutralizing components of B-cell epitopes. FLAVIdB is accessible at cvc.dfci.harvard.edu/flavi/ FLAVIdB represents a new generation of databases in which data and tools are integrated into a data min-ing infrastructures specifically designed to aid rational vaccine design by discovery of vaccine targets.     

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.     

Synthetic Long Peptide Influenza Vaccine Containing Conserved T and B Cell Epitopes Reduces Viral Load in Lungs of Mice and Ferrets

Currently licensed influenza vaccines mainly induce antibodies against highly variable epitopes. Due to antigenic drift, protection is subtype or strain-specific and regular vaccine updates are required. In case of antigenic shifts, which have caused several pandemics in the past, completely new vaccines need to be developed. We set out to develop a vaccine that provides protection against a broad range of influenza viruses. Therefore, highly conserved parts of the influenza A virus (IAV) were selected of which we constructed antibody and T cell inducing peptide-based vaccines. The B epitope vaccine consists of the highly conserved HA2 fusion peptide and M2e peptide coupled to a CD4 helper epitope. The T epitope vaccine comprises 25 overlapping synthetic long peptides of 26-34 amino acids, thereby avoiding restriction for a certain MHC haplotype. These peptides are derived from nucleoprotein (NP), polymerase basic protein 1 (PB1) and matrix protein 1 (M1). C57BL/6 mice, BALB/c mice, and ferrets were vaccinated with the B epitopes, 25 SLP or a combination of both. Vaccine-specific antibodies were detected in sera of mice and ferrets and vaccine-specific cellular responses were measured in mice. Following challenge, both mice and ferrets showed a reduction of virus titers in the lungs in response to vaccination. Summarizing, a peptide-based vaccine directed against conserved parts of influenza virus containing B and T cell epitopes shows promising results for further development. Such a vaccine may reduce disease burden and virus transmission during pandemic outbreaks.     

Predicting population coverage of T-cell epitope-based diagnostics and vaccines

Background  

T cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a particular pathogen-derived epitope. A given epitope will elicit a response only in individuals that express an MHC molecule capable of binding that particular epitope. MHC molecules are extremely polymorphic and over a thousand different human MHC (HLA) alleles are known. A disproportionate amount of MHC polymorphism occurs in positions constituting the peptide-binding region, and as a result, MHC molecules exhibit a widely varying binding specificity. In the design of peptide-based vaccines and diagnostics, the issue of population coverage in relation to MHC polymorphism is further complicated by the fact that different HLA types are expressed at dramatically different frequencies in different ethnicities. Thus, without careful consideration, a vaccine or diagnostic with ethnically biased population coverage could result.     

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates: E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 < 50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.     

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.     

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.