Sequence motifs, information content, logos, and HMM’s

Sequence motifs, information content, logos, and HMM’s
Morten Nielsen, CBS, BioSys, DTU

Objectives Visualization of binding motifs
Construction of sequence logos Understand the concepts of weight matrix construction One of the most important methods of bioinformatics Introduce Hidden Markov models and understand that they are just weight matrices with gaps

Outline Pattern recognition Information content
Regular expressions and probabilities Information content Sequence logos Multiple alignment and sequence motifs Weight matrix construction Sequence weighting Low (pseudo) counts Examples from the real world HMM’s Viterbi decoding Profile HMMs Links to HMM packages

Binding Motif. MHC class I with peptide
Anchor positions

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

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 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 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

Information content A R N D C Q E G H I L K M F P S T W Y V S I

Sequence logos Height of a column equal to I
Relative height of a letter is p Highly useful tool to visualize sequence motifs HLA-A0201 High information positions

Characterizing a binding motif from small data sets
10 MHC restricted peptides What can we learn? A at P1 favors binding? I is not allowed at P9? K at P4 favors binding? Which positions are important for binding? ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

Simple motifs Yes/No rules
10 MHC restricted peptides ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV Only 11 of 212 peptides identified! Need more flexible rules If not fit P1 but fit P2 then ok Not all positions are equally important We know that P2 and P9 determines binding more than other positions Cannot discriminate between good and very good binders

Simple motifs Yes/No rules
10 MHC restricted peptides ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV Example Two first peptides will not fit the motif. They are all good binders (aff< 500nM) RLLDDTPEV 84 nM GLLGNVSTV 23 nM ALAKAAAAL 309 nM

Extended motifs Fitness of aa at each position given by P(aa)
Example P1 PA = 6/10 PG = 2/10 PT = PK = 1/10 PC = PD = …PV = 0 Problems Few data Data redundancy/duplication ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV RLLDDTPEV 84 nM GLLGNVSTV 23 nM ALAKAAAAL 309 nM

Sequence information Raw sequence counting
ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

} Sequence weighting Poor or biased sampling of sequence space
ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV } Similar sequences Weight 1/5 Poor or biased sampling of sequence space Example P1 PA = 2/6 PG = 2/6 PT = PK = 1/6 PC = PD = …PV = 0 RLLDDTPEV 84 nM GLLGNVSTV 23 nM ALAKAAAAL 309 nM

Sequence weighting ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV
GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

Pseudo counts ALAKAAAAM
ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV 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

The Blosum matrix A R N D C Q E G H I L K M F P S T W Y V A R N D C Q E G H I L K M F P S T W Y V Some amino acids are highly conserved (i.e. C), some have a high change of mutation (i.e. I)

What is a pseudo count? Say V is observed at P2
A R N D C Q E G H I L K M F P S T W Y V A R N D C …. Y V Say V is observed at P2 Knowing that V at P2 binds, what is the probability that a peptide could have I at P2? P(I|V) = 0.16

Pseudo count estimation
ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV Calculate observed amino acids frequencies fa Pseudo frequency for amino acid b Example

Weight on pseudo count ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV 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

Weight on pseudo count Example
ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV 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

Sequence weighting and pseudo counts
ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV RLLDDTPEV 84nM GLLGNVSTV 23nM ALAKAAAAL 309nM P7P and P7S > 0

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 V

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

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 Which peptide is most likely to bind? Which peptide second? RLLDDTPEV GLLGNVSTV ALAKAAAAL 11.9 14.7 4.3 84nM 23nM 309nM

An example!! (See handout)

Estimation of pseudo counts

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

Take a deep breath Smile to you neighbor
End of first part Take a deep breath Smile to you neighbor

Hidden Markov Models Weight matrices do not deal with insertions and deletions In alignments, this is done in an ad-hoc manner by optimization of the two gap penalties for first gap and gap extension HMM is a natural frame work where insertions/deletions are dealt with explicitly

Why hidden? The unfair casino: Loaded die p(6) = 0.5; switch fair to load:0.05; switch load to fair: 0.1 Model generates numbers Does not tell which die was used Alignment (decoding) can give the most probable solution/path (Viterby) FFFFFFLLLLLL Or most probable set of states 1:1/6 2:1/6 3:1/6 4:1/6 5:1/6 6:1/6 Fair 1:1/10 2:1/10 3:1/10 4:1/10 5:1/10 6:1/2 Loaded 0.95 0.10 0.05 0.9

HMM (a simple example) ACA---ATG TCAACTATC ACAC--AGC AGA---ATC
Example from A. Krogh Core region defines the number of states in the HMM (red) Insertion and deletion statistics are derived from the non-core part of the alignment (black) ACA---ATG TCAACTATC ACAC--AGC AGA---ATC ACCG--ATC Core of alignment

HMM construction ACA---ATG TCAACTATC ACAC--AGC AGA---ATC ACCG--ATC
5 matches. A, 2xC, T, G 5 transitions in gap region C out, G out A-C, C-T, T out Out transition 3/5 Stay transition 2/5 ACA---ATG TCAACTATC ACAC--AGC AGA---ATC ACCG--ATC .4 A C G T .2 .4 .2 .2 .6 .6 A C G T .8 A C G T A C G T .8 A C G T 1 A C G T A C G T 1. 1. .4 1. 1. .8 .2 .8 .2 .2 .2 .2 .8 ACA---ATG 0.8x1x0.8x1x0.8x0.4x1x1x0.8x1x0.2 = 3.3x10-2

Align sequence to HMM ACA---ATG 0.8x1x0.8x1x0.8x0.4x1x0.8x1x0.2 = 3.3x10-2 TCAACTATC 0.2x1x0.8x1x0.8x0.6x0.2x0.4x0.4x0.4x0.2x0.6x1x1x0.8x1x0.8 = x10-2 ACAC--AGC = 1.2x10-2 Consensus: ACAC--ATC = 4.7x10-2, ACA---ATC = 13.1x10-2 Exceptional: TGCT--AGG = x10-2

Align sequence to HMM - Null model
Score depends strongly on length Null model is a random model. For length L the score is 0.25L Log-odds score for sequence S Log( P(S)/0.25L) Positive score means more likely than Null model ACA---ATG = 4.9 TCAACTATC = 3.0 ACAC--AGC = 5.3 AGA---ATC = 4.9 ACCG--ATC = 4.6 Consensus: ACAC--ATC = 6.7 ACA---ATC = 6.3 Exceptional: TGCT--AGG = -0.97 Note!

Aligning a sequence to an HMM
Find the path through the HMM states that has the highest probability For alignment, we found the path through the scoring matrix that had the highest sum of scores The number of possible paths rapidly gets very large making brute force search infeasible Just like checking all path for alignment did not work Use dynamic programming The Viterbi algorithm does the job

The Viterbi algorithm The unfair casino: Loaded dice p(6) = 0.5; switch fair to load:0.05; switch load to fair: 0.1 1:1/6 2:1/6 3:1/6 4:1/6 5:1/6 6:1/6 Fair 1:1/10 2:1/10 3:1/10 4:1/10 5:1/10 6:1/2 Loaded 0.95 0.10 0.05 0.9 Model generates numbers

Model decoding (Viterbi)
Example: 566. What was the series of dice used to generate this output? 1:1/6 2:1/6 3:1/6 4:1/6 5:1/6 6:1/6 Fair 1:1/10 2:1/10 3:1/10 4:1/10 5:1/10 6:1/2 Loaded 0.95 0.10 0.05 0.9 FFF = 0.5*0.167*0.95*0.167*0.95*0.167 = FFL = 0.5*0.167*0.95*0.167*0.05*0.5 = FLF = 0.5*0.167*0.05*0.5*0.1*0.167 = FLL = 0.5*0.167*0.05*0.5*0.9*0.5 = LFF = 0.5*0.1*0.1*0.167*0.95*0.167 = LFL = 0.5*0.1*0.1*0.167*0.05*0.5 = LLF = 0.5*0.1*0.9*0.5*0.1*0.167 = LLL = 0.5*0.1*0.9*0.5*0.9*0.5 = Brute force

Or in log space Example: 566. What was the series of dice used to generate this output? 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model Log(P(LLL|M)) = log(0.5*0.1*0.9*0.5*0.9*0.5) = Log(P(LLL|M)) = log(0.5)+log(0.1)+log(0.9)+log(0.5)+log(0.9)+log(0.5) Log(P(LLL|M)) = = -1.99

Example: What was the series of dice used to generate this output? 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model 5 6 1 2 3 4 F -1.08 L -1.30

Example: What was the series of dice used to generate this output? 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model FFF = 0.5*0.167*0.95*0.167*0.95*0.167 = Log(P(FFF))=-2.68 LLL = 0.5*0.1*0.9*0.5*0.9*0.5 = Log(P(LLL))=-1.99 5 6 1 2 3 4 F -1.08 -1.88 -2.68 L -1.30 -1.65 -1.99

Example: What was the series of dice used to generate this output? 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model 5 6 1 2 3 4 F -1.08 -1.88 -2.68 L -1.30 -1.65 -1.99

Example: What was the series of dice used to generate this output? 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model 5 6 1 2 3 4 F -1.08 -1.88 -2.68 -3.48 L -1.30 -1.65 -1.99

Now we can formalize the algorithm! 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0.78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model Old max score New match Transition

Model decoding (Viterbi).
Example: What was the series of dice used to generate this output? Fill out the table using the Viterbi recursive algorithm Add the arrows for backtracking Find the optimal path 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0-78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model 5 6 1 2 3 4 F -1.08 -1.88 -2.68 -3.48 L -1.30 -1.65 -1.99

Model decoding (Viterbi).
Example: What was the series of dice used to generate this output? Fill out the table using the Viterbi recursive algorithm Add the arrows for backtracking Find the optimal path 1:-0.78 2:-0.78 3:-0.78 4:-0.78 5:-0.78 6:-0-78 Fair 1:-1 2:-1 3:-1 4:-1 5:-1 6:-0.3 Loaded -0.02 -1 -1.3 -0.046 Log model LLLLFFFFF 5 6 1 2 3 4 F -1.08 -1.88 -2.68 -3.48 -4.12 -4.92 4.73 -6.53 -7.33 L -1.30 -1.65 -1.99 -2.34 -3.39 -4.32 -5.48 -6.52 -7.57

HMM’s and weight matrices
In the case of un-gapped alignments HMM’s become simple weight matrices To achieve high performance, the emission frequencies are estimated using the techniques of Sequence weighting Pseudo counts

Profile HMM’s Alignments based on conventional scoring matrices (BLOSUM62) scores all positions in a sequence in an equal manner Some positions are highly conserved, some are highly variable (more than what is described in the BLOSUM matrix) Profile HMM’s are ideal suited to describe such position specific variations

Sequence profiles TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDGMERNTAGVP

Example. (SGNH active site)

Sequence profiles Matching any thing but G => large negative score
Conserved Non-conserved Insertion ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASKISMSEEDLLN TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWHGLLVPFGMDGVPGVSFPG---I -TSMAPAFGVQEFYRTVKQGDEVTVTIT-----NIDQIED-VSHGFVVVNHGVSME---I IE--KMKYLTPEVFYTIKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD--- -TSVAPSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAME---V ASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPEGVEGFKSRINDE---- TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDGMERNTAGVP Matching any thing but G => large negative score Any thing can match

Example. (SGNH active site)

HMM vs. alignment Detailed description of core
Conserved/variable positions Price for insertions/deletions varies at different locations in sequence These features cannot be captured in conventional alignments

All M/D pairs must be visited once
Profile HMM’s All M/D pairs must be visited once L1- Y2A3V4R5- I6 P1D2P3P4I4P5D6P7

HMM packages NET-ID, HMMpro (http://www.netid.com/html/hmmpro.html)
HMMER ( S.R. Eddy, WashU St. Louis. Freely available. SAM ( R. Hughey, K. Karplus, A. Krogh, D. Haussler and others, UC Santa Cruz. Freely available to academia, nominal license fee for commercial users. META-MEME ( William Noble Grundy, UC San Diego. Freely available. Combines features of PSSM search and profile HMM search. NET-ID, HMMpro ( Freely available to academia, nominal license fee for commercial users. Allows HMM architecture construction. EasyGibbs ( Webserver for Gibbs sampling of proteins sequences

Sequence motifs, information content, logos, and HMM’s

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Sequence motifs, information content, logos, and HMM’s

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