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  • Administration of a substance to a person with the purpose of preventing a disease Traditionally composed of a killed or weakened micro organism Vaccination works by creating a type of immune response that enables the memory cells to later respond to a similar organism before it can cause disease
  • 31 patients with different ethnicity infected with different subtypes. 30 out of 31 had response towards at least one peptide. 116 of our 185 peptides (62%) did induce a response in at least one patient. 21 of the recognized peptides induced a response in >4 patients (13% of the study subjects).
  • Transcript

    • 1. Immunological Bioinformatics
    • 2. The Immunological Bioinformatics group
      • Immunological Bioinformatics group, CBS, Technical University of Denmark ( www.cbs.dtu.dk )
      • Ole Lund, Group Leader
      • Morten Nielsen, Associate Professor
      • Claus Lundegaard , Associate Professor
      • Jean Vennestrøm, post doc.
      • Thomas Blicher (50%), post doc.
      • Mette Voldby Larsen, PhD student
      • Pernille Haste Andersen, PhD student
      • Sune Frankild, PhD student
      • Sheila Tang, PhD student
      • Thomas Rask (50%), PhD student
      • Nicolas Rapin , PhD student
      • Ilka Hoff , PhD student
      • Jorid Sørli, PhD student
      • Hao Zhang, PhD student
      • MSc students
      • Collaborators
      • IMMI, University of Copenhagen
      • Søren Buus MHC binding
      • Mogens H Claesson Elispot Assay
      • La Jolla Institute of Allergy and Infectious Diseases
      • Allesandro Sette Epitope database
      • Bjoern Peters
      • Leiden University Medical Center
      • Tom Ottenhoff Tuberculosis
      • Michel Klein
      • Ganymed
      • Ugur Sahin Genetic library
      • University of Tubingen
      • Stefan Stevanovic MHC ligands
      • INSERM
      • Peter van Endert Tap binding
      • University of Mainz
      • Hansjörg Schild Proteasome
      • Schafer-Nielsen
      • Claus Schafer-Nielsen Peptide synthesis
      • ImmunoGrid
      • Elda Rossi Simulation of the
      • Vladimir Brusic Immune system
      • University of Utrectht
      • Can Kesmir Ideas
    • 3. Figure 1-20
    • 4. Effectiveness of vaccines 1958 start of small pox eradication program
    • 5. The Immune System
      • The innate immune system
      • The adaptive immune system
    • 6. The innate immune system
      • Unspecific
      • Antigen independent
      • Immediate response
      • No training/selection hence no memory
      • Pathogen independent (but response might be pathogen type dependent)
    • 7. The adaptive immune system
      • Pathogen specific
        • Humoral
        • Cellular
      http://www.uni-heidelberg.de/zentral/ztl/grafiken_bilder/bilder/e-coli.jpg Bacteria http://en.wikipedia.org/wiki/Image:Aids_virus.jpg Virus http://tpeeaupotable.ifrance.com/ma%20photo/bilharzoze.jpg Parasite
    • 8. Adaptive immune response
      • Signal induced
        • Pathogens
          • Antigens
            • Epitopes
      B Cell T Cell
    • 9. Humoral immunity Cartoon by Eric Reits
    • 10. Antibody - Antigen interaction Fab Antigen Epitope Antibody The antibody recognizes structural properties of the surface of the antigen Paratope
    • 11. Cellular Immunity
    • 12. MHC class I with peptide Anchor positions
    • 13. HLA specificity clustering A0201 A0101 A6802 B0702
    • 14. Prediction of HLA binding specificity Historical overview
      • Simple Motifs
        • Allowed/non allowed amino acids
      • Extended motifs
        • Amino acid preferences ( SYFPEITHI)
        • Anchor/Preferred/other amino acids
      • Hidden Markov models
        • Peptide statistics from sequence alignment
      • SVMs and neural networks
        • Can take sequence correlations into account
       
    • 15. Sequence information SLLPAIVEL YLLPAIVHI TLWVDPYEV GLVPFLVSV KLLEPVLLL LLDVPTAAV LLDVPTAAV LLDVPTAAV LLDVPTAAV VLFRGGPRG MVDGTLLLL YMNGTMSQV MLLSVPLLL SLLGLLVEV ALLPPINIL TLIKIQHTL HLIDYLVTS ILAPPVVKL ALFPQLVIL GILGFVFTL STNRQSGRQ GLDVLTAKV RILGAVAKV QVCERIPTI ILFGHENRV ILMEHIHKL ILDQKINEV SLAGGIIGV LLIENVASL FLLWATAEA SLPDFGISY KKREEAPSL LERPGGNEI ALSNLEVKL ALNELLQHV DLERKVESL FLGENISNF ALSDHHIYL GLSEFTEYL STAPPAHGV PLDGEYFTL GVLVGVALI RTLDKVLEV HLSTAFARV RLDSYVRSL YMNGTMSQV GILGFVFTL ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CLGGLLTMV FIAGNSAYE KLGEFYNQM KLVALGINA DLMGYIPLV RLVTLKDIV MLLAVLYCL AAGIGILTV YLEPGPVTA LLDGTATLR ITDQVPFSV KTWGQYWQV TITDQVPFS AFHHVAREL YLNKIQNSL MMRKLAILS AIMDKNIIL IMDKNIILK SMVGNWAKV SLLAPGAKQ KIFGSLAFL ELVSEFSRM KLTPLCVTL VLYRYGSFS YIGEVLVSV CINGVCWTV VMNILLQYV ILTVILGVL KVLEYVIKV FLWGPRALV GLSRYVARL FLLTRILTI HLGNVKYLV GIAGGLALL GLQDCTMLV TGAPVTYST VIYQYMDDL VLPDVFIRC VLPDVFIRC AVGIGIAVV LVVLGLLAV ALGLGLLPV GIGIGVLAA GAGIGVAVL IAGIGILAI LIVIGILIL LAGIGLIAA VDGIGILTI GAGIGVLTA AAGIGIIQI QAGIGILLA KARDPHSGH KACDPHSGH ACDPHSGHF SLYNTVATL RGPGRAFVT NLVPMVATV GLHCYEQLV PLKQHFQIV AVFDRKSDA LLDFVRFMG VLVKSPNHV GLAPPQHLI LLGRNSFEV PLTFGWCYK VLEWRFDSR TLNAWVKVV GLCTLVAML FIDSYICQV IISAVVGIL VMAGVGSPY LLWTLVVLL SVRDRLARL LLMDCSGSI CLTSTVQLV VLHDDLLEA LMWITQCFL SLLMWITQC QLSLLMWIT LLGATCMFV RLTRFLSRV YMDGTMSQV FLTPKKLQC ISNDVCAQV VKTDGNPPE SVYDFFVWL FLYGALLLA VLFSSDFRI LMWAKIGPV SLLLELEEV SLSRFSWGA YTAFTIPSI RLMKQDFSV RLPRIFCSC FLWGPRAYA RLLQETELV SLFEGIDFY SLDQSVVEL RLNMFTPYI NMFTPYIGV LMIIPLINV TLFIGSHVV SLVIVTTFV VLQWASLAV ILAKFLHWL STAPPHVNV LLLLTVLTV VVLGVVFGI ILHNGAYSL MIMVKCWMI MLGTHTMEV MLGTHTMEV SLADTNSLA LLWAARPRL GVALQTMKQ GLYDGMEHL KMVELVHFL YLQLVFGIE MLMAQEALA LMAQEALAF VYDGREHTV YLSGANLNL RMFPNAPYL EAAGIGILT TLDSQVMSL STPPPGTRV KVAELVHFL IMIGVLVGV ALCRWGLLL LLFAGVQCQ VLLCESTAV YLSTAFARV YLLEMLWRL SLDDYNHLV RTLDKVLEV GLPVEYLQV KLIANNTRV FIYAGSLSA KLVANNTRL FLDEFMEGV ALQPGTALL VLDGLDVLL SLYSFPEPE ALYVDSLFF SLLQHLIGL ELTLGEFLK MINAYLDKL AAGIGILTV FLPSDFFPS SVRDRLARL SLREWLLRI LLSAWILTA AAGIGILTV AVPDEIPPL FAYDGKDYI AAGIGILTV FLPSDFFPS AAGIGILTV FLPSDFFPS AAGIGILTV FLWGPRALV ETVSEQSNV ITLWQRPLV
    • 16. Scoring a sequence to a weight matrix
      • Score sequences to weight matrix by looking up and adding L values from the matrix
      A R N D C Q E G H I L K M F P S T W Y V 1 0.6 0.4 -3.5 -2.4 -0.4 -1.9 -2.7 0.3 -1.1 1.0 0.3 0.0 1.4 1.2 -2.7 1.4 -1.2 -2.0 1.1 0.7 2 -1.6 -6.6 -6.5 -5.4 -2.5 -4.0 -4.7 -3.7 -6.3 1.0 5.1 -3.7 3.1 -4.2 -4.3 -4.2 -0.2 -5.9 -3.8 0.4 3 0.2 -1.3 0.1 1.5 0.0 -1.8 -3.3 0.4 0.5 -1.0 0.3 -2.5 1.2 1.0 -0.1 -0.3 -0.5 3.4 1.6 0.0 4 -0.1 -0.1 -2.0 2.0 -1.6 0.5 0.8 2.0 -3.3 0.1 -1.7 -1.0 -2.2 -1.6 1.7 -0.6 -0.2 1.3 -6.8 -0.7 5 -1.6 -0.1 0.1 -2.2 -1.2 0.4 -0.5 1.9 1.2 -2.2 -0.5 -1.3 -2.2 1.7 1.2 -2.5 -0.1 1.7 1.5 1.0 6 -0.7 -1.4 -1.0 -2.3 1.1 -1.3 -1.4 -0.2 -1.0 1.8 0.8 -1.9 0.2 1.0 -0.4 -0.6 0.4 -0.5 -0.0 2.1 7 1.1 -3.8 -0.2 -1.3 1.3 -0.3 -1.3 -1.4 2.1 0.6 0.7 -5.0 1.1 0.9 1.3 -0.5 -0.9 2.9 -0.4 0.5 8 -2.2 1.0 -0.8 -2.9 -1.4 0.4 0.1 -0.4 0.2 -0.0 1.1 -0.5 -0.5 0.7 -0.3 0.8 0.8 -0.7 1.3 -1.1 9 -0.2 -3.5 -6.1 -4.5 0.7 -0.8 -2.5 -4.0 -2.6 0.9 2.8 -3.0 -1.8 -1.4 -6.2 -1.9 -1.6 -4.9 -1.6 4.5 RLLDDTPEV GLLGNVSTV ALAKAAAAL Which peptide is most likely to bind? Which peptide second? 11.9 14.7 4.3 84nM 23nM 309nM
    • 17. Example from real life
      • 10 peptides from MHCpep database
      • Bind to the MHC complex
      • Relevant for immune system recognition
      • Estimate sequence motif and weight matrix
      • Evaluate motif “correctness” on 528 peptides
      • ALAKAAAAM
      • ALAKAAAAN
      • ALAKAAAAR
      • ALAKAAAAT
      • ALAKAAAAV
      • GMNERPILT
      • GILGFVFTM
      • TLNAWVKVV
      • KLNEPVLLL
      • AVVPFIVSV
    • 18. Prediction accuracy Pearson correlation 0.45 Prediction score Measured affinity
    • 19. Predictive performance
    • 20. Higher order sequence correlations
      • Neural networks can learn higher order correlations!
        • What does this mean?
      S S => 0 L S => 1 S L => 1 L L => 0 No linear function can learn this (XOR) pattern Say that the peptide needs one and only one large amino acid in the positions P3 and P4 to fill the binding cleft How would you formulate this to test if a peptide can bind?
    • 21. Mutual information   313 binding peptides 313 random peptides
    • 22. Sequence encoding (continued)
      • Sparse encoding
        • V:0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
        • L:0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
        • V . L=0 (unrelated)
      • Blosum encoding
        • V: 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4
        • L:-1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1
        • R:-1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3
        • V . L = 0.88 (highly related)
        • V . R = -0.08 (close to unrelated)
    • 23. Evaluation of prediction accuracy
    • 24. Network ensembles
      • No one single network with a particular architecture and sequence encoding scheme, will constantly perform the best
      • Also for Neural network predictions will enlightened despotism fail
        • For some peptides, BLOSUM encoding with a four neuron hidden layer can best predict the peptide/MHC binding, for other peptides a sparse encoded network with zero hidden neurons performs the best
        • Wisdom of the Crowd
          • Never use just one neural network
          • Use Network ensembles
    • 25. Evaluation of prediction accuracy ENS : Ensemble of neural networks trained using sparse, Blosum, and hidden Markov model sequence encoding
    • 26. NetMHC-3.0 update
      • IEDB + more proprietary data
        • Higher accuracy for existing ANNs
        • More Human alleles
        • Non human alleles (Mice + Primates)
        • Prediction of 8mer binding peptides for some alleles
        • Prediction of 10- and 11mer peptides for all alleles
        • Outputs to spread sheet
    • 27. Prediction of 10- and 11mers using 9mer prediction tools
      • Approach:
      • For each peptide of length L create 6 pseudo peptides deleting a sliding window of L - 9 always keeping pos. 1,2,3, and 9
      • Example:
      • MLP QWESNT L = MLP WESNT L
      • MLPQESNTL
      • MLPQWSNTL
      • MLPQWENTL
      • MLPQWESTL
      • MLPQWESNL
    • 28. Prediction of 10- and 11mers using 9mer prediction tools
    • 29. Prediction of 10- and 11mers using 9mer prediction tools
      • Final prediction = average of the 6 log scores:
      • (0.477+0.405+0.564+0.505+0.559+0.521)/6
      • = 0.505
      • Affinity:
      • Exp(log(50000)*(1 - 0.505)) = 211.5 nM
    • 30. Prediction using ANN trained on 10mer peptides
    • 31. Prediction of 10- and 11mers using 9mer prediction tools
    • 32. Cellular Immunity
    • 33. Proteasome specificity
      • Low polymorphism
        • Constitutive & Immuno-proteasome
      • Evolutionary conserved
      • Stochastic and low specificity
        • Only 70-80% of the cleavage sites are reproduced in repeated experiments
    • 34. Proteasome specificity
      • NetChop is one of the best available cleavage method
        • www.cbs.dtu.dk/services/NetChop-3.0
    • 35. Predicting TAP affinity 9 meric peptides >9 meric Peters et el., 2003. JI, 171: 1741. ILR GTSFVY V -0.11 + 0.09 - 0.42 - 0.3 = -0.74
    • 36. Integrating all three steps (protesaomal cleavage, TAP transport and MHC binding) should lead to improved identification of peptides capable of eliciting CTL responses Integration?
    • 37. Identifying CTL epitopes 1 EBN3_EBV YQAYSSWMY 2.56 1.00 0.03 0.34 0.99 0.02 0.01 0.75 0.94 0.92 2.97 0 2.80 2 EBN3_EBV QSDETATSH 2.22 0.01 0.28 0.88 0.04 0.83 0.51 0.30 0.11 0.99 -0.80 0 2.28 3 EBN3_EBV PVSPAVNQY 1.55 0.01 0.97 0.01 0.22 0.21 1.00 0.02 0.04 1.00 2.63 0 1.78 4 EBN3_EBV AYSSWMYSY 1.31 0.34 0.99 0.02 0.01 0.75 0.94 0.92 0.09 1.00 3.28 1 1.58 5 EBN3_EBV LAAGWPMGY 1.02 1.00 0.97 0.22 0.01 0.18 0.01 0.06 0.01 1.00 3.01 0 1.27 6 EBN3_EBV IVQSCNPRY 0.99 0.10 0.97 0.50 0.05 0.01 0.01 0.01 0.02 0.93 3.19 0 1.24 7 EBN3_EBV FLQRTDLSY 0.94 0.46 0.99 0.02 0.82 0.07 0.01 0.63 0.01 0.96 2.79 0 1.18 8 EBN3_EBV YTDHQTTPT 1.15 1.00 0.01 0.42 0.02 0.04 0.01 0.02 0.54 0.14 -0.87 0 1.12 9 EBN3_EBV GTDVVQHQL 0.96 0.01 0.02 0.03 0.99 1.00 0.02 0.46 0.30 1.00 0.53 0 1.09 ... HLA affinity Proteasomal cleavage TAP affinity
    • 38.  
    • 39.  
    • 40. Large scale method validation HIV A3 epitope predictions
    • 41. Case I: SARS Sylvester-Hvid et al, Tissue Antigens. 2004
    • 42. Sars virus HLA ligands 75% of predicted peptides were binding with an IC 50 <500 nM
    • 43. Case II: Discovery of conserved Class I epitopes in Human Influenza Virus H1N1 Wang et al., Vaccine 2007
    • 44. Pox Strategy
    • 45. Influenza
      • We selected the Influenza peptides with the top 15 combined scores with conservation p9 > 70% for each pf the 12 supertypes.
      • 180 peptides select ed
      • 167 tested for binding and CTL response
      • 89 (53%) of the influenza peptides tested have an affinity better than 500nM
    • 46. Donors
      • 35 normal healthy blood donors
      • 35-65 years old
        • Expected to have had influenza more than 3 times
      • HLA typed by SBT for HLA A and B
    • 47. ELISPOT assay
      • Measure number of white blood cells that in vitro produce interferon-  in response to a peptide
      • A positive result means that the immune system have earlier reacted to the peptide (during a response of a vaccine/natural infection)
      FLDVMESM Two spots FLDVMESM FLDVMESM FLDVMESM FLDVMESM FLDVMESM
    • 48. Peptides positive in ELISPOT assay Protein Supertype Affinity PA SA Comb Cons_9 Polymerase PB1 A1 nt 0.62 3.25 3.74 0.90 Nucleoprotein NP A1 nt 0.55 2.89 3.35 0.80 Polymerase PB1 A2 51 0.46 1.09 1.25 0.76 Polymerase PB1 A26 5 11.3 1.59 2.05 0.98 Polymerase PB1 B27 246 0.43 1.54 2.02 0.97 Nucleoprotein NP B27 37 0.37 1.34 1.72 0.87 Matrix protein M1 B39 nt 7.08 0.99 1.29 0.84 Nucleoprotein NP B58 41 0.44 1.51 1.64 0.99 Polymerase PB1 B62 178 0.40 0.96 1.47 0.97 Polymerase PB1 B62 87 0.46 1.08 1.45 0.92 Polymerase PB1 B7 5 0.67 1.87 2.08 0.99 Polymerase PA B8 nb 7.81 1.05 1.28 0.77 Protein: Common name for protein Supertype: HLA supertype that the peptide is predicted to bind to Affinity: Measured affinity (kD in nM) PA: Predicted affinity SA: Scaled affinity Comb: Combined score calculated as in Larsen et al., 2005 cons_9: Fraction of clusters (with more than 98% sequence identity) that contain the 9mer nb: non binder nt: not tested
    • 49. Peptides positive in ELISPOT assay
    • 50. Conservation of epitopes
      • Number of 9mers 100% conserved:
        • 10/12 conserved in Influenza A virus (A/Goose/Guangdong/1/96(H5N1))
        • 11/12 conserved in Influenza A virus (A/chicken/Jilin/9/2004(H5N1))
    • 51. EpiSelect Genotype 1 Top Scoring Peptides Genotype 2 Genotype 3 Genotype 4 Genotype 5 Genotype 6 Select peptide with maximal coverage Select peptide with maximal coverage preferring uncovered strains Select peptide with maximal coverage preferring lowest covered strains Repeat until the desired number of peptides is selected
    • 52. HCV Results - B7 Genotype 1 Genotype 2 Genotype 3 Genotype 4 Genotype 5 Genotype 6 QPRGRRQPI Peptide Predicted affinity (nM) 5 SPRGSRPSW 43 Genome Coverage 5 4 DPRRRSRNL * 3 66 RARAVRAKL Peptides 6 3 TPAETTVRL * 38 3 3 3 2 3 4 3 * Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database
    • 53. Ongoing work
      • Selection of epitopes covering host (HLA) and pathogen variability
      • Selection of diagnostic peptides in TB
      • Predict cross reactivity (T and B cell)
        • Applications in epitope prediction, autoimmune diseases, transplantation
      • Virulence factor discovery by comparative genomics
      • Function-antigenecity studies
      • Bioinformatics immune system simulation
    • 54.  

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