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The Immune Response The humoral response involves interaction of B cells with antigen (Ag) and their differentiation into antibody-secreting plasma cells. The secreted antibody (Ab) binds to the antigen and facilitates its clearance from the body. The cell-mediated responses involve various subpopulations of T cells that recognize antigen presented on self-cells. Helper T cells respond to antigen by producing cytokines. Cytotoxic T cells respond to antigen by developing into cytotoxic T lymphocytes (CTLs), which mediate killing of altered self-cells (e.g., virusinfected cells). The MHC class I pathway Antigen Not all peptides binding to MHC molecules are epitopes, but all T-cell epitopes need to bind to MHC. Proteasome Identifying of T-cell epitopes is important for development of peptide-based vaccines, evaluation of subunit vaccines, diagnostic development Peptides TAP T-cell epitope ER MHC I TCD8+ Antigen Presenting Cell T cell receptor (TCR) Cytotoxic T lymphocyte (CTL) Xenoreactive Complex AHIII 12.2 TCR bound to P1049 (ALWGFFPVLS) /HLA-A2.1 T cell epitope – a short linear peptide or other chemical entity (native or denatured antigen) that binds MHC (class I binds 8-10 aa peptides; class II binds 11-25 aa peptides) and may be recognized by T-cell receptor (TCR). T-Cell Receptor V V Epitope is a peptide which able to elicit T cell response. T cell recognition of antigen involves tertiary complex “antigen-TCRMHC”. MHC class I -2-Microglobulin 1lp9 MHC class I facts MHC class I in human is called HLA I (Human Leukocyte Antigen) (in mouse H-2). Every normal (heterozygous) human expresses six different MHC class I molecules on every cell, containing α-chains derived from the two alleles of HLA-A, HLA-B, HLA-C genes that inherited from the parents. MHC genes are the most polymorphic in human genome. For each locus hundreds of different alleles exist. For today, there are known 489 HLA-A alleles, 830 HLA-B and 266 HLA-C alleles (1,670 alleles including non-classical 7 alleles). Some of these alleles are more closely related to the alleles found in chimpanzees than to another human alleles from the same gene. The role of MHC diversity in sexual selection: the more diverse the MHC genes of parents, the stronger the immune system of the offspring. A preference of mate of different MHC was demonstrated on both mice and humans. The MHC molecules of an individual do not discriminate between foreign and self peptides. Prediction of MHC class I binding peptides Regression models predicting the peptide-MHC binding affinity. Require a lot of experimental data pairs pairs {peptide – affinity value} for a given MHC allele. Such data are currently available for very restricted number of alleles (<50 for HLA class I). Sequence-based methods. Classification models distinguishing binders from non-binders. Do not require consistent quantitative binding data. QSAR, 3D-QSAR approach Average relative binding matrices (sequence-based approach) Structure-based methods: docking, threading (slow - atom energy function calculation, fast – knowledge-based residue contact scoring functions) etc. Performance measures for prediction methods ROC curve TP FP threshold FN TN sensitivity = TP / (TP + FN) = 6/7= 0.86 specificity = TN / (TN + FP) = 6/8 = 0.75 True positive rate, TP / (TP + FN) 1 0.9 0.8 0.7 0.6 0.5 AROC 0.4 0.3 0.2 0.1 0 0 0.2 0.4 0.6 0.8 False positive rate, FP / (FP + TN) 1 Sequence-based methods for prediction of peptide binding to MHC class I ALAKAAAAM ALAKAAAAN ALAKAAAAV ALAKAAAAT GMNERPILT GILGFVFTM TLNAWVKVV Gibbs sampling Sequence motifs, matrices Sequence weighted matrices Hidden Markov Models Artificial Neural Networks SVM 0.95 0.9 KLNEPVLLL AVVPFIVSV Peptides known to bind to the HLAA*0201 molecule. Performance (measured as AROC) depends on the number of training peptides Aroc 0.85 0.8 0.75 0.7 0.65 2 10 20 100 200 Number of training peptides 500 Benchmarking predictions of peptide binding to MHC I (Peters et al. PLoS Comput Biol. 2006 Jun 9;2(6):e65) Data: pairs {peptide – affinity value in terms of IC50 nM} for a given MHC allele 48 different mouse, human (35 HLA class I), macaque, and chimpanzee MHC class I alleles. Length of peptides 8 – 11 aa. 48,828 experimental data points. 20 different methods were evaluated. Peptide binding to MHC class I affinity prediction methods comparison Correlation coefficients (ARB=0.55, SMM=0.62, ANN=0.69) are significantly different (p<0.05 using a t test). Aroc values (ARB=0.934, SMM=0.952, ANN=0.957) are significantly different (p<0.05 using a paired t test on Aroc values generated by bootstrap). Predep – structure-based method of Schueler-Furman, Altuvia, Sette, Margalit (2000) Protein Sci. Structure-based methods for prediction of peptide-MHC I binding Contrarily to sequence-based methods structure-based methods are applicable to different peptide length and MHC alleles. Sequence-based methods have limited structural interpretability contrarily to structure-based methods. More than 40 X-ray structures of different peptide-MHC I complexes are available (only 10 different HLA class I allotypes). Learning MHC I – peptide binding Data: HLA I sequences Input: 37 3D-structures of MHC-peptide complexes MHC-binding affinity data: 870 data points (quantitative data) Binders and non-binders (peptides) from 3 databases known for particular HLA alleles (binary data) Method: threading of a peptide sequence onto 3D-structure of a complex of other peptide with the same or similar (by sequence) HLA molecule combined with machine learning on binding data binding energy is additive, the residue pairwise potentials depend only on the amino acids (not on their context), 20x20 matrix of pairwise potentials is derived from known 3D-structures and binding data Parameters of binding energy function are learned on binding data Results: Proposed method outperforms original threading method in case when both structure and binding data are available for the allele Proposed method performs similar to the threading when binding data are used for similar alleles; while adding of binary data known for the allele improves the prediction HLA Number of Peptide s AUC ADT AUC ANN Best Online Tool A_0101 1158 0.9657 0.9798 0.955 hla ligand A_0201 3090 0.9521 0.9564 0.922 hla_a2_smm A_0202 1448 0.9033 0.8988 0.793 multipredann A_0203 1444 0.9141 0.9203 0.788 multipredann A_0206 1438 0.9191 0.9261 0.735 multipredann A_0301 2095 0.9298 0.9366 0.851 multipredann A_1101 1986 0.9442 0.9511 0.869 multipredann A_2301 105 0.8044 0.8514 A_2402 198 0.7852 0.822 A_2403 255 0.8784 0.9175 A_2601 673 0.9224 A_2902 161 A_3001 multipredann 0.77 syfphethi 0.9552 0.736 pepdist 0.8866 0.9317 0.597 rankpep 670 0.941 0.945 A_3002 93 0.7633 0.744 A_3101 1870 0.9313 0.9274 0.829 bimas A_3301 1141 0.9363 0.9141 0.807 pepdist A_6801 1142 0.8847 0.8823 0.772 syfphethi A_6802 1435 0.8963 0.8986 0.643 mhcpred A_6901 834 0.8902 0.8803 HLA Number of Peptide s AUC ADT AUC ANN Best Online Tool B_0702 1263 0.9573 0.9636 0.942 hlaligand B_0801 709 0.854 0.9533 0.766 pepdist B_1501 979 0.9075 0.942 0.816 rankpep B_1801 119 0.8687 0.838 0.779 pepdist B_2705 970 0.9217 0.9371 0.926 bimas B_3501 737 0.8691 0.8739 0.792 bimas B_4001 1079 0.8933 0.9155 B_4002 119 0.8186 0.7524 0.775 rankpep B_4402 120 0.6775 0.7785 0.783 syfphethi B_4403 120 0.6239 0.7634 0.628 rankpep B_4501 115 0.8015 0.8609 B_5101 245 0.8474 0.8856 0.82 pepdist B_5301 255 0.8934 0.8974 0.861 rankpep B_5401 256 0.8457 0.9025 0.799 svmhc B_5701 60 0.832 0.8246 0.767 pepdist B_5801 989 0.94 0.96 0.899 bimas ANN method (Nielsen et al. 2003) outperformed all other sequence-based methods. The proposed methods can outperform ANN when the available training data for an allele is small. HIV virus evolves to modulate its binding to MHC molecules Hypothesis: HIV mutations correlate with the MHC types of the host in a way that the virus whose peptides bind well to a particular MHC molecule is typically under strong immune pressure in patients with this MHC type, and it is forced to mutate away (escape) from its fittest form towards a form that binds less well to MHC. Data: 246 HIV patients, >1,000 HIV sequences Results for the most frequent (in the Western world) MHC allele A0201: significant negative correlation between HIV peptide-MHC binding energy calculated values and viral load reflecting HIV mutations towards escaping of the immune response (lower MHC binding). The concept of MHC sypertypes MHC polymorphism is essential to protect the population from invasion by pathogens. But it generates problem for epitope-based vaccine design: a vaccine needs to contain a unique epitope binding each MHC allele. A factor that may reduce the number of epitopes necessary to include in a vaccine is that many of different HLA molecules have similar specificity, i.e. bind similar by sequence peptides. Such HLAs represents a supertype. HLA-A were classified on 5 supertypes (with a number of nonclassified alleles): A2 – hydrophobic amino acid in binding peptide position 9 A3 – basic aa in position 9 A26 – acidic aa in position 1 A1 – acidic aa in position 3 A24 – tyrosine in position 2