Magnitude of structural changes of the T-cell receptor binding regions determine the strength of T-cell antagonism: molecular dynamics simulations of HLA-DR4 (DRB1*0405) complexed with analogue peptide

Early relaxation dynamics in the LC 13 T cell receptor in reaction to 172 altered peptide ligands: a molecular dynamics simulation study

The interaction between the T cell receptor and the major histocompatibility complex is one of the most important events in adaptive immunology. Although several different models for the activation process of the T cell via the T cell receptor have been proposed, it could not be shown that a structural mechanism, which discriminates between peptides of different immunogenicity levels, exists within the T cell receptor. In this study, we performed systematic molecular dynamics simulations of 172 closely related altered peptide ligands in the same T cell receptor/major histocompatibility complex system. Statistical evaluations yielded significant differences in the initial relaxation process between sets of peptides at four different immunogenicity levels.

(E)-alkene and ethylene isosteres substantially alter the hydrogen-bonding network in class II MHC A(q)/glycopeptide complexes and affect T-cell recognition

The structural basis for antigen presentation by class II major histocompatibility complex (MHC) proteins to CD4(+) T-cells is important for understanding and possibly treating autoimmune diseases. In the work described in this paper, (E)-alkene and ethylene amide-bond isosteres were used to investigate the effect of removing hydrogen-bonding possibilities from the CII259-270 glycopeptide, which is bound by the arthritis-associated murine A(q) class II MHC protein. The isostere-modified glycopeptides showed varying and unexpectedly large losses of A(q) binding that could be linked to the dynamics of the system. Molecular dynamics (MD) simulations revealed that the backbone of CII259-270 and the A(q) protein are able to form up to 11 hydrogen bonds, but fewer than this number are present at any one time. Most of the strong hydrogen-bond interactions were formed by the N-terminal part of the glycopeptide, i.e., in the region where the isosteric replacements were made. The structural dynamics also revealed that hydrogen bonds were strongly coupled to each other; the loss of one hydrogen-bond interaction had a profound effect on the entire hydrogen-bonding network. The A(q) binding data revealed that an ethylene isostere glycopeptide unexpectedly bound more strongly to A(q) than the corresponding (E)-alkene, which is in contrast to the trend observed for the other isosteres. Analysis of the MD trajectories revealed that the complex conformation of this ethylene isostere was structurally different and had an altered molecular interaction pattern compared to the other A(q)/glycopeptide complexes. The introduced amide-bond isosteres also affected the interactions of the glycopeptide/A(q) complexes with T-cell receptors. The dynamic variation of the patterns and strengths of the hydrogen-bond interactions in the class II MHC system is of critical importance for the class II MHC/peptide/TCR signaling system.

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.

Side chain substitution benchmark for peptide/MHC interaction

The prediction of T-cell epitopes is an essential part in virtual immunology. Apart from sequence-based techniques, which achieve good results but fail to give insight into the binding behavior of a certain peptide binding to a major histocompatibility complex, structure-based approaches are another important technique. An essential step is the correct placement of the side chains for a given peptide in cases where no experimental data for the structure are available. To our knowledge, no benchmark for side chain substitution in the area of HLA has been reported in the literature. Here, we present a comparison of five different tools (SCWRL, SCATD, SPDBV, SCit, IRECS) applicable for side chain substitution. Each tool is tested on 29 different HLA-A2 structures with experimentally known side chain positions. Parts of the benchmark are correctness, reliability, runtime, and usability. For validation, the root mean square deviations between X-ray structures and predicted structures are used. All tools show different strengths and weaknesses.

Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high rpred2 = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide: can the alpha3 and beta2m domains be neglected?

Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histocompatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVYDGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available for this study allow us to compare directly differences in the behavior of very large molecular models; in this case, the entire extracellular portion of the peptide-MHC complex vs. the isolated peptide binding domain. Comparison of the results from the partial and the whole system simulations indicates that the peptide is less tightly bound in the partial system than in the whole system. From a detailed study of conformations, solvent-accessible surface area, the nature of the water network structure, and the binding energies, we conclude that, when considering the conformation of the alpha1-alpha2 domain, the alpha3 and beta2m domains cannot be neglected.

Computational tools for the study of allergens

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

Modeling MHC class II molecules and their bound peptides as expressed at the cell surface

A detailed insight to the structure of a given major histocompatibility complex (MHC)-peptide complex can strongly support and also improve the analysis of the peptide binding capabilities of the MHC molecule and the characterization of the developing T cell response. The number of MHC class II-peptide crystal structures is limited, therefore constructing and analyzing computer models can serve as efficient complementary tools when someone deals with experimentally determined binding and/or functional data. Commercial programs are available for modeling protein and protein-protein complexes, in general. However, more accurate results can be obtained if the parameters are directly optimized to a given complex, especially in the case of special proteins as MHC class II, an integral membrane protein, whose functional parts behave like regular globular proteins. Here, we present the optimization of an approach used for modeling MHC class II molecules complexed with various peptides fitting into the binding groove and several ways to analyze them with the help of experimental data.