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 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

Figure 1-20 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

Effectiveness of vaccines 1958 start of small pox eradication program

The Immune System The innate immune system The adaptive immune system

The innate immune system Unspecific Antigen independent Immediate response No training/selection hence no memory Pathogen independent (but response might be pathogen type dependent)

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

Adaptive immune response Signal induced Pathogens Antigens Epitopes B Cell T Cell

Humoral immunity Cartoon by Eric Reits

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

Cellular Immunity

MHC class I with peptide Anchor positions

HLA specificity clustering B0702

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

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

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? RLLDDTPEV GLLGNVSTV ALAKAAAAL 11.9 14.7 4.3 84nM 23nM 309nM

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

Prediction accuracy Measured affinity Prediction score Pearson correlation 0.45 Measured affinity Prediction score

Predictive performance

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

Mutual information   313 binding peptides 313 random peptides

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)

Evaluation of prediction accuracy

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

Evaluation of prediction accuracy ENS: Ensemble of neural networks trained using sparse, Blosum, and hidden Markov model sequence encoding

IEDB + more proprietary data 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

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

Prediction of 10- and 11mers using 9mer prediction tools

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

Prediction using ANN trained on 10mer peptides

Prediction of 10- and 11mers using 9mer prediction tools

Cellular Immunity

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

Proteasome specificity NetChop is one of the best available cleavage method www.cbs.dtu.dk/services/NetChop-3.0

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.

Integration? Integrating all three steps (protesaomal cleavage, TAP transport and MHC binding) should lead to improved identification of peptides capable of eliciting CTL responses

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 ...

Large scale method validation HIV A3 epitope predictions

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

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

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

Pox Strategy

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

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

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

Peptides positive in ELISPOT assay

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))

Repeat until the desired number of peptides is selected 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

HCV Results - B7 Genotype 1 Genotype 2 Genotype 3 Genotype 4 Peptides Genome Coverage Predicted affinity (nM) Peptide Genotype 1 4 QPRGRRQPI 5 5 Genotype 2 3 SPRGSRPSW 43 4 2 Genotype 3 DPRRRSRNL* 66 3 Genotype 4 3 RARAVRAKL 6 3 Genotype 5 3 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). TPAETTVRL* 38 3 Genotype 6 3 * Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database

Selection of epitopes covering host (HLA) and pathogen variability 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