Molecular dynamics study of the human insulin B peptide SHLVEALYLVCGERGG complexed with HLA-DQ8 reveals important hydrogen bond interactions

The basis of proper recognition of pathogens and tumours is provided by adaptive immunity. This immunological reaction of the recognition function of T-cell receptors on T lymphocytes detects antigenic peptides bound to major histocompatibility complex (MHC) molecules. Structural insight into this process has few grown considerably in the last years. In some of the cases, antigens are self-protein fragments causing autoimmunity diseases. Type 1 diabetes is such a disease connected with the human leukocyte antigen-DQ8 molecule, a class II MHC glycoprotein. Its crystal structure, complexed with LVEALYLVCGERGG peptide (insulin B peptide), has been solved, and important information about the significance of P1, P4 and P9 binding pockets has been discovered. The complex structure also revealed an unusual large number of intermolecular hydrogen bonds between insulin B peptide and MHC molecule. To further investigate the dynamics of peptide/MHC interactions, we perform molecular dynamic simulations in explicit water. Analysis of the results provided useful information of the binding of the peptide antigen to MHC molecule, which is supported by numerous hydrogen bonds besides the electrostatic (P1 and P9 pockets) or hydrophobic interactions (P4). Results also allowed some implications to be drawn for the role of residues located outside of the binding groove.     

Immunoinformatic Identification of Potential Epitopes Against Shigellosis

Diarrhoeal diseases due to Shigellosis account for deaths of ~1.5 million children every year in developing countries. Outer membrane proteins (OMPs) of Gram negative bacteria have been shown to be excellent subunit vaccine candidates against various pathogens. However, effective immune response can be generated using specific immunogenic determinants or peptides instead of whole protein or pathogen. In the present study, we chose six OMPs of Shigella flexneri 2a to predict peptides with good antigenic potential. Various tools were used in a systematic flow to predict B- and T-cell epitopes. Stringent selection criteria were used for epitope screening to ensure generation of both arms of immunity. These epitopes are predicted to be effective against a significantly large population of the diarrhoea afflicted countries in Southeast Asia. Most of the predicted epitopes are located towards the outer exposed region of proteins. The epitopes were docked with respective MHC Class I and II molecules to study peptide–MHC interactions. In conclusion, we have predicted an epitope ensemble against Shigellosis which can be experimentally validated for its immunogenic efficacy. We also propose a systematic workflow for immune-optimization to design effective peptide vaccines.     

Molecular modeling of a T-cell receptor bound to a major histocompatibility complex molecule: implications for T-cell recognition.

The main functions of the T-cell receptor (TCR) involve its specific interaction with short and linear antigenic peptides bound to the major histocompatibility complex (MHC) molecules. In the absence of a 3D structure for TCR and for the TCR/peptide/MHC complex, several attempts to characterize the structural components of the TCR/peptide/MHC interaction have been made. However, this subject is still troublesome. In this paper a computer-based 3D model for a TCR/peptide/MHC complex (5C.C7/moth cytochrome c [MCC] peptide 93-103/I-Ek) was obtained. The complex surface shows a high complementarity between the 5C.C7 structure and the peptide/I-Ek molecule. The mapping of residues involved in the TCR/peptide/MHC interaction shows close agreement with mutational experiments (Jorgensen JL, Reay PA, Ehrich EW, Davis MM, 1992b, Annu Rev Immunol 10:835-873). Moreover, the results are consistent with a recent variability analysis of TCR sequences using three variability indexes (Almagro JC, Zenteno-Cuevas R, Vargas-Madrazo E, Lara-Ochoa F, 1995b, Int J Pept Protein Res 45:180-186). Accordingly, the 3D model of the 5C.C7/MCC peptide 93-103/I-Ek complex provides a framework to generate testable hypotheses about TCR recognition. Thus, starting from this model, the role played by each loop that forms the peptide/MHC binding site of the TCR is discussed.     

A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach

The identification of MHC class II restricted peptide epitopes is an important goal in immunological research. A number of computational tools have been developed for this purpose, but there is a lack of large-scale systematic evaluation of their performance. Herein, we used a comprehensive dataset consisting of more than 10,000 previously unpublished MHC-peptide binding affinities, 29 peptide/MHC crystal structures, and 664 peptides experimentally tested for CD4+ T cell responses to systematically evaluate the performances of publicly available MHC class II binding prediction tools. While in selected instances the best tools were associated with AUC values up to 0.86, in general, class II predictions did not perform as well as historically noted for class I predictions. It appears that the ability of MHC class II molecules to bind variable length peptides, which requires the correct assignment of peptide binding cores, is a critical factor limiting the performance of existing prediction tools. To improve performance, we implemented a consensus prediction approach that combines methods with top performances. We show that this consensus approach achieved best overall performance. Finally, we make the large datasets used publicly available as a benchmark to facilitate further development of MHC class II binding peptide prediction methods.     

Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations

One key step in the immune response against infected or tumor cells is the recognition of the T-cell receptor (TCR) by class I major histocompatibility complexes. The complex between the HLA-B8 molecule and the immunodominant peptide with sequence FLRGRAYGL, derived from the Epstein-Barr virus, with the LC13 TCR has been determined by X-ray diffraction. The complex has been used as a starting point in a molecular dynamics study in order to investigate the dynamics of the complex association and to explore the specific interactions of the complex formation. The analyzed structures provided evidence that the peptide adopts an open type β-turn conformation close to C-terminal part, which dominates peptide/TCR interactions. Conformational energy landscape analysis indicated the presence of two conformational clusters in the peptide’s structure, underlying the backbone flexibility of the peptide despite being surrounded by two receptors. The peptide/MHC/TCR interface was found to hold significant number of solvent molecules, more specifically the peptide has been found to have approximately seventeen hydrogen bonds with water molecules. The molecular dynamics simulation indicated the disruption of some MHC/TCR contacts, mainly with the CDR1α loop. However, several other interactions emerged that resulted in a stable association during the 20 ns trajectory, as revealed by the buried surface area analysis.     

Structure of a covalently stabilized complex of a human alphabeta T-cell receptor, influenza HA peptide and MHC class II molecule, HLA-DR1

An alphabeta T-cell receptor (alphabetaTCR)/hemagglutinin (HA) peptide/human leukocyte antigen (HLA)-DR1 complex was stabilized by flexibly linking the HA peptide with the human HA1.7 alphabetaTCR, to increase the local concentration of the interacting proteins once the peptide has been loaded onto the major histocompatibility complex (MHC) molecule. The structure of the complex, determined by X-ray crystallography, has a binding mode similar to that of the human B7 alphabetaTCR on a pMHCI molecule. Twelve of the 15 MHC residues contacted are at the same positions observed earlier in class I MHC/peptide/TCR complexes. One contact, to an MHC loop outside the peptide-binding site, is conserved and specific to pMHCII complexes. TCR gene usage in the response to HA/HLA-DR appears to conserve charged interactions between three lysines of the peptide and acidic residues on the TCR.     

Targeting ICAM-1/LFA-1 interaction for controlling autoimmune diseases: designing peptide and small molecule inhibitors

This review describes the role of modulation of intracellular adhesion molecule-1 (ICAM-1)/leukocyte function-associated antigen-1 (LFA-1) interaction in controlling autoimmune diseases or inducing immunotolerance. ICAM-1/LFA-1 interaction is essential for T-cell activation as well as for migration of T-cells to target tissues. This interaction also functions, along with Signal-1, as a co-stimulatory signal (Signal-2) for T-cell activation, which is delivered by the T-cell receptors (TCR)-major histocompatibility complex (MHC)-peptide complex. Therefore, blocking ICAM-1/LFA-1 interaction can suppress T-cell activation in autoimmune diseases and organ transplantation. Many types of inhibitors (i.e. antibodies, peptides, small molecules) have been developed to block ICAM-1/LFA-1 interactions, and some of these molecules have reached clinical trials. Peptides derived from ICAM-1 and LFA-1 sequences have been shown to inhibit T-cell adhesion and activation. In addition, these inhibitors have been useful in elucidating the mechanism of ICAM-1/LFA-1 interaction. Besides binding to LFA-1, the ICAM-1 peptide can be internalized by LFA-1 receptors into the cytoplasmic domain of T-cells. Therefore, this ICAM-1 peptide can be utilized to selectively target toxic drugs to T-cells, thus avoiding harmful side effects. Finally, bi-functional inhibitory peptide (BPI), which is made by conjugating the antigenic peptide and an LFA-1 peptide, can alter the T-cell commitment from T-helper-1 (Th1) to T-helper-2 (Th2)-like cells, suggesting that this peptide may have a role in blocking the formation of the "immunological synapse."     

Identification of naturally processed ligands in the C57BL/6 mouse using large-scale mass spectrometric peptide sequencing and bioinformatics prediction

Most major histocompatibility complex (MHC) class I–peptide-binding motifs are currently defined on the basis of quantitative in vitro MHC–peptide-binding assays. This information is used to develop bioinformatics-based tools to predict the binding of peptides to MHC class I molecules. To date few studies have analyzed the performance of these bioinformatics tools to predict the binding of peptides determined by sequencing of naturally processed peptides eluted directly from MHC class I molecules. In this study, we performed large-scale sequencing of endogenous peptides eluted from H2K^b and H2D^b molecules expressed in spleens of C57BL/6 mice. Using sequence data from 281 peptides, we identified novel preferred anchor residues located in H2K^b and H2D^b-associated peptides that refine our knowledge of these H2 class I peptide-binding motifs. The analysis comparing the performance of three bioinformatics methods to predict the binding of these peptides, including artificial neural network, stabilized matrix method, and average relative binding, revealed that 61% to 94% of peptides eluted from H2K^b and H2D^b molecules were correctly classified as binders by the three algorithms. These results suggest that bioinformatics tools are reliable and efficient methods for binding prediction of naturally processed MHC class I ligands. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

The validity of predicted T-cell epitopes

High-performing MHC class I binding predictions have been available for more than a decade; however, their value in terms of actual epitope finding has only now been estimated in a large-scale investigation undertaken by the group of Sette. This work underlines the importance of bioinformatics as a resource-saving tool in the field of epitope discovery. In addition, the data can be used to benchmark the performance of other new or existing CTL epitope-prediction tools.     

MAPPP: MHC class I antigenic peptide processing prediction

MAPPP is a bioinformatics tool for the prediction of potential antigenic epitopes presented on the cell surface by major histocompatibility complex class I (MHC I) molecules to CD8 positive T lymphocytes. It combines existing predictions for proteasomal cleavage with peptide anchoring to MHC I molecules.     

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.     

ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

Design and use of conditional MHC class I ligands

Major histocompatibility complex (MHC) class I molecules associate with a variety of peptide ligands during biosynthesis and present these ligands on the cell surface for recognition by cytotoxic T cells. We have designed conditional MHC ligands that form stable complexes with MHC molecules but degrade on command, by exposure to a defined photostimulus. 'Empty MHC molecules' generated in this manner can be loaded with arrays of peptide ligands to determine MHC binding properties and to monitor antigen-specific T-cell responses in a high-throughput manner. We document the value of this approach by identifying cytotoxic T-cell epitopes within the H5N1 influenza A/Vietnam/1194/04 genome.     

Chemical complementarity between immune receptors and cancer mutants,independent of antigen presentation protein binding,is associated with increased survival rates

Uterine cancer has been associated with a T-cell immune response that leads to increased survival. Therefore, we used several bioinformatics approaches to explore specific interactions between T-cell receptor (TCR) and tumor mutant peptide sequences. Using endometrioid uterine cancer exome files from the The Cancer Genome Atlas database, we obtained tumor resident V-J recombinations for the T-Cell Receptor alpha gene (TRA). The charged-based, chemical complementarity for each patient''s LRP2 or TTN mutant amino acids (AAs) and the recovered, TRA complementarity determining region-3 (CDR3) sequences was calculated, allowing a division of patients into complementary and noncomplementary groups. Complementary groups with TTN mutants had increased disease-free survival and increased expression of complement genes. Furthermore, the survival distinction based on CDR3-mutant peptide complementarity was independent of programmatically assessed HLA class II binding and was not observable based on the CDR3 AA chemical features alone. The above approach provides a potential, highly efficient method for identifying TCR targets in uterine cancer and may aid in the development of novel prognostic tools.     

Modeling alternative binding registers of a minimal immunogenic peptide on two class II major histocompatibility complex (MHC II) molecules predicts polarized T-cell receptor (TCR) contact positions.

Several major histocompatibility complex class II (MHC II) complexes with known minimal immunogenic peptides have now been solved by X-ray crystallography. Specificity pockets within the MHC II binding groove provide distinct peptide contacts that influence peptide conformation and define the binding register within different allelic MHC II molecules. Altering peptide ligands with respect to the residues that contact the T-cell receptor (TCR) can drastically change the nature of the ensuing immune response. Here, we provide an example of how MHC II (I-A) molecules may indirectly effect TCR contacts with a peptide and drive functionally distinct immune responses. We modeled the same immunogenic 12-amino acid peptide into the binding grooves of two allelic MHC II molecules linked to distinct cytokine responses against the peptide. Surprisingly, the favored conformation of the peptide in each molecule was distinct with respect to the exposure of the N- or C-terminus of the peptide above the MHC II binding groove. T-cell clones derived from each allelic MHC II genotype were found to be allele-restricted with respect to the recognition of these N- vs. C-terminal residues on the bound peptide. Taken together, these data suggest that MHC II alleles may influence T-cell functions by restricting TCR access to specific residues of the I-A-bound peptide. Thus, these data are of significance to diseases that display genetic linkage to specific MHC II alleles, e.g. type 1 diabetes and rheumatoid arthritis.     

Analysis of key parameters for molecular dynamics of pMHC molecules

Molecular dynamics (MD) studies of human major histocompatibility complex (MHC) HLAB*2705 complexing two different peptides were performed. During simulation one peptide partially detached from the MHC while the other peptide switched back and forth between several different configurations. These different configurations relate to conformational substates and can be assigned to different levels of chemical activity or even the molecular mechanisms of immunological signalling. To ensure reliable immunological conclusions from MD simulations we prepare the methodological tools by carefully evaluating initial conditions, system simplification, solvation shell thickness, water model/force field combination and simulation length. We also derive a guideline for appropriate model selection. This kind of quality assessment is seen a mandatory prerequisite for coming studies linking peptide-loaded MHC dynamics to T-cell activation.     

Prediction of MHC binding peptide using Gibbs motif sampler,weight matrix and artificial neural network

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40¢), however above the random prediction (14¢). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.