Pitfalls of reductionism in the design of peptide-based vaccines

Prediction of Conformational and Linear B-Cell Epitopes on Envelop Protein of Zika Virus Using Immunoinformatics Approach

The current spread of Zika virus infection in India has become a public health issue due to the virus's possible link to birth abnormalities and neurological disorders. There is a need for enhanced vaccines or drugs as a result of its epidemic outbreak and the lack of potential medication. B-cell mediated adaptive immunity is capable of developing pathogen-specific memory that confers immunological protection. Therefore, in this study, the envelope protein of the Zika virus was retrieved from the NCBI protein database. The ABCpred and BepiPred software were used to discover linear B-cell epitopes on envelope protein. Conformational B-cell epitopes on envelope protein were identified using SEPPA 3.0 and Ellipro tools. Predicted B-cell epitopes were evaluated for allergenicity, toxicity, and antigenicity. Two consensus linear B-cell epitopes, envelope_165-180 (AKVEITPNSPRAEATL) and envelope_224-238 (PWHAGADTGTPHWNN) were identified using ABCpred and BepiPredtools. SEPPA 3.0 and Elliprotools predicted consensus conformational envelope_98-110 (DRGWGNGCGLFGK) and envelope_248-251 (AHAK) epitopes and one residue (⁷⁵PRO) within envelope protein as a component of B-cell epitopes. These predicted linear and conformational B-cell epitopes will help in designing peptide vaccines that will activate the humoral response. However, in-vitro and in-vivo laboratory experimental confirmations are still needed to prove the application's feasibility.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

Applications of molecular networks in biomedicine

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

Requirements for Empirical Immunogenicity Trials, Rather than Structure-Based Design, for Developing an Effective HIV Vaccine

The claim that it is possible to rationally design a structure-based HIV-1 vaccine is based on misconceptions regarding the nature of protein epitopes and of immunological specificity. Attempts to use reverse vaccinology to generate an HIV-1 vaccine on the basis of the structure of viral epitopes bound to monoclonal neutralizing antibodies have failed so far because it was not possible to extrapolate from an observed antigenic structure to the immunogenic structure required in a vaccine. Vaccine immunogenicity depends on numerous extrinsic factors such as the host immunoglobulin gene repertoire, the presence of various cellular and regulatory mechanisms in the immunized host and the process of antibody affinity maturation. All these factors played a role in the appearance of the neutralizing antibody used to select the epitope to be investigated as potential vaccine immunogen, but they cannot be expected to be present in identical form in the host to be vaccinated. It is possible to rationally design and optimize an epitope to fit one particular antibody molecule or to improve the paratope binding efficacy of a monoclonal antibody intended for passive immunotherapy. What is not possible is to rationally design an HIV-1 vaccine immunogen that will elicit a protective polyclonal antibody response of predetermined efficacy. An effective vaccine immunogen can only be discovered by investigating experimentally the immunogenicity of a candidate molecule and demonstrating its ability to induce a protective immune response. It cannot be discovered by determining which epitopes of an engineered antigen molecule are recognized by a neutralizing monoclonal antibody. This means that empirical immunogenicity trials rather than structural analyses of antigens offer the best hope of discovering an HIV-1 vaccine.

Databases for B-cell epitopes

Identification and characterization of B-cell epitopes in target antigens is one of the key steps in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. For localizing epitopes by experimental methods is time consuming and cost expensive, researchers have been developing in silico or computational models for the prediction of B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. A sufficient number of available B-cell epitopes are indispensable for establishing the prediction models. To our knowledge, some popular databases associated with the B-cell epitopes are proposed and widely used in the immunoinformatics. In this chapter, we present an overview of the important databases and introduce how to compile datasets for the development of B-cell epitope prediction tools.

Systems biology: a biologist's viewpoint

The debate over reductionism and antireductionism in biology is very old. Even the systems approach in biology is more than five decades old. However, mainstream biology, particularly experimental biology, has broadly sidestepped those debates and ideas. Post-genome data explosion and development of high-throughput techniques led to resurfacing of those ideas and debates as a new incarnation called Systems Biology. Though experimental biologists have co-opted systems biology and hailed it as a paradigm shift, it is practiced in different shades and understood with divergent meanings. Biology has certain questions linked with organization of multiple components and processes. Often such questions involve multilevel systems. Here in this essay we argue that systems theory provides required framework and abstractions to explore those questions. We argue that systems biology should follow the logical and mathematical approach of systems theory and transmogrification of systems biology to mere collection of higher dimensional data must be avoided. Therefore, the questions that we ask and the priority of those questions should also change. Systems biology should focus on system-level properties and investigate complexity without shying away from it.

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning

MOTIVATION

The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.

METHODS

In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.

RESULTS

Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.

AVAILABILITY

The web server and datasets are freely available at http://bcell.whu.edu.cn.

Requirements for empirical immunogenicity trials, rather than structure-based design, for developing an effective HIV vaccine

The claim that it is possible to rationally design a structure-based HIV-1 vaccine is based on misconceptions regarding the nature of protein epitopes and of immunological specificity. Attempts to use reverse vaccinology to generate an HIV-1 vaccine on the basis of the structure of viral epitopes bound to monoclonal neutralizing antibodies have failed so far because it was not possible to extrapolate from an observed antigenic structure to the immunogenic structure required in a vaccine. Vaccine immunogenicity depends on numerous extrinsic factors such as the host immunoglobulin gene repertoire, the presence of various cellular and regulatory mechanisms in the immunized host and the process of antibody affinity maturation. All these factors played a role in the appearance of the neutralizing antibody used to select the epitope to be investigated as potential vaccine immunogen, but they cannot be expected to be present in identical form in the host to be vaccinated. It is possible to rationally design and optimize an epitope to fit one particular antibody molecule or to improve the paratope binding efficacy of a monoclonal antibody intended for passive immunotherapy. What is not possible is to rationally design an HIV-1 vaccine immunogen that will elicit a protective polyclonal antibody response of predetermined efficacy. An effective vaccine immunogen can only be discovered by investigating experimentally the immunogenicity of a candidate molecule and demonstrating its ability to induce a protective immune response. It cannot be discovered by determining which epitopes of an engineered antigen molecule are recognized by a neutralizing monoclonal antibody. This means that empirical immunogenicity trials rather than structural analyses of antigens offer the best hope of discovering an HIV-1 vaccine.

Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature

Background

Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction.

Results

In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including the impact of interior residues, different contributions of adjacent residues, and the imbalanced data which contain much more non-epitope residues than epitope residues. In order to address above issues, we take following strategies. Firstly, a concept of 'thick surface patch' instead of 'surface patch' is introduced to describe the local spatial context of each surface residue, which considers the impact of interior residue. The comparison between the thick surface patch and the surface patch shows that interior residues contribute to the recognition of epitopes. Secondly, statistical significance of the distance distribution difference between non-epitope patches and epitope patches is observed, thus an adjacent residue distance feature is presented, which reflects the unequal contributions of adjacent residues to the location of binding sites. Thirdly, a bootstrapping and voting procedure is adopted to deal with the imbalanced dataset. Based on the above ideas, we propose a new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine. The experiments show that our method can predict conformational B-cell epitopes with high accuracy. Evaluated by leave-one-out cross validation (LOOCV), our method achieves the mean AUC value of 0.633 for the benchmark bound dataset, and the mean AUC value of 0.654 for the benchmark unbound dataset. When compared with the state-of-the-art prediction models in the independent test, our method demonstrates comparable or better performance.

Conclusions

Our method is demonstrated to be effective for the prediction of conformational epitopes. Based on the study, we develop a tool to predict the conformational epitopes from 3D structures, available at http://code.google.com/p/my-project-bpredictor/downloads/list.

Benchmarking B-cell epitope prediction for the design of peptide-based vaccines: problems and prospects

To better support the design of peptide-based vaccines, refinement of methods to predict B-cell epitopes necessitates meaningful benchmarking against empirical data on the cross-reactivity of polyclonal antipeptide antibodies with proteins, such that the positive data reflect functionally relevant cross-reactivity (which is consistent with antibody-mediated change in protein function) and the negative data reflect genuine absence of cross-reactivity (rather than apparent absence of cross-reactivity due to artifactual masking of B-cell epitopes in immunoassays). These data are heterogeneous in view of multiple factors that complicate B-cell epitope prediction, notably physicochemical factors that define key structural differences between immunizing peptides and their cognate proteins (e.g., unmatched electrical charges along the peptide-protein sequence alignments). If the data are partitioned with respect to these factors, iterative parallel benchmarking against the resulting subsets of data provides a basis for systematically identifying and addressing the limitations of methods for B-cell epitope prediction as applied to vaccine design.

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.

Pharmaceutical and immunological evaluation of human papillomavirus viruslike particle as an antigen carrier

We report the preparation and the immunogenicity of a conjugate vaccine obtained by chemically conjugating a variant of the extracellular peptide fragment of influenza type A M2 protein to the human papillomavirus (HPV) viruslike particle (VLP). Conjugates comprised of approximately 4,000 copies of the antigenic peptide per VLP are obtained as the result of the reaction between a C-terminal cysteine residue on the peptide and the maleimide-activated HPV VLP. The resulting conjugates have an average particle size slightly larger than the carrier and present enhanced overall stability against chemical and thermal-induced denaturation. The M2-HPV VLP conjugates lost the binding affinity for anti-HPV conformational antibodies but retained reactivity to a M2-specific monoclonal antibody. The conjugate vaccine formulated with aluminum adjuvant and delivered in two doses of 30-ng peptide was found to be highly immunogenic and conferred good protection against lethal challenge of influenza virus in mice. These results suggest that HPV VLP can be used as a carrier for synthetic or small antigens for the development of subunit vaccines.

Vaccination with a Human High Molecular Weight Melanoma-Associated Antigen Mimotope Induces a Humoral Response Inhibiting Melanoma Cell Growth In Vitro

Peptide mimics of a conformational epitope that is recognized by a mAb with antitumor activity are promising candidates for formulations of anticancer vaccines. These mimotope vaccines are able to induce a polyclonal Ab response focused to the determinant of the mAb. Such attempts at cancer immunotherapy are of special interest for malignant melanoma that is highly resistant to chemotherapy and radiotherapy. In this study, we describe for the first time the design and immunogenicity of a vaccine containing a mimotope of the human high m.w. melanoma-associated Ag (HMW-MAA) and the biological potential of the induced Abs. Mimotopes were selected from a pVIII-9mer phage display peptide library with the anti-HMW-MAA mAb 225.28S. The mimotope vaccine was then generated by coupling the most suitable candidate mimotope to tetanus toxoid as an immunogenic carrier. Immunization of rabbits with this vaccine induced a specific humoral immune response directed toward the epitope recognized by the mAb 225.28S on the native HMW-MAA. The induced Abs inhibited the in vitro growth of the melanoma cell line 518A2 up to 62%. In addition, the Abs mediated 26% lysis of 518A2 cells in Ab-dependent cellular cytotoxicity. Our results indicate a possible application of this mimotope vaccine as a novel immunotherapeutic agent for the treatment of malignant melanoma.

Epitope-Specific Antibody Response to Mel-CAM Induced by Mimotope Immunization

Peptide mimotopes of tumor antigen epitopes have been proposed as components of tumor vaccines. In this study, we determined the immunogenicity of melcam mim1 and melcam mim2, peptide mimics of an epitope of the melanoma cell-adhesion molecule (Mel-CAM). BALB/c mice were vaccinated either with mimotopes or mimotopes coupled to tetanus toxoid (TT). The antibody responses of mice to melcam mim1, melcam mim2, and recombinant Mel-CAM were analyzed by an ELISA and immunoblot analyses. TT-coupled mimotopes led to high titers of IgG mainly of the IgG2a subclass to melcam mim1 and melcam mim2. Immunization with each of the mimotope formulations induced antibodies that cross-reacted with recombinant Mel-CAM. Uncoupled mimotopes induced lymphocyte proliferation and cytokine production in spleen cell cultures indicating that both peptide mimotopes also contained T cell epitopes. TT-coupled mimotopes induced T helper (Th)1 (interleukin (IL)-2, interferon-gamma) and Th2 (IL-4, IL-5) cytokines, whereas uncoupled mimotopes induced a Th1-biased T cell response. Our results suggest that mimotopes potentially represent a novel vaccine approach to induce a tumor antigen-specific humoral and cellular response.

Computational peptide dissection of Melan-a/MART-1 oncoprotein antigenicity

We have mapped the linear antigenic determinant of a commercial MAb raised in the mouse against the melanoma-associated-antigen Melan-A/MART-1. The B cell epitope on the Melan-A/MART-1 oncoprotein is located in the 15-mer amino acid sequence 101-115 PPAYEKLSAEQSPPP, within residues 102-106. The definition of the antigenic sequence on Melan-A/MART-1 oncoprotein was reached following analyses of MHC II binding potential and similarity level to the mouse proteome, that put into evidence the 15-mer amino acid sequence 101-115 PPAYEKLSAEQSPPP as the top scoring peptide in binding H2-A(d) molecules and the epitopic sequence residues 102-106 (i.e., the peptide sequence PAYEK) as having low-similarity level to the mouse proteome. Dot-blot epitope mapping immunoassay identified proline residue 102 as critical, based on its effect on antibody recognition. The present study adds to previous companion reports in validating the hypothesis that low-similarity to the host's proteome and binding potential to MHC II molecules are essential concurring factors in the modulation of the pool of epitopic sequences.

NMR probing of in silico identification of anti-HPV16 E7 mAb linear peptide epitope

A proteomics-based approach was exploited in order to individuate peptide sequences having the immunogenic potential to evoke humoral response. The epitope search utilized two parameters: the similarity level of the peptide sequence to the host's proteins, and the peptide capability to bind to the major histocompatibility complex class II molecules. By this approach, the human papillomavirus 16 E7(49-63) RAHYNIVTFCCKCDS peptide was individuated as the immunogenic epitope recognized by an anti-HPV16 E7 monoclonal antibody raised against the full-length viral oncoprotein. In this report, two-dimensional nuclear magnetic resonance spectroscopic experiments unequivocally probe the HPV16 E7 epitope individuation.

Drug targeting by macromolecules without recognition unit?

his review will summarize available information on the ability of macromolecular conjugates containing no specific recognition motifs to deliver anthracyclines (daunomycin, adriamycin) or methotrexate to target cells such as tumour cells or macrophages. Conjugates with natural (proteins, DNA, carbohydrates) and synthetic macromolecules (linear and branched chain poly-alpha-amino acids, non-biodegradable DIVEMA, HPMA etc.) will be reviewed. Experimental data from several laboratories indicate that these conjugates are taken up by cells mainly by fluid-phase or adsorptive endocytosis. It is believed that these processes do not involve 'specific receptors'. Two examples of methotrexate and daunomycin conjugates will be discussed to show the effect of the chemical structure of branched chain polypeptides on the uptake and antitumour or antiparasitic (Leishmania donovani infection) efficacy of conjugates.

Overview of the needs and realities for developing new and improved vaccines in the 21st century

The science of present day vaccinology is based on the pioneering discoveries of the late 18th and late 19th centuries and the technologic breakthroughs of the past 60 years. The driving force for the development of new vaccines resides in technologic feasibility, public need and economic incentive for translating the basic knowledge into a product. Past efforts by government to define which particular vaccines to develop were mostly irrelevant to the realistic choices which were made. There is a vast array of viral, bacterial, parasitic and fungal disease agents against which preventative vaccines may be developed, and to this may be added cancer and certain amyloidoses such as Alzheimer's and 'mad cow' diseases. The proven past for vaccines has relied on live, killed, protein and polysaccharide antigens plus the single example of recombinant-expressed hepatitis B vaccine. The validity of redirection of vaccinology to exploration of simplified vaccines such as recombinant vectored and DNA preparations and reductionist vaccines based on peptides of contrived epitope composition remains to be proved. Reductionism imposes vastly increased complexity and difficulty on vaccine development and might not be capable of achievement. The challenge in the 21st century will involve new and uncertain pathways toward worthwhile accomplishments.

Rapid monoclonal antibody generation via dendritic cell targeting in vivo

Dendritic cells (DC) are the professional antigen-presenting cells of the immune system. Previous studies have demonstrated that targeting foreign antigens to DC leads to enhanced antigen (Ag)-specific responses in vivo. However, the utility of this strategy for the generation of MAbs has not been investigated. To address this question we immunized mice with IgG-peptide conjugates prepared with the hamster anti-murine CD11c MAb N418. Synthetic peptides corresponding to two different exposed regions of DC-specific ICAM-3 grabbing nonintegrin (DC-SIGN), a human C-type lectin, were conjugated to N418 using thiol-based chemistry. The N418 MAb served as the targeting molecule and synthetic peptides as the Ag (MAb-Ag). A rapid and peptide specific serum IgG response was produced by Day 7 when the synthetic peptides were linked to the N418 MAb, compared to peptide co-delivered with the N418 without linkage. Spleen cells from N418-peptide immunized mice were fused on Day 10, and three IgG1/k monoclonal antibodies (MAbs) were selected to one of the peptide epitopes (MID-peptide). One of the MAbs, Novik 2, bound to two forms of recombinant DC-SIGN protein in enzyme-linked immunosorbent assay (ELISA), and was specifically inhibited by the MID-peptide in solution. Two of these MAbs show specific binding to DC-SIGN expressed by cultured human primary DC. We conclude that in vivo DC targeting enhances the immunogenicity of synthetic peptides and is an effective method for the rapid generation of MAbs to predetermined epitopes.

Reductionism and the search for structure-function relationships in antibody molecules

One of the claims of reductionism is that it can explain all the features of living systems by an analysis of their physico-chemical constituents. Such a claim disregards the existence in biological systems of emergent properties that do not exist in their isolated components but which allow autonomous organisms to be directively organized in a self-regulated and integrated manner. It is not possible to describe biological systems adequately without using functional language that is meaningless in the physical sciences. The description of biological functions is also an essential part of immunology and functional explanations are more useful than causal explanations also in this discipline. Since causality is not a relation between a material object and an event, the structure of an antibody cannot be the cause of its binding activity. When structure-function relationships are analysed, the search should be for correlations rather than for causal relations. Methods used to find correlations between the atomic structure of antibody binding sites and their binding activity are mostly based on mutagenesis studies. Since the effect of any mutation depends on the molecular context, it is usually very difficult to predict the effects of multiple mutations on antibody function. Our knowledge of the molecular basis of antigen-antibody recognition has led to the expectation that it may be possible to develop new vaccines using molecular design principles. Such unwarranted hopes arise because of a confusion between antigenicity and immunogenicity. Although knowledge of antibody structure is of little use in vaccine design, it may help to develop therapeutic inhibitors and antibodies effective in the passive immunotherapy of viral infection.

Immunogenically fit subunit vaccine components via epitope discovery from natural peptide libraries

Antigenic peptides that bind pathogen-specific Abs are a potential source of subunit vaccine components. To be effective the peptides must be immunogenically fit: when used as immunogens they must elicit Abs that cross-react with native intact pathogen. In this study, antigenic peptides obtained from phage display libraries through epitope discovery were systematically examined for immunogenic fitness. Peptides selected from random peptide libraries, in which the phage-displayed peptides are encoded by synthetic degenerate oligonucleotides, had marginal immunogenic fitness. In contrast, 50% of the peptides selected from a natural peptide library, in which phage display segments of actual pathogen polypeptides, proved very successful. Epitope discovery from natural peptide libraries is a promising route to subunit vaccines.

Peptide mimotopes as surrogate antigens of carbohydrates in vaccine discovery

Carbohydrate antigens are immune targets associated with a variety of pathogens and tumor cells. Unfortunately, most carbohydrates are intrinsically T cell-independent antigens, which diminishes their efficacy as immunogens. The conversion of carbohydrate epitopes to peptide mimotopes is one means to overcome the T cell-independent nature of carbohydrate antigens because peptides have an absolute requirement for T cells. Although such conversion has great potential for the development of veterinarian and human vaccines, there are issues related to the use of peptide-based immunogens as functional surrogates. Some of these issues are fundamental, pertaining to how mimicry comes about at the molecular level, and some are application oriented, directed at elucidating important immunological mechanisms. In this article the potential and caveats of this technology regarding its application in vaccine discovery are analyzed.

The development of a mimotope-based synthetic peptide vaccine against respiratory syncytial virus

Respiratory syncytial virus (RSV) is the most important cause of bronchiolitis and pneumonia in infants and young children worldwide and the development of a synthetic peptide epitope-based vaccine to induce virus-neutralising antibodies against RSV would seem to be a valid approach to the production of an effective vaccine against infection.A combinatorial solid-phase peptide library has been screened with a virus-neutralising, protective monoclonal antibody (MAb19) directed towards a conserved and conformationally-determined epitope of the Fusion (F) protein of the virus. Two of the sequences identified from the peptide library using MAb19 reacted specifically with the antibody and amino acid substitution experiments identified four sequences from one of the mimotopes which showed increased reactivity with MAb19. Immunisation of BALB/c mice with these mimotopes, presented as MAPs, resulted in the induction of anti-peptide antibodies that inhibited the binding of MAb19 to the virus and neutralised viral infection in vitro, with titres equivalent to those in sera from RSV-infected animals. Following RSV challenge of mimotope-immunised mice, a significant reduction in the titre of virus and a greatly reduced cell infiltration into the lungs of immunised mice compared to that in controls was observed. The induction of virus-specific cytotoxic T-lymphocyte responses as well as virus-specific antibodies are likely to be necessary in an effective vaccine. The incorporation of a peptide representing a CTL epitope from the M2 protein of the virus together with peptides inducing T-helper and anti-mimotope responses in a peptide cocktail vaccine resulted in a more effective clearance of the virus from immunised, challenged mice than peptide-induced humoral or cellular immunity alone.

Antigenicity and immunogenicity of synthetic peptides

The ability of a peptide to react specifically with the functional binding site of a complementary antibody is known as its antigenic reactivity or antigenicity. Our understanding of peptide antigenicity has improved considerably in recent years mainly through the X-ray crystallographic analysis of peptide-monoclonal antibody complexes. This knowledge is obtained along reductionist lines by turning the biological question of antigen recognition into the purely chemical phenomenon of protein-peptide interactions described in terms of atomic forces and non-covalent bonds. This makes it possible to improve the degree of steric complementarity between a peptide and a single monoclonal antibody and thus to improve the peptide's antigenicity following structure-based rational design principles. The situation is quite different with immunogenicity which is the ability of the peptide to induce an immune response in a competent host. Whereas antigenicity can be reduced to the level of chemistry, such a reduction is not achievable in the case of immunogenicity which depends on many complex interactions with various elements of the host immune system. These cellular and regulatory mechanisms cannot be controlled by adjusting the structure of the peptide in a predetermined manner. For this reason, it is not possible to develop a synthetic peptide vaccine using molecular design principles.