predicting peptide mhc interactions

Predicting peptide MHC interactions

Morten Nielsen,CBS, Depart of Systems

Biology, DTU

MHC Class I pathwayFinding the needle in the haystack

Figure by Eric A.J. Reits

1/200 peptides make to the surface

Or, Finding the needle in the haystack

Objectives

• Visualization of binding motifs– Construction of sequence logos

• Understand the concepts of weight matrix construction– One of the most important methods of

bioinformatics• A few word on Artificial neural networks• MHC binding rules

– No other factors in the MHC (I and II) pathways are (as) decisive for T cell epitope identification

• All known T cell epitopes have specific MHC restrictions matching their host

• MHC binding is the single most important feature for understanding cellular immunity

Anchor positions

Binding Motif. MHC class I with peptide

SLLPAIVEL YLLPAIVHI TLWVDPYEV GLVPFLVSV KLLEPVLLL LLDVPTAAV LLDVPTAAV LLDVPTAAVLLDVPTAAV VLFRGGPRG MVDGTLLLL YMNGTMSQV MLLSVPLLL SLLGLLVEV ALLPPINIL TLIKIQHTLHLIDYLVTS ILAPPVVKL ALFPQLVIL GILGFVFTL STNRQSGRQ GLDVLTAKV RILGAVAKV QVCERIPTIILFGHENRV ILMEHIHKL ILDQKINEV SLAGGIIGV LLIENVASL FLLWATAEA SLPDFGISY KKREEAPSLLERPGGNEI ALSNLEVKL ALNELLQHV DLERKVESL FLGENISNF ALSDHHIYL GLSEFTEYL STAPPAHGVPLDGEYFTL GVLVGVALI RTLDKVLEV HLSTAFARV RLDSYVRSL YMNGTMSQV GILGFVFTL ILKEPVHGVILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CLGGLLTMV FIAGNSAYE KLGEFYNQMKLVALGINA DLMGYIPLV RLVTLKDIV MLLAVLYCL AAGIGILTV YLEPGPVTA LLDGTATLR ITDQVPFSVKTWGQYWQV TITDQVPFS AFHHVAREL YLNKIQNSL MMRKLAILS AIMDKNIIL IMDKNIILK SMVGNWAKVSLLAPGAKQ KIFGSLAFL ELVSEFSRM KLTPLCVTL VLYRYGSFS YIGEVLVSV CINGVCWTV VMNILLQYVILTVILGVL KVLEYVIKV FLWGPRALV GLSRYVARL FLLTRILTI HLGNVKYLV GIAGGLALL GLQDCTMLVTGAPVTYST VIYQYMDDL VLPDVFIRC VLPDVFIRC AVGIGIAVV LVVLGLLAV ALGLGLLPV GIGIGVLAAGAGIGVAVL IAGIGILAI LIVIGILIL LAGIGLIAA VDGIGILTI GAGIGVLTA AAGIGIIQI QAGIGILLAKARDPHSGH KACDPHSGH ACDPHSGHF SLYNTVATL RGPGRAFVT NLVPMVATV GLHCYEQLV PLKQHFQIVAVFDRKSDA LLDFVRFMG VLVKSPNHV GLAPPQHLI LLGRNSFEV PLTFGWCYK VLEWRFDSR TLNAWVKVVGLCTLVAML FIDSYICQV IISAVVGIL VMAGVGSPY LLWTLVVLL SVRDRLARL LLMDCSGSI CLTSTVQLVVLHDDLLEA LMWITQCFL SLLMWITQC QLSLLMWIT LLGATCMFV RLTRFLSRV YMDGTMSQV FLTPKKLQCISNDVCAQV VKTDGNPPE SVYDFFVWL FLYGALLLA VLFSSDFRI LMWAKIGPV SLLLELEEV SLSRFSWGAYTAFTIPSI RLMKQDFSV RLPRIFCSC FLWGPRAYA RLLQETELV SLFEGIDFY SLDQSVVEL RLNMFTPYINMFTPYIGV LMIIPLINV TLFIGSHVV SLVIVTTFV VLQWASLAV ILAKFLHWL STAPPHVNV LLLLTVLTVVVLGVVFGI ILHNGAYSL MIMVKCWMI MLGTHTMEV MLGTHTMEV SLADTNSLA LLWAARPRL GVALQTMKQGLYDGMEHL KMVELVHFL YLQLVFGIE MLMAQEALA LMAQEALAF VYDGREHTV YLSGANLNL RMFPNAPYLEAAGIGILT TLDSQVMSL STPPPGTRV KVAELVHFL IMIGVLVGV ALCRWGLLL LLFAGVQCQ VLLCESTAVYLSTAFARV YLLEMLWRL SLDDYNHLV RTLDKVLEV GLPVEYLQV KLIANNTRV FIYAGSLSA KLVANNTRLFLDEFMEGV ALQPGTALL VLDGLDVLL SLYSFPEPE ALYVDSLFF SLLQHLIGL ELTLGEFLK MINAYLDKLAAGIGILTV FLPSDFFPS SVRDRLARL SLREWLLRI LLSAWILTA AAGIGILTV AVPDEIPPL FAYDGKDYIAAGIGILTV FLPSDFFPS AAGIGILTV FLPSDFFPS AAGIGILTV FLWGPRALV ETVSEQSNV ITLWQRPLV

Sequence information

Sequence Information• Say that a peptide must have L at P2 in order to bind, and that A,F,W,and Y are found at P1. Which position has most information? • How many questions do I need to ask to tell if a peptide binds looking at only P1 or P2?

Sequence Information• Say that a peptide must have L at P2 in order to bind, and that A,F,W,and Y are found at P1. Which position has most information? • How many questions do I need to ask to tell if a peptide binds looking at only P1 or P2?• P1: 4 questions (at most)• P2: 1 question (L or not)• P2 has the most information

Sequence Information

• Calculate pa at each position• Entropy

• Information content

• Conserved positions– PV=1, P!v=0 => S=0,

I=log(20)• Mutable positions

– Paa=1/20 => S=log(20), I=0

• Say that a peptide must have L at P2 in order to bind, and that A,F,W,and Y are found at P1. Which position has most information? • How many questions do I need to ask to tell if a peptide binds looking at only P1 or P2?• P1: 4 questions (at most)• P2: 1 question (L or not)• P2 has the most information

Information content

A R N D C Q E G H I L K M F P S T W Y V S I1 0.10 0.06 0.01 0.02 0.01 0.02 0.02 0.09 0.01 0.07 0.11 0.06 0.04 0.08 0.01 0.11 0.03 0.01 0.05 0.08 3.96 0.372 0.07 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.00 0.08 0.59 0.01 0.07 0.01 0.00 0.01 0.06 0.00 0.01 0.08 2.16 2.163 0.08 0.03 0.05 0.10 0.02 0.02 0.01 0.12 0.02 0.03 0.12 0.01 0.03 0.05 0.06 0.06 0.04 0.04 0.04 0.07 4.06 0.264 0.07 0.04 0.02 0.11 0.01 0.04 0.08 0.15 0.01 0.10 0.04 0.03 0.01 0.02 0.09 0.07 0.04 0.02 0.00 0.05 3.87 0.455 0.04 0.04 0.04 0.04 0.01 0.04 0.05 0.16 0.04 0.02 0.08 0.04 0.01 0.06 0.10 0.02 0.06 0.02 0.05 0.09 4.04 0.286 0.04 0.03 0.03 0.01 0.02 0.03 0.03 0.04 0.02 0.14 0.13 0.02 0.03 0.07 0.03 0.05 0.08 0.01 0.03 0.15 3.92 0.407 0.14 0.01 0.03 0.03 0.02 0.03 0.04 0.03 0.05 0.07 0.15 0.01 0.03 0.07 0.06 0.07 0.04 0.03 0.02 0.08 3.98 0.348 0.05 0.09 0.04 0.01 0.01 0.05 0.07 0.05 0.02 0.04 0.14 0.04 0.02 0.05 0.05 0.08 0.10 0.01 0.04 0.03 4.04 0.289 0.07 0.01 0.00 0.00 0.02 0.02 0.02 0.01 0.01 0.08 0.26 0.01 0.01 0.02 0.00 0.04 0.02 0.00 0.01 0.38 2.78 1.55

Sequence logos

•Height of a column equal to I•Relative height of a letter is p•Highly useful tool to visualize sequence motifs

High information positions

HLA-A0201

http://www.cbs.dtu.dk/~gorodkin/appl/plogo.html

Characterizing a binding motif from small data sets

ALAKAAAAMALAKAAAANALAKAAAARALAKAAAATALAKAAAAVGMNERPILTGILGFVFTMTLNAWVKVVKLNEPVLLLAVVPFIVSV

Sequence weighting


• Poor or biased sampling of sequence space

• Example P1PA = 2/6PG = 2/6PT = PK = 1/6PC = PD = …PV = 0

}Similar sequencesWeight 1/5

RLLDDTPEV 84 nMGLLGNVSTV 23 nMALAKAAAAL 309 nM

Sequence weightingALAKAAAAMALAKAAAANALAKAAAARALAKAAAATALAKAAAAVGMNERPILTGILGFVFTMTLNAWVKVVKLNEPVLLLAVVPFIVSV

Pseudo counts


• I is not found at position P9. Does this mean that I is forbidden (P(I)=0)?

• No! Use Blosum substitution matrix to estimate pseudo frequency of I at P9

A R N D C Q E G H I L K M F P S T W Y V A 0.29 0.03 0.03 0.03 0.02 0.03 0.04 0.08 0.01 0.04 0.06 0.04 0.02 0.02 0.03 0.09 0.05 0.01 0.02 0.07 R 0.04 0.34 0.04 0.03 0.01 0.05 0.05 0.03 0.02 0.02 0.05 0.12 0.02 0.02 0.02 0.04 0.03 0.01 0.02 0.03 N 0.04 0.04 0.32 0.08 0.01 0.03 0.05 0.07 0.03 0.02 0.03 0.05 0.01 0.02 0.02 0.07 0.05 0.00 0.02 0.03 D 0.04 0.03 0.07 0.40 0.01 0.03 0.09 0.05 0.02 0.02 0.03 0.04 0.01 0.01 0.02 0.05 0.04 0.00 0.01 0.02 C 0.07 0.02 0.02 0.02 0.48 0.01 0.02 0.03 0.01 0.04 0.07 0.02 0.02 0.02 0.02 0.04 0.04 0.00 0.01 0.06 Q 0.06 0.07 0.04 0.05 0.01 0.21 0.10 0.04 0.03 0.03 0.05 0.09 0.02 0.01 0.02 0.06 0.04 0.01 0.02 0.04 E 0.06 0.05 0.04 0.09 0.01 0.06 0.30 0.04 0.03 0.02 0.04 0.08 0.01 0.02 0.03 0.06 0.04 0.01 0.02 0.03 G 0.08 0.02 0.04 0.03 0.01 0.02 0.03 0.51 0.01 0.02 0.03 0.03 0.01 0.02 0.02 0.05 0.03 0.01 0.01 0.02 H 0.04 0.05 0.05 0.04 0.01 0.04 0.05 0.04 0.35 0.02 0.04 0.05 0.02 0.03 0.02 0.04 0.03 0.01 0.06 0.02 I 0.05 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.01 0.27 0.17 0.02 0.04 0.04 0.01 0.03 0.04 0.01 0.02 0.18 L 0.04 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.01 0.12 0.38 0.03 0.05 0.05 0.01 0.02 0.03 0.01 0.02 0.10 K 0.06 0.11 0.04 0.04 0.01 0.05 0.07 0.04 0.02 0.03 0.04 0.28 0.02 0.02 0.03 0.05 0.04 0.01 0.02 0.03 M 0.05 0.03 0.02 0.02 0.02 0.03 0.03 0.03 0.02 0.10 0.20 0.04 0.16 0.05 0.02 0.04 0.04 0.01 0.02 0.09 F 0.03 0.02 0.02 0.02 0.01 0.01 0.02 0.03 0.02 0.06 0.11 0.02 0.03 0.39 0.01 0.03 0.03 0.02 0.09 0.06 P 0.06 0.03 0.02 0.03 0.01 0.02 0.04 0.04 0.01 0.03 0.04 0.04 0.01 0.01 0.49 0.04 0.04 0.00 0.01 0.03 S 0.11 0.04 0.05 0.05 0.02 0.03 0.05 0.07 0.02 0.03 0.04 0.05 0.02 0.02 0.03 0.22 0.08 0.01 0.02 0.04 T 0.07 0.04 0.04 0.04 0.02 0.03 0.04 0.04 0.01 0.05 0.07 0.05 0.02 0.02 0.03 0.09 0.25 0.01 0.02 0.07 W 0.03 0.02 0.02 0.02 0.01 0.02 0.02 0.03 0.02 0.03 0.05 0.02 0.02 0.06 0.01 0.02 0.02 0.49 0.07 0.03 Y 0.04 0.03 0.02 0.02 0.01 0.02 0.03 0.02 0.05 0.04 0.07 0.03 0.02 0.13 0.02 0.03 0.03 0.03 0.32 0.05 V 0.07 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.16 0.13 0.03 0.03 0.04 0.02 0.03 0.05 0.01 0.02 0.27

The Blosum (substitution frequency) matrix

Some amino acids are highly conserved (i.e. C), some have a high change of mutation (i.e. I)

• Calculate observed amino acids frequencies fa

• Pseudo frequency for amino acid b

• Example


Pseudo count estimation

€

gI = 0.2⋅ qI |M + 0.1⋅ qI |R + ...+ 0.3⋅ qI |V + 0.1⋅ qI |LgI = 0.2⋅ 0.1+ 0.1⋅ 0.02 + ...+ 0.3⋅ 0.16 + 0.1⋅ 0.12 = 0.094


Weight on pseudo count• Pseudo counts are important when

only limited data is available• With large data sets only “true”

observation should count

• is the effective number of sequences (N-1), is the weight on prior

– In clustering = #clusters -1– In heuristics = <# different amino

acids in each column> -1

• Example

• If large, p ≈ f and only the observed data defines the motif

• If small, p ≈ g and the pseudo counts (or prior) defines the motif

• is [50-200] normally


Weight on pseudo count

Sequence weighting and pseudo countsALAKAAAAMALAKAAAANALAKAAAARALAKAAAATALAKAAAAVGMNERPILTGILGFVFTMTLNAWVKVVKLNEPVLLLAVVPFIVSV

Position specific weighting

• We know that positions 2 and 9 are anchor positions for most MHC binding motifs– Increase weight on high

information positions

• Motif found on large data set

Weight matrices

• Estimate amino acid frequencies from alignment including sequence weighting and pseudo count

• What do the numbers mean?– P2(V)>P2(M). Does this mean that V enables binding more than M.– In nature not all amino acids are found equally often

• In nature V is found more often than M, so we must somehow rescale with the background

• qM = 0.025, qV = 0.073• Finding 7% V is hence not significant, but 7% M highly

significant

A R N D C Q E G H I L K M F P S T W Y V1 0.08 0.06 0.02 0.03 0.02 0.02 0.03 0.08 0.02 0.08 0.11 0.06 0.04 0.06 0.02 0.09 0.04 0.01 0.04 0.082 0.04 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.11 0.44 0.02 0.06 0.03 0.01 0.02 0.05 0.00 0.01 0.103 0.08 0.04 0.05 0.07 0.02 0.03 0.03 0.08 0.02 0.05 0.11 0.03 0.03 0.06 0.04 0.06 0.05 0.03 0.05 0.074 0.08 0.05 0.03 0.10 0.01 0.05 0.08 0.13 0.01 0.05 0.06 0.05 0.01 0.03 0.08 0.06 0.04 0.02 0.01 0.055 0.06 0.04 0.05 0.03 0.01 0.04 0.05 0.11 0.03 0.04 0.09 0.04 0.02 0.06 0.06 0.04 0.05 0.02 0.05 0.086 0.06 0.03 0.03 0.03 0.03 0.03 0.04 0.06 0.02 0.10 0.14 0.04 0.03 0.05 0.04 0.06 0.06 0.01 0.03 0.137 0.10 0.02 0.04 0.04 0.02 0.03 0.04 0.05 0.04 0.08 0.12 0.02 0.03 0.06 0.07 0.06 0.05 0.03 0.03 0.088 0.05 0.07 0.04 0.03 0.01 0.04 0.06 0.06 0.03 0.06 0.13 0.06 0.02 0.05 0.04 0.08 0.07 0.01 0.04 0.059 0.08 0.02 0.01 0.01 0.02 0.02 0.03 0.02 0.01 0.10 0.23 0.03 0.02 0.04 0.01 0.04 0.04 0.00 0.02 0.25

Weight matrices• A weight matrix is given as

Wij = log(pij/qj)– where i is a position in the motif, and j an amino acid. qj is

the background frequency for amino acid j.

• W is a L x 20 matrix, L is motif length

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

• 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

Scoring a sequence to a weight matrix

RLLDDTPEVGLLGNVSTVALAKAAAAL

Which peptide is most likely to bind?Which peptide second?

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


Prediction accuracy

Pearson correlation 0.45

Prediction score

Mea

sure

d affi

nity

Predictive performance

00.10.20.30.40.50.60.70.8

Pearsons correlation

CC 0.45 0.5 0.6 0.65 0.79

Simple Seq.W Seq.W+SCSeq.W+SC+PW

Large dataset

Summary I. PSSMs

• Sequence logo is a power tool to visualize (binding) motifs– Information content identifies essential

residues for function and/or structural stability

• Weight matrices and sequence profiles can be derived from very limited number of data using the techniques of– Sequence weighting– Pseudo counts

Is there anything beyond weight matrices • The effect on the binding affinity

of having a given amino acid at one position can be influenced by the amino acids at other positions in the peptide (sequence correlations). – Two adjacent amino acids may for

example compete for the space in a pocket in the MHC molecule.

• Artificial neural networks (ANN) are ideally suited to take such correlations into account

Higher order sequence correlations

Neural networks can learn higher order correlations!– What does this mean?

S S => 0L S => 1S L => 1L 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?

Linear functions (like PSSM’s) cannot learn higher order signals

XOR function:0 0 => 01 0 => 10 1 => 11 1 => 0

(1,1)

(1,0)(0,0)

(0,1)

No linear function can separate the points OR

AND

XOR

Error estimates

XOR 0 0 => 01 0 => 10 1 => 11 1 => 0

(1,1)

(1,0)(0,0)

(0,1)Predict0111

Error0001

Mean error: 1/4

Neural networks

v1 v2

Linear function

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y = x1 ⋅v1 + x2 ⋅v2

Neural networks. How does it work?

w12

v1

w21 w2

2

v2

wt2

wt1w11

vt

Input 1 (Bias){

€

O = 11+ exp(−o)

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o = x i∑ ⋅wi

Neural networks

Neural network learning higher order correlations

• How is mutual information calculated?• Information content was calculated as• Gives information in a single position

• Similar relation for mutual information• Gives mutual information between two positions

Mutual information

€

I = paa

∑ log( paqa

)

€

I = paba,b∑ log( pab

pa ⋅ pb)

Mutual information. Example

ALWGFFPVAILKEPVHGVILGFVFTLTLLFGYPVYVGLSPTVWLSYMNGTMSQV GILGFVFTL WLSLLVPFVFLPSDFFPS

P1 P6

P(G1) = 2/9 = 0.22, ..P(V6) = 4/9 = 0.44,..P(G1,V6) = 2/9 = 0.22, P(G1)*P(V6) = 8/81 = 0.10

log(0.22/0.10) > 0€

I = paba,b∑ log( pab

pa ⋅ pb)

Knowing that you have G at P1 allows you to make an educated guess on what you will find at P6. P(V6) = 4/9. P(V6|G1) = 1.0!

313 binding peptides 313 random peptides

Mutual information

• A Network contains a very large set of parameters

– A network with 5 hidden neurons predicting binding for 9meric peptides has more than 9x20x5=900 weights

• Over fitting is a problem• Stop training when test performance is optimal

Neural network training

yearsTe

mpe

ratu

re

Neural network training. Cross validation

Cross validation

Train on 4/5 of dataTest on 1/5=>Produce 5 different neural networks each with a different prediction focus

Neural network training curve

Maximum test set performanceMost cable of generalizing

Network ensembles

5 fold training

0.7

0.75

0.8

0.85

0.9

0.95

syn.00 syn.01 syn.02 syn.03 syn.04

Pearsons correlation

TrainTest

Which network to choose?

5 fold training

0.7

0.75

0.8

0.85

0.9

0.95

syn.00 syn.01 syn.02 syn.03 syn.04 ens

Pearson correlation

TrainTestEval

The Wisdom of the Crowds• The Wisdom of Crowds. Why the Many

are Smarter than the Few. James Surowiecki

One day in the fall of 1906, the British scientist Fracis Galton left his home and headed for a

country fair… He believed that only a very few people had the characteristics necessary to keep

societies healthy. He had devoted much of his career to measuring those characteristics, in fact, in order to prove that the vast majority of people

did not have them. … Galton came across a weight-judging competition…Eight hundred people tried

their luck. They were a diverse lot, butchers, farmers, clerks and many other no-experts…The

crowd had guessed … 1.197 pounds, the ox weighted 1.198

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

Motif PSSM NN-ensemble0.5

0.6

0.7

0.8

0.9

1

NN-ensemble: Ensemble of neural networks trained using sparse, Blosum

NetMHCwww.cbs.dtu.dk/services/NetMHC

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

Figure by Melani Zolfagharian Khodaie and Mikael Holm Thomsen


• 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


9-10 mer approximation

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

approach

Pearson correlation coefficient

9mer apprx 10mer

Predicting binding for longer-mersAllele Length Peptide % RankH2-Db 11 SGVENPGGYCL 1.5H2-Ld 12 IPQSLDSWWTSL 0.1

HLA-A*0101 11 YSEHPTFTSQY 0.05HLA-A*0101 11 SSDYVIPIGTY 0.05HLA-A*0101 11 FLEGNEVGKTY 1.5HLA-A*0201 11 MLMAQEALAFL 0.15HLA-A*0201 11 GLAPPQHLIRV 0.8HLA-A*0201 11 LLPENNVLSPL 1HLA-A*0301 11 RLRDLLLIVTR 1HLA-A*1101 11 ACQGVGGPGHK 15HLA-A*1101 12 SVLGPISGHVLK 0.1HLA-A*3101 11 STLPETTVVRR 0.8HLA-A*6801 11 FVFPTKDVALR 0.1HLA-B*0702 11 SPSVDKARAEL 0.4HLA-B*0702 11 RPHERNGFTVL 0.05HLA-B*0702 13 RPQGGSRPEFVKL 0.3HLA-B*2702 11 RRARSLSAERY 0.3HLA-B*3501 11 HPVGEADYFEY 0.1HLA-B*3501 11 EPLPQGQLTAY 0.2HLA-B*3501 14 LPAVVGLSPGEQEY 0.05HLA-B*3501 12 TPRLPSSADVEF 0.8HLA-B*3508 13 LPEPLPQGQLTAY 0.1HLA-B*3508 12 CPSQEPMSIYVY 0.05HLA-B*4402 11 SELFRSGLDSY 0.15HLA-B*5703 11 KAFSPEVIPMF 0.4

So we can find the needle in the haystack

• At least is some haystacks

• Within a host limited number of loci (genes)• only 6 different class I molecules (two A, B and C)• only upto 12 different class II molecules

• Within a population > 100 alleles per locus

Polymorphism of MHC

~1% probability that an MHC molecule binds a peptide Different hosts sample different peptides from same pathogen.

More MHC molecules: more diversity in the presented peptides

Figure by Thomas Blicher ([email protected]

MHC polymorphism

• Heterozygote advantage– Heterozygotes have a selective advantage

because they can present more peptides (Hughes.n88).

• Coevolution– Pathogens avoid presentation on common MHC

alleles (HIV)– Frequency dependent selection

Immunological benefits of MHC polymorphism

Variations among populations• Allele frequency varies between populations• Databases of HLA and MHC frequencies– allelefrequencies.net– dbMHC

http://allelefrequencies.net/

http://www.ncbi.nlm.nih.gov/gv/mhc/

• Few human beings will share the same set of HLA alleles– Different persons will react to a pathogen

infection in a non-similar manner• A CTL based vaccine must include

epitopes specific for each HLA allele in a population– A CTL based vaccine must consist of ~800

HLA class I epitopes and ~400 class II epitopes

Heterozygote disadvantage!(for vaccine design)

HLA polymorphism

• The IMGT/HLA Sequence Database currently encompass more than 1500 HLA class I proteins

Source: http://www.anthonynolan.com/HIG/index.html

HLA specificity clustering

A0201

A0101

A6802

B0702

HLA supertypes

Clustering in: O Lund et al., Immunogenetics. 2004 55:797-810

Supertype Selected alleleA1 A*0101A2 A*0201A3 A*1101A24 A*2401A26 (new*) A*2601B7 B*0702B8 (new*) B*0801B27 B*2705B39(new*) B*3901B44 B*4001B58 B*5801B62 B*1501

How little we know

• Alleles characterized with 5 or more data points• 3% covered

• ~70 HLA alleles are characterized by binding data

• Reliable MHC class I binding predictions (NetMHC-3.2) for ~50 HLA A and B molecules

• No methods for HLA-C, and HLA-E• Long way to cover 2500!

HLA polymorphism

HLA polymorphism!B0807 B4804 B0710 B1513 A6817 B5130 A0204 B3503 A2415 B0740 B3929 A0250 B5204 A2420 B1804 B3523 B3502 A3202 B0802 A3601 B4047 A6601 A0268 B0817 B5002 B5602 B3811 B4810 A0103 B1530 B4415 A3111 B7803 A6804 B3520 B3528 A2610 A6802 A2404 A7406 B0744 B3701 B4058 B1803 B1527 B3801 A6826 B5606 B0725 B5603 A0110 B1586 A3205 A0212 B3511 A2603 B5120 A0251 A3106 A6801 B5135 B1567 B4012 A3401 B5106 B3912 B1525 B5703 B4402 B0733 A2901 B0711 A6603 B3907 B4023 B2717 B4507 B4502 B4807 A2438 B1312 B1590 A0258 B5310 B5124 B4103 B0811 B3927 B4104 A1110 B1553 A2621 B5115 B1599 A0102 B5102 A0207 B4444 A3002 A6813 B5709 B5515 B4439 B1561 A2618 B2728 A3404 A6820 A3107 A2430 A0235 A2914 B1301 B4004 A2620 B1573 A0259 B0804 B1548 A2616 B5401 B0707 A2453 A2609 B3554 A0245 B4411 A0220 B1510 A2433 B5512 B5306 B1540 B5114 B3934 B5510 B1521 B0810 B5137 B3932 B4802 B4044 B3709 B3915 B2729 B3810 A0238 B0729 B3537 A2314 B0734 B3702 A0214 B4805 A0269 A3102 B5206 A6819 B3707 A3011 A1123 B1822 A6823 A4301 B3917 B4702 B5118 B3708 A0265 B5203 A3013 B3530 B4701 B4061 A0316 B4814 B2710 A7411 B3930 B0702 B5702 A1107 B7801 A0246 B3534 A0228 B1596 A3305 B2711 B3526 B4445 A0216 B1539 A3308 A2455 A0206 B4605 B2725 A0310 B4037 A1104 A2622 B5607 B4504 B4602 B1598 A3112 B0813 B5113 A0237 A3602 B0805 A6808 B4505 B1544 A0285 A3108 B5402 B6701 A6901 B0730 B4056 B5205 B1310 B5805 B1404 A2435 A2614 A7405 B1520 B3920 A0254 B2702 A6815 A3201 B1570 A0255 B5708 B4033 B4435 A2405 B4007 B4034 B4806 B5615 A0218 B3527 B3512 B0814 B5301 A6829 B4904 B4038 A0304 A7408 B7805 B3549 B1503 B4420 A1120 B1815 B5129 B0801 B0827 B5001 A3402 A0314 B4405 A2305 B4438 B4052 B0823 A8001 B1302 B4021 A2909 B3933 B4408 B4105 B0727 B5508 B4108 A3405 B1315 B3517 A1116 B0731 B4053 B1516 B4704 B1403 A6830 B5610 A3009 B0714 B1303 B1566 B2714 B3923 B5801 A2439 B2719 A0219 A2602 A2413 B1821 A0260 B4410 A6605 B1309 B8202 B4426 A2623 B4042 B1805 B3902 A2503 B1536 A0302 A3209 A0205 B2715 B5131 A0262 A6805 B5201 A1119 B1402 A0270 A2450 A1111 A3008 B3806 A6822 A0202 B5503 B0826 B3926 A2428 A1114 A2414 A3301 A0239 B4054 B0825 A0308 B3563 A0305 B4036 B1589 B1314 B1563 B4005 A3104 B4440 B5122 A3206 B7804 B0718 B4446 B4905 B9509 A0112 A0256 A6604 B4029 B1807 B5901 A2906 B1304 B3501 A2502 B5509 B4107 B2707 A0117 B4032 B3914 B3509 A3306 A6602 B1504 B5611 A2904 B3535 A2447 B6702 B1572 A2417 B1811 A2452 B3542 A2612 B1542 B1507 B5406 B3911 A2421 A2443 B4404 A3015 B5704 B4437 B4427 B8101 B4002 B3901 A1103 B3928 A2408 A6827 B1517 B0824 B1576 B4601 A2303 B4811 B4003 A2605 B1505 B4808 A7407 B1809 A0222 B4031 B1511 B4429 B1564 A2406 B1515 B5601 A2301 B4101 B3506 A0113 B5710 A7404 B3531 A0201 B4902 B1581 A2907 B4431 A0252 B4102 A2601 A6825 B5116 B5608 B4201 B5110 B4422 B2720 B2727 A3304 B1306 A2425 B5501 A0233 B0736 A2423 B1549 A1109 B3558 B5134 B5139 A0289 B5121 B4208 A0271 B2705 A2407 B4501 B3550 A2410 B2706 B1552 A1101 A0273 B1546 B3905 B4409 B5808 A2313 B0706 B1534 B5138 B0803 A2429 B5507 A6810 B1405 B2713 B3547 B4013 A3003 B5119 A3010 B0726 A3204 B3552 B3802 A3105 B4062 B4018 B4403 B1550 A0317 B4432 B4433 B3551 B9505 B8201 A3303 B5804 B4008 A0208 A0230 B1819 B2726 B3533 B4428 B5404 A0267 B1529 B4046 A0106 B9507 B3505 B4016 B3922 A7410 B1509 B0822 A3012 A0319 B4503 B5207 B1531 B3904 A2910 B5613 B0717 A2403 A2912 B3510 B0818 B5806 B0724 B7802 B3561 B0728 B1585 B2730 B4030 B4604 B3513 B3809 B5403 B3529 A2617 A3110 B5128 B3504 B3924 B3539 B5511 B5103 B5109 B5604 B1575 A3007 A2627 B3536 A2437 B3805 B4812 A1113 B5518 B3803 A0313 B3514 B9502 A6816 B3808 A2911 A0108 B1524 A2606 B1578 B1538 A2504 B1813 B4407 A0244 B1556 B5307 A0272 A2608 B2723 A2913 A2619 A0231 B2721 B4051 B1551 B5112 B4035 B2701 A0209 B0806 B4418 A2454 A2902 B8301 B4057 B5520 A2903 A6824 B1545 A0275 B4417 A0114 B3548 A0322 B0732 B4059 B3918 A0241 B5132 A2444 B4430 B0739 A3006 B2724 B1818 A2418 A3103 B5514 B0723 A2456 B4060 B5308 B3559 B1547 B5616 B4205 A7402 B4421 B4001 B1597 B5101 B1308 B4406 B4015 A2309 B8102 B0720 B4813 B3557 A6812 A2419 A0277 B4703 B5605 B9506 B3545 A0261 A2615 B5504 B4436 A7403 B1502 B3935 A2312 B4441 A3307 B1592 B0703 B4803 B0708 B5133 B1587 A0225 B5311 B0745 B5519 A0263 B1562 A2458 A2501 B4020 B4009 A6803 A0278 A3004 B4606 B1574 B1535 B1583 B1820 B3909 A2427 B5208 A0234 B0715 B0743 B0709 B5305 A0236 A0274 A2310 B4901 B5706 A2441 B5126 A2426 A1102 A2446 A0307 B1554 A0318 A3001 B1588 B3524 B3936 B3519 B4603 A2442 B1812 A0227 A2424 B0741 A1117 B3546

B1513 B3811 A3106 B3912 B5102 A3107 B3709 A2314 A7411 A0216 A3108 A2405 B4052 B4408 B4426 A0302 B4036 B5901 A2904 A3001 B1515 B4422 A0273 B4403 B5207 B3514 B1578 A6824 B2724 B5605 A2458 B0709 A2442

HLA polymorphism!

X

Predicting the specificityAlign A3001 (365) versus A3002 (365). Aln score 2445.000 Aln len 365 Id 0.9890 A3001 0 MAVMAPRTLLLLLSGALALTQTWAGSHSMRYFSTSVSRPGSGEPRFIAVGYVDDTQFVRFDSDAA ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: A3002 0 MAVMAPRTLLLLLSGALALTQTWAGSHSMRYFSTSVSRPGSGEPRFIAVGYVDDTQFVRFDSDAA

A3001 65 SQRMEPRAPWIEQERPEYWDQETRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQIMYGCDVGSD :::::::::::::::::::::::::::: ::::: ::::::::::::::::::::::::::::: A3002 65 SQRMEPRAPWIEQERPEYWDQETRNVKAHSQTDRENLGTLRGYYNQSEAGSHTIQIMYGCDVGSD

A3001 130 GRFLRGYEQHAYDGKDYIALNEDLRSWTAADMAAQITQRKWEAARWAEQLRAYLEGTCVEWLRRY ::::::::::::::::::::::::::::::::::::::::::::: ::::::::::::::::::: A3002 130 GRFLRGYEQHAYDGKDYIALNEDLRSWTAADMAAQITQRKWEAARRAEQLRAYLEGTCVEWLRRY

A3001 195 LENGKETLQRTDPPKTHMTHHPISDHEATLRCWALGFYPAEITLTWQRDGEDQTQDTELVETRPA ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: A3002 195 LENGKETLQRTDPPKTHMTHHPISDHEATLRCWALGFYPAEITLTWQRDGEDQTQDTELVETRPA

A3001 260 GDGTFQKWAAVVVPSGEEQRYTCHVQHEGLPKPLTLRWELSSQPTIPIVGIIAGLVLLGAVITGA ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: A3002 260 GDGTFQKWAAVVVPSGEEQRYTCHVQHEGLPKPLTLRWELSSQPTIPIVGIIAGLVLLGAVITGA

A3001 325 VVAAVMWRRKSSDRKGGSYTQAASSDSAQGSDVSLTACKV :::::::::::::::::::::::::::::::::::::::: A3002 325 VVAAVMWRRKSSDRKGGSYTQAASSDSAQGSDVSLTACKV

HLA-A*3001

HLA-A*3002

NetMHCpan - a pan-specific method

NetMHC NetMHCpan

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence. Nielsen et al.

PLoS ONE 2007

ExamplePeptide Amino acids of HLA pockets HLA Aff VVLQQHSIA YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.131751SQVSFQQPL YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.487500SQCQAIHNV YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.364186LQQSTYQLV YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.582749LQPFLQPQL YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.206700VLAGLLGNV YFAVLTWYGEKVHTHVDTLVRYHY A0201 0.727865VLAGLLGNV YFAVWTWYGEKVHTHVDTLLRYHY A0202 0.706274VLAGLLGNV YFAEWTWYGEKVHTHVDTLVRYHY A0203 1.000000VLAGLLGNV YYAVLTWYGEKVHTHVDTLVRYHY A0206 0.682619VLAGLLGNV YYAVWTWYRNNVQTDVDTLIRYHY A6802 0.407855

Evaluation. MHC ligands from SYFPEITHI

Sort on binding

Top Rank: F-rank=0.0Random Rank: F-rank=0.5

SYFPEITHI benchmark (1400 ligands restricted to 46 HLA molecules)

More than 90% of ligands are predicted with a rank less than 2.5%.

If you select 5 peptides from a source protein, the ligand will in 90%

of the cases be part of the pool.

Pan-specific predictions

• Pan-specific MHC peptide binding prediction is the single most important recent (in silico) development for understanding presentation of T cell epitopes/ligands

NetMHCpanwww.cbs.dtu.dk/services/NetMHCpan

NetMHCpan output

SKADVIAKY. Known BoLA Tp5 CTL epitope

What is the % rank score

1% rank (percentile) score1% rank (percentile) score

Rational epitope discovery

• Forward epitope discovery– Identify antigens using overlapping peptides– Identify epitope using peptide truncations

• Reverse epitope discovery– Predict potential epitopes using bioinformatics

tool– Validate predictions using tetra-mers

• Forward/Backwards epitope discovery– Identify antigens using overlapping peptides– Use bioinformatics tool to predict epitopes– Validate predictions using tetra-mers

Forward epitope discovery

• Some numbers– YF 3,411 amino acids precursor protein– ~ 900 15mers overlapping with 11 amino acids– One positive 15mer peptide will contain to 26

submer peptides of length 8-11– Testing all 26 submer peptides to each of the 6

HLA alleles requires 156 validations

Reverse discovery

• Problems– Which alleles to include in selection of

potential epitopes– Use HLA supertypes, predict 8-11mer,

select top 5% predicted binder => 8200 peptides

– And you might miss a lot• Supertypes are not perfect, i.e. HLA-

A*11:01 and HLA-A*03:01 do not bind the same set of peptides

• Predictions are not perfect. Less than 80% of predicted binders turn out to be actual binders

Forward/Backwards epitope discovery

• ~ 900 15mers overlapping with 11 amino acids

• Identify immunogenic peptides using peptide pools

• Identify HLA restriction and minimal epitope using bioinformatic tools– Reduces peptide set by 95% at a

sensitivity of 92%

Peptide pools

The HLArestrictorwww.cbs.dtu.dk/services/HLArestrictor

Output

Known B42:01 epitope

Binding threshold (%)

Per

cent

pre

dict

ed

7.1%

9.7%

8.7 %

1.8 %

23.1%

25.9%

30.3 %

37.5 %

34.7 %

44.8 %

37.5 %

48.0 %

38.1%

48.5 %

0.2 %

43.8 %

22.5 %11.7 %

12.8 % 13.3 %

0

20

40

60

80

100

0.5 1 2 5 10

ANot predicted

BC

92% of positive EliSpot responses are identified at a 2 %rank threshold5 predicted positive per peptides228 potential positive per peptide=> Reduction of 98%

5145 18mer HIV EliSpot positive peptides(Kiepiela et al. 2004)

Tetra-mer validations

Tetra-mer validationsPatient HIV 18’mer with ELIspot

response Validated epitope Validated

allele Pred. affinity

Pred. %rank

N080 PRTLNAWVKVIEEKAF VKVIEEKAF B15:03 155 nM 6.0 % N080 YHCLVCFQTKGLGISYGR FQTKGLGISY B15:03 8 nM 0.8 % N080 VKAACWWAGIQQEFGIPY IQQEFGIPY B15:03 4 nM 0.1 % N080 AVFIHNFKRKGGIGGYSA FKRKGGIGGY B15:03 24 nM 1.5 % H044 WVKVIEEKAFSPEVIPMF KAFSPEVIPMF B57:03 NA 0.4 % N021 ELKQEAVRHFPRPWLHGL FPRPWLHGL B42:01 49 nM 0.05 % N012 ACQGVGGPSHKARVLAEA GPSHKARVL* B07:02 36 nM 0.3 % R050 / N012 CRAIRNIPRRIRQGL IPRRIRQGL* B07:02 10 nM 0.1 % R050 NYTPGPGVRYPLTFGWCF TPGPGVRYPL B07:02 45 nM 0.3 % R050 QGWKGSPAIFQSSMTKIL SPAIFQSSM B07:02 10 nM 0.1 % R050 / R039 WVKVIEEKAFSPEVIPMF KAFSPEVIPMF B57:01 67 nM 0.1 % R039 PVGEIYKRWIILGLNKIV KRWIILGLNK B27:05 22 nM 0.05 % R039 AVFIHNFKRKGGIGGYSA KRKGGIGGY B27:05 289 nM 1.0 % R035 ELKNEAVRHFPRIWLHSL VRHFPRIWL B27:05 357 nM 1.0 % R014 MASEFNLPPIVAKEIVA LPPIVAKEI B42:01 NA 1.5 % N067 TGSEELRSLYNTVATLY SLYNTVATL A02:01 436 nM 4.0 % N067 / N096 ELAENREILKEPVHGVYY ILKEPVHGV A02:01 42 nM 1.5 % N096 SDIAGTTSTLQEQIAWM TSTLQEQIAW B58:01 32 nM 0.2 %

Visualization of binding motifs

The MHC motif viewer: a visualization tool for MHC binding motifs. Rapin N, Hoof I, Lund O, Nielsen M. Curr Protoc Immunol. 2010 Feb;Chapter 18:Unit 18.17.

www.cbs.dtu.dk/biotools/MHCMotifViewer

Going beyond humans

BoLA epitopes the hard way

0

10

20

30

40

50

60

70

80

90

1000 100 10 1 0.1

peptide conc. ng/ml

% Cytotoxicity

QRSPMFEGTL

RSPMFEGTL

RSPMFEGTLG

RSPMFEGT

SPMFEGTL

Trimming prior to binding

QRSPMFEGTL - Rank=6%RSPMFEGTL – Rank =0.1%

BoLA Class I epitopes, Work by Ivan Morrison and co-workers

BoLA Class I epitopes, Work by Ivan Morrison and co-workers

Trimming happens in both ends

-10

0

10

20

30

40

50

60

70

80

90

100ng 10ng 1ng 0.1ng 0.01ng

SKFPKMRMG

SKFPKMRMGKG

SKFPKMRMGK

KFPKMRMGK

SKFPKMRM

KFPKMRMG

Processing effect: addition of GKG and possibly the G

SKFPKMRMG – Rank 16% SKFPKMRM - Rank 1%

BoLA CTL epitopes - the rational way

Average predicted rank of 16 CTL BoLA

restricted epitopes is 3%

Fran

k

So, we can find the needle in the haystack

• Given a protein sequence and an HLA molecule, we can accurately predict with peptides will bind (70-95%)

• 15-80% of these will in turn be epitopes

Conclusions II. MHC binding

• Pan-specific MHC prediction method can deal with the immense MHC polymorphism and is (in my opinion) the most significant recent contribution to our understanding of cellular immune responses

• Rational epitope discovery is feasible– Prediction methods are an important guide for

epitope identification– Given a protein sequence and an HLA molecule,

we can predict the peptide binders (find the needle in the haystack)

What defines a T cell epitope?

• Processing (Proteasomal cleavage, TAP)?

• MHC binding• Other proteases• T cell repertoire• MHC:peptide complex stability• Source protein abundance, cellular

location and function

Evaluation. MHC ligands from SYFPEITHI

Sort on binding

Top Rank: F-rank=0.0Random Rank: F-rank=0.5

ProcessingDo proteasomal cleavage and TAP matter?

NetCTL, MHC-pathway said yes (in 2005)

NetCTL, 2005

Wcl=0.05, Wt=0.1 (AUC)

2010, NetCTLpan says maybe

– Wcl=0, Wt=0 (AUC) – Wcl=0.225, Wt= 0.025 (AUC0.1)

Benchmark (Ligands and HIV epitopes)

€

S = MHC +wcl ⋅ClC −term +wtap ⋅TAPWcl=0.225, wtap=0.025

MHC class I pathway co-evolution

Nielsen, Kesmir, Immunogenetics, (2005) 57: 33–41

Going pan-specific does most of it

Objectives

Visualization of binding motifsConstruction of sequence logos

Understand the concepts of weight matrix constructionOne of the most important methods of

bioinformatics A few word on Artificial neural networks MHC binding rules

No other factors in the MHC (I and II) pathways are (as) decisive for T cell epitope identification

All known T cell epitopes have specific MHC restrictions matching their host

MHC binding is the single most important feature for understanding cellular immunity

Class II MHC binding• Binds peptides of length 9-18 (even whole proteins can bind!)• Binding cleft is open• Binding core is 9 aa• Binding motif highly generate• Amino acids flanking the binding core affect binding• Peptide structure might determine binding

Gibbs samplerwww.cbs.dtu.dk/biotools/EasyGibbs

100 10mer peptides2100~1030 combinations

€

E = Cpa logppaqap,aa

∑

Monte Carlo simulations can do

it

The problem. Where is the binding core?

PEPTIDE IC50(nM)VPLTDLRIPS 48000GWPYIGSRSQIIGRS 45000ILVQAGEAETMTPSG 34000HNWVNHAVPLAMKLI 120SSTVKLRQNEFGPAR 8045NMLTHSINSLISDNL 47560LSSKFNKFVSPKSVS 4GRWDEDGAKRIPVDV 49350ACVKDLVSKYLADNE 86NLYIKSIQSLISDTQ 67IYGLPWMTTQTSALS 11QYDVIIQHPADMSWC 15245

Effect of Peptide Flanking Residues

• PFR’s can affect binding dramatically– RFYKTLRAEQASQ 34 nM – YKTLRAEQA >10000 nM

NN-align

PEPTIDE Pred Meas VPLTDLRIPS 0.00 0.03 GWPYIGSRSQIIGRS 0.19 0.08 ILVQAGEAETMTPSG 0.07 0.24 HNWVNHAVPLAMKLI 0.77 0.59 SSTVKLRQNEFGPAR 0.15 0.19 NMLTHSINSLISDNL 0.17 0.02 LSSKFNKFVSPKSVS 0.81 0.97 GRWDEDGAKRIPVDV 0.39 0.45 ACVKDLVSKYLADNE 0.58 0.57 NLYIKSIQSLISDTQ 0.84 0.66IYGLPWMTTQTSALS 1.00 0.93QYDVIIQHPADMSWC 0.12 0.11

Predict binding affinity and core

Calculate predictionerror

Update method toMinimize prediction

error

GRWDEDGAKRIPVDV

GRWDEDGAKRIP

RWDEDGAKRIPVG

WDEDGAKRIPVDGR

DEDGAKRIPVDVGRW

0.450.150.030.390.05

Nielsen et al. BMC Bioinformatics 2009, 10:296

NetMHCII (NN-align)

P<0.05P<0.001

Nielsen et al. BMC Bioinformatics 2009, 10:296

P<0.05

Network ensembles

Pan NN-align

• Add MHC pseudo sequence to training

• The contact residues are defined as being within 4.0 Å of the peptide in any of a representative set of HLA-DR, -DQ, and DP structures with peptides.

• Only polymorphic residues are included

• Pseudo-sequence consisting of 25 amino acid residues.

• Include polymorphic residues in potential contact with the bound peptide

NetMHCIIPan-2.0www.cbs.dtu.dk/services/NetMHCIIpan

But, can we find the haystack?

MTB (mycobacterium tuberculosis)

• Bacterial genome coding for more then 4000 proteins

• 700 known epitopes, found in only 30 proteins (ORFs)

TBW epitopes

MTB (mycobacterium tuberculosis)

• Bacterial genome coding for more then 4000 proteins

• 700 known epitopes, found in only 30 proteins (ORFs)

• Is this biology, or history?– More than 500.000 unique 9mer peptides– Where to start?

• Each HLA allele will binding ~5000 of these peptides..

Functional bias in TB epitope proteins

Tang et al. J Immunol. 2011 Jan 15;186(2):1068-80.

Where are the epitopes?

Larsen MV et al., PLoS One. 2010 Sep 14;5

Conclusions

• Rational epitope discovery is feasible– Prediction methods are an important guide for epitope

identification– Given a protein sequence and an HLA molecule, we

can predict the peptide binders (find the needle in the haystack)

• Pan-specific MHC prediction method can deal with the immense MHC polymorphism

• All CTL epitopes have specific MHC restrictions matching their host– There is no such thing as a non-binding CTL epitope

• Processing have little impact in predicting of CTL epitopes

• For large pathogens, we still have no good handle on how to select immunogenic proteins

CBS immunology web serverswww.cbs.dtu.dk/services

Acknowledgements• Immunological Bioinformatics

group, CBS, DTU– Ole Lund - Group leader– Claus Lundegaard - Data bases,

HLA binding predictions• Collaborators

– IMMI, University of Copenhagen• Søren Buus: MHC binding

– La Jolla Institute of Allergy and Infectious Diseases• A. Sette, B. Peters: Epitope

database• and many, many more

www.cbs.dtu.dk/services

predicting peptide mhc interactions

Documents

mhc class

specific mhc restrictions

hostmhc binding

peptide binds

t cell epitopes

important feature

cellular immunity

systems biology