Prediction of peptide affinity to HLA DRB1*0401

Prediction of T-cell epitope

The prevailing methods to predict T-cell epitopes are reviewed. Motif matching, matrix, support vector machine (SVM), and empirical scoring function methods are mainly reviewed; and the thermodynamic integration (TI) method using all-atom molecular dynamics (MD) simulation is mentioned briefly. The motif matching method appeared first and developed with the increased understanding of the characteristic structure of MHC-peptide complexes, that is, pockets aligned in the groove and corresponding residues fitting on them. This method is now becoming outdated due to the insufficiency and inaccuracy of information. The matrix method, the generalization of interaction between pockets of MHC and residues of bound peptide to all the positions in the groove, is the most prevalent one. Efficiency of calculation makes this method appropriate to scan for candidates of T-cell epitopes within whole expressed proteins in an organ or even in a body. A large amount of experimental binding data is necessary to determine a matrix. SVM is a relative of the artificial neural network, especially direct generalization of a linear Perceptron. By incorporating non-binder data and adopting encoding that reflects the physical properties of amino acids, its performance becomes quite high. Empirical scoring functions apparently seem to be founded on a physical basis. However, the estimates directly derived from the method using only structural data are far from practical use. Through regression with binding data of a series of ligands and receptors, this method predicts binding affinity with appropriate accuracy. The TI method using MD requires only structural data and a general atomic parameter, that is, force field, and hence theoretically most consistent; however, the extent of perturbation, inaccuracy of the force field, the necessity of an immense amount of calculations, and continued difficulty of sampling an adequate structure hamper the application of this method in practical use.

Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications

Prediction of which peptides can bind major histocompatibility complex (MHC) molecules is commonly used to assist in the identification of T cell epitopes. However, because of the large numbers of different MHC molecules of interest, each associated with different predictive tools, tool generation and evaluation can be a very resource intensive task. A methodology commonly used to predict MHC binding affinity is the matrix or linear coefficients method. Herein, we described Average Relative Binding (ARB) matrix methods that directly predict IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction. A computer program was developed to automate the generation and evaluation of ARB predictive tools. Using an in-house MHC binding database, we generated a total of 85 and 13 MHC class I and class II matrices, respectively. Results from the automated evaluation of tool efficiency are presented. We anticipate that this automation framework will be generally applicable to the generation and evaluation of large numbers of MHC predictive methods and tools, and will be of value to centralize and rationalize the process of evaluation of MHC predictions. MHC binding predictions based on ARB matrices were made available at http://epitope.liai.org:8080/matrix web server.

Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

The role of the DR4 shared epitope in selection and commitment of autoreactive T cells in rheumatoid arthritis

The mechanistic basis for HLA associations with RA is still unknown in spite of 20 years of disease association studies and a detailed characterization of HLA class II alleles associated with disease. Analysis of the structural interactions between DR4 susceptibility molecules and T cells specific for the peptide-MHC complex suggests a mechanism for directed T-cell selection and amplification in which RA-associated genetic polymorphisms bias intermolecular recognition. New immunologic models for illustrating the importance of regulated thresholds for T-cell activation based on avidity between the TCR, MHC, and peptide offer insight into a potential mechanism in which the disease-associated HLA molecules create an autoimmune-prone individual by virtue of a biased TCR selection and T-cell amplification process. New tools such as the use of HLA-DR4 tetramers provide the ability to identify and monitor the presence of such autoreactive T cells in the periphery of individuals and patients and should assist in further testing of the multistep model for RA pathways presented in this article.