Sequence alignments 2 cb1 alignments2 - Rostlab · 2017. 5. 16. · /134 © Burkhard Rost 1 title: Computational Biology 1 - Protein structure: Sequence alignments 2 short title:

/134© Burkhard Rost

1

title: Computational Biology 1 - Protein structure:   Sequence alignments 2

short title: cb1_alignments2

lecture: Protein Prediction 1 - Protein structure  Computational Biology 1 - TUM Summer 2016


Videos: YouTube / www.rostlab.org  THANKS :  . EXERCISES: Special lectures: • 07/xx Predrag Radivojac - Indiana Univ. • 06/xx Yana Bromberg - Rutgers Univ. No lecture: • 05/09 no lecture • 05/15 Ascension day • 05/23 Student assembly (SVV) • 06/06 Whitsun holiday • 06/15 Corpus Christi LAST lecture: bef: Jul 11  after: Jul 28 Examen: WEDNESDAY(!!) July 12: 18:00-19:30 TBA • Makeup: TBC: Oct 17 & 19, 2017 - lecture time

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CONTACT: Lothar Richter [email protected]

Dmitrij Nechaev

Lothar Richter

Christian Dallago

http://www.rostlab.orgmailto:[email protected]?subject=

© Burkhard Rost (TUM Munich)

Recap: 3D comparison

3


4

Notation: protein structure 1D, 2D, 3DPQITLWQRPLVTIKIGGQLKEALLDTGADDTVL

PP PQQQYFFQVISSIVRLLSTLWWQEDRKQAKRRRPQPPPPPVVTKFVVLIITTKEKAALIVHYKKFIILVIEENGGGGGTGQQKRRPPLWWVVFKVEESKKVVGLGLLILLLLLVVDDDDDTTTTTGGGGGAAAAADDDDDDDAKESSTTVIIVIVVVIVL

1281757077

120238169200247114740

904

466268

11831

1241

292449726217

102691

140

1109760691481976248590

690

730

415371597395000

5851300

79586900

EEEEE

EEEEEE

EEEEEEE

EE

EEEEE

EEEEEE

EE

kcal/mol0 -1 -2 -3 -4 -5

1 10 20 30 40 50 60 70 80 90

1

10

20

30

40

50

60

70

80

90

1D1D 2D2D 3D3D


5

Dynamic programming: Global

GGQLAKEEALEGQPVEVL

GGQLAKEEAL EGQPVEVL

GGQLAKEEAL..EGQPVEVL


6

Dynamic programming concept no gaps

G G Q L A K E E A LE 1 1G 1 1 1 1Q 1 2 1 1P 1 2 1V 1 2E 1 2V 1 2L 1 2P 1 2

© Burkhard Rost /134

allowing for gaps

7

GGQLAKEEAL GQ..PVEVL

GGQLAKEEAL GQPVEVL

no gap with gap


8

Dynamic programming concept gaps

G G Q L A K E E A LE 1 1G 1 1 1 1Q 2 1 1P 2 1V 2E 3V 3L 4PScore = ∑ 𝛿ij - Go * Ngap

GQPVE•V•LGQLAKEEAL

GQGQ


9

Dynamic programming: optimal alignment

€

SW = MUkTkk=1

Lali∑ - Go ⋅ Ngap - Ge ⋅ (Lgap - Ngap )

• Global/no gap:

SB Needleman and CD Wunsch 1970 J Mol Biol 48, 443-53

• Local/Gap:

TF Smith and MS Waterman 1981 J Mol Biol 147, 195-197


Many more substitution matrices exist today

Most use: BLOSUM62


12

Interactive software toolIgnacio Ibarra & Francisco Melo:  Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming  Bioinformatics 2010, 26:1664-5  

http:/melolab.org/sat 

http:/melolab.org/sat


13

BLAST: fast matching of single ‘words’

TTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKTIYKLILQGRTIKAELITEGVDGATGEKVYKQYGNQNAVDAEYTYDNATRTFTITQK

Default “word” size for “seeds” = 3

#1 seed=3

#2 extendTTYKLIL AATAEKVFKQYA WTYDDATKTFTIYKLIL GATGEKVYKQYG YTYDNATRTF

? ?


the major challenge for word search algorithms is to get the statistics right

14

Word searches: challenge: statistics


15

Significance of match (e.g. BLAST E-values)

Hits


Sequence comparisons:

multiple alignment

16


How accurate are pairwise alignments?

17


18

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eprofile - pro

filesequence - profile

sequence - sequence

sequ

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->

stru

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B Rost (1997) Fold Des 2:S19-24

B Rost (1999) Protein Eng 12:85-94


19

Zones

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eprofile - pro


sequence - sequence

sequ

ence

sim

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->

stru

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20

Zones

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eprofile - pro


sequence - sequence

sequ

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->

stru

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21

40

50

60

70

80

90

100

15 20 25 30 35

Schematic: structural similarity in Twilight zone

not true data: just to illustrate the idea of the twilight

zone

True Positives=pairs of proteins with similar structure

Percentage:

accuracy

-or-

specificity


22

1

10

100

1,000

15 20 25 30 35

Schematic: true and false in Twilight zone

True Positives = pairs of proteins with similar structureFalse Positives=pairs of proteins with DIFFERENT structure

Number of

pairs:


23

All-vs-all: PDB

3D =

structural alignment

1D =

sequence alignment

0.5nm rmsd

SAME 3D

ignore

DIFFER in 3D


24

PDB all-against-all ok?


25

Databases biased: MUST remove bias!


26


Day

light

Zo

ne

Twili

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Zone

Mid

nigh

t Zo

ne

redundancy-reduced vs.

redundancy-reduced?


27



28

Sequence conservation of protein structure

B Rost 1999 Prot Engin 12, 85-94 C Sander & R Schneider 1991 Proteins 9:56-69


29

Raw data: density? lesson learned?



30

How to estimate performance from the curves?

?gro

ups

groups

groupsgroupsgroups


31

Distance from new HSSP-curve



32

Twilight zone = true positives explode

101

102

103

104

105

106

-15 -10 -5 0 5 10

10 15 20 25 30 35

Num

ber o

f pro

tein

pai

rs

Distance from HSSP threshold

Percentage sequence identity

0

20

40

60

80

100

50 100 150 200 250 300 350 400

%

Seq

uenc

e id

entit

y

Number of residues aligned

+25+20

+15+10

+ 5 0

- 5- 10

- 15- 20

B Rost 1999 Prot Engin 12, 85-94


33

Twilight zone = false positives bazoom!!

101

102

103

104

105

106

-15 -10 -5 0 5 10

10 15 20 25 30 35

Num

ber o

f pro

tein

pai

rs

Distance from HSSP threshold

Percentage sequence identity

0

20

40

60

80

100

50 100 150 200 250 300 350 400

%

Seq

uenc

e id

entit

y

Number of residues aligned

+25+20

+15+10

+ 5 0

- 5- 10

- 15- 20

B Rost 1999 Prot Engin 12, 85-94


Multiple alignment:

simple hacks

34


Dynamic programming? for 3 sequences: O(N1 x N2 x N3) NP-complete (L Wang & T Jiang (1994) JCB 1: 337-48)

35

Multiple alignments

G G Q L A K E E A L

E 1 1

G 1 1 1 1

Q 2 1 1P 2 1

V 2

E 3

V

L 4

P3D


Dynamic programming? for 3 sequences: O(N1 x N2 x N3) NP-complete (L Wang & T Jiang (1994) JCB 1: 337-48)

claim: computer: up to 6  ~60 TB main memory no quote-> unsure 

36

Multiple alignments

G G Q L A K E E A LE 1 1G 1 1 1 1Q 2 1 1P 2 1V 2E 3VL 4P


Dynamic programming? for 3 sequences: O(N1 x N2 x N3) NP-complete (L Wang & T Jiang (1994) JCB 1: 337-48) hack 1:  dynamic programming: pairwise, only space in vicinity of intersection searched n-wise – H Carrillo & DJ Lipman (1988) SIAM J Applied Math. 48: 1073-82

– DJ Lipman, SF Altschul, JC Kececioglu (1989) PNAS 86:4412-5

37

Multiple alignments


Dynamic programming? for 3 sequences: O(N1 x N2 x N3) NP-complete (L Wang & T Jiang (1994) JCB 1: 337-48) hack 1:  dynamic programming: pairwise, only space in vicinity of intersection searched n-wise – H Carrillo & DJ Lipman (1988) SIAM J Applied Math. 48: 1073-82

– DJ Lipman, SF Altschul, JC Kececioglu (1989) PNAS 86:4412-5

hack 2: map to tree / pairwise Russell Doolittle, UCSD

38

Multiple alignments

Russell Doolittle

Shapers and Shakers


39

Multiple alignment: progressive 1

A B C DA 90 80 70B 90 80C 90D

GGQLAKEEALGGQLAKDEALGGQIAKDEALGGQIAKDEAI


40

Multiple alignment: progressive

A B C DA 90 80 70B 90 80C 90D


GGQLAKEEALGGQLAKDEALggqlakeeal

Step 1

Step 1


41

Multiple alignment: progressive

A B C DA 90 80 70B 90 80C 90D



Step 1

Step 2

Step 1 Step 2GGQIAKDEALGGQIAKDEAIggqiakdeal


42


A B C DA 90 80 70B 90 80C 90D



Step 1

Step 2

Step 1 Step 2GGQIAKDEALGGQIAKDEAIggqiakdeal

ggqlakeealggqiakdeal

Step 3

Step 3


43


A B C DA 90 80 70B 90 80C 90D



Step 1

Step 1


44


A B C DA 90 80 70B 90 80C 90D



Step 1

Step 2

Step 1 Step 2ggqlakeealGGQIAKDEAL ggqlakeeal


45


A B C DA 90 80 70B 90 80C 90D



Step 1

Step 2

Step 1 Step 2ggqlakeealGGQIAKDEAL ggqlakeeal

Step 3

Step 3ggqlakeealGGQIAKDEAI


Profiles: concept

46


47

Pairwise vs. multiple: built-up

GGQQLAKEEALGGQQLAKDEAL G.GQQLAKEEAL

GGAQGLAKDEAL


48



GGAQGLAKDEAL

GGQQLAKEEALGGQQLAKDEAL

GGAQGLAKDEAL


49



GGAQGLAKDEAL

GGQQLAKEEALGGQQLAKDEAL

GGAQGLAKDEAL

GG.QQLAKEEALGG.QQLAKDEALGGAQGLAKDEAL


50

Profiles profit from relation of “families”


51

Computationally: motifs

K Robison et al (1998) JMB 284, 241-54retrieved from http://weblogo.berkeley.edu/examples.html

Transcription factor binding sites

Following slides:

thanks kudos to Theresa Wirth

http://weblogo.berkeley.edu/examples.html


52Fig. 7: M Zvelebil & JO Baum (2008) Understanding Bioinformatics, Garland

Sequence motifs

Any AAOne-letter code

Two or more possible AA Disallowed AA Repetition range X(n,m) of X

Matching sequences e.g.

GLLMSACVVVGILMSAYPPGLLMSAES

Representation of a sequence with more than one possible amino acid (AA) or nuclear acid (NA) at a single position

G-[LI]-L-M-S-A-{RK}-X(1,3)

slide: Theresa Wirth


53S Henikoff & J Henikoff (1992) PNAS 89:10915-9

Recap: substitution matrix (BLOSUM)

GENERIC (for all proteins)


54

Scoring matrix: generic vs. specific


Generic

(here Blossum62)

Position-specific

scoring matrix

PSSM


• Matrix of numbers with scores for each residue or nucleotide at each position

55

PSSM - Position specific substitution matrix: concept

PSSM: Position-specific scoring matrix

Building PSSM:

◦ Absolute frequencies

◦ Add pseudo-counts if necessary

◦ Relative frequencies

◦ Log likelihoods

Starting point: 123456

ATGCTA

ATTGCT

TCTGAG

GTTGAG

CCATCC



56

PSSM - Position specific substitution matrix: one solution

Absolutefrequency

A:2T:1G:1C:1

Relativefrequency

A:0.4T:0.2G:0.2C:0.2

Σ=1

1 2 3 4 5 6

A 0.4 0 0.2 0 0.4 0.2

T 0.2 0.6 0.6 0.2 0.2 0.2

G 0.2 0 0.2 0.6 0 0.4

C 0.2 0.4 0 0.2 0.4 0.2

1 2 3 4 5 6

A 0.47 ∞ -0.22 ∞ 0.47 -0.22

T -0.22 0.86 0.86 -0.22 -0.22 -0.22

G -0.22 ∞ -0.22 0.86 ∞ 0.47

C -0.22 0.47 ∞ -0.22 0.47 -0.22

Logodds



57Olga Zhaxybayeva http://carrot.mcb.uconn.edu/~olgazh/bioinf2010/class10.html

PSSM - Position specific substitution matrix: example


http://carrot.mcb.uconn.edu/~olgazh/bioinf2010/class10.htmlhttp://carrot.mcb.uconn.edu/~olgazh/bioinf2010/class10.htmlhttp://carrot.mcb.uconn.edu/~olgazh/bioinf2010/class10.html


PSI-Blast

Position-Specific Iterative-

Basic Local Alignment Tool

58


59

Profile-based comparison

1 50fyn_human VTLFVALYDY EARTEDDLSF HKGEKFQILN SSEGDWWEAR SLTTGETGYIyrk_chick VTLFIALYDY EARTEDDLSF QKGEKFHIIN NTEGDWWEAR SLSSGATGYIfgr_human VTLFIALYDY EARTEDDLTF TKGEKFHILN NTEGDWWEAR SLSSGKTGCIyes_chick VTVFVALYDY EARTTDDLSF KKGERFQIIN NTEGDWWEAR SIATGKTGYIsrc_avis2 VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_aviss VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_avisr VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_chick VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIstk_hydat VTIFVALYDY EARISEDLSF KKGERLQIIN TADGDWWYAR SLITNSEGYIsrc_rsvpa .......... ESRIETDLSF KKRERLQIVN NTEGTWWLAH SLTTGQTGYIhck_human ..IVVALYDY EAIHHEDLSF QKGDQMVVLE ES.GEWWKAR SLATRKEGYIblk_mouse ..FVVALFDY AAVNDRDLQV LKGEKLQVLR .STGDWWLAR SLVTGREGYVhck_mouse .TIVVALYDY EAIHREDLSF QKGDQMVVLE .EAGEWWKAR SLATKKEGYIlyn_human ..IVVALYPY DGIHPDDLSF KKGEKMKVLE .EHGEWWKAK SLLTKKEGFIlck_human ..LVIALHSY EPSHDGDLGF EKGEQLRILE QS.GEWWKAQ SLTTGQEGFIss81_yeast.....ALYPY DADDDdeISF EQNEILQVSD .IEGRWWKAR R.ANGETGIIabl_mouse ..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVabl1_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVsrc1_drome..VVVSLYDY KSRDESDLSF MKGDRMEVID DTESDWWRVV NLTTRQEGLImysd_dicdi.....ALYDF DAESSMELSF KEGDILTVLD QSSGDWWDAE L..KGRRGKVyfj4_yeast....VALYSF AGEESGDLPF RKGDVITILK ksQNDWWTGR V..NGREGIFabl2_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YNQNGEWSEV RSKNG.QGWVtec_human .EIVVAMYDF QAAEGHDLRL ERGQEYLILE KNDVHWWRAR D.KYGNEGYIabl1_caeel..LFVALYDF HGVGEEQLSL RKGDQVRILG YNKNNEWCEA RlrLGEIGWVtxk_human .....ALYDF LPREPCNLAL RRAEEYLILE KYNPHWWKAR D.RLGNEGLIyha2_yeastVRRVRALYDL TTNEPDELSF RKGDVITVLE QVYRDWWKGA L..RGNMGIFabp1_sacex.....AEYDY EAGEDNELTF AENDKIINIE FVDDDWWLGE LETTGQKGLF


60BLOSUM: S Henikoff & J Henikoff (1992) PNAS 89:10915-9

Alignment scores use generic scoring matrix

Generic scoring matrix

(here BLOSUM62)


61

Idea: replace generic by specific scoring

BLOSUM: S Henikoff & J Henikoff (1992) PNAS 89:10915-9

Generic scoring matrix

(here BLOSUM62)



concept of PSI-BLAST

62


1. fast hashing

63

PSI-BLAST in steps


64

Like BLAST match ‘words’

TTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKTTYKLILLLLLLLLLLLLLLLLAWTVEKAFKTFAAAAAAAAAWTVEKAFKTFAAAAA

TTYKLILTTYKLIL

WTYDDATKTFWTVEKAFKTF

AATAEKVFKQYAAWTVEKAFKTFA

? ?

Default “word” size for “seeds” = 3

#1 seed=3

#2 extend


1. fast hashing 2. dynamic programming extension between

matches

65

PSI-BLAST in steps


66

BLAST + Smith-Waterman


TTYKLILTTYKLIL



#1 seed=3

#2 extend

dynamic programming to extend


67

BLAST + Smith-Waterman


TTYKLILTTYKLIL



#1 seed=3

#2 extend

dynamic programming to extend

Why is this fast?



matches 3. compile statistics 

EVAL - Expectation values

68

PSI-BLAST in steps

Hits



matches 3. compile statistics 4. collect all pairs and build profile

69

PSI-BLAST in steps


70

Sequence-profile comparison


YDFHGVGEDDISIKRG

PSI-BLAST SF Altschul 1997 Nucl Acids Res 25 3389-3402

PS- position specific



matches 3. compile statistics 4. collect all pairs and build profile 5. ?

71

PSI-BLAST in steps

??? 1 50fyn_human VTLFVALYDY EARTEDDLSF HKGEKFQILN SSEGDWWEAR SLTTGETGYIyrk_chick VTLFIALYDY EARTEDDLSF QKGEKFHIIN NTEGDWWEAR SLSSGATGYIfgr_human VTLFIALYDY EARTEDDLTF TKGEKFHILN NTEGDWWEAR SLSSGKTGCIyes_chick VTVFVALYDY EARTTDDLSF KKGERFQIIN NTEGDWWEAR SIATGKTGYIsrc_avis2 VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_aviss VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_avisr VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_chick VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIstk_hydat VTIFVALYDY EARISEDLSF KKGERLQIIN TADGDWWYAR SLITNSEGYIsrc_rsvpa .......... ESRIETDLSF KKRERLQIVN NTEGTWWLAH SLTTGQTGYIhck_human ..IVVALYDY EAIHHEDLSF QKGDQMVVLE ES.GEWWKAR SLATRKEGYIblk_mouse ..FVVALFDY AAVNDRDLQV LKGEKLQVLR .STGDWWLAR SLVTGREGYVhck_mouse .TIVVALYDY EAIHREDLSF QKGDQMVVLE .EAGEWWKAR SLATKKEGYIlyn_human ..IVVALYPY DGIHPDDLSF KKGEKMKVLE .EHGEWWKAK SLLTKKEGFIlck_human ..LVIALHSY EPSHDGDLGF EKGEQLRILE QS.GEWWKAQ SLTTGQEGFIss81_yeast.....ALYPY DADDDdeISF EQNEILQVSD .IEGRWWKAR R.ANGETGIIabl_mouse ..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVabl1_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVsrc1_drome..VVVSLYDY KSRDESDLSF MKGDRMEVID DTESDWWRVV NLTTRQEGLImysd_dicdi.....ALYDF DAESSMELSF KEGDILTVLD QSSGDWWDAE L..KGRRGKVyfj4_yeast....VALYSF AGEESGDLPF RKGDVITILK ksQNDWWTGR V..NGREGIFabl2_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YNQNGEWSEV RSKNG.QGWVtec_human .EIVVAMYDF QAAEGHDLRL ERGQEYLILE KNDVHWWRAR D.KYGNEGYIabl1_caeel..LFVALYDF HGVGEEQLSL RKGDQVRILG YNKNNEWCEA RlrLGEIGWVtxk_human .....ALYDF LPREPCNLAL RRAEEYLILE KYNPHWWKAR D.RLGNEGLIyha2_yeastVRRVRALYDL TTNEPDELSF RKGDVITVLE QVYRDWWKGA L..RGNMGIFabp1_sacex.....AEYDY EAGEDNELTF AENDKIINIE FVDDDWWLGE LETTGQKGLF

YDFHGVGEDDISIKRG



matches 3. compile statistics 4. collect all pairs and build profile 5. iterate

72

PSI-BLAST in steps


73



YDFHGVGEDDISIKRG

PSI-BLAST SF Altschul 1997 Nucl Acids Res 25 3389-3402

PSI- position specific iteration


74

Steps involved for profile-based alignments

sequence-sequence query database using substitution metric

results -> profile


75



results -> profile

profile-sequence query database using PSSM/profile

results -> update profile


76



results -> profile

profile-sequence query database using PSSM/profile

results -> update profile ite

rate


PSI-BLAST fast, partial dynamic programming SF Altschul (1997) NAR 25:3389-3402

ClustalW/ClustalX slow, dynamic programming, for experts JD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80

77

Sequence-profile methods


all against all (pairs)  by dynamic programming  (varying substitution matrices) build phylogenetic tree

78

Clustal (ClustalW, ClustalX)

A B C DA 90 80 70B 90 80C 90D A B C D

JD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80




MaxHom relatively slow, dynamic programming, good first guess C Sander & R Schneider (1991) Proteins 9:56-69

79





SAM/HMMer slow, need preprocess, HMM (statistics), very accurate R Hughey & A Krogh (1996) CABIOS 12:95-107  S Eddy (1998) Bioinformatics 14:755-63

80



81

Zones

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profile - profile

sequence - profilesequence - sequence

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stru

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another way to do math: HMM -

Hidden Markov

Models82


83SR Eddy (1998) Bioinformatics 755-63

HMM: Hidden Markov Models

Fig. 1. A toy HMM, modeling sequences of as and bs as two regions of potentially different residue composition. The model is drawn (top) with circles for states and arrows for state transitions. A possible state sequence generated from the model is shown, followed by a possible symbol sequence. The joint probability P(x,π|HMM) of the symbol sequence and the state sequence is a product of all the transition and emission probabilities. Notice that another state sequence (1-2-2) could have generated the same symbol sequence, though probably with a different total probability. This is the distinction between HMMs and a standard Markov model with nothing to hide: in an HMM, the state sequence (e.g. the biologically meaningful alignment) is not uniquely determined by the observed symbol sequence, but must be inferred probabilistically from it.


84

ProfileHMM: example footballFC Augsburg 2013/14 Bundesliga Fixtures

Date Status Home Score Away Attendance Competition

Aug 10 FT FC Augsburg 0-4 Borussia Dort. 30,660 Bundesliga

Aug 17 FT Werder Bremen 1-0 FC Augsburg 40,112 Bundesliga

Aug 25 FT FC Augsburg 2-1 VfB Stuttgart 30,030 Bundesliga

Aug 31 FT Nurnberg 0-1 FC Augsburg 37,239 Bundesliga

Sep 14 FT FC Augsburg 2-1 SC Freiburg 28,453 Bundesliga

Sep 21 FT Hannover 96 2-1 FC Augsburg 39,200 Bundesliga

Sep 27 FT FC Augsburg 2-2 Borussia Mon. 30,352 Bundesliga

Oct 5 FT Schalke 04 4-1 FC Augsburg 60,731 Bundesliga

Oct 20 FT FC Augsburg 1-2 VfL Wolfsburg 27,554 Bundesliga

Oct 26 FT Bayer Leverk 2-1 FC Augsburg 27,811 Bundesliga

Nov 3 FT FC Augsburg 2-1 Mainz 28,007 Bundesliga

Nov 9 FT Bayern Munich 3-0 FC Augsburg 71,000 Bundesliga

slide: Marco Punta


85















slide: Marco Punta

W

L

D

WW

W

L

LLL

L

L


86

ProfileHMM: example football

slide: Marco Punta

W D LProbabilistic

W D L


87


slide: Marco Punta

Our model for Augsburg’s Bundesliga results:

3 states: W, D, L

S(t)=F(S(t-1))

States S connected by probabilities

pij≥0; j

pij=1Σ


88















slide: Marco Punta

W

L

D

WW

W

L

LLL

L

L


89















slide: Marco Punta

W

L

D

WW

W

L

LLL

L

L

H

A

A

H

H

H

A

H

A

H

A

A


90


slide: Marco Punta

W D L

H A


91

ProfileHMM: formalism

slide: Marco Punta

HMMs are probabilistic models defined by:

! A finite set S of states

! A discrete alphabet A of symbols (observed objects)

! A probability transition matrix T=(tij) , i,j states

! A probability emission matrix E=(eix), i state, x symbol

… …

states

symbols

tij tij tij tij

eix eix eix eix


92

ProfileHMM - intro

Profile-HMMs are probabilistic models…

Model -> simulates a system Probabilistic -> produces outcomes based on probabilities


93

ProfileHMM: example 2: traffic light


94


R Y G


95


R Y G

Deterministic


96

ProfileHMM: example 3: weather


97


S C R


98


S C R

Probabilistic

*In fact, chaotic, deterministic

*


99


Probabilistic

pijTransition probability from symbol i to symbol j

*

*In fact, chaotic, deterministic


100

Probabilistic, 1st order Markov model

Example 2: The weather

P(Xn+1=x|X1=x1,…,Xn=xn)=P(Xn+1=x|Xn=xn)


HMM for alignment

101


102M Zvelebil & JO Baum (2008) Understanding Bioinformatics, Garland

Generic Profile-HMM for alignment

M1 M2 M3 M4

I0 I1 I3I2 I4

D1 D4D3D2

START END

◦ Captures matches, insertions and deletions

◦ Transition and emission probabilites

◦ Gap penalty handled by variation of transition probabilities

◦ Calculation of probability by multiplication of path variables



consider residue position i  BEFORE any amino acid is aligned,  

103

Entropy in alignment

0

1

2

bits

sav

ed

1 2CPQNDHWERKTMSGAVIYLF

3HCYMFPQRDEGNKILVAST

4CMYFPIVLHQARETKGSDN

5HQNPDCYREGKMSFTALIV

6MYCHFPIQRVLDEKNGTAS

7WHQYPMERDNKFGTSIVLAC



10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13

WHQYPMERDNKFGTSIVLAC

14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20

WHNPDCQEGYRSKTAMFVIL

21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36

CPMDQNGEWRTKSAIHVLFY

37MYCHFPI

QRVLDEKNGTAS

© Kevin Karplus UCSC


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 

104



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 now consider same column AFTER alignment posterior probability Pi + priors -> Hi 

105



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML


31MFYHIPVTNDLGQSAERK



34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 now consider same column AFTER alignment posterior probability Pi + priors -> Hi if position i conserved: Hi =? 

106



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML





34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 now consider same column AFTER alignment posterior probability Pi + priors -> Hi if position i conserved: Hi -> 0 if position i completely varied: Hi=? 

107



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML





34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 now consider same column AFTER alignment posterior probability Pi + priors -> Hi if position i conserved: Hi -> 0 if position i completely varied: Hi -> H0 

108



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML





34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS


consider residue position i BEFORE any amino acid is aligned,  we expect a particular acid according to some prior or background probability, P0, with entropy H0 now consider same column AFTER alignment posterior probability Pi + priors -> Hi if conserved: Hi -> 0; if varied: Hi -> H0 Hi-H0 reflects the “bits saved” by the alignment

109



0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML





34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS


• few members / little divergence  entropy dominated by priors -> the background signal dominates

110

0

1

2

bits

sav

ed









10

MYCHFPI

QRVLDEKNGTAS

11

MFYHIPVTNDLGQSAERK

12

MFYIH

PVGLTNRSAQKDE

13


14

PCHNDMQERKTSVIA

GLFYW

15

MYCHFPI

QRVLDEKNGTAS

16

HQNPDCYREGKMSFTALIV

17


18

FYMPIHVGTNLDSARKEQ

19

CMYFPHIVDGTNSALQEKR

20


21

CMPIVTFDGLQSAKRENYH

22

CMYFPIVLHQARETKGSDN

23

HCYMFPQRDEGNKILVAST

24

MYCHFPI

QRVLDEKNGTAS

25

CMYFPHIVDGTNSALQEKR

26

WMCYHFPQI

RVELTDKNSAG

27

MFYHIPVTNDLGQSAERK

28


29

CHPDNYEGQRSKTFAVIML

30

CMYFPIVLHQARETKGSDN

31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34CMYFPHIVDGTNSALQEKR


36CPMDQNGEWRTKSAIHVLFY

37MYCHFPI

QRVLDEKNGTAS

Alignment entropy for small families

0

1

2

bits

sav

ed

1MHYFQPIN

RGDKELVAST

2MFYHIVPLNGTQDSAERK

3CMYHFIQNRVTLDGKESAP

4CMPQDNGHWERKTSIAVLFY

5MFYHI

PVNGDTLQSAEKR

6WMCFYIHVQLPRTKENDSAG

7MIPFVQTYGNLDARSEKH

8MFYHI

PVNGDTLQSAEKR

9CDQNPHGEKRMWSTAVIYLF

10

MHYFQPINRGDKELVAST

11

MFYHIV

PLNGTQDSAERK

12

MFYHIVLPGNTRQSAKDE

13

FIYHVPLQRATKESGDN

14

WHCNDQGPYRMKESFTALIV

15

MFYHI

PVNGDTLQSAEKR

16

WHCNDQGPREKYSMTFAVLI

17

CWHNDQGPERSKYTMAFVIL

18

MFYHIVLPGNTRQSAKDE

19

MFHYIQVLPRNKDEGTAS

20

CPMQHDNKEGRTSAIVLYFW

21

CDQNPHGEKRMWSTAVIYLF

22

HCMYFNQRPDIKTELSGVA

23

MFYHIV

PLNGTQDSAERK

24

FIYHVPLQRATKESGDN

25

WHCNDQGPREKYSMTFAVLI

26

MFYHIVLPGNTRQSAKDE

27

FIYHVPLQRATKESGDN

28

CMYHFI

QNRVTLDGKESAP

29

CMPQDNGHWERKTSIAVLFY

30CWHNDQGPERSKYTMAFVIL

31MFIYHVLPRQTAKGSNED

32MHYFQPINRGDKELVAST

33

MFYHIV

PLNGTQDSAERK

34

WMCFYIHVQLPRTKENDSAG

35


36

MFYHIVLPGNTRQSAKDE

37

FIYHVPLQRATKESGDN

38


39

CHNDPGQYREKSTFAIVLM

40

MFYHIV

PLNGTQDSAERK

41

FIYHVPLQRATKESGDN

42

MHYFQPINRGDKELVAST

0

1

2

3

4

bits

sav

ed

1HVLPTNGQASDREK

2LDPATNSEQGRK 3IYVHPLTANGDSQEKR

4YIVPHDTLSANGEQKR

5PGK 6FMYIPVHDGTNLSAEQKR

7GYEFKRQSIAVLT

8SIVTAL 9PQGMNEKRSHWTAVLIYF

10

LHQRAGPNKDEST

11

PGQNTADERSK

12

ASE

13

FPYGNIDRSTLVKEAQ

14

QEYFSKARIVTL

15

QEVAL

16

QFKSRTIVLAE

17

CTYAFVMIL

18

HIPGVLTSNADQRKE

19

HPLGTQDNSAERK

20

AVLE

21

RTSHAMVIWLYF

22

AL

23

REAVKFL

24

PRAGQKEHDTSN

25

AIVL

26

LDTRASEK

27 28

EANSQKRP

29

MPDQGHENSI

RATVFLKY

30

NGQEKRYSMTFAVIPL

31

HPQRKAEGNDTS

32

SEIAVLR

33

RSAE

34

PMIGNLVSTADQKRE

35

DPHGMNYFQESTAVILKR

36

KRTVAILE

37

GVNTDLRSKAQE

38

CKSYTAFMVIL

39

HYFNDQCPRMKEILTVGSA

40

TDNVLSQRAEK

41

VEKASL

42

NPGQEKCRYMSFAIVTL



111

SAM-T98: Build alignment


Reestimate the alignment with the new homologs

Use the model to search for additional homologs

Build a model from the sequence or alignment

SAM-T98 Alignment Building

(Iterations 1 - 3)

Start: a single sequence

End: a SAM-T98 alignment(Iteration 4)




SAM/HMMer slow, need preprocess, HMM (statistics), very accurate R Hughey & A Krogh (1996) CABIOS 12:95-107/ S Eddy (1998) Bioinformatics 14:755-63

T-Coffee much slower, requires preprocessing, Genetic Algorithm Cedric Notredame, DG Higgins, Jaap Heringa (2000) JMB 302:205-17

112



Genetic Algorithm for alignment

113


114

Independence assumption


dynamic programming

(smith-waterman) PSI-BLAST sequence-sequence

or

sequence-profile

SAM

HMMer


115

Independence assumption


dynamic programming

(smith-waterman) PSI-BLAST sequence-sequence

or

sequence-profile

SAM

HMMer

ALL assume that alignment at position i independent of alignment at position j


E E R E E D

K K K E E R


Genetic algorithm does not make the

independence assumption

116


Genetic algorithm operates on segments

117


118

Genetic algorithm - concept

The 2006 NASA ST5 spacecraft antenna. This complicated shape was found through a genetic algorithm optimizing the radiation

pattern. It is known as an Evolved antenna.

© Wikipedia

http://en.wikipedia.org/wiki/Space_Technology_5http://en.wikipedia.org/wiki/Evolved_antenna


119

Genetic algorithm - concept

The 2006 NASA ST5 spacecraft antenna.

This complicated shape was found through a genetic algorithm optimizing the

radiation pattern. It is known as an Evolved

antenna.

© W

ikipe

dia

Initialize population

Compute fitness

STOP

? • roulette parents

• cross-over to children

• mutate children

• compute fitness

START

END

Crossover

parents

child

mutation

http://en.wikipedia.org/wiki/Space_Technology_5http://en.wikipedia.org/wiki/Evolved_antennahttp://en.wikipedia.org/wiki/Evolved_antenna


Local Alignment Global Alignment

Extension

Multiple Sequence Alignment

121

T-Coffee: Mix local and global alignment

© Cedric Notredame, CRG Barcelona


Local Alignment Global Alignment

Multiple Sequence Alignment

Multiple Alignment

StructuralSpecialist

122

T-Coffee: Use more information

© Cedric Notredame, CRG Barcelona




MaxHom relatively slow, dynamic programming, good first guess C Sander & R Schneider (1991) Proteins 9:56-69

T-Coffee much slower, requires preprocessing, Genetic Algorithm Cedric Notredame, DG Higgins, Jaap Heringa (2000) JMB 302:205-17

SSEARCH/PSI-Search SSEARCH - similar to MaxHom with SW only

PSI-Search: iterated SSEARCH W Liu, H McWilliam, M Goujon, A Cowley, R Lopez, WR Pearson (2013) Bioinformatics 28:1650-1

123



124



YDFHGVGEDDISIKRG


125

Zones

Day

light

Zon

e

Twili

ght Z

one

Mid

nigh

t Zon

e

profile - profile

sequence - profilesequence - sequence

sequ

ence

sim

ilar

->

stru

ctur

e sim

ilar




pairwise multiple sequence-profile ?

126

evolution of alignment methods


Profile-profile alignments

127


128

Profile-profile comparison









Documents

Sequence alignments 2 cb1 alignments2 - Rostlab · 2017. 5. 16. · /134 © Burkhard Rost 1 title: Computational Biology 1 - Protein structure: Sequence alignments 2 short title: