Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction

MOTIVATION: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. RESULTS: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 peptides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r(pred) = 0.593 and r(pred) = 0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. AVAILABILITY: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHCPred.     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: The identification of T-cell epitopes can be crucial for vaccine development. An epitope is a peptide segment that binds to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. Predicting which peptide segments bind MHC molecules is the first step in epitope prediction. RESULTS: An iterative stepwise discriminant analysis meta-algorithm explores a large molecular database to derive quantitative motifs for peptide binding. The applications presented here demonstrate the algorithm's versatility by producing four closely related models for HLA-DR1. Two models use an expert initial estimate and two do not; two models use amino acid residues as the only predictors and two use amino acid groupings as additional predictors. Each model correctly classifies >90% of the peptides in the database. AVAILABILITY: Software is available commercially; data are free over the Internet.     

Structural requirements and biological significance of interactions between peptides and the major histocompatibility complex

Previous studies indicate that T cells recognize a complex between the major histocompatibility complex (MHC) restriction-element and peptide-antigen fragments. Two aspects of this complex formation are considered in this paper: (1) what is the nature of the specificity of the interactions that allows a few MHC molecules to serve as restriction elements for a large universe of antigens; and (2) what is the relative contribution of determinant selection (i.e. antigen-MHC complex formation) and T-cell repertoire in determining the capacity of an individual to respond to an antigen? By analysing single amino acid substitution analogues of a peptide antigen (Ova 325-335) as well as by analysing the structural similarities between unrelated peptides capable of binding to the same MHC molecule, we have been able to document the very permissive nature of the antigen-MHC interaction. Despite this permissiveness of binding, it is possible to define certain structural features of peptides that are associated with the capacity to bind to a particular MHC specificity. With respect to the question of the relative role of 'determinant selection' and 'holes in the T-cell repertoire' in determining immune responsiveness, we present data that suggest both mechanisms operate in concert with one another. Thus only about 30% of a collection of peptides that in sum represent the sequence of a protein molecule were found to bind to Ia. Although immunogenicity was restricted to those peptides that were capable of binding to Ia (i.e. determinant selection was operative), we found that about 40% of Ia-binding peptides were not immunogenic (i.e. there were also 'holes in the T-cell repertoire').     

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. AVAILABILITY: The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1     

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.     

Quantitative approaches to computational vaccinology

This article reviews the newly released JenPep database and two new powerful techniques for T-cell epitope prediction: (i) the additive method; and (ii) a 3D-Quantitative Structure Activity Relationships (3D-QSAR) method, based on Comparative Molecular Similarity Indices Analysis (CoMSIA). The JenPep database is a family of relational databases supporting the growing need of immunoinformaticians for quantitative data on peptide binding to major histocompatibility complexes and to the Transporters associated with Antigen Processing (TAP). It also contains an annotated list of T-cell epitopes. The database is available free via the Internet (http://www.jenner.ac.uk/JenPep). The additive prediction method is based on the assumption that the binding affinity of a peptide depends on the contributions from each amino acid as well as on the interactions between the adjacent and every second side-chain. In the 3D-QSAR approach, the influence of five physicochemical properties (steric bulk, electrostatic potential, local hydrophobicity, hydrogen-bond donor and hydrogen-bond acceptor abilities) on the affinity of peptides binding to MHC molecules were considered. Both methods were exemplified through their application to the well-studied problem of peptides binding to the human class I MHC molecule HLA-A*0201.     

Rapid identification of antigenic T-cell epitopes by extracellular acidification rate signals

We used a silicon-based biosensor, a microphysiometer, to measure real-time extracellular acidification rate signals associated with T lymphocyte responses to peptide ligands interacting with the T-cell receptor (TCR). We compared these effector responses with those of interferon-gamma (IFN-gamma) production, and T-cell proliferation. Within minutes, major histocompatibility complex (MHC)-bound peptides on antigen-presenting cells (APCs) engaged the TCR to increase acidification rates of the extracellular media was measured by microphysiometer. We exposed two myelin peptide-specific human T-cell clones, MSF132E11 (DRB1*1501 restricted) and TOM3A6 (DRB5*0101 restricted), to truncated analogues of the parent MBP 84-102 peptide, in the presence of MHC restricted human antigen-presenting cells, and measured the extracellular acidification rate signal changes, IFN-gamma production and T-cell proliferation. The core epitopes recognized by these clones were identified by microphysiometer and found to be MBP 88-100 and MBP 91-100, respectively. These epitopes were identical to those identified by the IFN-gamma and proliferation assays. We conclude that measurement of real-time extracellular acidification rate signals by the microphysiometer may facilitate rapid identification of human T-cell epitopes involved in immune disorders and the development of specific T-cell antagonists.     

An empirical method for the prediction of T-cell epitopes

Identification of T-cell epitopes from foreign proteins is the current focus of much research. Methods using simple two or three position motifs have proved useful in epitope prediction for major histocompatibility complex (MHC) class I, but to date not for MHC class II molecules. We utilized data from pool sequence analysis of peptides eluted from two HLA-DR13 alleles to construct a computer algorithm for predicting the probability that a given sequence will be naturally processed and presented on these alleles. We assessed the ability of this method to predict know self-peptides from these DR-13 alleles, DRB1 ^*1301 and *1302, as well as an immunodominant T-cell epitope. We also compared the predictions of this scoring procedure with the measured binding affinities of a panel of overlapping peptides from hepatitis B virus surface antigen. We concluded that this method may have wide application for the prediction of T-cell epitopes for both MHC class I and class II molecules.     

Prediction of MHC class II binding affinity using SMM-align,a novel stabilization matrix alignment method

Background  

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles.     

Methods and protocols for prediction of immunogenic epitopes

T-cell recognition of peptide/major histocompatibility complex (MHC) is a prerequisite for cellular immunity. Recently, there has been an influx of bioinformatics tools to facilitate the identification of T-cell epitopes to specific MHC alleles. This article examines existing computational strategies for the study of peptide/MHC interactions. The most important bioinformatics tools and methods with relevance to the study of peptide/MHC interactions have been reviewed. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.     

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.     

Flow cytometric analysis of peptide binding to major histocampatibility complex class I for hepatitis C virus core T-cell epitopes

BACKGROUND/METHODS: To characterize the repertoire of T-cell epitopes on the hepatitis C virus (HCV) core protein, we studied major histocompatibility complex (MHC) class I binding of 75 decapeptides on 20 human B-cell lines and murine spleen cells using a flow cytometric assay. The results were compared with MHC class I stabilization on T2 cells, the SYFPEITHI algorithm, and known T-cell epitopes from the literature. RESULTS: Binding of peptides proved to be specific for MHC class I molecules. We observed peak fluorescence signals at positions amino acids (aa) 35-44, aa 87-96, aa 131-140, and aa 167-176 in virtually all HLA-A2-positive cell lines. These sites corresponded to T-cell epitopes predicted by SYFPEITHI and the positions of known T-cell epitopes, whereas T2 stabilization was at variance for two peptides. The assay was applied to HLA-A2-negative cells and murine spleen cells without further modification, and identified additional peptides, corresponding to known T-cell epitopes. CONCLUSIONS: Peptide binding to different MHC class I alleles can be mapped rapidly by a flow cytometric assay and enables a first orientation on the sites of possible T-cell epitopes. Application of this assay to HCV core suggests a rather limited repertoire of epitopes in the Caucasoid population.     

Receptor proximity, not intermolecular orientation, is critical for triggering T-cell activation

Engagement of antigen receptors on the surface of T-cells with peptides bound to major histocompatibility complex (MHC) proteins triggers T-cell activation in a mechanism involving receptor oligomerization. Receptor dimerization by soluble MHC oligomers is sufficient to induce several characteristic activation processes in T-cells including internalization of engaged receptors and up-regulation of cell surface proteins. In this work, the influence of intermolecular orientation within the activating receptor dimer was studied. Dimers of class II MHC proteins coupled in a variety of orientations and topologies each were able to activate CD4+ T-cells, indicating that triggering was not dependent on a particular receptor orientation. In contrast to the minimal influence of receptor orientation, T-cell triggering was affected by the inter-molecular distance between MHC molecules, and MHC dimers coupled through shorter cross-linkers were consistently more potent than those coupled through longer cross-linkers. These results are consistent with a mechanism in which intermolecular receptor proximity, but not intermolecular orientation, is the key determinant for antigen-induced CD4+ T-cell activation.     

Molecular analysis of TCR and peptide/MHC interaction using P18-I10-derived peptides with a single D-amino acid substitution

For the structural analysis of T-cell receptor (TCR) and peptide/MHC interaction, a series of peptides with a single amino acid substitution by a corresponding D-amino acid, having the same weight, size, and charge, within P18-I10 (aa318-327: RGPGRAFVTI), an immunodominant epitope of HIV-1 IIIB envelope glycoprotein, restricted by the H-2Dd class I MHC molecule, has been synthesized. Using those peptides, we have observed that the replacement at positions 324F, 325V, 326T, and 327I with each corresponding D-amino acid induced marked reduction of the potency to sensitize targets for P18-I10-specific murine CD8+ cytotoxic T lymphocytes (CTLs), LINE-IIIB, recognition. To analyze further the role of amino acid at position 325, the most critical site for determining epitope specificity, we have developed a CTL line [LINE-IIIB(325D)] and its offspring clones specific for the epitope I-10(325v) having a D-valine (v) at position 325. Taking advantage of two distinct sets of CD8+ CTLs restricted by the same Dd, three-dimensional structural analysis on TCR and peptide/MHC complexes by molecular modeling was performed, which indicates that the critical amino acids within the TCRs for interacting with 325V or 325v appear to belong to the complementarity-determining region 1 but not to the complementarity-determining region 3 of Vbeta chain.     

Crystal structures of HLA-A*0201 complexed with antigenic peptides with either the amino- or carboxyl-terminal group substituted by a methyl group

The crystal structures of class I major histocompatibility complex (MHC) molecules complexed with antigenic peptides revealed a network of hydrogen bonds between the charged amino- and carboxyl-termini of the peptides and conserved MHC residues at both ends of the peptide binding site. These interactions were shown to contribute substantially to the stability of class I MHC/peptide complexes by thermal denaturation studies using synthetic peptides in which either the amino- or carboxyl-terminal group is substituted by a methyl group. Here we report crystal structures of HLA-A*0201 complexed with these terminally modified synthetic peptides showing that they adopt the same bound conformation as antigenic peptides. A number of variations in peptide conformation were observed for the terminally modified peptides, including in one case, a large conformational difference in four central peptide residues that is apparently caused by the lattice contact. This is reminiscent of the way binding a T-cell receptor changed the conformation of central residues of an MHC-bound peptide. The structures determined identify which conserved hydrogen bonds are eliminated in terminally substituted peptides and suggest an increased energetic importance of the interactions at the peptide termini for MHC-peptide stability. Proteins 33:97–106, 1998. © 1998 Wiley-Liss, Inc.     

POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties

MOTIVATION: Both modeling of antigen-processing pathway including major histocompatibility complex (MHC) binding and immunogenicity prediction of those MHC-binding peptides are essential to develop a computer-aided system of peptide-based vaccine design that is one goal of immunoinformatics. Numerous studies have dealt with modeling the immunogenic pathway but not the intractable problem of immunogenicity prediction due to complex effects of many intrinsic and extrinsic factors. Moderate affinity of the MHC-peptide complex is essential to induce immune responses, but the relationship between the affinity and peptide immunogenicity is too weak to use for predicting immunogenicity. This study focuses on mining informative physicochemical properties from known experimental immunogenicity data to understand immune responses and predict immunogenicity of MHC-binding peptides accurately. RESULTS: This study proposes a computational method to mine a feature set of informative physicochemical properties from MHC class I binding peptides to design a support vector machine (SVM) based system (named POPI) for the prediction of peptide immunogenicity. High performance of POPI arises mainly from an inheritable bi-objective genetic algorithm, which aims to automatically determine the best number m out of 531 physicochemical properties, identify these m properties and tune SVM parameters simultaneously. The dataset consisting of 428 human MHC class I binding peptides belonging to four classes of immunogenicity was established from MHCPEP, a database of MHC-binding peptides (Brusic et al., 1998). POPI, utilizing the m = 23 selected properties, performs well with the accuracy of 64.72% using leave-one-out cross-validation, compared with two sequence alignment-based prediction methods ALIGN (54.91%) and PSI-BLAST (53.23%). POPI is the first computational system for prediction of peptide immunogenicity based on physicochemical properties. AVAILABILITY: A web server for prediction of peptide immunogenicity (POPI) and the used dataset of MHC class I binding peptides (PEPMHCI) are available at http://iclab.life.nctu.edu.tw/POPI     

Alloreactivity of an OVA-specific T-cell clone

A T-cell clone (Lyl-03) derived from BALB/cBy mice, though highly specific for OVA/A^d, reacted to allogeneic spleen cells of 6 of 12 H-2 haplotypes tested. The reactivity to each particular H-2 haplotype required the expression of a non-major histocompatibility complex (MHC) gene product present on the B cells of certain strains of mice. All the alloreactive responses were MHC restricted and were inhibited by class II-specific and L3T4-specific monoclonal antibodies. The non-MHC gene product, X, is a new lymphocyte-stimulating determinant that is not expressed in mice with the xid defect. We favor a model that proposes two independent sites (or receptors) for X and the class II molecule. Contrary to previous models for alloreactivity, the anti-MHC site is not directed to a polymorphic receptor for self-class II epitope on the foreign class II molecule, but rather to a conserved determinant present on both self- and allo-class II molecules. If there is only one antigen receptor on the T-cell clone Lyl-03, then anti-X receptor must bind to a cross-reactive determinant found on immunogenic OVA and the non-MHC coded gene product expressed on the cell surface membrane. We further postulate that class II plus X recognition may be a general rule for alloreactive as well as autoreactive responses. Thus, both allo-class II and allo-class I reactive T cells are similar in that both bind a non-MHC coded gene product prior to activation.Abbreviations used in this paper: APC antigen-presenting cell(s) - Con A concanavalin A - Cl. clone - DME Dulbecco's modified Eagle's medium - FCS fetal calf serum - H-2 histocompatibility-2 - MHC major histocompatibility complex - MLR mixed lymphocyte response - Mls mixed lymphocyte stimulating - OVA chicken ovalbumin - X unknown cell-surface antigen - xid immunodeficiency mapped to the X chromosome     

Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.     

TCR internalization induced by peptide/MHC ligands requires the transmembrane domains of alphabeta chains of TCR, but not the expression of CD8 and Thy-1 molecules

T-cell receptor (TCR) internalization occurs via TCR recognition of the peptide/MHC molecule complex on antigen presenting cell (APC). In this study, the requirements for inducing the internalization of TCR molecules on Ld major histocompatibility complex (MHC) class I-restricted T-cells were investigated with 2C cytotoxic T-lymphocyte (CTL) clones with defined peptides as the antigen. To evaluate the function of the transmembrane region of TCR alphabeta chains in TCR internalization, we generated T-cell transfectants expressing the wild type and glycosylphosphatidyl inositol (GPI)-linked form of 2C TCR. Among all peptides forming proper ligands to 2C TCR, only the Qp2Ca peptide induced TCR internalization, which was known to have the highest affinity to both Ld MHC class I molecules and TCR in association with Ld molecules. Such TCR internalization was not observed in cells expressing the GPI-linked form of 2C TCR. Furthermore, the expression of CD8 coreceptor and Thy-1 accessory molecules were both not required for Qp2Ca-induced TCR internalization, and these molecules did not accompany TCR internalization. Altogether, these results suggest that TCR internalization on CTL is not a prerequisite for CTL function.