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 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.     

Effect of class I MHC binding peptides on recognition by natural killer cells.

NK cells can recognize and destroy a broad range of cells, including many tumor cells and virally infected cells, yet spare most normal cells. Identification of the target structure recognized by these cells has proved elusive. An attractive hypothesis is that, unlike B cells and T cells that recognize a specific foreign marker, NK cells respond to the absence of a "self" marker. Class I MHC molecules have been implicated as the self markers whose absence can trigger lysis. We show here that normal cells are lysed on incubation with IL-2-activated NK cells if peptides that can bind to the class I MHC molecules of the normal cells are also included in the assay, and speculate that this binding is somehow removing a self marker that normally protects a cell from lysis. NK cells were derived from splenocytes of young (5 to 8 wk old) athymic nude BALB/c (H-2d) or nude C57Bl/6 (H-2b) mice incubated with 1000 U/ml rIL-2, and target cells were derived from splenocytes of normal BALB/c or C57Bl/6 mice incubated with Con A. Peptides were from xenogeneic, viral, self, and mutated self protein sequences and included sequences specific for Kd, Kb, Db, and Ld. All peptides increased lysability of those targets to which they could bind.     

Presentation of antigenic peptides by MHC class I molecules

The basis for the immune response against intracellular pathogens is the recognition by cytotoxic T lymphocytes of antigenic peptides derived from cytosolic proteins, which are presented on the cell surface by major histocompatibility complex (MHC) class I molecules. The understanding of MHC class I-restricted peptide presentation has recently improved dramatically with the elucidation of the structural basis for the specificity of peptide binding to MHC class I molecules and the identification of proteins encoded in the class II region of the MHC that are putatively involved in the production of peptides and their transport into the endoplasmic reticulum, where they assemble with class I molecules.     

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.     

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.     

Disulfide bond engineering to trap peptides in the MHC class I binding groove

Immunodominant peptides in CD8 T cell responses to pathogens and tumors are not always tight binders to MHC class I molecules. Furthermore, antigenic peptides that bind weakly to the MHC can be problematic when designing vaccines to elicit CD8 T cells in vivo or for the production of MHC multimers for enumerating pathogen-specific T cells in vitro. Thus, to enhance peptide binding to MHC class I, we have engineered a disulfide bond to trap antigenic peptides into the binding groove of murine MHC class I molecules expressed as single-chain trimers or SCTs. These SCTs with disulfide traps, termed dtSCTs, oxidized properly in the endoplasmic reticulum, transited to the cell surface, and were recognized by T cells. Introducing a disulfide trap created remarkably tenacious MHC/peptide complexes because the peptide moiety of the dtSCT was not displaced by high-affinity competitor peptides, even when relatively weak binding peptides were incorporated into the dtSCT. This technology promises to be useful for DNA vaccination to elicit CD8 T cells, in vivo study of CD8 T cell development, and construction of multivalent MHC/peptide reagents for the enumeration and tracking of T cells-particularly when the antigenic peptide has relatively weak affinity for the MHC.     

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.     

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.     

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.     

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.     

Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors

We are able to make reliable predictions of the efficiency with which peptides of arbitrary lengths will be transported by TAP. The pressure exerted by TAP on Ag presentation thus can be assessed by checking to what extent MHC class I (MHC-I)-presented epitopes can be discriminated from random peptides on the basis of predicted TAP transport efficiencies alone. Best discriminations were obtained when N-terminally prolonged epitope precursor peptides were included and the contribution of the N-terminal residues to the score were down-weighted in comparison with the contribution of the C terminus. We provide evidence that two factors may account for this N-terminal down-weighting: 1) the uncertainty as to which precursors are used in vivo and 2) the coevolution in the C-terminal sequence specificities of TAP and other agents in the pathway, which may vary among the various MHC-I alleles. Combining predictions of MHC-I binding affinities with predictions of TAP transport efficiency led to an improved identification of epitopes, which was not the case when predictions of MHC-I binding affinities were combined with predictions of C-terminal cleavages made by the proteasome.     

Building proteomic tool boxes to monitor MHC class I and class II peptides

Major histocompatibility complex Class I (MHCI) and Class II (MHCII) presented peptides powerfully modulate T cell immunity and play a vital role in generating effective anti‐tumor and anti‐viral immune responses in mammals. Characterizing these MHCI or MHCII presented peptides can help generate therapeutic treatments, afford information on T cell mediated biomarkers, provide insight into disease progression, and reduce adverse anti‐drug side effects from engineered biotherapeutics. Here, we explore the tools and techniques commonly employed to discover both MHCI‐ and MHCII‐presented peptides. We describe complementary strategies that enhance the characterization of these peptides and the informatics tools employed for both predicting and characterizing MHCI‐ and MHCII‐presented epitopes. The evolution of methodologies for isolating MHC‐presented peptides is discussed, as are the mass spectrometric workflows that can be employed for their characterization. We provide a perspective on where this field is headed, and how these tools may be applicable to the discovery and monitoring of epitopes in a variety of scenarios.     

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.     

Soluble class I MHC with beta2-microglobulin covalently linked peptides: specific binding to a T cell hybridoma

Soluble forms of the mouse MHC class I molecule, Dd, were produced in which the peptide binding groove was uniformly occupied by peptides attached via a covalent flexible peptide linker to the N terminus of the associated beta2-microglobulin. The MHC heavy chain and beta2-microglobulin were firmly associated, and the molecules displayed an Ab epitope requiring proper occupancy of the peptide binding groove. Soluble Dd containing a covalent version of a well-characterized Dd-binding peptide from HIV stimulated a T cell hybridoma specific for this combination. Furthermore, a tetravalent version of this molecule bound specifically with apparent high avidity to this hybridoma.     

Exploration of peptides bound to MHC class I molecules in melanoma

Advancements in high‐resolution HPLC and mass spectrometry have reinvigorated the application of this technology to identify peptides eluted from immunopurified MHC class I molecules. Three melanoma cell lines were assessed using w6/32 isolation, peptide elution and HPLC purification; peptides were identified by mass spectrometry. A total of 13 829 peptides were identified; 83–87% of these were 8–11 mers. Only approximately 15% have been described before. Subcellular locations of the source proteins showed even sampling; mRNA expression and total protein length were predictive of the number of peptides detected from a single protein. HLA‐type binding prediction for 10 078 9/10 mer peptides assigned 88–95% to a patient‐specific HLA subtype, revealing a disparity in strength of predicted binding. HLA‐B*27‐specific isolation successfully identified some peptides not found using w6/32. Sixty peptides were selected for immune screening, based on source protein and predicted HLA binding; no new peptides recognized by antimelanoma T cells were discovered. Additionally, mass spectrometry was unable to identify several epitopes targeted ex vivo by one patient's T cells.     

Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: Implications for vaccine design

T cell recognition of the peptide–MHC complex initiates a cascade of immunological events necessary for immune responses. Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms based on sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex (MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aim of obtaining a better differentiation between binding and nonbinding sequence. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. We used the Miyazawa & Jernigan and Betancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria (Nearest atom ≫ 4.0 Å & C-beta ≫ 7.0 Å) for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highly ranked. The predictions from threading improved when used multiple templates and average scoring scheme. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein. The proposed scheme may significantly reduce the number of peptides to be tested in wet laboratory for epitope based vaccine design.