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

Enzymatic discovery of a HER-2/neu epitope that generates cross-reactive T cells

Patients with HER-2/neu-expressing breast cancer remain at risk for relapse following standard therapy. Vaccines targeting HER-2/neu to prevent relapse are in various phases of clinical testing. Many vaccines incorporate the HER-2/neu HLA-A2-binding peptide p369-377 (KIFGSLAFL), because it has been shown that CTLs specific for this epitope can directly kill HER-2/neu-overexpressing breast cancer cells. Thus, understanding how tumors process this epitope may be important for identifying those patients who would benefit from immunization. Proteasome preparations were used to determine if p369-377 was processed from larger HER-2/neu-derived fragments. HPLC, mass spectrometry, cytotoxicity assays, IFN-γ ELISPOT, and human breast cancer cell lines were used to assess the proteolytic fragments. Processing of p369-377 was not detected by purified 20S proteasome and immunoproteasome, indicating that tumor cells may not be capable of processing this Ag from the HER-2/neu protein and presenting it in the context of HLA class I. Instead, we show that other extracellular domain HER-2/neu peptide sequences are consistently processed by the proteasomes. One of these sequences, p373-382 (SLAFLPESFD), bound HLA-A2 stronger than did p369-377. CTLs specific for p373-382 recognized both p373-382 and p369-377 complexed with HLA-A2. CTLs specific for p373-382 also killed human breast cancer cell lines at higher levels than did CTLs specific for p369-377. Conversely, CTLs specific for p369-377 recognized p373-382. Peptide p373-382 is a candidate epitope for breast cancer vaccines, as it is processed by proteasomes and binds HLA-A2.

An assessment on epitope prediction methods for protozoa genomes

Background

Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix; b) integrating data from experimentally validated and in silico predicted epitopes; and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes.

Results

A database-driven epitope prediction method was developed with built-in functions that were capable of: a) removing experimental data redundancy; b) parsing algorithms predictions and storage experimental validated and predict data; and c) evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value of 0.77. For T CD8+ epitope predictors, the combined prediction of NetCTL and NetMHC reached an AUC value of 0.64. Finally, regarding the subcellular localization prediction, the best performance is achieved when the combined prediction of Sigcleave, TargetP and WoLF PSORT is used.

Conclusions

Our study indicates that the combination of B cells epitope predictors is the best tool for predicting epitopes on protozoan parasites proteins. Regarding subcellular localization, the best result was obtained when the three algorithms predictions were combined. The developed pipeline is available upon request to authors.

CD8 T cell autoreactivity to preproinsulin epitopes with very low human leucocyte antigen class I binding affinity

Beta cells presenting islet epitopes are recognized and destroyed by autoreactive CD8 T cells in type 1 diabetes. These islet-specific T cells are believed to react with epitopes binding with high affinity to human leucocyte antigen (HLA) expressed on beta cells. However, this assumption might be flawed in case of islet autoimmunity. We evaluated T cell recognition of the complete array of preproinsulin (PPI) peptides with regard to HLA binding affinity and T cell recognition. In a comprehensive approach, 203 overlapping 9-10mer PPI peptides were tested for HLA-A2 binding and subjected to binding algorithms. Subsequently, a high-throughput assay was employed to detect PPI-specific T cells in patient blood, in which conditional HLA ligands were destabilized by ultraviolet irradiation and HLA molecules refolded with arrays of PPI peptides, followed by quantum-dot labelling and T cell staining. Analysis of patient blood revealed high frequencies of CD8 T cells recognizing very low HLA binding peptides. Of 28 peptides binding to HLA-A2, a majority was predicted not to bind. Unpredicted peptides bound mainly with low affinities. HLA binding affinity and immunogenicity may not correlate in autoimmunity. Algorithms used to predict high-affinity HLA peptide binders discount the majority of low-affinity HLA binding epitopes. Appreciation that peptides binding HLA with very low affinity can act as targets of autoreactive T cells may help to understand loss of tolerance and disease pathogenesis and possibly point to tissue-specific immune intervention targets.

Residue analysis of a CTL epitope of SARS-CoV spike protein by IFN-gamma production and bioinformatics prediction

Background

Severe acute respiratory syndrome (SARS) is an emerging infectious disease caused by the novel coronavirus SARS-CoV. The T cell epitopes of the SARS CoV spike protein are well known, but no systematic evaluation of the functional and structural roles of each residue has been reported for these antigenic epitopes. Analysis of the functional importance of side-chains by mutational study may exaggerate the effect by imposing a structural disturbance or an unusual steric, electrostatic or hydrophobic interaction.

Results

We demonstrated that N50 could induce significant IFN-gamma response from SARS-CoV S DNA immunized mice splenocytes by the means of ELISA, ELISPOT and FACS. Moreover, S366-374 was predicted to be an optimal epitope by bioinformatics tools: ANN, SMM, ARB and BIMAS, and confirmed by IFN-gamma response induced by a series of S358-374-derived peptides. Furthermore, each of S366-374 was replaced by alanine (A), lysine (K) or aspartic acid (D), respectively. ANN was used to estimate the binding affinity of single S366-374 mutants to H-2 Kd. Y367 and L374 were predicated to possess the most important role in peptide binding. Additionally, these one residue mutated peptides were synthesized, and IFN-gamma production induced by G368, V369, A371, T372 and K373 mutated S366-374 were decreased obviously.

Conclusions

We demonstrated that S366-374 is an optimal H-2 Kd CTL epitope in the SARS CoV S protein. Moreover, Y367, S370, and L374 are anchors in the epitope, while C366, G368, V369, A371, T372, and K373 may directly interact with TCR on the surface of CD8-T cells.

MRKAd5 HIV-1 Gag/Pol/Nef vaccine-induced T-cell responses inadequately predict distance of breakthrough HIV-1 sequences to the vaccine or viral load

Background

The sieve analysis for the Step trial found evidence that breakthrough HIV-1 sequences for MRKAd5/HIV-1 Gag/Pol/Nef vaccine recipients were more divergent from the vaccine insert than placebo sequences in regions with predicted epitopes. We linked the viral sequence data with immune response and acute viral load data to explore mechanisms for and consequences of the observed sieve effect.

Methods

Ninety-one male participants (37 placebo and 54 vaccine recipients) were included; viral sequences were obtained at the time of HIV-1 diagnosis. T-cell responses were measured 4 weeks post-second vaccination and at the first or second week post-diagnosis. Acute viral load was obtained at RNA-positive and antibody-negative visits.

Findings

Vaccine recipients had a greater magnitude of post-infection CD8+ T cell response than placebo recipients (median 1.68% vs 1.18%; p = 0·04) and greater breadth of post-infection response (median 4.5 vs 2; p = 0·06). Viral sequences for vaccine recipients were marginally more divergent from the insert than placebo sequences in regions of Nef targeted by pre-infection immune responses (p = 0·04; Pol p = 0·13; Gag p = 0·89). Magnitude and breadth of pre-infection responses did not correlate with distance of the viral sequence to the insert (p>0·50). Acute log viral load trended lower in vaccine versus placebo recipients (estimated mean 4·7 vs 5·1) but the difference was not significant (p = 0·27). Neither was acute viral load associated with distance of the viral sequence to the insert (p>0·30).

Interpretation

Despite evidence of anamnestic responses, the sieve effect was not well explained by available measures of T-cell immunogenicity. Sequence divergence from the vaccine was not significantly associated with acute viral load. While point estimates suggested weak vaccine suppression of viral load, the result was not significant and more viral load data would be needed to detect suppression.

Mark-specific proportional hazards model with multivariate continuous marks and its application to HIV vaccine efficacy trials

For time-to-event data with finitely many competing risks, the proportional hazards model has been a popular tool for relating the cause-specific outcomes to covariates (Prentice and others, 1978. The analysis of failure time in the presence of competing risks. Biometrics 34, 541-554). Inspired by previous research in HIV vaccine efficacy trials, the cause of failure is replaced by a continuous mark observed only in subjects who fail. This article studies an extension of this approach to allow a multivariate continuum of competing risks, to better account for the fact that the candidate HIV vaccines tested in efficacy trials have contained multiple HIV sequences, with a purpose to elicit multiple types of immune response that recognize and block different types of HIV viruses. We develop inference for the proportional hazards model in which the regression parameters depend parametrically on the marks, to avoid the curse of dimensionality, and the baseline hazard depends nonparametrically on both time and marks. Goodness-of-fit tests are constructed based on generalized weighted martingale residuals. The finite-sample performance of the proposed methods is examined through extensive simulations. The methods are applied to a vaccine efficacy trial to examine whether and how certain antigens represented inside the vaccine are relevant for protection or anti-protection against the exposing HIVs.

Degenerate T-cell recognition of peptides on MHC molecules creates large holes in the T-cell repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes ([Formula: see text]0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the "holes in the T-cell repertoire" that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself.

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.

Cancer genome sequencing and its implications for personalized cancer vaccines

New DNA sequencing platforms have revolutionized human genome sequencing. The dramatic advances in genome sequencing technologies predict that the $1,000 genome will become a reality within the next few years. Applied to cancer, the availability of cancer genome sequences permits real-time decision-making with the potential to affect diagnosis, prognosis, and treatment, and has opened the door towards personalized medicine. A promising strategy is the identification of mutated tumor antigens, and the design of personalized cancer vaccines. Supporting this notion are preliminary analyses of the epitope landscape in breast cancer suggesting that individual tumors express significant numbers of novel antigens to the immune system that can be specifically targeted through cancer vaccines.

Diabetogenic T lymphocytes in human Type 1 diabetes

The field of Type 1 diabetes research has been quick to embrace the era of translational medicine in the recent epoch. Building upon some 30 years of intense immunological research, the past decade has been marked by a series of clinical trials designed to evaluate the potential beneficial effects of a range of immune intervention and prevention strategies [1(••),2-5]. At the heart of Type 1 diabetes is an autoimmune process, the consequence of which is immune-mediated destruction of islet β-cells. Although understanding the pathogenesis of islet autoimmunity is critical, there are also good reasons to focus research onto the β-cell destructive process itself. Measuring preservation of function of insulin-producing cells is currently the best means available to evaluate potential beneficial effects of immunotherapy, but there is an urgent need to discover and monitor immunological correlates of this β-cell destructive process. Whilst the best approach to intervention and prevention has yet to emerge, it is logical that future attempts to intelligently design therapeutics for Type 1 diabetes will need to be predicated on a clear understanding of the process of β-cell destruction and the immune components involved. For these reasons, this review will focus on the role of diabetogenic T lymphocytes in this disease-defining event.

Short communication an interferon-γ ELISPOT assay with two cytotoxic T cell epitopes derived from HTLV-1 tax region 161-233 discriminates HTLV-1-associated myelopathy/tropical spastic paraparesis patients from asymptomatic HTLV-1 carriers in a Peruvian population

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic and progressive disorder caused by the human T-lymphotropic virus type 1 (HTLV-1). In HTLV-1 infection, a strong cytotoxic T cell (CTL) response is mounted against the immunodominant protein Tax. Previous studies carried out by our group reported that increased IFN-γ enzyme-linked immunospot (ELISPOT) responses against the region spanning amino acids 161 to 233 of the Tax protein were associated with HAM/TSP and increased HTLV-1 proviral load (PVL). An exploratory study was conducted on 16 subjects with HAM/TSP, 13 asymptomatic carriers (AC), and 10 HTLV-1-seronegative controls (SC) to map the HAM/TSP-associated CTL epitopes within Tax region 161-233. The PVL of the infected subjects was determined and the specific CTL response was evaluated with a 6-h incubation IFN-γ ELISPOT assay using peripheral blood mononuclear cells (PBMCs) stimulated with 16 individual overlapping peptides covering the Tax region 161-233. Other proinflammatory and Th1/Th2 cytokines were also quantified in the supernatants by a flow cytometry multiplex assay. In addition, a set of human leukocyte antigen (HLA) class I alleles that bind with high affinity to the CTL epitopes of interest was determined using computational tools. Univariate analyses identified an association between ELISPOT responses to two new CTL epitopes, Tax 173-185 and Tax 181-193, and the presence of HAM/TSP as well as an increased PVL. The HLA-A*6801 allele, which is predicted to bind to the Tax 181-193 peptide, was overpresented in the HAM/TSP patients tested.

Immunological responses induced by a DNA vaccine expressing RON4 and by immunogenic recombinant protein RON4 failed to protect mice against chronic toxoplasmosis

The development of an effective vaccine against Toxoplasma gondii infection is an important issue due to the seriousness of the related public health problems, and the economic importance of this parasitic disease worldwide. Rhoptry neck proteins (RONs) are components of the moving junction macromolecular complex formed during invasion. The aim of this study was to evaluate the vaccine potential of RON4 using two vaccination strategies: DNA vaccination by the intramuscular route, and recombinant protein vaccination by the nasal route. We produced recombinant RON4 protein (RON4S2) using the Schneider insect cells expression system, and validated its antigenicity and immunogenicity. We also constructed optimized plasmids encoding full length RON4 (pRON4), or only the N-terminal (pNRON4), or the C-terminal part (pCRON4) of RON4. CBA/J mice immunized with pRON4, pNRON4 or pCRON4 plus a plasmid encoding the granulocyte-macrophage-colony-stimulating factor showed high IgG titers against rRON4S2. Mice immunized by the nasal route with rRON4S2 plus cholera toxin exhibited low levels of anti-RON4S2 IgG antibodies, and no intestinal IgA antibodies specific to RON4 were detected. Both DNA and protein vaccination generated a mixed Th1/Th2 response polarized towards the IgG1 antibody isotype. Both DNA and protein vaccination primed CD4+ T cells in vivo. In addition to the production of IFN-γ, and IL-2, Il-10 and IL-5 were also produced by the spleen cells of the immunized mice stimulated with RON4S2, suggesting that a mixed Th1/Th2 type immune response occurred in all the immunized groups. No cytokine was detectable in stimulated mesenteric lymph nodes from mice immunized by the nasal route. Immune responses were induced by both DNA and protein vaccination, but failed to protect the mice against a subsequent oral challenge with T. gondii cysts. In conclusion, strategies designed to enhance the immunogenicity and to redirect the cellular response towards a Th1 type response against RON4 could lead to more encouraging results.

Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools

Binding of short antigenic peptides to major histocompatibility complex (MHC) molecules is a core step in adaptive immune response. Precise identification of MHC-restricted peptides is of great significance for understanding the mechanism of immune response and promoting the discovery of immunogenic epitopes. However, due to the extremely high MHC polymorphism and huge cost of biochemical experiments, there is no experimentally measured binding data for most MHC molecules. To address the problem of predicting peptides binding to these MHC molecules, recently computational approaches, called pan-specific methods, have received keen interest. Pan-specific methods make use of experimentally obtained binding data of multiple alleles, by which binding peptides (binders) of not only these alleles but also those alleles with no known binders can be predicted. To investigate the possibility of further improvement in performance and usability of pan-specific methods, this article extensively reviews existing pan-specific methods and their web servers. We first present a general framework of pan-specific methods. Then, the strategies and performance as well as utilities of web servers are compared. Finally, we discuss the future direction to improve pan-specific methods for MHC-peptide binding prediction.

Cross-protective peptide vaccine against influenza A viruses developed in HLA-A*2402 human immunity model

Background

The virus-specific cytotoxic T lymphocyte (CTL) induction is an important target for the development of a broadly protective human influenza vaccine, since most CTL epitopes are found on internal viral proteins and relatively conserved. In this study, the possibility of developing a strain/subtype-independent human influenza vaccine was explored by taking a bioinformatics approach to establish an immunogenic HLA-A24 restricted CTL epitope screening system in HLA-transgenic mice.

Methodology/Principal Findings

HLA-A24 restricted CTL epitope peptides derived from internal proteins of the H5N1 highly pathogenic avian influenza A virus were predicted by CTL epitope peptide prediction programs. Of 35 predicted peptides, six peptides exhibited remarkable cytotoxic activity in vivo. More than half of the mice which were subcutaneously vaccinated with the three most immunogenic and highly conserved epitopes among three different influenza A virus subtypes (H1N1, H3N2 and H5N1) survived lethal influenza virus challenge during both effector and memory CTL phases. Furthermore, mice that were intranasally vaccinated with these peptides remained free of clinical signs after lethal virus challenge during the effector phase.

Conclusions/Significance

This CTL epitope peptide selection system can be used as an effective tool for the development of a cross-protective human influenza vaccine. Furthermore this vaccine strategy can be applicable to the development of all intracellular pathogens vaccines to induce epitope-specific CTL that effectively eliminate infected cells.

Identifying Schistosoma japonicum excretory/secretory proteins and their interactions with host immune system

Schistosoma japonicum is a major infectious agent of schistosomiasis. It has been reported that large number of proteins excreted and secreted by S. japonicum during its life cycle are important for its infection and survival in definitive hosts. These proteins can be used as ideal candidates for vaccines or drug targets. In this work, we analyzed the protein sequences of S. japonicum and found that compared with other proteins in S. japonicum, excretory/secretory (ES) proteins are generally longer, more likely to be stable and enzyme, more likely to contain immune-related binding peptides and more likely to be involved in regulation and metabolism processes. Based on the sequence difference between ES and non-ES proteins, we trained a support vector machine (SVM) with much higher accuracy than existing approaches. Using this SVM, we identified 191 new ES proteins in S. japonicum, and further predicted 7 potential interactions between these ES proteins and human immune proteins. Our results are useful to understand the pathogenesis of schistosomiasis and can serve as a new resource for vaccine or drug targets discovery for anti-schistosome.

Visceral leishmaniasis: immunology and prospects for a vaccine

Human visceral leishmaniasis (HVL) is the most severe clinical form of a spectrum of neglected tropical diseases caused by protozoan parasites of the genus Leishmania. Caused mainly by L. donovani and L. infantum/chagasi, HVL accounts for more than 50 000 deaths every year. Drug therapy is available but costly, and resistance against several drug classes has evolved. Here, we review our current understanding of the immunology of HVL and approaches to and the status of vaccine development against this disease.

Demographic processes affect HIV-1 evolution in primary infection before the onset of selective processes

HIV-1 transmission and viral evolution in the first year of infection were studied in 11 individuals representing four transmitter-recipient pairs and three independent seroconverters. Nine of these individuals were enrolled during acute infection; all were men who have sex with men (MSM) infected with HIV-1 subtype B. A total of 475 nearly full-length HIV-1 genome sequences were generated, representing on average 10 genomes per specimen at 2 to 12 visits over the first year of infection. Single founding variants with nearly homogeneous viral populations were detected in eight of the nine individuals who were enrolled during acute HIV-1 infection. Restriction to a single founder variant was not due to a lack of diversity in the transmitter as homogeneous populations were found in recipients from transmitters with chronic infection. Mutational patterns indicative of rapid viral population growth dominated during the first 5 weeks of infection and included a slight contraction of viral genetic diversity over the first 20 to 40 days. Subsequently, selection dominated, most markedly in env and nef. Mutants were detected in the first week and became consensus as early as day 21 after the onset of symptoms of primary HIV infection. We found multiple indications of cytotoxic T lymphocyte (CTL) escape mutations while reversions appeared limited. Putative escape mutations were often rapidly replaced with mutually exclusive mutations nearby, indicating the existence of a maturational escape process, possibly in adaptation to viral fitness constraints or to immune responses against new variants. We showed that establishment of HIV-1 infection is likely due to a biological mechanism that restricts transmission rather than to early adaptive evolution during acute infection. Furthermore, the diversity of HIV strains coupled with complex and individual-specific patterns of CTL escape did not reveal shared sequence characteristics of acute infection that could be harnessed for vaccine design.

Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

In all vertebrate animals, CD8⁺ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.

Predicting MHC-II binding affinity using multiple instance regression

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir.

HLA class I allele promiscuity revisited

The peptide repertoire presented on human leukocyte antigen (HLA) class I molecules is largely determined by the structure of the peptide binding groove. It is expected that the molecules having similar grooves (i.e., belonging to the same supertype) might present similar/overlapping peptides. However, the extent of promiscuity among HLA class I ligands remains controversial: while in many studies T cell responses are detected against epitopes presented by alternative molecules across HLA class I supertypes and loci, peptide elution studies report minute overlaps between the peptide repertoires of even related HLA molecules. To get more insight into the promiscuous peptide binding by HLA molecules, we analyzed the HLA peptide binding data from the large epitope repository, Immune Epitope Database (IEDB), and further performed in silico analysis to estimate the promiscuity at the population level. Both analyses suggest that an unexpectedly large fraction of HLA ligands (>50%) bind two or more HLA molecules, often across supertype or even loci. These results suggest that different HLA class I molecules can nevertheless present largely overlapping peptide sets, and that “functional” HLA polymorphism on individual and population level is probably much lower than previously anticipated.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

Genetic impact of vaccination on breakthrough HIV-1 sequences from the STEP trial

We analyzed HIV-1 genome sequences from 68 newly-infected volunteers in the Step HIV-1 vaccine trial. To determine whether the vaccine exerted selective T-cell pressure on breakthrough viruses, we identified potential T-cell epitopes in the founder sequences and compared them to epitopes in the vaccine. We found greater distances for sequences from vaccine recipients than from placebo recipients (p-values ranging from < 0.0001 to 0.09). The most significant signature site distinguishing vaccine from placebo recipients was Gag-84, a site encompassed by several epitopes contained in the vaccine and restricted by HLA alleles common in the cohort. Moreover, the extended divergence was confined to the vaccine components of the virus (Gag, Pol, Nef) and not found in other HIV-1 proteins. These results represent the first evidence of selective pressure from vaccine-induced T-cell responses on HIV-1 infection.

Advances of bioinformatics tools applied in virus epitopes prediction

In recent years, the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein, a general overview of different tools currently available, including T cell and B cell epitopes prediction tools, is presented. And the principles of different prediction algorithms are reviewed briefly. Finally, several examples are present to illustrate the application of the prediction tools.

Predicting peptide binding to Major Histocompatibility Complex molecules

The Major Histocompatibility Complex (MHC) constitutes an important part of the human immune system. During infection, pathogenic proteins are processed into peptide fragments by the antigen processing machinery. These peptides bind to MHC molecules and the MHC-peptide complex is then transported to the cell membrane where it elicits an immune response via T-cell binding. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. One of the most challenging aspects of this area of research is understanding the specificity and sensitivity of the binding process. An empirical approach to the problem is unfeasible as there are over 512 billion potential binding peptides for each MHC molecule. Computational approaches offer the promise of predicting peptide binding, thus dramatically reducing the number of peptides proceeding to experimental verification. Various bioinformatic approaches have been developed to predict whether or not a particular peptide will bind to a particular MHC allele. Currently, peptide binding prediction methods can be categorised into three major groups: motif- and scoring matrix-based methods, artificial intelligence- (AI-) based methods, and structure-based methods. The first two are sequence-based approaches and are generally based on common sequence motifs in peptides known to bind to MHC molecules. The structure-based approach concerns the structural features and the distribution of energy between the binding peptide and the MHC molecule. Although knowledge of the molecular structure of the MHC molecules is expected to lead to better predictions of peptide binding, the development of structure-based methods has been relatively slow compared to sequence-based methods. Comparisons of various methods showed that the best sequence-based methods significantly outperform structure-based methods. This may be improved by producing more structures and binding data desperately needed by many alleles, especially class II molecules. On the other hand, the large number of verification methods and indicators used by structure-based studies hinders critical evaluation of the methods. Adopting commonly used assessment procedures can demonstrate the relative performance of structure-based methods in a straightforward comparison with other methods. This review provides an overview of current methods for predicting peptide binding to the MHC, with a focus on structure-based methods, and explores the potential for future development in this area.

Mapping the landscape of host-pathogen coevolution: HLA class I binding and its relationship with evolutionary conservation in human and viral proteins

The high diversity of HLA binding preferences has been driven by the sequence diversity of short segments of relevant pathogenic proteins presented by HLA molecules to the immune system. To identify possible commonalities in HLA binding preferences, we quantify these using a novel measure termed "targeting efficiency," which captures the correlation between HLA-peptide binding affinities and the conservation of the targeted proteomic regions. Analysis of targeting efficiencies for 95 HLA class I alleles over thousands of human proteins and 52 human viruses indicates that HLA molecules preferentially target conserved regions in these proteomes, although the arboviral Flaviviridae are a notable exception where nonconserved regions are preferentially targeted by most alleles. HLA-A alleles and several HLA-B alleles that have maintained close sequence identity with chimpanzee homologues target conserved human proteins and DNA viruses such as Herpesviridae and Adenoviridae most efficiently, while all HLA-B alleles studied efficiently target RNA viruses. These patterns of host and pathogen specialization are both consistent with coevolutionary selection and functionally relevant in specific cases; for example, preferential HLA targeting of conserved proteomic regions is associated with improved outcomes in HIV infection and with protection against dengue hemorrhagic fever. Efficiency analysis provides a novel perspective on the coevolutionary relationship between HLA class I molecular diversity, self-derived peptides that shape T-cell immunity through ontogeny, and the broad range of viruses that subsequently engage with the adaptive immune response.

CTLs' repertoire shaping in the thymus: a Monte Carlo simulation

MOTIVATION

The human immune system evolved a multi-layered control mechanism to eliminate self-reactive cells. Of these so-called tolerance induction mechanisms, lymphocytes T education in the thymus gland represents the very first one. This complicated process is not fully understood and quantitative models able to help in this endeavor are lacking. Here, we present a stochastic computational model of the thymus which combines data-driven prediction methods and a novel method based on protein-protein potential measurements for assessing molecular binding among cell receptors, major histocompatibility complex (MHC) molecules, and self-peptides.

RESULTS

Of all possible specificities of immature T cells entering the thymus, only a small fraction is actually selected for maturation. Monte Carlo simulations of thymocytes selection in the thymus are performed varying the size of the self and a parameter determining the number of encounter with antigen-presenting cells (APCs). We score the fraction of self-reacting thymocytes leaving the thymus as mature naive T cells and show that self-reactivity is only marginally dependent on the number of self-molecules presented by APCs, while it is strongly affected by a parameter proportional to the time spent in the thymus. We study how this measure changes when we vary the number of MHC alleles and found an optimal number not too different from what we have in reality. The main result of this study is more methodological than biological as we show that immunoinformatics data and methods can be used in systemic level simulation of immune processes.

Predicting interactions between T cell receptors and MHC-peptide complexes

Conserved interactions between T cell receptors (TCRs) and major histocompatibility complex (MHC) proteins with bound peptide antigens are not well understood. In order to gain a better understanding of the interaction modes of human TCR variable (V) regions, we have performed a structural analysis of the TCRs bound to their MHC-peptide ligands in human, using the available structural models determined by X-ray crystallography. We identified important differences to previous studies in which such interactions were evaluated. Based on the interactions found in the actual experimental structures we developed the first rule-based approach for predicting the ability of TCR residues in the complementarity-determining region (CDR) 1, CDR2, and CDR3 loops to interact with the MHC-peptide antigen complex. Two relatively simple algorithms show good performance under cross validation.

State of the art and challenges in sequence based T-cell epitope prediction

Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell epitopes. We will focus on some of the most accurate methods and their basic background.

Concept and application of a computational vaccinology workflow

BACKGROUND

The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.

RESULTS

We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.

CONCLUSION

Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.

An integrated approach to epitope analysis I: Dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches

Background

Operation of the immune system is multivariate. Reduction of the dimensionality is essential to facilitate understanding of this complex biological system. One multi-dimensional facet of the immune system is the binding of epitopes to the MHC-I and MHC-II molecules by diverse populations of individuals. Prediction of such epitope binding is critical and several immunoinformatic strategies utilizing amino acid substitution matrices have been designed to develop predictive algorithms. Contemporaneously, computational and statistical tools have evolved to handle multivariate and megavariate analysis, but these have not been systematically deployed in prediction of MHC binding. Partial least squares analysis, principal component analysis, and associated regression techniques have become the norm in handling complex datasets in many fields. Over two decades ago Wold and colleagues showed that principal components of amino acids could be used to predict peptide binding to cellular receptors. We have applied this observation to the analysis of MHC binding, and to derivation of predictive methods applicable on a whole proteome scale.

Results

We show that amino acid principal components and partial least squares approaches can be utilized to visualize the underlying physicochemical properties of the MHC binding domain by using commercially available software. We further show the application of amino acid principal components to develop both linear partial least squares and non-linear neural network regression prediction algorithms for MHC-I and MHC-II molecules. Several visualization options for the output aid in understanding the underlying physicochemical properties, enable confirmation of earlier work on the relative importance of certain peptide residues to MHC binding, and also provide new insights into differences among MHC molecules. We compared both the linear and non-linear MHC binding prediction tools to several predictive tools currently available on the Internet.

Conclusions

As opposed to the highly constrained user-interaction paradigms of web-server approaches, local computational approaches enable interactive analysis and visualization of complex multidimensional data using robust mathematical tools. Our work shows that prediction tools such as these can be constructed on the widely available JMP^® platform, can operate in a spreadsheet environment on a desktop computer, and are capable of handling proteome-scale analysis with high throughput.

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.

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.

HLA class I binding of HBZ determines outcome in HTLV-1 infection

CD8(+) T cells can exert both protective and harmful effects on the virus-infected host. However, there is no systematic method to identify the attributes of a protective CD8(+) T cell response. Here, we combine theory and experiment to identify and quantify the contribution of all HLA class I alleles to host protection against infection with a given pathogen. In 432 HTLV-1-infected individuals we show that individuals with HLA class I alleles that strongly bind the HTLV-1 protein HBZ had a lower proviral load and were more likely to be asymptomatic. We also show that in general, across all HTLV-1 proteins, CD8(+) T cell effectiveness is strongly determined by protein specificity and produce a ranked list of the proteins targeted by the most effective CD8(+) T cell response through to the least effective CD8(+) T cell response. We conclude that CD8(+) T cells play an important role in the control of HTLV-1 and that CD8(+) cells specific to HBZ, not the immunodominant protein Tax, are the most effective. We suggest that HBZ plays a central role in HTLV-1 persistence. This approach is applicable to all pathogens, even where data are sparse, to identify simultaneously the HLA Class I alleles and the epitopes responsible for a protective CD8(+) T cell response.

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.

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system

We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.

MHC class II epitope predictive algorithms

SUMMARY

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason for this is that the MHC-II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC-II-bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data-driven methods. Thousands of different MHC-II alleles exist in humans. Recently developed pan-specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.

Predicting MHC class I epitopes in large datasets

Background

Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening.

Results

We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC₅₀ cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC₅₀ less than 10 nM) and clear non-binders (IC₅₀ greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders.

Conclusions

All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders.

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

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

Discovery of naturally processed and HLA-presented class I peptides from vaccinia virus infection using mass spectrometry for vaccine development

An important approach for developing a safer smallpox vaccine is to identify naturally processed immunogenic vaccinia-derived peptides rather than live whole vaccinia virus. We used two-dimensional liquid chromatography coupled to mass spectrometry to identify 116 vaccinia peptides, encoded by 61 open reading frames, from a B-cell line (homozygous for HLA class I A*0201, B*1501, and C*03) after infection with vaccinia virus (Dryvax). Importantly, 68 of these peptides are conserved in variola, providing insight into the peptides that induce protection against smallpox. Twenty-one of these 68 conserved peptides were 11 amino acids long or longer, outside of the range of most predictive algorithms. Thus, direct identification of naturally processed and presented HLA peptides gives important information not provided by current computational methods for identifying potential vaccinia epitopes.

Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior

BACKGROUND

Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure of similarity to improve peptide:MHC binding prediction methods. This should help compensate for holes and bias in the sequence space coverage of existing peptide binding datasets.

RESULTS

Here, a novel amino acid similarity matrix (PMBEC) is directly derived from the binding affinity data of combinatorial peptide mixtures. Like BLOSUM62, this matrix captures well-known physicochemical properties of amino acid residues. However, PMBEC differs markedly from existing matrices in cases where residue substitution involves a reversal of electrostatic charge. To demonstrate its usefulness, we have developed a new peptide:MHC class I binding prediction method, using the matrix as a Bayesian prior. We show that the new method can compensate for missing information on specific residues in the training data. We also carried out a large-scale benchmark, and its results indicate that prediction performance of the new method is comparable to that of the best neural network based approaches for peptide:MHC class I binding.

CONCLUSION

A novel amino acid similarity matrix has been derived for peptide:MHC binding interactions. One prominent feature of the matrix is that it disfavors substitution of residues with opposite charges. Given that the matrix was derived from experimentally determined peptide:MHC binding affinity measurements, this feature is likely shared by all peptide:protein interactions. In addition, we have demonstrated the usefulness of the matrix as a Bayesian prior in an improved scoring-matrix based peptide:MHC class I prediction method. A software implementation of the method is available at: http://www.mhc-pathway.net/smmpmbec.

Towards in silico design of epitope-based vaccines

BACKGROUND

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

METHODS

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

CONCLUSION

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

FRED--a framework for T-cell epitope detection

Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.

AVAILABILITY

FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.