Distribution of tripeptides in MHC binding peptides

Major Histocompatibility Complex (MHC) molecules are cell surface glycoproteins that are central to the process of immunity. MHC Class I and II molecules differ in their peptide binding specificity. In this study we have analyzed a non redundant set of MHC binding peptides derived from MHCPEP database, in terms of tripeptides and their positional preference. Results indicate that certain tripeptides have a preference to appear at a particular position for a specific allele. Further, the distribution of rigid tripeptides across all binding sequences was also analyzed and their positions were correlated with anchor residue positions.     

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.     

Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.     

Structure-based design and evaluation of MHC class II binding peptides.

Structural information regarding binding of peptides to the major histocompatibility complex (MHC) class II molecule is of great use for the design of compounds that intervene in the interaction between the MHC-peptide-T-cell receptor (TCR) complex. These compounds can be applied in the treatment of T-cell-mediated auto-immune disease for specific modulation of the disease process. In case no crystal structure of the MHC molecule is available, homology models of the MHC molecule can be of importance. Here we describe the construction of a homology model of the MHC class II molecule and binding of the peptide, that are involved in experimental auto-immune encephalomyelitis, a rat model for human multiple sclerosis. The validity of the model was investigated using experimental data of peptides binding to this MHC molecule.     

Prediction of MHC class I binding peptides, using SVMHC

Background  

T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested.     

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.     

Prediction of MHC class I binding peptides with a new feature encoding technique

The recognition of specific peptides, bound to major histocompatibility complex (MHC) class I molecules, is of particular importance to the robust identification of T-cell epitopes and thus the successful design of protein-based vaccines. Here, we present a new feature amino acid encoding technique termed OEDICHO to predict MHC class I/peptide complexes. In the proposed method, we have combined orthonormal encoding (OE) and the binary representation of selected 10 best physicochemical properties of amino acids derived from Amino Acid Index Database (AAindex). We also have compared our method to current feature encoding techniques. The tests have been carried out on comparatively large Human Leukocyte Antigen (HLA)-A and HLA-B allele peptide binding datasets. Empirical results show that our amino acid encoding scheme leads to better classification performance on a standalone classifier.     

Transporter associated with antigen processing preselection of peptides binding to the MHC: a bioinformatic evaluation

TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.     

A probabilistic meta-predictor for the MHC class II binding peptides

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.     

Kinetics of registry selection of chimeric peptides binding to MHC II

Major histocompatability complex type II proteins (MHC II) are alphabeta-heterodimeric glycoproteins that present peptides to the T cell receptor (TCR) of CD4(+) T-cells. This presentation may result in activation of these T-cells, depending on the nature of the peptide. Peptides interact specifically with MHC II with nine peptide amino acid positions, and the corresponding MHC II pocket positions are usually labeled P1-P9. However, the length of peptides binding to MHC II may be greater than nine amino acids, and therefore these peptides may potentially bind to the MHC II in more than one registry. To investigate the mechanism by which a long peptide binds to I-E(k), a murine MHC II, a chimeric peptide with two nonoverlapping registries, f-IAYLKQATKQLRMATPLLMR was designed. The IAYLKQATK peptide segment is based on moth cytochrome c 95-103 (MCC 95-103), and the QLRMATPLLMR segment is based on murine Ii CLIP 89-99 M90L (Ii CLIP 89-99 M90L). This chimeric peptide forms two isomeric complexes. The MCC and Ii CLIP registries dissociate from I-E(k) with t(1/2) values of >800 and 4.94 h, respectively. The registry composition of this MHC II/chimeric peptide complex was found to change as a function of time in approaching thermodynamic equilibrium: the results are consistent with a kinetic model that involves no intramolecular isomer interconversion. The model depicts uncorrelated binding to the MHC II determined by relative association rates to the two registries. This is followed by dissociation and subsequent rebinding, leading ultimately to a preponderance of the most stable complex. Similar results were obtained at pH 5.3. The behavior of this chimeric peptide approximates the binding of a 1:1 solution mixture of two peptides to MHC II, where the more stable complex is selected over time. We have also found that a chimeric peptide and a human MHC II, HLA-DR40401, form isomers with relative association rates to DR0401 at pH 5.3 of 15% for one isomer and 85% for the second isomer.     

MHC class II binding of peptides derived from HLA-DR 1.

The aim of these studies was to determine whether auto- and alloreactivity can arise from T cell recognition of MHC-peptides in context of syngeneic MHC. Four synthetic peptides derived from the first domain of the HLA-DR beta 1 * 0101 chain were used in limiting dilution analysis to prime T cells from HLA-DR1- and HLA-DR1+ responders. The frequency of T cells responding to these four peptides was similar in individuals with or without HLA-DR1. In both cases, the peptide corresponding to the nonpolymorphic sequence 43-62, was less immunogenic than peptides corresponding to the three hypervariable regions 1-20, 21-42, and 66-90, eliciting a lower number of reactive T cells. Experiments using a T cell line with specific reactivity to peptide 21-42 showed, however, that this response can be efficiently blocked by adding to the culture a nonpolymorphic sequence peptide. This suggests that alloreactivity can be blocked by use of monomorphic (self) peptides. The binding of both "monomorphic" and "polymorphic" synthetic DR1 peptides to affinity purified HLA-DR 1 and DR 11 molecules was measured using radiolabeled peptides and high performance size exclusion chromatography. The data showed that the polymorphic as well as monomorphic synthetic DR1 peptides bound to both DR1 and DR11 molecules. Competitive inhibition studies indicated that the monomorphic 43-62 peptide can block the binding of the polymorphic peptides, consistent with the results obtained in T cell cultures. Taken together these data suggest that anti-MHC autoreactive T cells are present in the periphery and that both auto and alloreactivity can be elicited by MHC peptides binding to MHC class II molecules.     

Prediction of MHC class I binding peptides using probability distribution functions

Binding of peptides to specific Major Histo-compatibility Complex (MHC) molecule is important for understanding immunity and has applications to vaccine discovery and design of immunotherapy. Artificial neural networks (ANN) are widely used by predictions tools to classify the peptides as binders or non­binders (BNB). However, the number of known binders to a specific MHC molecule is limited in many cases, which poses a computational challenge for prediction of BNB and hence, needs improvement in learning of ANN. Here, we describe, the application of probability distribution functions to initialize the weights and biases of the artificial neural network in order to predict HLA­A*0201 binders and non­binders. The 10­fold cross validation has been used to validate the results. It is evident from the results that the A_ROC for 90% of test cases for Weibull, Uniform and Rayleigh distributions is in the range 0.90-1.0. Further, the standard deviation for AROC was minimum for Weibull distribution, and may be used to train the artificial neural network for HLA­A*0201 MHC Class­I binders and non­binders prediction.     

MHCPEP: a database of MHC-binding peptides.

MHCPEP is a curated database comprising over 4000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the source protein (when known), an estimate of binding affinity and critical anchor residues (if identified), and is fully referenced. The present format of the database allows test string matching searches. The database can be accessed via Internet using gopher.     

MPID: MHC-Peptide Interaction Database for sequence-structure-function information on peptides binding to MHC molecules

SUMMARY: Binding of short antigenic peptides to Major histocompatibility complex (MHC) proteins is the first step in T-cell mediated immune response. To understand the structural principles governing MHC-specific peptide recognition and binding, we have developed the MHC-Peptide Interaction Database (MPID), containing sequence-structure-function information. MPID (version 1.2) contains curated x-ray crystallographic data on 86 MHC peptide complexes, with precomputed interaction parameters (solvent accessibility, hydrogen bonds, gap volume and gap index). A user-friendly web interface and query tools will facilitate the development of predictive algorithms for MHC-peptide binding from a structural viewpoint. AVAILABILITY: Freely accessible from http://surya.bic.nus.edu.sg/mpid.     

Planar molecular arrangements aid the design of MHC class II binding peptides

The coupling between peptides and MHC-II proteins in the human immune system is not well understood. This work presents an evidence-based hypothesis of a guiding intermolecular force present in every human MHC-II protein (HLA-II). Previously, we examined the spatial positions of the fully conserved residues in all HLA-II protein types. In each one, constant planar patterns were revealed. These molecular planes comprise of amino acid groups of the same chemical species (for example, Gly) distributed across the protein structure. Each amino acid plane has a unique direction and this directional element offers spatial selectivity. Constant within all planes, too, is the presence of an aromatic residue possessing electrons in movement, leading the authors to consider that the planes generate electromagnetic fields that could serve as an attractive force in a single direction. Selection and attraction between HLA-II molecules and antigen peptides would, therefore, be non-random, resulting in a coupling mechanism as effective and rapid as is clearly required in the immune response. On the basis of planar projections onto the HLA-II groove, modifications were made by substituting the key residues in the class II-associated invariant chain peptide—a peptide with a universal binding affinity—resulting in eight different modified peptides with affinities greater than that of the unmodified peptide. Accurate and reliable prediction of MHC class II-binding peptides may facilitate the design of universal vaccine-peptides with greatly enhanced binding affinities. The proposed mechanisms of selection, attraction and coupling between HLA-II and antigen peptides are explained further in the paper.     

MHC binding peptides for designing of vaccines against Japanese encephalitis virus: A computational approach

Japanese encephalitis (JE), a viral disease has seen a drastic and fatal enlargement in the northern states of India in the current decade. The better and exact cure for the disease is still in waiting. For the cause an in silico strategy in the development of the peptide vaccine has been taken here for the study. A computational approach to find out the Major Histocompatibility Complex (MHC) binding peptide has been implemented. The prediction analysis identified MHC class I (using propred I) and MHC class II (using propred) binding peptides at an expectable percent predicted IC (50) threshold values. These predicted Human leukocyte antigen [HLA] allele binding peptides were further analyzed for potential conserved region using an Immune Epitope Database and Analysis Resource (IEDB). This analysis shows that HLA-DRB1*0101, HLA-DRB3*0101, HLA-DRB1*0401, HLA-DRB1*0102 and HLA-DRB1*07:01% of class II (in genotype 2) and HLA-A*0101, HLA-A*02, HLA-A*0301, HLA-A*2402, HLA-B*0702 and HLA-B*4402% of HLA I (in genotype 3) bound peptides are conserved. The predicted peptides MHC class I are ILDSNGDIIGLY, FVMDEAHFTDPA, KTRKILPQIIK, RLMSPNRVPNYNLF, APTRVVAAEMAEAL, YENVFHTLW and MHC class II molecule are TTGVYRIMARGILGT, NYNLFVMDEAHFTDP, AAAIFMTATPPGTTD, GDTTTGVYRIMARGI and FGEVGAVSL found to be top ranking with potential super antigenic property by binding to all HLA. Out of these the predicted peptide FVMDEAHFTDPA for allele HLA-A*02:01 in MHC class I and NYNLFVMDEAHFTDP for allele HLA-DRB3*01:01 in MHC class II was observed to be most potent and can be further proposed as a significant vaccine in the process. The reported results revealed that the immune-informatics techniques implemented in the development of small size peptide is useful in the development of vaccines against the Japanese encephalitis virus (JEV).     

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