1 / 37

Computer-aided design of T- cell epitope vaccines

Computer-aided design of T- cell epitope vaccines. Pedro Antonio Reche Gallardo, PhD. INDEX. 1. T-cell immunology background. Adaptive immune system T cell immune recognition Thymic selection Processing. • • • •. 2. Prediction of T-cell epitopes. 1. Methods 2. Problems

gdanielle
Télécharger la présentation

Computer-aided design of T- cell epitope vaccines

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computer-aided design of T- cell epitope vaccines Pedro Antonio Reche Gallardo, PhD

  2. INDEX 1. T-cell immunology background Adaptive immune system T cell immune recognition Thymic selection Processing • • • • 2. Prediction of T-cell epitopes 1. Methods 2. Problems 3. Applications to HIV

  3. The Adaptive immune system Lymph node DC CD8 Skin/Mucosa CD8 CD8 CD8 CD8 CD8 CD8 CD4 CD8 CD4 B DC B CD4 B B CD4 B CD4 CD4 CD4 CD4 B cellular immunity Bonne marrow IgM IgG B AFC B IgA IgE Humoral response

  4. T-cell immune biology  T cells are activated by activated by Dendritic cells  There are two types of T cells: CD4 T cells and CD8 T cells  CD8 T cells are able to kill cells invaded by pathogens (viruses) and tumoral cells. Important for controlling viral infection and tumors  CD4 T cells (T-helper cells) provide help to both CD8-T-cells and B-cells through the production of cytokines. They are important for controlling bacterial infections  What are the molecular basis for T cell recognition/activation?

  5. Background: T-cell immune responses  T-cells immune responses are triggered by the recognition of foreign peptides in the context of MHC molecules. The recognition is mediated via the T-cell Receptor (TCR) of the T-cell  CD8 and CD4 T cells recognize peptides in the context of two distinct classes of MHC molecules CD8 T-cell CD4 T-cell TCR TCR CD4 CD8 MHCI MHCII APC Antigen Presenting Cell (APC)

  6. Structural recognition of peptide-MHCI by TCR CD8+ T-CELL CD4 T-CELL TCR TCR CD4 pMHC CD8 pMHCI b2m APC APC CD4 T-cell CD8 T-cell TCR TCR CD4 CD8 MHCII MHCI APC APC

  7. Structural features of TCR Recognition of pMHCI complexes TCR F Y K A T Gp33-41 Db 1 4 6 7 8 TCR CONTACTS Gp33-41 K A V Y N F A T C General pMHCI/TCR interactions TCR 1 2 3 4 5 6 7 8 9 MHCI

  8. Structural recognition of peptide-MHC MHCI MHCII C N TCR T C R 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 MHCI M H C I

  9. MHCI- and MHCII- peptide binding modes MHC I MHC II • Peptide extend beyond the limits of the MHCII binding groove. Variable peptide length (9-22). a • Only a core of nine residues bind to the MHCII grove. a • Peptide usually have between 4 anchor residues. Position is the most important. b2m • Major contribution to the binding energy comes from peptide backbone. • Peptide is pinned into the MHCI binding groove by their N- and C-terminal ends. • Peptide repertoire is larger than that of MHCI molecules. • Peptide length is fixed (8-11). • Peptide usually have between 3 and 4 anchor residues. • Peptide anchor residues have a major contribution to the total binding energy. a • Peptide repertoire is restricted and motifs are easy to identify.

  10. T cells: Priming versus effector function Pathogen CD8+ CD4+ Dendritic cell Antigen Presenting Cell T-cell activation is based on the recognition of foreign peptides in the context of MHC molecules T helper Lymphocytes (Th1/2) Cytotoxic T Lymphocytes (CTL) CTL effectors function is predicated in the recognition of pMHC/TCR Killing The effector function is predicated on the production of cytokines

  11. TCR-pMHC fit and self tolerance of T-cells is adquired during Thymic selection Thymocyte selection is mediated by peptide/MHC/TCR interactions 1. 2. 3. APC APC APC MHC Ø +++ + Interact. TCR T T T Death Neglect Death – selection Survival + selection Nature Reviews Immunology 4; 278-289 (2004);

  12. ANTIGEN PRESENTATION: OVERVIEW ER ER Golgi Nucleus Nucleus TAP Ii Proteasome Golgi Cytosol Cytosol ANY CELL DC, MACROPHAGES, B CELL CLASS II MHC CLASS I MHC

  13. T cell epitopes  T cell epitopes are peptides derived from the processing of proteins that are able to activate T cells in a detectable manner APC PEPTIDE P 1. Processed (released) from a protein in vivo 2. is presented by the MHC molecule in the cell surface of the APC MHC MHC-p-TCR P 3. peptide-MHC is recognized by T cell, and recogniztion result in activation of the T cell in a manner that can be detected TCR T CELL  Identification of T-cell epitopes is important for understanding disease pathogenesis and for vaccine design

  14. Experimental Identification of T-cell epitopes is very expensive  Overlapping peptides: Full length amino acid sequence of a protein is covered by making 20 mer peptides overlapping 10 residues, and T response is determined NH3+ P1 20 aa 10 aa ACTIVATION READ OUT APC T-CELL P2 COO-  Prediction of T-cell epitopes

  15. MHCI and MHCII peptide binding A B N C C N T C R T C R 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 M H C I M H C I MHCI and MHCII impose constraints on the type of peptides they bind

  16. Prediction of MHC-peptide binding using sequence patterns  Most widely used method: simple ( [AS]-X(2)-[W]-[FTS] )  Losses information. Pattern matching algorithms are quite rigid  Generation of motif is manual

  17. Prediction of peptide-MHC-binding using PSSMs  Peptides that bind to the same MHC are related by sequence similarity and thereby a Position Specific Scoring Matrix (PSSM) or profile can be applied to the prediction of MCH binding MUS10165 FQPQNGQFI MUS1117A FQPQNGCFI 24mdm2 GRPKNGCIV MUS1195E VNIRNCCYI 29Der FGISNYCQI MUS117A9 YSNENMDAM 9iavnp ASNENMDAM MUS1005C ASNENMETM DbPEPTIDES wij= ln ( fobs/ fexp) PSSM A C D … T V W Y P1 1.941 5.635 -4.904 … -1.657 -2.563 5.905 -1.073 P2 3.065 1.813 -4.667 … -1.106 -1.006 -5.868 -0.989 P3 -1.012 -4.061 -5.640 … -4.712 1.676 0.703 -0.690 P4 -2.624 4.497 -0.749 … -0.490 -0.429 4.873 1.732 P5 -7.366 -7.593 -4.054 … -1.876 -8.438 -9.188 -7.853 P6 1.209 3.057 -0.825 … 0.054 0.140 2.529 0.895 P7 0.899 6.183 4.968 … 1.723 -0.396 P8 -0.799 1.723 -5.113 … 2.248 1.433 4.143 P9 -1.289 1.062 -6.286 … -2.107 1.069 -3.953 -3.338 PROTEIN A V W Y D D V W Y E T E T T i i +1 2.951 -4.715 4.361 Si = 1.941+ -1.006 + 0.703 + 1.732 + -4.054 + -0.825 + -0.396 + 4.143 + -3.338 SCORE <=> SIMILARITY TO ALIGNMENT OF BINDERS <=> FUNCTION (MHC BINDING)

  18. Defining profile motifs from peptides binding to MHC molecules EPIMHC http://bio.dfci.harvard.edu/epimhc/ MHC II MHC I MHC sub-setting MHCIIx MHCIX Peptide length sub-setting meme EM algorithm Motif size: 9 ALN ALN l (peptide length) MHCIX(l) MHCIIX(9) Create PSSM PSSM MHCIX(l) PSSM MHCIIX(9)

  19. Predictions Tests: Results PSSM are able to predict known T-cell epitopes within their protein sources among the top scoring peptides. MHCI Predicted epitopes (%) MHC II Predicted epitopes (%) 120 120 100 100 80 80 60 60 40 40 20 20 0 0 0.5% 1% 2% 3% 5% 10% 20% 0.5% 1% 2% 3% 5% 10% 20% Threshold (%) Threshold (%) Performance on cross-validation SE (3%) 89 % ± 8 SP (3%) 92% ± 7 AUC 0.85 ± 0.09 MHCI MHCII 80% ± 9 89% ± 8 0.80 ± 0.1

  20. Prediction of peptide-MHC binding using RANKEP http://bio.dfci.harvard.edu/Tools/rankpep.html • Most used peptide-mhc binding tool on the internet (google) • Prediction peptide binding to MHCI and MHCII. • Largest number of predictors (44 MHCI and 40 MHCII PSSMs, respectively) • Flexibility • Sorting by percentage or total number of molecular weight • Restriction of searches by MW • Prediction of proteasome cleavage of peptide binding to MHCI. • Immunodominance Filter • INPUT: single file of protein/s in fasta format or multiple sequence alignment. Variability masking Reche et al. Human Immunol. 2002, 63:701-9 Reche et al. Immunogenetics 2004, 56:405-19

  21. In humans, MHC molecules are extremely polymorphic HLA Chr6 B C A DP DQ DR HLA I HLA II HLA-A*0101 HLA MOLECULE SEQUENCES CLASS I Black Caucas. Hispan. Nat.Ame Asian HLA-A 230 HLA-B 447 GF 5.6% 15.1% 6.0% 7.5% 1.5% HLA-C 97 CLASS II PF HLA-B*4402 10.8% 27.9% 11.6% 14.4% 3.0% HLA-DPA 12 HLA-DPB 90 HLA-DQA 17 HLA-DQB 42 Black Caucas. Hispan. Nat.Ame Asian HLA-DRA 2 GF 2.0% 11.75% 6.7% 3.4% 0.7% HLA-DRB1 271 HLA-DRB3 30 PF 3.9 % 22.0 % 12.39% 8.3 % 1.3 % HLA-DRB4 7 HLA-DRB5 14

  22. HLA polymorphisms match peptide binding residues HLA-DR1(DRA*0101xDRB1*0101) a1 a1 b11 b37 b1 b13 b70-71 b1 VAR DRB1*0101 R F L E Y S T S E C H F F N G T E R V R F L D R Y F Y N Q E E Y V R F D S D V G E Y R A V T E L G R P D A E YWN S Q K D L L E D R R A A V D T Y C R H N Y G V G E S F T V Q 6 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 S1 S2 S3 S4 H1a H1b Reche and Reinherz, J Mol Biol. 2005;331(3):623-41

  23. HLA polymorphisms complicate epitope based immunotherapy  Vaccine coverage. Required HLA restriction and ethnic variation in HLA distribution, suggest that epitope vaccines might not be effective across populations.  Epitope prediction. T-cell epitopes are anticipated on the basis of their binding to MHC molecules.  Which MHC allele to choose?  It would appear that peptide predictions to many MHC molecules will have to be provided. How to limit the number of peptides?  HLA supertypes: Peptide binding specificities HLA molecules can be largely overlapping (promiscuity) HLA molecules with similar binding specificities are termed as HLA supertypes Defining HLA supertypes by clustering the overlap between their peptide- binding repertoires

  24. Defining HLA supertypes by clustering the peptide-binding repertoires PSSMj(HLA Ij) PSSMi(HLA Ii) protein (1) (2) i j ni,j i (3) di,j j k (4) ( N - ni,j) g n (1) Prediction of the peptide biding repertoire (i,j sets in figure). (2) Compute common peptides between the binding repertoire of any two HLA I molecules. (3) Build a distance matrix. (4) Use a phylogenic clustering algorithm to compute and visualize HLA I supertypes (clusters of HLA I molecules with overlapping peptide binding repertoires).

  25. HLA I Promiscuity: Supertypes A0204 A2 A6802 A0209 A0207 A0214 A0203 A0206 A0201 A0202 Cw0102 Cw0304 B2704 A0205 A6601 B27 B2705 B2703 A3301 A3101 A6801 B2706 B2701 A3 B2709 B2702 A1101 B3909 A0301 B1517 Cw0702 B1501 B62 B4402 B44 B1508 B4403 B1502 B5801 B15 B5702 A*0101 B5701 B1513 B1516 B57 B0702 B5401 B4002 B5301 B3501 A2902 B5101 B5103 B39011 B5502 B5102 B1510 B8 B7 B1509 A2402 B3801 BX A24 Supertype Alleles Blacks Caucasians Hispanics *N.A.Natives Asians Promiscuous epitopes A2 A3 B7 B15 A24 A*0201-7, A*6802 A*0301, A*1101, A*3101, A*3301, A*6801, A*6601 35.4% B*0702, B*3501, B*5101-02, B*5301, B*5401 A*0101, B*1501_B62, B1502 A*2402, B*3801 43.7% 49.9% 46.9% 42.2% 37.80% 17.28% 51.8% 41.5% 40.5% 16.75% 25.85% 52.4% 40.7% 52.0% 27.26% 41.94% 44.7% 47.9% 31.3% 21.04% 35.0% 0.8% 0.6% 1.2% 0.6% 0.5% 45.9% 13.06% 15.5%

  26. PEPVAC Features http://bio.dfci.harvard.edu/PEPVAC/ SUPERTYPES AND COVERAGE: A2: A*0201, A*0202, A*0205, A*0206 27.8% A3: A*0301, A*1101, A*3101, A*3301, A*6801 32.1% A24:A*2301, A*2402, A*2403, A*2405, A*2407 15.5% B7: B*0702, B*3501, B*5101, B*5301, B*5401 30.6% B15:A*0101, B*1501_B62, B1502 13.1% ALL: >95.0% NUMBER OF PEPTIDES (2% THRESHOLD) ( Influenza Virus A (PR/8); 11 ORF; 4617 amino acids) PROTEOSOMA OFF : 210 (4.56% of all 9mers) PROTEOSOMA ON: 148 (3.21% of all 9mers)  Reche, P. A., and Reinherz, E. L. (2004). Definition of MHC supertypes through clustering of MHC peptide binding repertoires. Proceedings of 3rd International Conference on Artificial Immune Systems. ICARIS 2004, LNCS 3239, pp. 189-196. Eds. G. Nicosia, V. Cutello, P. J. Bentley and T. Timmis. Springer-Verlag Berling Heidelberg. Publisher Abstract.  Reche, P. A., and Reinherz, E. L. (2004). PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands. Nucleic Acids Res. In press

  27. Computer-aided vaccine design flow Pathogenic target IN SILICO Epitope selection: MHC- binding, processing, conservation, coverage Experimental determination Binding analysis population coverage corrections EX SILICO Immunogenicity experiments Vaccine optimization Binding analysis and HLA-restriction mapping must be determined unambiguously

  28. Genome wide identification of T cell epitopes in an Influenza disease model in H2b Mouse (Kband DbMHCI) Zhong et al. J Biol Chem. 2003 Nov 14;278(46):45135-44. 1. 2. 3. 172 peptides were predicted to bind to Kbor Db 80% of peptides were found to bind to Kband Db Only 10% were immunogenic (activated T cells) and only 5 were immunodominants) CTL ¦ +++ +++ Immunog. Immunodominant Pept PA224-233 NP366-374 Sequence SSLENFRAYV/Db ASNENMETM/Db Cleav * * Bind* +++ +++ SSYRRPVGI/Kb PB1703- * +++ ++ ~ +++ 711 MGLIYNRM/Kb SSISFCGV/Kb FSVIFDRL/Kb M1128-135 NA425-432 NS1133- * * * +++ +++ +++ - ~ ± ± ± Subdominant 140 NS2114- RTFSFQLI/Kb * ++ + 121 HP43-50 NA181-189 NA335-343 PB1214- GGLPFSLL/Kb SGPDNGAVAV/Db YRYGNGVWI/Db RSYLIRAL/Kb * * * * ++ ++ TBD + + + + + 221 PA238-245 PB2689- NGYIEGKL/Kb VLRGFLIL/Kb * * + + + ± 696 *+++: strong. ++: intermediate. +: weak. -: non-binder. ¦ +++: ~ 14%. ++: 5-14%. +: 1.5-3.0%. ±:~1% in intracellular IFNg staining assay.

  29. T cell epitope predictions 1. Algorithms for the prediction of T cell epitopes can narrow the number of potential epitopes by 95% 1. Low false negative rate 2. Aproximadetly 10% of predicted epitopes are immunogenic (activate T cell epitopes) 1. High False positive rate 3. Lack of immunogenicity of predicted epitopes is due to lack of appropriated processing. 4. For pathogens such as HIV-1 there are hundreds of experimentally determined epitopes (immunogenic in vivo) identified from patients 5. Optimizing T-cell epitope selection for vaccine design?

  30. CTL EPITOPES IN HIV INFECTED HUMANS •AIDS is a sexually transmitted infectious disease caused by the HIV virus. The virus infects primeraly cells of the immune system (CD4-T-cells) and it is subject of extreme sequence variability which is basis for the HIV immune evasion. • Los Alamos HIV database is depositary of T-cell epitopes from HIV and SIV http://hiv-web.lanl.gov/content/index • CTL epitope example record Displaying record number 1 HXB2 Location p17(18-26) Author Location p17(18-26 IIIB) Sequence KIRLRPGGK Species(HLA) human (A3) Immunogen HIV-1 infection Keywords Notes References • 1567 CTL epitope records => 592 unique seq =>195 9mers Immunogen HIV-1 infection Species(HLA) human • CTL epitopes from HIV infected patients infected could be used as the basis of a vaccine against HIV1. How?

  31. Strategy for Development of a HIV1 Vaccine Using CTL Epitopes 1. Collect CTL epitope HIV-database  Immunogen:HIV1 INFECTION;  SPECIE: Human 2. Select only conserved CTL epitopes (9 mers) 3. Determine the extended HLAI binding profile of each CTL epitope (experimental from HIV + predicted) 4. Determine population coverage for each peptide 5. Vaccine against HIV1 should be made from a combination of peptides providing 95 % population coverage including all ethnics

  32. Selection of Conserved CTL HIV1 GAG POL ENV VIF TAT REV VPU/VPX VPR NEF GAG.seq POL.seq ENV.seq VIF.sea TAT.seq REV.seq VPU.seq VPR.seq NEF.seq GAG.aln POL.aln ENV.aln VIF.aln TAT.aln REV.aln VPU.aln VPR.aln NEF.aln Mask variability Clustalw QuickT ime™ an d a T IFF (Un co mp re ssed ) d ecompr esso r a re ne ed ed to se e thi s p ictur e. 2 1.6 1.8 I W D N MH=1 1.2 1.4 1 0.6 0.8 0.2 0.4 0 G G I W G C S G K L I I C C T T N V P W N S S W S N K S Q S E I W G C S G K L T T N V P W N S S W S N K S Q S E I W D N M Shannon filter G I W G C S G K L I C T T . V P W N S S W S N K S . . E I W . N M Peptide binding scores are given only for segments without "dots"

  33. Conserved HIV1 CTL Epitopes From HIV1 Infection 1. Collect CTL epitope HIV-database (195 9mers) 2. Select only conserved CTL epitopes (Shannon Filter 0-4.3) H < 0.5 POS 16-24 H < 1 CTL SOURCE ALLELES (HIVDB) B0702 B7 A0301 A0301 A0201 A2 B3501 B35 B1501 B60 A0201 A0201 A1101 A0201 A0201 B5101 B62 A0301 A0201 A2 A3002 A30 Conser. SOURCE POS HIV CTL SPRTLNAWV:p24 AVFIHNFKR:Integrase 179-187 MAVFIHNFK:Integrase 178-186 TLFCASDAK:gp160 FPVRPQVPL:Nef RAMASDFNL:Integrase 20-28 KLTPLCVTL:gp160 TLNAWVKVI:p24 VIYQYMDDL:RT LVGPTPVNI:Protease TVLDVGDAY:RT PLVKLWYQL:RT TLNFPISPI:POL NTPVFAIKK:RT SEGATPQDL:p24 EKEGKISKI:RT LLWKGEGAV:Integrase 241-249 KLVGKLNWA:RT LTFGWCFKL:Nef YQYMDDLYV:RT GPKVKQWPL:RT WASRELERF:p17 RAIEAQQHL:gp160 GLNKIVRMY:p24 KEKGGLEGL:Nef YFPDWQNYT:Nef WYIKIFIMI:gp160 YVDRFFKTL:p24 FVNTPPLVK:RT DRFFKTLRA:p24 KIQNFRVYY:Integrase 219-227 KLNWASQIY:RT QGWKGSPAI:RT IRLRPGGKK:p17 DLSHFLKEK:Nef KIRLRPGGK:p17 GIPHPAGLK:RT MTKILEPFR:RT AETFYVDGA:RT EEKAFSPEV:p24 CRAPRKKGC:p2p7p1p6 ITLWQRPLV:Protease ALLELES (HIVDB) B0702 A0301 A0301 A0301 B3501 A0201 A0201 A0201 A0201 A0201 B3501 A0201 A0201 A0301 SPRTLNAWV p24 AVFIHNFKR Integrase 179-187 MAVFIHNFK Integrase 178-186 LVGPTPVNI Protease TVLDVGDAY RT GLNKIVRMY p24 SEGATPQDL p24 PLVKLWYQL RT LLWKGEGAV Integrase 241-249 FVNTPPLVK RT KLVGKLNWA RT YQYMDDLYV RT QGWKGSPAI RT GIPHPAGLK RT LTFGWCFKL Nef TLNFPISPI POL KLNWASQIY RT Total: 17 peptides 16-24 51-59 68-76 76-84 107-115 137-145 44-52 421-429 121-129 19-27 179-187 76-84 107-115 421-429 POL 57-65 44-52 42-50 416-424 259-267 181-189 151-159 93-101 137-145 B5101 A0201 A0201 A0201 A0201 B0801 B3501 B5101 B1501 C0304 B1501 B4002 A1 B3701 B5701 A2402 A2601 A1101 B1402 A3002 A3002 B5101 B2705 A0301 A0301 B0301 A0301 A0301 B4501 B4415 B1402 259-267 137-145 181-189 18-26 36-44 557-565 137-145 92-100 120-128 680-688 164-172 416-424 166-174 263-271 263-271 151-159 19-27 86-94 18-26 93-101 164-172 437-445 28-36 42-50 03-11 Experimental HLAI binding from HIV database Total: 42 peptides

  34. Extended HLAI binding Profile of Conserved HIV1 CTL Epitopes From HIV1 Infection H < 0.5 POS 16-24 CTL SOURCE ALLELES (HIVDB) B0702 B7 A0301 A0301 A0201 A2 B3501 B35 B1501 B60 A0201 A0201 A1101 A0201 A0201 B5101 B62 A0301 A0201 A2 A3002 A30 HLA NÞ % COV 8 6 6 6 4 3 5 3 4 1 1 1 1 1 1 2 2 ALLELES (HIVDB + PREDICTED) B0702 B3501 B5101 B5102 B5103 B5301 B5401 B5502 A0301 A1101 A3101 A3301 A6601 A6801 A0301 A1101 A3101 A3301 A6601 A6801 A0201 A0202 A0205 A0209 B1501 B1516 B1501 B3501 B5701 C0304 A0203 A1 B1501 A2902 B39011 B4002 B4402 B4403 A0201 A0202 A0203 A0201 A0204 A0205 A0209 A1101 A0201 A0201 B5101 A0301 A0201 A0201 A0207 A1 A3002 SPRTLNAWV p24 AVFIHNFKR Integrase 179-187 MAVFIHNFK Integrase 178-186 LVGPTPVNI Protease TVLDVGDAY RT GLNKIVRMY p24 SEGATPQDL p24 PLVKLWYQL RT LLWKGEGAV Integrase 241-249 FVNTPPLVK RT KLVGKLNWA RT YQYMDDLYV RT QGWKGSPAI RT GIPHPAGLK RT LTFGWCFKL Nef TLNFPISPI POL KLNWASQIY RT 0.35 0.35 0.35 0.27 0.26 0.13 0.2 0.26 0.18 0.05 0.18 0.18 0.02 0.02 0.18 0.23 0.03 76-84 107-115 137-145 44-52 421-429 416-424 259-267 181-189 151-159 93-101 137-145 263-271 Apply an algorithm algorithm to identify combinations of epitopes providing a population coverage of 95% A minimum of 5 peptides are required to cover the whole population

  35. Selected HIV1 Epitopes # 37 epitopes resulting from parsing out all epitopes with H ≤ 1 sites with regard to a HIV1 all clades consensus sequence # Epitope selection: There are 52 different combinations of 5 peptides each with a predicted 95% coverage. PREDICTIONS Restriction EXPERIMENTAL Position 16-24 179-187 51-59 68-76 20-28 19-27 179-187 76-84 107-115 421-429 EPITOPE SPRTLNAWV K AVFIHNFKR K TLFCASDAK A FPVRPQVPL R RAMASDFNL P TLNAWVKVI E VIYQYMDDL Y LVGPTPVNI I TVLDVGDAY F PLVKLWYQL E TLNFPISPI E NTPVFAIKK K EKEGKISKI G LLWKGEGAV V LTFGWCFKL V YQYMDDLYV G GPKVKQWPL T RAIEAQQHL L GLNKIVRMY S YFPDWQNYT P WYIKIFIMI V YVDRFFKTL R FVNTPPLVK L KIQNFRVYY R DRFFKTLRA E COV 0.35 B0702 B3501 B5101 B5102 B5103 B5301 B5401 B5502 0.35 A0301 A1101 A3101 A3301 A6601 A6801 0.32 A0301 A1101 A3101 A3301 A6801 0.32 A2902 B0702 B3501 B5101 B5102 B5103 B5301 B5401 0.31 A0201 B2709 C0304 0.29 A0201 A0202 A0203 A0204 A0206 0.28 A0201 A0205 A0207 A0214 0.27 A0201 A0202 A0205 A0209 B1501 B1516 0.26 B1501 B3501 B5701 C0304 0.26 A0201 A0202 A0203 0.23 A0201 A0207 0.22 A0301 A6601 C0102 0.19 B2701 B3801 B39011 B3909 B4402 B5101 B8 0.18 A0201 A0204 A0205 A0209 0.18 A0201 0.18 A0201 0.17 B0702 B0801 B3501 B8 0.13 B1501 B1517 B5101 C0304 0.13 A0203 A1 B1501 0.07 A1 B3701 B5701 0.05 A0203 A0206 A2402 0.05 A0203 A0204 A0207 A2601 B3801 0.05 A1101 0.03 A1 A3002 0.03 B1402 B2701 B2702 B2703 B2704 B2705 B2709 Source Restriction p24 Integrase gp160 Nef Integrase p24 RT Protease RT RT POL RT RT Integrase Nef RT RT gp160 p24 Nef gp160 p24 RT Integrase p24 B0702 A0301 A0301 B3501 A0201 A0201 A0201 A0201 B3501 A0201 57-65 42-50 241-249 137-145 181-189 18-26 557-565 137-145 120-128 680-688 164-172 416-424 219-227 166-174 A0301 B5101 A0201 A0201 A0201 B0801 B5101 B1501 C0304 B1501 A1 B3701 B5701 A2402 A2601 A1101 A3002 B1402

  36. Possible caveats in designing epitopes based vaccines  Processing: Epitopes have been isolated from a population, and to be presented by an individual with the right MHC specificity processing must be conserved. Will two individual with the same HLAI profile present the same peptides?  TCR repertoire, immunodominance/competition issues. Is the TCR repertoire large enough to recognize any peptide that is processed and presented? Is immunodominance conserved in individual with the same HLAI profile? Can immunodominance be modulated by priming with a peptides? Are these the right epitopes? These epitopes were isolated from infected people that usually became ill and die: How do we know that these epitopes are "the good ones". Peptide predictions suggest the presence of new unidentified T-cell epitopes

  37. MIF BIOINFORMATICS Jonn-Paul Glutting Hong Zhang Ellis Reinherz

More Related