Poor correspondence between predicted and experimental binding of peptides to class I MHC molecules

Validating Immunomodulatory Responses of r-LdODC Protein and Its Derived HLA-DRB1 Restricted Epitopes against Visceral Leishmaniasis in BALB/c Mice

Vaccination is considered the most appropriate way to control visceral leishmaniasis (VL). With this background, the r-LdODC protein as well as its derived HLA-DRB1-restricted synthetic peptides (P1: RLMPSAHAI, P2: LLDQYQIHL, P3: GLYHSFNCI, P4: AVLEVLSAL, and P5: RLPASPAAL) were validated in BALB/c mice against visceral leishmaniasis. The study was initiated by immunization of the r-LdODC protein as well as its derived peptides cocktail with adjuvants (r-CD2 and MPL-A) in different mice groups, separately. Splenocytes isolated from the challenged and differentially immunized mice group exhibited significantly higher IFN-γ secretion, which was evidenced by the increase in the expression profile of intracellular CD4+IFN-γ T cells. However, the IL-10 secretion did not show a significant increase against the protein and peptide cocktail. Subsequently, the study confirmed the ability of peptides as immunoprophylactic agents, as the IE-I/AD-I molecule overexpressed on monocytes and macrophages of the challenged mice group. The parasitic load in macrophages of the protein and peptides cocktail immunized mice groups, and T cell proliferation rate, further established immunoprophylactic efficacy of the r-LdODC protein and peptide cocktail. This study suggests that the r-LdODC protein, as well as its derived HLA-DRB1-restricted synthetic peptides, have immunoprophylactic potential and can activate other immune cells' functions towards protection against visceral leishmaniasis. However, a detailed study in a humanized mice model can explore its potential as a vaccine candidate.

Characterization of T-cell responses against IκBα in cancer patients

The nuclear factor κ light chain enhancer of activated B cells (NFκB) is constitutively active in most cancers, controlling multiple cellular processes including proliferation, invasion and resistance to therapy. NFκB is primarily regulated through the association with inhibitory proteins that are known as inhibitors of NFκB (IκBs). Increased NFκB activity in tumor cells has been correlated with decrease stability of IκB proteins, in particular IκBα. In responso to a large number of stimuli, IκB proteins are degraded by the proteasome. Cytotoxic T lymphocytes (CTLs) recognize HLA-restricted antigenic peptides that are generated by proteasomal degradation in target cells. In the present study, we demonstrate the presence of naturally occurring IκBα -specific T cells in the peripheral blood of patients suffering from several unrelated tumor types, i.e., breast cancer, malignant melanoma and renal cell carcinoma, but not of healthy controls. Furthermore, we show that such IBα-specific T cells are granzyme B-releasing, cytotoxic cells. Hence, the increased proteasomal degradation of IκBα in cancer induces IκBα-specific CTLs.

GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking

Motivation

The identification of T-cell epitopes has many profound translational applications in the areas of transplantation, disease diagnosis, vaccine/therapeutic protein development and personalized immunotherapy. While data-driven methods have been widely used for the prediction of peptide binders with notable successes, the structural modeling of peptide binding to MHC molecules is crucial for understanding the underlying molecular mechanism of the immunological processes.

Results

We developed GradDock, a structure-based method for the rapid and accurate modeling of peptide binding to MHC Class I (pMHC-I). GradDock explicitly models diverse unbound peptides in vacuo and inserts them into the MHC-I groove through a steered gradient descent with a topological correction process. The simulation process yields diverse structural conformations including native-like peptides. We completely revised the Rosetta score terms and developed a new ranking function specifically for pMHC-I. Using the diverse peptides, a linear programming approach is applied to find the optimal weights for the individual Rosetta score terms. Our examination revealed that a refinement of the dihedral angles and a modification of the repulsion can dramatically improve the modeling quality. GradDock is five-times faster than a Rosetta-based docking approach for pMHC-I. We also demonstrate that the predictive capability of GradDock with the re-weighted Rosetta ranking function is consistently more accurate than the Rosetta-based method with the standard Rosetta score (approximately three-times better for a cross-docking set).

Availability and implementation

GradDock is freely available for academic purposes. The program and the ranking score weights for Rosetta are available at http://bel.kaist.ac.kr/research/GradDock.

Contact

hskim76@kaist.ac.kr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effect of secondary anchor amino acid substitutions on the immunogenic properties of an HLA-A*0201-restricted T cell epitope derived from the Trypanosoma cruzi KMP-11 protein

The TcTLE peptide (TLEEFSAKL) is a CD8(+) T cell HLA-A*0201-restricted epitope derived from the Trypanosoma cruzi KMP-11 protein that is efficiently processed, presented and recognized by CD8(+) T cells from chagasic patients. Since the immunogenic properties of wild-type epitopes may be enhanced by suitable substitutions in secondary anchor residues, we have studied the effect of introducing specific mutations at position 3, 6 and 7 of the TcTLE peptide. Mutations (E3L, S6V and A7F) were chosen on the basis of in silico predictions and in vitro assays were performed to determine the TcTLE-modified peptide binding capacity to the HLA-A*0201 molecule. In addition, the functional activity of peptide-specific CD8(+) T cells in HLA-A2(+) chagasic patients was also interrogated. In contrast to bioinformatics predictions, the TcTLE-modified peptide was found to have lower binding affinity and stability than the original peptide. Nevertheless, CD8(+) T cells from chronic chagasic patients recognized the TcTLE-modified peptide producing TNF-α and INF-γ and expressing CD107a/b, though in less extension than the response triggered by the original peptide. Overall, although the amino acids at positions 3, 6 and 7 of TcTLE are critical for the peptide affinity, they have a limited effect on the immunogenic properties of the TcTLE epitope.

Systems vaccinology for cancer vaccine development

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

Noncanonical peptides in complex with MHC class I

The major histocompatibility complex (MHC) presents canonical medium-high-affinity peptides on the surface of antigen-presenting cells to T-cells. Recognition of peptide MHC by T-cells initiates a cascade of signals which maintains a T-cell-dependent immune response. In the design of vaccines, there is need for an understanding of how peptides bind to MHC class I molecules. Herein, the presentation of canonical anchor motif peptides to MHC class I, noncanonical anchor motif peptides, low-affinity peptides, peptides making use of new pockets, short peptides, long peptides, glycopeptides, retro-inverso peptides and prediction programs for peptides binding to MHC class I molecules is discussed. All this information will aid in the design of new and improved peptide-based vaccines.

Use of proteomics to define targets of T-cell immunity

The mammalian immune system has evolved to display peptides derived from microbial antigens to immune effector cells. Liberated from the intact antigens through distinct proteolytic mechanisms, these peptides are subsequently transported to the cell surface while bound to chaperone-like receptors known as major histocompatibility complex molecules. These complexes are then scrutinized by T-cells that express receptors with specificity for specific major histocompatibility complex-peptide complexes. In normal uninfected cells, this process of antigen processing and presentation occurs continuously, with the resultant array of self-antigen-derived peptides displayed on the surface of these cells. Changes in this cellular peptide array alert the immune system to changes in the intracellular environment that may be associated with infection, oncogenesis or other abnormal cellular processes, resulting in a cascade of events that result in the elimination of the abnormal cell. Since peptides play such an essential role in informing the immune system of infection with viral or microbial pathogens and the transformation of cells in malignancy, the tools of proteomics, in particular mass spectrometry, are ideally suited to study these immune responses at a molecular level. Recent advances in studies of immune responses that have utilized mass spectrometry and associated technologies are reviewed. The authors gaze into the future and look at current challenges and where proteomics will impact in immunology over the next 5 years.

Advances in the study of HLA-restricted epitope vaccines

Vaccination is a proven strategy for protection from disease. An ideal vaccine would include antigens that elicit a safe and effective protective immune response. HLA-restricted epitope vaccines, which include T-lymphocyte epitopes restricted by HLA alleles, represent a new and promising immunization approach. In recent years, research in HLA-restricted epitope vaccines for the treatment of tumors and for the prevention of viral, bacterial, and parasite-induced infectious diseases have achieved substantial progress. Approaches for the improvement of the immunogenicity of epitope vaccines include (1) improving the accuracy of the methods used for the prediction of epitopes, (2) making use of additional HLA-restricted CD8(+) T-cell epitopes, (3) the inclusion of specific CD4(+) T-cell epitopes, (4) adding B-cell epitopes to the vaccine construction, (5) finding more effective adjuvants and delivery systems, (6) using immunogenic carrier proteins, and (7) using multiple proteins as epitopes sources. In this manuscript, we review recent research into HLA-restricted epitope vaccines.

Prediction of MHC class I binding peptides by a query learning algorithm based on hidden markov models

A query learning algorithm based on hidden Markov models (HMMs) isdeveloped to design experiments for string analysis and prediction of MHCclass I binding peptides. Query learning is introduced to aim at reducingthe number of peptide binding data for training of HMMs. A multiple numberof HMMs, which will collectively serve as a committee, are trained withbinding data and used for prediction in real-number values. The universeof peptides is randomly sampled and subjected to judgement by the HMMs.Peptides whose prediction is least consistent among committee HMMs aretested by experiment. By iterating the feedback cycle of computationalanalysis and experiment the most wanted information is effectivelyextracted. After 7 rounds of active learning with 181 peptides in all,predictive performance of the algorithm surpassed the so far bestperforming matrix based prediction. Moreover, by combining the bothmethods binder peptides (log Kd < -6) could be predicted with84% accuracy. Parameter distribution of the HMMs that can be inspectedvisually after training further offers a glimpse of dynamic specificity ofthe MHC molecules.

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.

Characterization of Spontaneous Immune Responses against Long Peptides Derived from Bcl-X(L) in Cancer Patients Using Elispot

In recent years we and others have used the ELISPOT assay successfully to identify novel tumor antigens by the characterization of spontaneous HLA class I restricted immune responses against a number of minimal 9–10 amino acid long peptide epitopes. In the present study, we examined the capability of using longer peptides when scrutinizing Peripheral Blood Mononuclear Cells (PMBC) from melanoma patients for spontaneous immunity by means of ELISPOT IFN-γ secretion assay. To this end, we examined PBMC for the presence of specific T-cell responses against long peptides derived from the tumor associated antigen BCL-X(L). The protein product of the larger BCL-X(L) differs from Bcl-X(S) protein by an inserted region (amino acids 126–188). Thus, we scrutinized eight long peptides covering this inserted region for spontaneous immunity. The peptides were overlapping and consisted of 20–23 amino acids. PBMC were pre-stimulated with peptide-pulsed autologous dendritic cells (DC) and subjected to the IFN-γ ELISPOT assay. Four of the BCL-X(L) derived peptides elicited very frequent responses in several patients. Additionally, in all patients responses against more than one of the peptides could be detected. In conclusion several long BCL-X(L) derived peptide epitopes exist, which may be used in anti-cancer immunity. Furthermore, the ELISPOT assay offers an attractive and sensitive method for the characterization of spontaneous immune reactivity against long peptides.

Prediction of epitopes using neural network based methods

In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the background for these methods, and the rationale behind the different optimization steps implemented in the methods. We go through the practical use of the methods, which are publicly available in the form of relatively fast and simple web interfaces. Furthermore, we will review results obtained in actual epitope discovery projects where previous implementations of the described methods have been used in the initial selection of potential epitopes. Selected potential epitopes were all evaluated experimentally using ex vivo assays.

Identification of a cyclin B1-derived CTL epitope eliciting spontaneous responses in both cancer patients and healthy donors

With the aim to identify cyclin B1-derived peptides with high affinity for HLA-A2, we used three in silico prediction algorithms to screen the protein sequence for possible HLA-A2 binders. One peptide scored highest in all three algorithms, and the high HLA-A2-binding affinity of this peptide was verified in an HLA stabilization assay. By stimulation with peptide-loaded dendritic cells a CTL clone was established, which was able to kill two breast cancer cell lines in an HLA-A2-dependent and peptide-specific manner, demonstrating presentation of the peptide on the surface of cancer cells. Furthermore, blood from cancer patients and healthy donors was screened for spontaneous T-cell reactivity against the peptide in IFN-γ ELISPOT assays. Patients with breast cancer, malignant melanoma, or renal cell carcinoma hosted powerful and high-frequency T-cell responses against the peptide. In addition, when blood from healthy donors was tested, similar responses were observed. Ultimately, serum from cancer patients and healthy donors was analyzed for anti-cyclin B1 antibodies. Humoral responses against cyclin B1 were frequently detected in both cancer patients and healthy donors. In conclusion, a high-affinity cyclin B1-derived HLA-A2-restricted CTL epitope was identified, which was presented on the cell surface of cancer cells, and elicited spontaneous T-cell responses in cancer patients and healthy donors.

Major histocompatibility complex class I binding predictions as a tool in epitope discovery

SUMMARY

Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained, highlighting the most useful and historically important. Selected case stories, where these 'reverse immunology' systems have been used in actual epitope discovery, are briefly reviewed. We conclude that this new generation of epitope discovery systems has become a highly efficient tool for epitope discovery, and recommend that the less accurate prediction systems of the past be abandoned, as these are obsolete.

The immune system strikes back: cellular immune responses against indoleamine 2,3-dioxygenase

BACKGROUND

The enzyme indoleamine 2,3-dioxygenase (IDO) exerts an well established immunosuppressive function in cancer. IDO is expressed within the tumor itself as well as in antigen-presenting cells in tumor-draining lymph nodes, where it promotes the establishment of peripheral immune tolerance to tumor antigens. In the present study, we tested the notion whether IDO itself may be subject to immune responses.

METHODS AND FINDINGS

The presence of naturally occurring IDO-specific CD8 T cells in cancer patients was determined by MHC/peptide stainings as well as ELISPOT. Antigen specific cytotoxic T lymphocytes (CTL) from the peripheral blood of cancer patients were cloned and expanded. The functional capacity of the established CTL clones was examined by chrome release assays. The study unveiled spontaneous cytotoxic T-cell reactivity against IDO in peripheral blood as well as in the tumor microenvironment of different cancer patients. We demonstrate that these IDO reactive T cells are indeed peptide specific, cytotoxic effector cells. Hence, IDO reactive T cells are able to recognize and kill tumor cells including directly isolated AML blasts as well as IDO-expressing dendritic cells, i.e. one of the major immune suppressive cell populations.

CONCLUSION

IDO may serve as an important and widely applicable target for anti-cancer immunotherapeutic strategies. Furthermore, as emerging evidence suggests that IDO constitutes a significant counter-regulatory mechanism induced by pro-inflammatory signals, IDO-based immunotherapy holds the promise to boost anti-cancer immunotherapy in general.

Discovery of novel targets for multi-epitope vaccines: screening of HIV-1 genomes using association rule mining

Background

Studies have shown that in the genome of human immunodeficiency virus (HIV-1) regions responsible for interactions with the host's immune system, namely, cytotoxic T-lymphocyte (CTL) epitopes tend to cluster together in relatively conserved regions. On the other hand, "epitope-less" regions or regions with relatively low density of epitopes tend to be more variable. However, very little is known about relationships among epitopes from different genes, in other words, whether particular epitopes from different genes would occur together in the same viral genome. To identify CTL epitopes in different genes that co-occur in HIV genomes, association rule mining was used.

Results

Using a set of 189 best-defined HIV-1 CTL/CD8+ epitopes from 9 different protein-coding genes, as described by Frahm, Linde & Brander (2007), we examined the complete genomic sequences of 62 reference HIV sequences (including 13 subtypes and sub-subtypes with approximately 4 representative sequences for each subtype or sub-subtype, and 18 circulating recombinant forms). The results showed that despite inclusion of recombinant sequences that would be expected to break-up associations of epitopes in different genes when two different genomes are recombined, there exist particular combinations of epitopes (epitope associations) that occur repeatedly across the world-wide population of HIV-1. For example, Pol epitope LFLDGIDKA is found to be significantly associated with epitopes GHQAAMQML and FLKEKGGL from Gag and Nef, respectively, and this association rule is observed even among circulating recombinant forms.

Conclusion

We have identified CTL epitope combinations co-occurring in HIV-1 genomes including different subtypes and recombinant forms. Such co-occurrence has important implications for design of complex vaccines (multi-epitope vaccines) and/or drugs that would target multiple HIV-1 regions at once and, thus, may be expected to overcome challenges associated with viral escape.

Mining the Leishmania genome for novel antigens and vaccine candidates

Leishmaniasis is a neglected disease with an estimated 12 million infected people. The recent completion of the sequencing of the Leishmania major genome has opened opportunities for the identification of targets for vaccine development. We present here the first attempt at identifying novel vaccine candidates by whole genome analysis. We predicted CD8(+) T cell epitopes from the L. major proteome and validated in vivo in mice the immunogenicity of some of the best predicted epitopes. Consensus epitope predictions from 8272 annotated protein sequences with 5-8 different algorithms allowed the identification of 78 class I CD8(+) epitopes. BALB/c mice were immunized with 26 synthetic peptides corresponding to the most likely epitopes. Fourteen (54%) resulted immunogenic, with eight being strong inducers of T cell IFNgamma production. None of the proteins from which the epitopes are derived are differentially expressed, only two may be surface proteins, eight have putative enzymatic, and metabolic activities. These epitopes and proteins represent new antigen candidates for further studies. While pathogen genomes have not yet delivered their full promise in terms of human health applications, our study opens the way for extensive genome mining for antigen identification and vaccine development against Leishmania and other pathogens.

A modified epitope identified for generation and monitoring of PSA-specific T cells in patients on early phases of PSA-based immunotherapeutic protocols

Efficacy of vaccination in cancer patients on immunotherapeutic protocols can be difficult to evaluate. The aim of this study was therefore to identify a single natural or modified epitope in prostate-specific antigen (PSA) with the ability to generate high levels of PSA-specific T cells to facilitate monitoring in patients after vaccination against prostate cancer. To the best of our knowledge, this study describes for the first time the peptide specificity of T cells stimulated by endogenously processed PSA antigen. The peptide specificity of HLA-A*0201-restricted CD8(+) T cells against human and rhesus PSA was investigated both in vivo after DNA vaccination in HLA-A*0201-transgenic mice and in vitro after repetitive stimulation of human T cells with DNA-transfected human dendritic cells (DCs). One of seven native PSA peptides, psa53-61, was able to activate high levels of PSA-specific CD8(+) T cells in HLA-A*0201-transgenic mice after PSA DNA vaccination. Psa53-61 was also the only peptide that induced human T cells to produce IFNgamma after stimulation with PSA transfected DCs, however not in all donors. Therefore, plasmids encoding modified epitopes in predicted HLA-A*0201 sequences were constructed. One of these modified PSA plasmids consistently induced IFNgamma producing CD8(+) T cells to the corresponding modified peptide as well as to the corresponding native peptide, in all murine and human T cell cultures. This study demonstrates a novel concept of introducing a modified epitope within a self-tumor antigen, with the purpose of eliciting a reliable T cell response from the non-tolerized immune repertoire, to facilitate monitoring of vaccine efficacy in cancer patients on immunotherapeutic protocols. The purpose of such a modified epitope is thus not to induce therapeutically relevant T cells but rather to, in case of weak or divergent T cell responses to self antigens/peptides, help answer questions about efficacy of vaccine delivery and about the possibility to induce immune responses in the selected and often immunosuppressed cancer patients.

Shift-invariant adaptive double threading: learning MHC II-peptide binding

The major histocompatibility complex (MHC) plays important roles in the workings of the human immune system. Specificity of MHC binding to peptide fragments from cellular and pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. In this paper we propose a novel approach to predicting binding configurations and energies for MHC class II molecules, whose epitopes are generally predicted less well than the MHC I epitopes due in part to larger variation in bound peptide length. We treat the relative position of the peptide as a hidden variable, and model the ensemble of different binding configurations, rather than use a separate alignment procedure to narrow it down to one. Thus, our predictor infers a distribution over peptide positions from the MHC II and peptide sequences, and computes the total binding affinity. The training procedure iterates the predictions with re-estimation of the parameters of the binding groove model. For a given relative peptide position, any MHC class I prediction model can be used. Here we choose the physics based model of Jojic et al. (2006). We show that the parameters of the binding model can be learned efficiently from the training data and then used to estimate binding energies for previously untested peptides. Our technique performs on par with previous approaches to MHC II epitope prediction. Furthermore, our model choice allows generalization to new MHC class II alleles, which were not a part of the training set.

RhoC a new target for therapeutic vaccination against metastatic cancer

Most cancer deaths are due to the development of metastases. Increased expression of RhoC is linked to enhanced metastatic potential in multiple cancers. Consequently, the RhoC protein is an attractive target for drug design. The clinical application of immunotherapy against cancer is rapidly moving forward in multiple areas, including the adoptive transfer of anti-tumor-reactive T cells and the use of "therapeutic" vaccines. The over-expression of RhoC in cancer and the fact that immune escape by down regulation or loss of expression of this protein would reduce the morbidity and mortality of cancer makes RhoC a very attractive target for anti-cancer immunotherapy. Herein, we describe an HLA-A3 restricted epitope from RhoC, which is recognized by cytotoxic T cells. Moreover, RhoC-specific T cells show cytotoxic potential against HLA-matched cancer cells of different origin. Thus, RhoC may serve as an important and widely applicable target for anti-cancer immunotherapeutic strategies.

New T cell epitopes identified from an anti-idiotypic antibody mimicking ovarian cancer associated antigen

Anti-idiotype (Id) antibodies can be used to induce specific cellular immune responses against tumor antigens, but the mechanism of antigenicity is not always clear. We previously reported an anti-Id antibody, 6B11, which mimics human ovarian cancer associated antigen OC166-9. To explore the molecular basis of cellular immune response induced by 6B11, a panel of peptides derived from complementarity determining region (CDR) of 6B11 were synthesized. After a series of immunologic experiments, we found that the light chain CDR3 peptide and heavy chain CDR3 peptide were the MHC class I and class II epitopes of 6B11, respectively. The combination of MHC class I and class II epitopes is more effective than 6B11 in inducing specific cellular immune response against ovarian cancer. Our study provided the structural basis of antigenicity of 6B11. The identification of antigen-specific T cell eptitopes in 6B11 should facilitate the design of epitope-based vaccine against human ovarian cancer.

Synthetic multi-epitope peptides identified in silico induce protective immunity against multiple influenza serotypes

Influenza causes yearly epidemics of mild disease and, occasionally, pandemics with millions of fatalities. Currently, no vaccine is effective against all influenza strains. Analysis of influenza sequences from animal and human isolates using CLUSTALW and a novel proprietary epitope prediction algorithm identified six conserved T cell-reactive regions in several proteins. Immunisation of transgenic mice with a preparation of these six regions as chemically synthesised peptides (FLU-v) induced a specific HLA-A*0201-mediated CD8(+) T cell response. This T cell population also reacted against human cells infected with three non-related influenza strains, confirming that the identified regions contain epitopes naturally presented by infected human cells and conserved amongst non-related viruses. Moreover, FLU-v immunisation significantly increased survival of transgenic mice against lethal challenge with influenza. Overall, FLU-v represents a promising influenza vaccine candidate, obviating the need for yearly vaccinations and allowing the stockpiling and initiation of a worldwide vaccination program in advance of a pandemic outbreak.

More than one reason to rethink the use of peptides in vaccine design

The use of peptides as therapeutics is experiencing renewed enthusiasm owing to advances in delivery, stability and design. Moreover, there is a growing emphasis on the use of peptides in vaccine design as insights into tissue-specific processing of the immunogenic epitopes of proteins and the discovery of unusually long cytotoxic T-lymphocyte epitopes broaden the range of targets and give clues to enhancing peptide immunogenicity. Peptides can also be synthesized with known post-translational modifications and/or deliberately introduced protease-resistant peptide bonds to regulate their processing independent of tissue-specific proteolysis and to stabilize these compounds in vivo. We discuss the potential of peptide-based vaccines for the treatment of chronic viral diseases and cancer, and review recent developments in the field of peptide-based vaccines.

Mycobacterium tuberculosis-specific CD8+ T cells and their role in immunity

There are more cases of tuberculosis in the world today than at any other time in history. The global epidemic has generated intense interest into the immunological mechanisms that control infection. Although CD4+ T cells play a critical role in host immunity to Mycobacterium tuberculosis, there is considerable interest in understanding the role of other T cell subsets in preventing disease development following infection. CD8+ T cells are required for optimum host defense following M. tuberculosis infection, which has led to investigation into how this protective effect is mediated. A critical review of recent literature regarding the role of CD8+ T cells in protective immunity to M. tuberculosis infection is now required to address the strengths and weaknesses of these studies. In this article, we evaluate the evidence that CD8+ T cells are critical in immunity to M. tuberculosis infection. We discuss the specific mycobacterial proteins that are recognized by CD8+ T cells elicited during infection. Finally, we examine the effector mechanisms of CD8+ T cells generated during infection and synthesize recent studies to consider the protective roles that these T cells serve in vivo.

Assessment of the extent of variation in influenza A virus cytotoxic T-lymphocyte epitopes by using virus-specific CD8+ T-cell clones

The influenza A virus nucleoprotein (NP) and matrix protein are major targets for human virus-specific cytotoxic T-lymphocyte (CTL) responses. Most of the CTL epitopes that have been identified so far are conserved. However, sequence variation in CTL epitopes of the NP has recently been demonstrated to be associated with escape from virus-specific CTLs. To assess the extent of variation in CTL epitopes during influenza A virus evolution, 304 CTL clones derived from six study subjects were obtained with specificity for an influenza A/H3N2 virus isolated in 1981. Subsequently, the frequency of the CTL clones that failed to recognize a more recent influenza virus strain isolated in 2003 was determined. In four of six study subjects, CTLs were found to be specific for variable epitopes, accounting for 2.6 % of all CTL clones. For some of these CTL clones, the minimal epitope and the residues responsible for abrogation of T-cell recognition were identified.

Cytotoxic T cells generated against heteroclitic peptides kill primary tumor cells independent of the binding affinity of the native tumor antigen peptide

Heteroclitic peptide modifications increase immunogenicity, allowing generation of cytotoxic T lymphocytes (CTLs) against weakly immunogenic tumor-associated antigens (TAAs). A critical issue is whether T cells generated against heteroclitic peptides retain the ability to recognize and kill tumor cells expressing the original weak TAAs, and whether there is a lower threshold of binding affinity of the native peptides, below which such CTLs can still kill primary tumor cells. To examine this we used a model examining the ability of native and heteroclitic immunoglobulin (Ig)-derived peptides to generate CTLs that can kill chronic lymphocytic leukemia (CLL) cells. We demonstrate that CTLs generated against heteroclitic peptides have enhanced killing of CD40-activated B cells pulsed with either heteroclitic (P < .001) or native peptide (P = .04) and primary CLL cells (P = .01). The novel finding reported here is that the rate-limiting factor appears to be the ability to generate CTLs and that once generated, CTL lysis of primary tumor cells is independent of the binding affinity of the native peptide. These findings have implications for vaccination strategies in malignancies and are currently being further examined in vivo in murine models.

Peptide binding motif predictive algorithms correspond with experimental binding of leukemia vaccine candidate peptides to HLA-A*0201 molecules

The ability to reliably identify the peptides that can bind to MHC molecules is of practical importance for rapid vaccine development. Several computer-based prediction methods have been applied to study the interaction of MHC class I/peptide binding. Here we have compared the binding of peptides predicted by three algorithms (BIMAS, SYFPEITHI and Rankpep) to the binding of the peptides to HLA-A*0201 molecules in vitro, assessed using a MHC stabilization assay on live T2 cells. Fifty HLA-A*0201 peptides were selected from several target oncoproteins: Wilms' tumor protein (WT1), native and imatinib-mutated bcr-abl p210, JAK2 protein and Ewing's sarcoma fusion protein type 1. The sensitivity and specificity of BIMAS, SYFPEITHI and Rankpep respectively, were: 86%, and 82%; 75% and 73%; 64% and 82%. Combining two or more computer methods did not appear to significantly improve the predictive value.

Virus-epitope vaccine design: informatic matching the HLA-I polymorphism to the virus genome

Attempts to develop peptide vaccines, based on a limited number of peptides face two problems: HLA polymorphism and the high mutation rate of viral epitopes. We have developed a new genomic method that ensures maximal coverage and thus maximal applicability of the peptide vaccine. The same method also promises a large number of epitopes per HLA to prevent escape via mutations. Our design can be applied swiftly in order to face rapidly emerging viral diseases. We use a genomic scan of all candidate peptides and join them optimally. For a given virus, we use algorithms computing: peptide cleavage probability, transfer through TAP and MHC binding for a large number of HLA alleles. The resulting peptide libraries are pruned for peptides that are not conserved or are too similar to self peptides. We then use a genetic algorithm to produce an optimal protein composed of peptides from this list properly ordered for cleavage. The selected peptides represent an optimal combination to cover all HLA alleles and all viral proteins. We have applied this method to HCV and found that some HCV proteins (mainly envelope proteins) represent much less peptide than expected. A more detailed analysis of the peptide variability shows a balance between the attempts of the immune system to detect less mutating peptides, and the attempts of viruses to mutate peptides and avoid detection by the immune system. In order to show the applicability of our method, we have further used it on HIV-I, Influenza H3N2 and the Avian Flu Viruses.

The use of reverse immunology to identify HLA-A2 binding epitopes in Tie-2

A potential target for a cancer vaccine would be receptors, such as Tie-2 which are over expressed on tumour endothelium. Using computer aided motif predictions for possible HLA class I epitopes, we have identified peptides from Tie-2 that should bind with a range of affinities to HLA-A*0201. No direct correlation between predicted values and actual binding affinities was observed. Although, the programs did produce a number of false positives, two epitopes were predicted that bound with relatively high affinity when compared with an influenza peptide. We have previously identified a Tie-2 epitope and shown that it was only immunogenic when we substituted preferred amino acids at key anchor residues to increase binding affinity. In this study we used a similar approach to generate modified epitopes. When HLA-A2 transgenic mice were immunised with peptides, CTL killing of the target cells was only achieved when the wild type epitope was presented at moderate levels. Moreover, the efficiency of immunisation was increased when we linked CD4 epitopes to CD8 epitopes. Caution should therefore be employed in the use of both reverse immunology and anchor modification of CTL epitopes in the identification of CTL epitopes for cancer vaccines.

PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions

BACKGROUND

Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design.

METHODS

Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically, we propose to use DistBoost, which is a semi-supervised distance learning algorithm.

RESULTS

We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, our method's performance gain, when compared to other computational methods, is even more pronounced. We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles. The webserver which can be found at http://www.pepdist.cs.huji.ac.il is powered by a prediction engine which was trained using the framework presented in this paper.

CONCLUSION

The results obtained suggest that learning a single distance function over an entire family of proteins achieves higher prediction accuracy than learning a set of binary classifiers for each of the proteins separately. We also show the importance of obtaining information on experimentally determined non-binders. Learning with real non-binders generalizes better than learning with randomly generated peptides that are assumed to be non-binders. This suggests that information about non-binding peptides should also be published and made publicly available.

Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies

In silico antibody-antigen binding predictions are generally employed in research to rationalize epitope development. These techniques are widely spread despite their technical limitations. To validate the results of these bioinformatic calculations evidence based comparative in vitro studies are necessary. We have used a well-conserved mitochondrial inner membrane antigen-citrate synthase to develop a model for comparative analysis of the predicted and the immunoserologically verified epitopes of circulating autoantibodies. Epitopes were predicted using accepted tools: the GCG Wisconsin package and TEPITOPE 2000. An overlapping multipin ELISA assay--covering 49% of the citrate synthase molecule--was developed to map autoantibody epitopes of individuals (healthy, systemic autoimmune, and heart transplanted) in different immunopathological conditions. From the 40 synthesized decapeptides 34 were predicted in silico and 27 were validated in vitro. Thirty-two percent of epitopes were recognized by majority of sera 47% by at least one sera. False positive predictions were 21%. There was major difference in the recognized epitope pattern under different immunopathological conditions. Our results suggest that special databases are needed for training and weighing prediction methods by clinically well-characterized samples, due to the differences in the immune response under different health status. The development of these special algorithms needs a new approach. A high number of samples under these special immunological conditions are to be mapped and then used for the "fine tuning" of different prediction algorithms.

Spontaneous Immunity against Bcl-xL in Cancer Patients

It is well-established that peptide epitopes derived from human tumor-associated Ags can be recognized by CTL in the context of the MHC molecule. However, the vast majority of Ags described are not vital for survival and growth of the tumor cells, and immunoselection of Ag-loss variants during immunotherapy has been demonstrated in several cases. Malfunctions in death pathways observed in human cancers are often due to overexpression of antiapoptotic proteins in the Bcl-2 protein family, i.e., Bcl-2, Mcl-1, and Bcl-xL. These antiapoptotic proteins are implicated in cancer development, tumor progression, and drug resistance. The general overexpression of the antiapoptotic members of the Bcl-2 family in cancer and the fact that down-regulation or loss of expression of these proteins as a means of immune escape would impair sustained tumor growth makes them very attractive targets for anticancer immunotherapy. Recently, we identified spontaneous T cell responses against Bcl-2- and Mcl-1-derived peptides in patients suffering from cancers of different origin. In this study, we demonstrate that Bcl-xL is a target for T cell recognition in cancer patients. Thus, we describe spontaneous HLA-A2-restricted cytotoxic T cell responses against peptide epitopes derived from Bcl-xL by means of ELISPOT and flow cytometry stainings, whereas no responses were detected against any of the Bcl-xL epitopes in any healthy controls. Moreover, Bcl-xL-specific T cells are cytotoxic against HLA-matched cancer cells of different origin. Thus, cellular immune responses against apoptosis inhibitors like the Bcl-2 family proteins appear to represent a general feature in cancer.

Expression mapping using a retroviral vector for CD8+ T cell epitopes: definition of a Mycobacterium tuberculosis peptide presented by H2-Dd

Identification of CD8+ T cell epitopes is important because detection of specific CD8+ T cells after infection or immunization requires prior knowledge of epitope specificity. Furthermore, identification of CD8+ T cell epitopes permits the development of specific preventive and therapeutic approaches to both infections and tumors. Thus far, CD8+ T cell epitopes have been identified either using an overlapping peptide library covering an entire protein, or using algorithms designed to identify likely peptides that bind to major histocompatibility complex (MHC) class I molecules. The synthesis of overlapping peptides can be prohibitively expensive, and the algorithm programs used to predict CD8+ T cell epitopes are not always accurate. Here we describe a retroviral expression system that specifically allows longer polypeptides and shorter peptides to be expressed in the cytoplasm, and thereby to be processed onto class I MHC molecules. T cells from mice that were immunized with a DNA vaccine encoding MPT-51 were probed against MHC-compatible cell lines retrovirally transduced with overlapping gene fragments encoding 120-140 amino acids of the MPT-51 molecule. After further testing of shorter peptide sequences, we identified a CD8+ T cell epitope using cell lines expressing a relatively small number of algorithm-predicted candidate epitopes. We found that one of the requirements for cell surface display of the 20-mer peptide was the need for cotranslational ubiquitination. The restriction molecule was identified as Dd following transduction with MHC class I genes followed by transduction with the oligonucleotide encoding the epitope. The retroviral expression system described here is cost-effective, particularly if the target molecule is large, and could be adapted to identifying T cell epitopes recognized in infectious disease and against tumor cell antigens.

T-cell epitope repertoire as predicted from human and viral genomes

During thymic education, strongly self-reactive T cells are selected against, while weakly self-reactive cells are positively selected. However, the probability of an antigen being self derived and the number of self-peptides have never been properly defined. We merge algorithms for: cleavage prediction, TAP binding probability estimates and MHC binding properties to estimate the number and distribution of all MHC binding peptides. We show that the number of self-peptides with a high affinity to a given human MHC-I molecule is between 200 and almost 200,000 and is much less than the estimated total number of peptide sequences. This result suggests that MHC molecules are selected through evolution in order to reduce the number of self-peptides presented. The number of viral peptides presented is also low and varies between zero and a few hundred per virus for a given HLA allele. These low numbers explain the need for multiple alleles within an individual. We show that six codominantly expressed MHC-I alleles are sufficient to present at least one or two peptides per virus for the vast majority of viruses. Viruses can escape detection either by using peptides that cannot be presented on MHC molecules or by using peptides whose presented segments overlap significantly with self. Most viral families (such as influenza, HIV, Hepatitis and HPV) present as many peptides as predicted from their genome length, and overlap minimally with the human self-peptide repertoire. However, a few latent viruses, such as herpes and adenovirus share considerable peptide sequence homology with their human hosts.

Spontaneous T-cell responses against peptides derived from the Taxol resistance-associated gene-3 (TRAG-3) protein in cancer patients

Expression of the cancer-testis antigen Taxol resistance-associated gene-3 (TRAG-3) protein is associated with acquired paclitaxel (Taxol) resistance, and is expressed in various cancer types; e.g., breast cancer, leukemia, and melanoma. Thus, TRAG-3 represents an attractive target for immunotherapy of cancer. To identify HLA-A*02.01-restricted epitopes from TRAG-3, we screened cancer patients for spontaneous cytotoxic T-cell responses against TRAG-3-derived peptides. The TRAG-3 protein sequence was screened for 9mer and 10mer peptides possessing HLA-A*02.01-binding motifs. Of 12 potential binders, 9 peptides were indeed capable of binding to the HLA-A*02.01 molecule, with binding affinities ranging from strong to weak binders. Subsequently, lymphocytes from cancer patients (9 breast cancer patients, 12 melanoma patients, and 13 patients with hematopoietic malignancies) were analyzed for spontaneous reactivity against the panel of peptides by ELISpot assay. Spontaneous immune responses were detected against 8 epitope candidates in 7 of 9 breast cancer patients, 7 of 12 melanoma patients, and 5 of 13 patients with hematopoietic malignancies. In several cases, TRAG-3-specific CTL responses were scattered over several epitopes. Hence, no immunodominance of any single peptide was observed. Furthermore, single-peptide responses were detected in 2 of 12 healthy HLA-A2(+) donors, but no responses were detectable in 9 HLA-A2(-) healthy donors or 4 HLA-A2(-) melanoma patients. The identified HLA-A*02.01-restricted TRAG-3-derived epitopes are targets for spontaneous immune responses in breast cancer, hematopoietic cancer, and melanoma patients. Hence, these epitopes represent potential target structures for future therapeutic vaccinations against cancer, possibly appropriate for strategies that combine vaccination and chemotherapy; i.e., paclitaxel treatment.

Immunogenicity of Bcl-2 in patients with cancer

B-cell lymphoma 2 (Bcl-2) is a pivotal regulator of apoptotic cell death and it is overexpressed in many cancers. Consequently, the Bcl-2 protein is an attractive target for drug design, and Bcl-2–specific antisense oligonucleotides or small-molecule Bcl-2 inhibitors have shown broad anticancer activities in preclinical models and are currently in several clinical trials. The clinical application of immunotherapy against cancer is rapidly moving forward in multiple areas, including the adoptive transfer of anti–tumor-reactive T cells and the use of “therapeutic” vaccines. The overexpression of Bcl-2 in cancer and the fact that immune escape by down-regulation or loss of expression of this protein would impair sustained tumor growth makes Bcl-2 a very attractive target for anticancer immunotherapy. Herein, we describe spontaneous T-cell reactivity against Bcl-2 in peripheral blood from patients suffering from unrelated tumor types (ie, pancreatic cancer, breast cancer, acute myeloid leukemia [AML], and chronic lymphocytic leukemia [CLL]). Additionally, we show that these Bcl-2–reactive T cells are indeed peptide-specific, cytotoxic effector cells. Thus, Bcl-2 may serve as an important and widely applicable target for anticancer immunotherapeutic strategies (eg, in the combination with conventional radiotherapy and chemotherapy).

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

Novel strategy for identification of candidate cytotoxic T-cell epitopes from human preproinsulin

We describe a strategy for identifying ligands of human leukocyte antigen (HLA) class I molecules based on a peptide library-mediated in vitro assembly of recombinant class I molecules. We established a microscale class I assembly assay and used a capture ELISA to quantify the assembled HLA-peptide complexes. The identity of the bound ligands was then deduced by mass spectrometry. In this method, HLA complexes assembled in vitro in the presence of components of a mixture of peptides were immunoprecipitated and the bound peptide(s) identified by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. This process of epitope extraction is robust and can be used with complex mixtures containing in excess of 300 candidate ligands. A library of overlapping peptides representing all potential octamers, nonamers and decamers from human preproinsulin was synthesized using unique library chemistry. Peptides from the library were used to initiate assembly of recombinant HLA-B8, HLA-B15 and HLA-A2, facilitating the identification of candidate T-cell epitopes from preproinsulin.

The melanoma inhibitor of apoptosis protein: a target for spontaneous cytotoxic T cell responses

The identification of tumor antigens which expression is essential for the survival of tumor cells is a new avenue to prevent antigen loss variants emerging due to immunoselection, particularly during immune therapy. The melanoma inhibitor of apoptosis protein, ML-IAP (also named livin) counteracts apoptosis induced by death receptors, hypooxgenic conditions, or chemotherapeutic agents. Thus, elevated expression of ML-IAP renders melanoma cells resistant to apoptotic stimuli and thereby potentially contributes to the oncogenic phenotype. Here, we demonstrate that T cells in a large proportion of melanoma patients infiltrating the tumor or circulating in the peripheral blood specifically recognize ML-IAP-derived peptides. Interestingly, the responses against the peptide epitope ML-IAP280-289 were not restricted to melanoma patients but present among peripheral blood T cells in a few healthy controls. In situ peptide/HLA-A2 multimer staining, however, confirmed the infiltration of ML-IAP-reactive cells into the tumor microenvironment. Moreover, ML-IAP-reactive T cells isolated by magnetic beads coated with peptide/HLA-A2 complexes were cytotoxic against HLA-matched melanoma cells. In conclusion, out data strongly indicate ML-IAP as a suitable target for immunologic intervention.

Immunoproteomics: Mass spectrometry-based methods to study the targets of the immune response

The mammalian immune system has evolved to display fragments of protein antigens derived from microbial pathogens to immune effector cells. These fragments are typically peptides liberated from the intact antigens through distinct proteolytic mechanisms that are subsequently transported to the cell surface bound to chaperone-like receptors known as major histocompatibility complex (MHC) molecules. These complexes are then scrutinized by effector T cells that express clonally distributed T cell receptors with specificity for specific MHC-peptide complexes. In normal uninfected cells, this process of antigen processing and presentation occurs continuously, with the resultant array of self-antigen-derived peptides displayed on the surface of these cells. Changes in this peptide landscape of cells act to alert immune effector cells to changes in the intracellular environment that may be associated with infection, malignant transformation, or other abnormal cellular processes, resulting in a cascade of events that result in their elimination. Because peptides play such a crucial role in informing the immune system of infection with viral or microbial pathogens and the transformation of cells in malignancy, the tools of proteomics, in particular mass spectrometry, are ideally suited to study these immune responses at a molecular level. Here we review recent advances in the studies of immune responses that have utilized mass spectrometry and associated technologies, with specific examples from collaboration between our laboratories.

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.

Optimization and immune recognition of multiple novel conserved HLA-A2, human immunodeficiency virus type 1-specific CTL epitopes

MHC-I-restricted cytotoxic responses are considered a critical component of protective immunity against viruses, including human immunodeficiency virus type 1 (HIV-1). CTLs directed against accessory and early regulatory HIV-1 proteins might be particularly effective; however, CTL epitopes in these proteins are rarely found. Novel artificial neural networks (ANNs) were used to quantitatively predict HLA-A2-binding CTL epitope peptides from publicly available full-length HIV-1 protein sequences. Epitopes were selected based on their novelty, predicted HLA-A2-binding affinity and conservation among HIV-1 strains. HLA-A2 binding was validated experimentally and binders were tested for their ability to induce CTL and IFN-gamma responses. About 69 % were immunogenic in HLA-A2 transgenic mice and 61 % were recognized by CD8(+) T-cells from 17 HLA-A2 HIV-1-positive patients. Thus, 31 novel conserved CTL epitopes were identified in eight HIV-1 proteins, including the first HLA-A2 minimal epitopes ever reported in the accessory and regulatory proteins Vif, Vpu and Rev. Interestingly, intermediate-binding peptides of low or no immunogenicity (i.e. subdominant epitopes) were found to be antigenic and more conserved. Such epitope peptides were anchor-optimized to improve immunogenicity and further increase the number of potential vaccine epitopes. About 67 % of anchor-optimized vaccine epitopes induced immune responses against the corresponding non-immunogenic naturally occurring epitopes. This study demonstrates the potency of ANNs for identifying putative virus CTL epitopes, and the new HIV-1 CTL epitopes identified should have significant implications for HIV-1 vaccine development. As a novel vaccine approach, it is proposed to increase the coverage of HIV variants by including multiple anchor-optimized variants of the more conserved subdominant epitopes.

Dissecting the role of peptides in the immune response: theory, practice and the application to vaccine design

Analytical biochemistry and synthetic peptide based chemistry have helped to reveal the pivotal role that peptides play in determining the specificity, magnitude and quality of both humoral (antibody) and cellular (cytotoxic and helper T cell) immune responses. In addition, peptide based technologies are now at the forefront of vaccine design and medical diagnostics. The chemical technologies used to assemble peptides into immunogenic structures have made great strides over the past decade and assembly of highly pure peptides which can be incorporated into high molecular weight species, multimeric and even branched structures together with non-peptidic material is now routine. These structures have a wide range of applications in designer vaccines and diagnostic reagents. Thus the tools of the peptide chemist are exquisitely placed to answer questions about immune recognition and along the way to provide us with new and improved vaccines and diagnostics.

Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides

The ability to predict MHC-binding peptides remains limited despite ever expanding demands for specific immunotherapy against cancers, infectious diseases, and autoimmune disorders. Previous analyses revealed position-specific preference of amino acids but failed to detect sequence patterns. Efforts to use computational analysis to identify sequence patterns have been hampered by the insufficiency of the number/quality of the peptide binding data. We propose here a dynamic experiment design to search for sequence patterns that are common to the MHC class I-binding peptides. The method is based on a committee-based framework of query learning using hidden Markov models as its component algorithm. It enables a comprehensive search of a large variety (20(9)) of peptides with a small number of experiments. The learning was conducted in seven rounds of feedback loops, in which our computational method was used to determine the next set of peptides to be analyzed based on the results of the earlier iterations. After these training cycles, the algorithm enabled a real number prediction of MHC binding peptides with an accuracy surpassing that of the hitherto best performing positional scanning method.

Peptide libraries for T cell epitope screening and characterization

Screening with synthetic peptide libraries is the fastest and most efficient method for locating helper T cell (Th) and cytotoxic T cell (Tc) epitopes in proteins of known sequence. Epitope detection and analysis can be performed on clones, lines or freshly isolated ex-vivo T cells. Novel approaches to peptide library design ensure that the peptides are presented in a format which is optimized for Th or Tc epitope detection and analysis. The major histocompatibility complex (MHC) binding motif can be determined by binding or functional assays using analogs of the minimal active sequence.