1. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequence motifs, information content, logos, and HMM’s Morten Nielsen, CBS, BioCentrum, DTU

2. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Outline Multiple alignments and sequence motifs Weight matrices and consensus sequence –Sequence weighting –Low (pseudo) counts Information content –Sequence logos –Mutual information Example from the real world HMM’s and profile HMM’s –TMHMM (trans-membrane protein) –Gene finding Links to HMM packages

3. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Multiple alignment and sequence motifs Core Consensus sequence Weight matrices Problems –Sequence weights –Low counts ----------MLEFVVEADLPGIKA-------- ----------MLEFVVEFALPGIKA-------- ----------MLEFVVEFDLPGIAA-------- -------------YLQDSDPDSFQD-------- ---GSDTITLPCRMKQFINMWQE---------- ---RNQEERLLADLMQNYDPNLR---------- -------YDPNLRPAERDSDVVNVSLK------ ----------NVSLKLTLTNLISLNEREEA--- ----EREEALTTNVWIEMQWCDYR--------- ----------WCDYRLRWDPRDYEGLWVLR--- --LWVLRVPSTMVWRPDIVLEN----------- ------------IVLENNVDGVFEVALYCNVL- -------------YCNVLVSPDGCIYWLPPAIF ---------PPAIFRSACSISVTYFPFDW---- ********* FVVEFDLPG Consensus

4. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequences weighting 1 - Clustering ----------MLEFVVEADLPGIKA-------- ----------MLEFVVEFALPGIKA-------- ----------MLEFVVEFDLPGIAA-------- -------------YLQDSDPDSFQD-------- ---GSDTITLPCRMKQFINMWQE---------- ---RNQEERLLADLMQNYDPNLR---------- -------YDPNLRPAERDSDVVNVSLK------ ----------NVSLKLTLTNLISLNEREEA--- ----EREEALTTNVWIEMQWCDYR--------- ----------WCDYRLRWDPRDYEGLWVLR--- --LWVLRVPSTMVWRPDIVLEN----------- ------------IVLENNVDGVFEVALYCNVL- -------------YCNVLVSPDGCIYWLPPAIF ---------PPAIFRSACSISVTYFPFDW---- ********* } Homologous sequences Weight = 1/n (1/3) Consensus sequence YRQELDPLV Previous FVVEFDLPG

5. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequences weighting 2 - (Henikoff & Henikoff) w FVVEADLPG 0.37 FVVEFALPG 0.43 FVVEFDLPG 0.32 YLQDSDPDS 0.59 MKQFINMWQ 0.90 LMQNYDPNL 0.68 PAERDSDVV 0.75 LKLTLTNLI 0.85 VWIEMQWCD 0.84 YRLRWDPRD 0.51 WRPDIVLEN 0.71 VLENNVDGV 0.59 YCNVLVSPD 0.71 FRSACSISV 0.75 w aa ’ = 1/rs r: Number of different aa in a column s: Number occurrences Normalize so  w aa = 1 for each column Sequence weight is sum of w aa F: r=7 (FYMLPVW), s=4 w’=1/28, w = 0.055 Y: s=3, w`=1/21, w = 0.073 M,P,W: s=1, w’=1/7, w = 0.218 L,V: s=2, w’=1/14, w = 0.109

6. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Low count correction --------MLEFVVEADLPGIKA-------- --------MLEFVVEFALPGIKA-------- --------MLEFVVEFDLPGIAA-------- -----------YLQDSDPDSFQD-------- -GSDTITLPCRMKQFINMWQE---------- -RNQEERLLADLMQNYDPNLR---------- -----YDPNLRPAERDSDVVNVSLK------ --------NVSLKLTLTNLISLNEREEA--- --EREEALTTNVWIEMQWCDYR--------- --------WCDYRLRWDPRDYEGLWVLR--- LWVLRVPSTMVWRPDIVLEN----------- ----------IVLENNVDGVFEVALYCNVL- -----------YCNVLVSPDGCIYWLPPAIF -------PPAIFRSACSISVTYFPFDW---- ********* Limited number of data Poor sampling of sequence space I is not found at position P1. Does this mean that I is forbidden? No! Use Blosum matrix to estimate pseudo frequency of I P1

7. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Low count correction using Blosum matrices # I L V L 0.1154 0.3755 0.0962 V 0.1646 0.1303 0.2689 Blosum62 substitution frequencies Every time for instance L/V is observed, I is also likely to occur Estimate low (pseudo) count correction using this approach As more data are included the pseudo count correction becomes less important N L = 2, N V =2, N eff =12 => f I = (2*0.1154 + 2*0.1646)/12 = 0.05 p I * = (N eff * p I +  * f I )/(N eff +  ) = (12*0 + 10*0.05)/(12+10) = 0.02

8. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Information content Information and entropy –Conserved amino acid regions contain high degree of information (high order == low entropy) –Variable amino acid regions contain low degree of information (low order == high entropy) Shannon information D = log 2 (N) +  p i log 2 p i (for proteins N=20, DNA N=4) Conserved residue p A =1, p i<>A =0, D = log 2 (N) ( = 4.3 for proteins) Variable region p A =0.05, p C =0.05,.., D = 0

9. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Sequence logo Height of a column equal to D Relative height of a letter is p A Highly useful tool to visualize sequence motifs High information position MHC class II Logo from 10 sequences http://www.cbs.dtu.dk/~gorodkin/appl/plogo.html

10. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU More on logos Information content D =  p i log 2 (p i /q i ) Shannon, q i = 1/N = 0.05 D =  p i log 2 (p i ) -  p i log 2 (1/N) = log 2 N -  p i log 2 (p i ) Kullback-Leibler, q i = background frequency –V/L/A more frequent than for instance C/H/W A R N D C Q E G H I L K M F P S T W Y V 2 1 1 1 1 1 1 1 1 4 16 1 6 15 7 1 2 7 18 13 8 19 1 1 7 2 2 2 1 3 15 13 6 2 1 2 2 7 1 8 3 2 7 2 1 17 13 2 1 8 14 3 1 1 7 7 2 0 1 8 8 13 13 14 1 2 13 2 1 2 3 3 1 7 1 3 7 0 1 7 4 1 7 7 7 1 2 2 1 13 15 2 6 6 1 7 2 7 7 4 5 2 8 23 1 6 3 2 1 3 3 2 1 1 1 13 8 0 1 18 2 1 7 13 1 1 2 2 1 8 14 2 6 1 20 7 2 7 1 3 3 7 7 8 7 1 7 8 1 2 8 2 1 1 13 7 2 7 1 7 3 2 7 19 1 6 2 8 1 9 9 2 1 1 1 7 2 0 1 18 Frequency matrix

11. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Mutual information I(i,j) =  aa i  aa j P(aa i, aa j ) * log[P(aa i, aa j )/P(aa i )*P(aa j )] P(G 1 ) = 2/9 = 0.22,.. P(V 6 ) = 4/9 = 0.44,.. P(G 1,V 6 ) = 2/9 = 0.22, P(G 1 )*P(V 6 ) = 8/81 = 0.10 log(0.22/0.10) > 0 ALWGFFPVA ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS YMNGTMSQV GILGFVFTL WLSLLVPFV FLPSDFFPS P1P6

12. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Mutual information 313 binding peptides313 random peptides

13. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Mutual information at anchor position is low Mutual information between anchor positions 2 and 9 and other residues low –At pos 2 we know that L,M,T,V and I are the most frequent amino acids. –At pos 9 V,L,I and A are most frequent –313 Rammensee + Buus pep P(L 2 ) = 0.51, P(V 9 )=0.48, P(L 2,V 9 ) = 0.23 P(L 2,V 9 )/(P(L 2 )*P(V 9 ) )=0.23/0.24 = 1.0 Knowing that we have L at position 2 does not tell us which one of V,L or I is placed on position 9 => NO mutual information

14. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Weight matrices Estimate amino acid frequencies from alignment inc. sequence weighting and pseudo counts Now a weight matrix is given as W ij = log(p ij /q j ) Here i is a position in the motif, and j an amino acid. q j is the background frequency for amino acid j. W is a L x 20 matrix, L is motif length Score sequences to weight matrix by looking up and adding L values from matrix

15. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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 on 528 peptides ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

16. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Example (cont.) Raw sequence counting –No sequence weighting –No pseudo count –Prediction accuracy 0.45 Sequence weighting –No pseudo count –Prediction accuracy 0.5 ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

17. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Example (cont.) Sequence weighting and pseudo count –Prediction accuracy 0.60 Motif found on all data (485) –Prediction accuracy 0.79 ALAKAAAAM ALAKAAAAN ALAKAAAAR ALAKAAAAT ALAKAAAAV GMNERPILT GILGFVFTM TLNAWVKVV KLNEPVLLL AVVPFIVSV

18. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

19. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU HMM (a simple example) ACA---ATG TCAACTATC ACAC--AGC AGA---ATC ACCG--ATC Example from A. Krogh Core region defines the number of states in the HMM (red) Insertion and deletion statistics is derived from the non-core part of the alignment (blue) Core of alignment

20. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU.8.2 ACGTACGT ACGTACGT ACGTACGT ACGTACGT ACGTACGT ACGTACGT.8.2 1 ACGTACGT.4 1..4 1..6.4 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 0.8x1x0.8x1x0.8x0.4x1x0.8x1x0.2 = 3.3x10 -2

21. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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 = 0.0075x10 -2 ACAC--AGC = 1.2x10 -2 AGA---ATC = 3.3x10 -2 ACCG--ATC = 0.59x10 -2 Consensus: ACAC--ATC = 4.7x10 -2, ACA---ATC = 13.1x10 -2 Exceptional: TGCT--AGG = 0.0023x10 -2

22. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Align sequence to HMM - Null model Score depends strongly on length Null model is a random model. For length L the score is 0.25 L Log-odd score for sequence S Log( P(S)/0.25 L ) 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!

23. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU HMM’s and weight matrices In the case of un-gapped alignments HMM’s become simple weight matrices It still might be useful to use a HMM tool package to estimate a weight matrix –Sequence weighting –Pseudo counts

24. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Profile HMM’s Alignments based on conventional scoring matrices (BLOSUM62) scores all positions in a sequence in an equal manner Some position are highly conserved, some are highly flexible (more than what is described in the BLOSUM matrix) Profile HMM’s are ideal suited to describe such position specific variations

25. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Example Sequence profiles Alignment of 1PLC._ to 1GYC.A Blast e-value > 1000 Profile alignment –Align 1PLC._ against Swiss-prot –Make position specific weight matrix from alignment –Use this matrix to align 1PLC._ against 1GYC.A E-value > 10 -22. Rmsd=3.3

26. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Example continued Score = 97.1 bits (241), Expect = 9e-22 Identities = 13/107 (12%), Positives = 27/107 (25%), Gaps = 17/107 (15%) Query: 3 VLLGADDGSLAFVPSEFSISPGEKI------VFKNNAGFPHNIVFDEDSIPSGVDASKIS 56 V+ G F + G++ N+ + +G + + Sbjct: 26 VVNG------VFPSPLITGKKGDRFQLNVVDTLTNHTMLKSTSIHWHGFFQAGTNWADGP 79 Query: 57 MSEEDLLNAKGETFEVAL---SNKGEYSFYCSP--HQGAGMVGKVTV 98 A G +F G + ++ G+ G V Sbjct: 80 AFVNQCPIASGHSFLYDFHVPDQAGTFWYHSHLSTQYCDGLRGPFVV 126 Rmsd=3.3

27. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU EM55_HUMAN WWQGRVEGSSKESAGLIPSPELQEWRVASMAQSAP--SEAPSCSPFGKKKK-YKDKYLAK CSKP_HUMAN WWQGKLENSKNGTAGLIPSPELQEWRVACIAMEKTKQEQQASCTWFGKKKKQYKDKYLAK KAPB_MOUSE -----PENLLIDHQGYIQVTDFGFAKRVKG------------------------------ NRC2_NEUCR -----PENILLHQSGHIMLSDFDLSKQSDPGGKPTMIIGKNGTSTSSLPTIDTKSCIANF EM55_HUMAN HSSIFDQLDVVSYEEVVRLPAFKRKTLVLIGASGVGRSHIKNALLSQNPEKFVYPVPYTT CSKP_HUMAN HNAVFDQLDLVTYEEVVKLPAFKRKTLVLLGAHGVGRRHIKNTLITKHPDRFAYPIPHTT KAPB_MOUSE RTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSG NRC2_NEUCR RTNSFVGTEEYIAPEVIKGSGHTSAVDWWTLGILIYEMLYGTTPFKGKNRNATFANILRE EM55_HUMAN RPPRKSEEDGKEYHFISTEEMTRNISANEFLEFGSYQGNMFGTKFETVHQIHKQNKIAIL CSKP_HUMAN RPPKKDEENGKNYYFVSHDQMMQDISNNEYLEYGSHEDAMYGTKLETIRKIHEQGLIAIL KAPB_MOUSE KVRFPSHF-----SSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQR NRC2_NEUCR DIPFPDHAGAPQISNLCKSLIRKLLIKDENRRLG-ARAGASDIKTHPFFRTTQWALI--R EM55_HUMAN NNGVDETLKKLQEAFDQACSSPQWVPVSWVY CSKP_HUMAN NNEIDETIRHLEEAVELVCTAPQWVPVSWVY KAPB_MOUSE EKCGKEFCEF--------------------- NRC2_NEUCR ENAVDPFEEFNSVTLHHDGDEEYHSDAYEKR Profile HMM’s Insertion Deletion Conserved

28. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Profile HMM’s All M/D pairs must be visited once

29. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU TMHMM (trans-membrane HMM) (Sonnhammer, von Heijne, and Krogh) Model TM length distribution. Power of HMM. Difficult in alignment.

30. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Combination of HMM’s - Gene finding x ccc xxxxxxxxATGccc cccTAAxxxxxxxx Inter-genic region Region around start codon Coding region Region around stop codon Start codon Stop codon

31. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU HMM packages HMMER (http://hmmer.wustl.edu/) –S.R. Eddy, WashU St. Louis. Freely available. SAM ( http://www.cse.ucsc.edu/research/compbio/sam.html) –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 ( http://metameme.sdsc.edu/) –William Noble Grundy, UC San Diego. Freely available. Combines features of PSSM search and profile HMM search. NET-ID, HMMpro ( http://www.netid.com/html/hmmpro.html) –Freely available to academia, nominal license fee for commercial users. –Allows HMM architecture construction.

32. CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Simple Hmmer command hmmbuild --gapmax 0.0 --fast A2.hmmer A2.fsa hmmbuild - build a hidden Markov model from an alignment HMMER 2.2g (August 2001) - - - - - - - - - - - - - - - - - - Alignment file: A2.fsa File format: a2m Search algorithm configuration: Multiple domain (hmmls) Model construction strategy: Fast/ad hoc (gapmax 0.0) Null model used: (default) Sequence weighting method: G/S/C tree weights - - - - - - - - - - - - - - - - Alignment: #1 Number of sequences: 232 Number of columns: 9 Determining effective sequence number... done. [192] Weighting sequences heuristically... done. Constructing model architecture... done. Converting counts to probabilities... done. Setting model name, etc.... done. [A2.fasta] Constructed a profile HMM (length 9) Average score: -6.42 bits Minimum score: -15.47 bits Maximum score: -0.84 bits Std. deviation: 2.72 bits >HLA-A.0201 16 Example_for_Ligand SLLPAIVEL >HLA-A.0201 16 Example_for_Ligand YLLPAIVHI >HLA-A.0201 16 Example_for_Ligand TLWVDPYEV >HLA-A.0201 16 Example_for_Ligand SXPSGGXGV >HLA-A.0201 16 Example_for_Ligand GLVPFLVSV