T-Cell Epitope Prediction.

An epitope is a part of an immunogenic protein that can be recognized by the immune system. The peptides that can be recognized by the T-cell receptors after a particular antigen has been intracellularly processed, bound to at least one MHC molecule and expressed on the surface of the antigen presenting cell as a MHC-peptide complex, are called a T-cell epitope. Individuals who have at least one MHC molecule able to most avidly bind to allergenic amino acid sequences from an allergen, and at the same time have the appropriate T-cell clone that can recognize this MHC-peptide complex, are expected to be genetically prone to allergic reactions against that allergen. This possibility can be examined in silico by utilizing modern computational techniques that are based on sophisticated mathematics and statistics. The design principles of these techniques are different and therefore variations in their predictions are expected. The available software programs that have been developed on this basis are able to combine the increasing amount and complexity of the available experimental data that have been organized in immunoinformatics databases to predict potential allergen T-cell epitopes. All relevant T-cell epitope prediction methods can be accessed online as a freeware.

[1]  Bjoern Peters,et al.  Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries , 2008, Immunome research.

[2]  John Sidney,et al.  A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach , 2008, PLoS Comput. Biol..

[3]  J. Thornton,et al.  Continuous and discontinuous protein antigenic determinants , 1986, Nature.

[4]  Morten Nielsen,et al.  NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction , 2009, BMC Bioinformatics.

[5]  H. Sampson,et al.  Peanut T-cell epitope discovery: Ara h 1. , 2016, The Journal of allergy and clinical immunology.

[6]  Morten Nielsen,et al.  The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding , 2009, Bioinform..

[7]  Morten Nielsen,et al.  Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan , 2008, PLoS Comput. Biol..

[8]  U. Şahin,et al.  Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices , 1999, Nature Biotechnology.

[9]  Morten Nielsen,et al.  Peptide binding predictions for HLA DR, DP and DQ molecules , 2010, BMC Bioinformatics.

[10]  Magdalini Moutaftsi,et al.  A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus , 2006, Nature Biotechnology.

[11]  Morten Nielsen,et al.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.

[12]  Alessandro Sette,et al.  Properties of MHC Class I Presented Peptides That Enhance Immunogenicity , 2013, PLoS Comput. Biol..

[13]  R. Valenta,et al.  Autoallergy: a pathogenetic factor in atopic dermatitis? , 2000, Current problems in dermatology.

[14]  O. Lund,et al.  novel sequence representations Reliable prediction of T-cell epitopes using neural networks with , 2003 .

[15]  Clemencia Pinilla,et al.  Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior , 2009, BMC Bioinformatics.

[16]  Hiroshi Mamitsuka,et al.  Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools , 2011, Briefings Bioinform..

[17]  O. Lund,et al.  NetMHCpan, a method for MHC class I binding prediction beyond humans , 2008, Immunogenetics.

[18]  Morten Nielsen,et al.  Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method , 2007, BMC Bioinformatics.

[19]  P. Martus,et al.  IgE Mediated Autoallergy against Thyroid Peroxidase – A Novel Pathomechanism of Chronic Spontaneous Urticaria? , 2011, PloS one.

[20]  Alessandro Sette,et al.  Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method , 2005, BMC Bioinformatics.

[21]  H. Sampson,et al.  In silico prediction of Ara h 2 T cell epitopes in peanut‐allergic children , 2013, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[22]  Ellis L Reinherz,et al.  Definition of MHC Supertypes Through Clustering of MHC Peptide Binding Repertoires , 2004, ICARIS.