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Immunological Bioinformatics

Immunological Bioinformatics. The Immunological Bioinformatics group. 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

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Immunological Bioinformatics

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  1. Immunological Bioinformatics

  2. The Immunological Bioinformatics group • 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 • 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

  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 Parasite http://tpeeaupotable.ifrance.com/ma%20photo/bilharzoze.jpg Bacteria Virus http://en.wikipedia.org/wiki/Image:Aids_virus.jpg http://www.uni-heidelberg.de/zentral/ztl/grafiken_bilder/bilder/e-coli.jpg

  8. Adaptive immune response • Signal induced • Pathogens • Antigens • Epitopes B Cell T Cell

  9. Humoral immunity Cartoon by Eric Reits

  10. Paratope Antibody - Antigen interaction Antigen The antibody recognizes structural properties of the surface of the antigen Fab Epitope Antibody

  11. Cellular Immunity

  12. Anchor positions MHC class I with peptide

  13. HLA specificity clustering A0201 A6802 A0101 B0702

  14. Prediction of HLA binding specificityHistorical 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 Which peptide is most likely to bind? Which peptide second? 84nM 23nM 309nM 11.9 14.7 4.3 RLLDDTPEV GLLGNVSTV ALAKAAAAL

  17. 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 Example from real life

  18. Prediction accuracy Pearson correlation 0.45 Measured affinity Prediction score

  19. Predictive performance

  20. Higher order sequence correlations • Neural networks can learn higher order correlations! • What does this mean? 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? S S => 0 L S => 1 S L => 1 L L => 0 No linear function can learn this (XOR) pattern

  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: • MLPQWESNTL = MLPWESNTL • 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 ILRGTSFVYV -0.11 + 0.09 - 0.42 - 0.3 = -0.74 Peters et el., 2003. JI, 171: 1741.

  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 HLA affinity Proteasomal cleavage TAP affinity 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 ...

  38. Large scale method validation HIV A3 epitope predictions

  39. Case I: SARS Sylvester-Hvid et al, Tissue Antigens. 2004

  40. Sars virus HLA ligands 75% of predicted peptides were binding with an IC50 <500 nM

  41. Case II:Discovery of conserved Class I epitopes in Human Influenza Virus H1N1 Wang et al., Vaccine 2007

  42. Pox Strategy

  43. Influenza • We selected the Influenza peptides with the top 15 combined scores with conservation p9 > 70% for each pf the 12 supertypes. • 180 peptides selected • 167 tested for binding and CTL response • 89 (53%) of the influenza peptides tested have an affinity better than 500nM

  44. 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

  45. ELISPOT assay • Measure number of white blood cells that in vitro produce interferon-g 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 FLDVMESM FLDVMESM FLDVMESM FLDVMESM FLDVMESM Two spots

  46. Peptides positive in ELISPOT assay

  47. 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))

  48. EpiSelect Select peptide with maximal coverage Top Scoring Peptides Genotype 1 Genotype 2 Select peptide with maximal coverage preferring uncovered strains Genotype 3 Genotype 4 Genotype 5 Genotype 6 Select peptide with maximal coverage preferring lowest covered strains Repeat until the desired number of peptides is selected

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