rostlab.org · © burkhard rost (tu munich) /82 proﬁle-based comparison 1 50 fyn_human vtlfvalydy...

© Burkhard Rost (TU Munich) /821

title: Alignments - Profile-based

short title: alignments_2

lecture: Protein Prediction I - Protein Structure / Burkhard Rost, TUM, 2011

summer

Thursday June 9, 2011


Announcements

Videos: SciVee www.rostlab.orgTHANKS : Tim Karl + Haitham Sohby NO lectures: ?LAST lecture: Jul 7Examen: Jul 12 (?), 10:30 (likely this room)

• Makeup: likely: October 13 - morning

CONTACT: Marlena Drabik [email protected]

2Thursday June 9, 2011

http://www.rostlab.org


mailto:[email protected]



Today: Secondary structure prediction 1

LAST WEEKs• Secondary structure prediction

THIS WEEK• Alignments and “reach of comparative modeling”

NEXT WEEK• Comparative modeling



Sequence comparisons:

multiple alignment/profile-based



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



Sequence-profile methods

PSI-BLAST fast, partial dynamic programmingStephen F Altschul, TL Madden, Alejandro A Schaeffer, Jinghui Zhang, Zheng Zhang, Webb Miller & David J Lipman (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. NAR 25:3389-3402

>32,000 citations in Google Scholar May 2010



concept of PSI-BLAST



PSI-BLAST in steps

8

1. fast hashing



Like BLAST match ‘words’

TTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKTTYKLILLLLLLLLLLLLLLLLAWTVEKAFKTFAAAAAAAAAWTVEKAFKTFAAAAA

Default “word” size for “seeds” = 3



Like BLAST match ‘words’


TTYKLILTTYKLIL

WTYDDATKTFWTVEKAFKTF

AATAEKVFKQYAAWTVEKAFKTFA

Default “word” size for “seeds” = 3



PSI-BLAST in steps

10

1. fast hashing2. extend in between matches by dynamic programming



BLAST + Smith-Waterman


dynamic programming to extend



BLAST + Smith-Waterman


TTYKLILTTYKLIL

WTYDDATKTFWTVEKAFKTF

AATAEKVFKQYAAWTVEKAFKTFA

dynamic programming to extend



PSI-BLAST in steps

12

1. fast hashing2. extend in between matches by dynamic programming3. compiles statistics



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



PSI-BLAST in steps

14

1. fast hashing2. extend in between matches by dynamic programming3. compiles statistics4. collect all pairs and build profile



Sequence-profile comparison


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

PS-positionspecific





YDFHGVGEDDISIKRG


PS-positionspecific



PSI-BLAST in steps

16

1. fast hashing2. extend in between matches by dynamic programming3. compiles statistics4. collect all pairs and build profile5. iterate






PSI-positionspecificiteration





YDFHGVGEDDISIKRG


PSI-positionspecificiteration



Expanding in sequence space: dynamics of PSI-

BLAST18



Profile-based database search

Family U

U

B Rost 2001 J Struct Biol 134, 204-21Thursday June 9, 2011



Family U

safe forpairwise

safe zo

ne




zonereached throughposition-specific

family profileFamily U

safe forpairwise

safe zo

neU






safe forpairwise

safe zo

neUlost afteriteration






safe forpairwise

safe zo

neU

safe zonesof close

homologues

lost afteriteration





YDFHGVGEDDISIKRG






YDFHGVGEDDISIKRG


LWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTTLWYGQQAR KSQDKAKHAF AQHKRLQSTT




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

ClustalW/ClustalXslow, dynamic programming, for expertsJD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80



Clustal (ClustalW, ClustalX)

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

28

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



INSERT: reproduce phylogeny?



Reproduce phylogeny




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

34

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





all pairs (dynamic programming with varying substitution matrices)create phylogenetic treecluster and dynamic programming

35

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

C


D




over 30,000 citations in GoogleScholar

36

Desmond G Higgins & Paul M Sharp (1988) GeneDesmond G Higgins, AJ Bleasby & Reiner Fuchs (1992) Bioinformatics 8:189-91Julie D Thompson, Desmond G Higgins, Tobby J Gibson (1994) NAR 22:4673-80F Jeanmougin, Julie D Thompson, M Gouy, Des G Higgins & Toby J Gibson (1998) TIBS 23:403-5

Clustal

ClustalV

ClustalW

ClustalX

2402

2197

31056

1559

GoogleScholar May 2010




over 30,000 citations in GoogleScholar

37

Desmond G Higgins & Paul M Sharp (1988) GeneDesmond G Higgins, AJ Bleasby & Reiner Fuchs (1992) Bioinformatics 8:189-91Julie D Thompson, Desmond G Higgins, Tobby J Gibson (1994) NAR 22:4673-80F Jeanmougin, Julie D Thompson, M Gouy, Des G Higgins & Toby J Gibson (1998) TIBS 23:403-5

Des Higgins

Toby Gibson

Julie Dawn Thompson

Shapers and Shakers




PSI-BLAST fast, partial dynamic programmingSF Altschul (1997) NAR 25:3389-3402ClustalW/ClustalXslow, dynamic programming, for expertsJD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80MaxHomrelatively slow, dynamic programming, good first guessC Sander & R Schneider (1991) Proteins 9:56-69



Maxhom/HSSP

Homology-derived protein structures and the structural meaning of sequence alignment

39

Chris Sander & Reinhard Schneider (1991) Proteins 9:56-69C Sander & R Schneider (1993) NAR 21:3105-9Reinhard Schneider (1994) Sequenz- und Struktur Vergleiche und deren Anwendung für die Struktur- und Funktionsvorhersage von Proteinen (PhD Heidelberg University)

Reinhard SchneiderChris Sander

Shapers and Shakers



Maxhom/HSSP

40

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

A B

CA

DA

-> Profile (P0)conservation weight (cw0)

Sweep 1 Sweep 2P0cw0 B

P1cw0 C

P1cw0 D




PSI-BLAST fast, partial dynamic programmingSF Altschul (1997) NAR 25:3389-3402ClustalW/ClustalXslow, dynamic programming, for expertsJD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80MaxHomrelatively slow, dynamic programming, good first guessC Sander & R Schneider (1991) Proteins 9:56-69SAM/HMMerslow, need preprocess, HMM (statistics), very accurateR Hughey & A Krogh (1996) CABIOS 12:95-107S Eddy (1998) Bioinformatics 14:755-63



HMM & biology: SAM & HMMerJohn A Hertz, Richard G Palmer, Anders Krogh: Introduction to the Theory of Neural Computation, Westview Press

A Krogh, IS Mian, David Haussler (1994) NAR 22:4768-78R Durbin, S Eddy, A Krog & G Mitchison: Probabilistic models of proteins and nucleic acids, Cambridge University Press

42

Anders Krogh

David Haussler

Sean EddyKevin Karplus

Shapers and Shakers



Hidden Markov Models (HMM) - SAM

• A Krogh, M Brown, IS Mian, K Sjölander and D Haussler (1994) J Mol Biol 235 1501-31• K Karplus, C Barrett and R Hughey (1998) Bioinformatics 14 846-56• SR Eddy (1998) Bioinformatics 14 755-63

• K Karplus, R Karchin, J Draper, J Casper, Y Mandel-Gutfreund, M Diekhans and R Hughey (2003) Proteins: Structure, Function, and Genetics 53 491-6

SAM-T02 web site, UCSC, Kevin Karplus



Thanks for slides

Following slides cut out from an ISMB Tutorial given by Kevin Karplus 1999 in Heidelberg

44

© Kevin Karplus UCSChttp://www.sccrtc.org/photos/awards/karplus00-2001.jpg

http://users.soe.ucsc.edu/~karplus/bike/karplus_recumbent.gif


http://www.sccrtc.org/photos/awards/karplus00-2001.jpg





SAM-T98: Build alignment

45

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)

© Kevin Karplus UCSCThursday June 9, 2011


Background: entropy

46

Entropy: log (# microstates in a system)

H(x) = - SUM/x(i) { P(x(i)) log P(x(i)) }

minimal for PEAK distribution

maximal for uniform distribution

2 x1

1.0

P(x)

2 x1

1.0

P(x)

2 x1

1.0

P(x)

2 x1

1.0

P(x)



Entropy in alignment

consider column c at a residue position BEFORE any amino acid is aligned, we expect a particular acid according to some prior or background probability, P0, with entropy H0now consider same column AFTER alignmentposterior probability Pc + priors -> Hcif c conserved: Hc->0, if c totally varied: Hc->H0Hc-H0 reflects the “bits saved” by the alignment

47

0

1

2

bits

sav

ed

1 2CPQNDHWERKTMSGAVIYLF

3HCYMFPQRDEGNKILVAST

4CMYFPIVLHQARETKGSDN

5HQNPDCYREGKMSFTALIV

6MYCHFPI

QRVLDEKNGTAS

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


31MFYHIPVTNDLGQSAERK

32MFYHIPVTNDLGQSAERK


34

CMYFPHIVDGTNSALQEKR

35


36

CPMDQNGEWRTKSAIHVLFY

37

MYCHFPI

QRVLDEKNGTAS

0

1

2

bits

sav

ed





6MYCHFPI

QRVLDEKNGTAS




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



Hidden Markov Models (HMM) - SAM

• A Krogh, M Brown, IS Mian, K Sjölander and D Haussler (1994) J Mol Biol 235 1501-31• K Karplus, C Barrett and R Hughey (1998) Bioinformatics 14 846-56• SR Eddy (1998) Bioinformatics 14 755-63

• K Karplus, R Karchin, J Draper, J Casper, Y Mandel-Gutfreund, M Diekhans and R Hughey (2003) Proteins: Structure, Function, and Genetics 53 491-6

SAM-T02 web site, UCSC, Kevin Karplus



Alignment entropy for small families

49

0

1

2

bits

sav

ed





6MYCHFPI

QRVLDEKNGTAS




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


29CHPDNYEGQRSKTFAVIML


31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS

• In alignments with few family members or little divergence the entropy signal will be dominated by the priors-> the background signal dominates

0

1

2

bits

sav

ed

1MHYFQPINRGDKELVAST

2MFYHIV

PLNGTQDSAERK

3CMYHFI

QNRVTLDGKESAP

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

23MFYHIV

PLNGTQDSAERK

24FIYHVPLQRATKESGDN

25WHCNDQGPREKYSMTFAVLI

26MFYHIVLPGNTRQSAKDE

27

FIYHVPLQRATKESGDN

28

CMYHFI

QNRVTLDGKESAP

29

CMPQDNGHWERKTSIAVLFY

30


31

MFIYHVLPRQTAKGSNED

32

MHYFQPINRGDKELVAST

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 3IYVHPL

TANGDSQEKR

4YIVPHDTLSANGEQKR

5PGK 6FMYIPVHDGTNLSAEQKR

7GYEFKRQSIAVLT

8SIVTAL 9PQGMNEKRS

HWTAVLIYF

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



Alignment entropy for large families

50

0

1

2

bits

sav

ed





6MYCHFPI

QRVLDEKNGTAS




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


29CHPDNYEGQRSKTFAVIML


31

MFYHIPVTNDLGQSAERK

32

MFYHIPVTNDLGQSAERK

33


34

CMYFPHIVDGTNSALQEKR

35


36


37

MYCHFPI

QRVLDEKNGTAS

• In alignments with many family members and/or high divergence the entropy signal will be dominated by the observed profile-> profile dominates

• problem: possible over-training

0

1

2

bits

sav

ed

1MHYFQPINRGDKELVAST

2MFYHIV

PLNGTQDSAERK

3CMYHFI

QNRVTLDGKESAP

4CMPQDNGHWERKTSIAVLFY

5MFYHI

PVNGDTLQSAEKR

6WMCFYIHVQLPRTKENDSAG

7MIPFVQTYGNLDARSEKH

8MFYHI

PVNGDTLQSAEKR

9CDQNPHGEKRMWSTAVIYLF

10

MHYFQPINRGDKELVAST

11

MFYHIV

PLNGTQDSAERK

12

MFYHIVLPGNTRQSAKDE

13

FIYHVPLQRATKESGDN

14

WHCNDQGPYRMKESFTALIV

15

MFYHI

PVNGDTLQSAEKR

16


17


18

MFYHIVLPGNTRQSAKDE

19

MFHYIQVLPRNKDEGTAS

20

CPMQHDNKEGRTSAIVLYFW

21

CDQNPHGEKRMWSTAVIYLF

22

HCMYFNQRPDIKTELSGVA

23

MFYHIV

PLNGTQDSAERK

24

FIYHVPLQRATKESGDN

25


26

MFYHIVLPGNTRQSAKDE

27

FIYHVPLQRATKESGDN

28

CMYHFI

QNRVTLDGKESAP

29

CMPQDNGHWERKTSIAVLFY

30


31

MFIYHVLPRQTAKGSNED

32

MHYFQPINRGDKELVAST

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 3IYVHPL

TANGDSQEKR

4YIVPHDTLSANGEQKR

5PGK 6FMYIPVHDGTNLSAEQKR

7GYEFKRQSIAVLT

8SIVTAL 9PQGMNEKRS

HWTAVLIYF

10

LHQRAGPNKDEST

11

PGQNTADERSK

12

ASE

13

FPYGNIDRSTLVKEAQ

14

QEYFSKARIVTL

15

QEVAL

16

QFKSRTIVLAE

17

CTYAFVMIL

18

HIPGVLTSNADQRKE

19

HPLGTQDNSAERK

20

AVLE

21RTSHAMVIWLYF

22AL

23REAVKFL

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



HMM: some issues

Sequence weightingColumn regularizers

Transition regularizers

51

2crdExample 1

XFTNVSCTTSKECWSVCQRLHNTSRG.KCMNKKCRCYS

------CTTSKECWSVCQRLHNTSKG.WCDHRGCICES

XFTNVSCTTSKECWSVCQRLHNTSRG.KCMNKKCRCYS

XFTNVSCTTSKEXWSVCQRLHNTSRG.KCMNKKXRCYS

XFTQESCTASNQCWSICKRLHNTNRG.KCMNKKCRCYSXFTNVSCSASSQCWPVCKKLFGTYRG.KCMNSKCRCYS

XFTDVKCTGSKQCWPVCKQMFGKPNG.KCMNGKCRCYS

----VSCTGSKDCYAPCRKQTGCPNA.KCINKSCKCYG

TIINVKCTSPKQCSKPCKELYGSSAGaKCMNGKCKCYN

VGINVKCKHSGQCLKPCKDA-GMRFG.KCINGKCDCTP

2crd1sxm1cmr1bah1txm

2bmt1bkt

1lir

1big

Example 2




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

ClustalW/ClustalXslow, dynamic programming, for expertsJD Thompson, DG Higgins, TJ Gibson (1994) NAR 22:4673-80

MaxHomrelatively slow, dynamic programming, good first guessC Sander & R Schneider (1991) Proteins 9:56-69

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

T-Coffeemuch slower, requires preprocessing, Genetic AlgorithmCedric Notredame, DG Higgins, Jaap Heringa (2000) JMB 302:205-17



T-Coffee

T-Coffeemuch slower, requires preprocessing, Genetic AlgorithmCedric Notredame, DG Higgins, Jaap Heringa (2000) JMB 302:205-17

53

Des Higgins

Cedric Notredame

Jaap Heringa

Shapers and Shakers



Local Alignment Global Alignment

Extension

Multiple Sequence Alignment

T-Coffee: Mix local and global alignment

54© Cedric Notredame, CRG BarcelonaThursday June 9, 2011


Local Alignment Global Alignment

Multiple Sequence Alignment

Multiple Alignment

StructuralSpecialist

© Cedric Notredame, CRG Barcelona

T-Coffee: Use more information





YDFHGVGEDDISIKRG




Anything more fancy?



Profile-profile comparison





Sequence comparisons:homology



Why bother to align sequences?



Sequence alignments

61

Why do we need to align sequences?



Sequence alignments

61

Why do we need to align sequences?

Homology!



Zones



Relations in protein space



Sequence -> Structure

structurespace sequence

space



Similar (Sequence,Structure,Function)

67

Similar Sequence

Similar StructureSimilar Function



From 3D twilight to 3D midnight

zone



Zones



PDB all-against-all



Databases biased: MUST remove bias!



Hypothetical distribution of similar structures



0 25 50 75 1000

20

40

60

Percentage of identical residues



0 25 50 75 1000

20

40

60

Percentage of identical residues

FAKE DATA



Midnight zone: real - random

0

20

40

60

0 5 10 15 20 25

Num

ber o

f pai

rs

Percentage identical residues

B Rost 1997 Folding & Design 2, S19-S24Thursday June 9, 2011


Midnight zone: real - random

0

20

40

60

0 5 10 15 20 25

Num

ber o

f pai

rs

Percentage identical residues

B Rost 1997 Folding & Design 2, S19-S24 AS Yang and B Honig 2000 J Mol Biol 301, 679-689Thursday June 9, 2011


Evolution into the Midnight zone

0

400

800

1200

1600

0 5 10 15 20 25

Num

ber o

f stru

ctur

e pa

irs

Percentage pairwise sequence identity

25 50 75 100

0

B Rost 1997 Folding & Design 2, S19-S24Thursday June 9, 2011


Protein structures evolved at random - almost

average < 10%• -> most pairs have ‘random’ identity levels

• 3 - 4% anchor residues4 billion years of evolution reached equilibrium

• rate of creating new structures slower than drift towards meanaverages for convergent and divergent evolution similar

• convergent evolution may have been a major event



Secondary structure



Str 3......

3DPDB

EEH

HEEH

HEHH

EHHÉHE

FosfosProfile 1D Projection

sec acc

1aap1tcp

1btr

Seq (U) PHD 3

...

...

1DPHD

PHD 1

PHD 2

PHD n

Str 1Str 2

Str n

Two paths to fold recognition



TOPITS

good match to one of the known structures?=> • predict fold of matching structure

• model 3D coordinates by homology

LWQRPLVTIKIGGQLKEALLDTGAD

LWQRPLVTIKIGGQLKEALLDTGADLWRRPVVTAHIEGQLVEVLLDTGAD DRPLVRVILTNTGstALLDSGADLEKRPTTIVLINDTPLNVLLDTGAD :

-----EEEEE-----EEHHHH----o•oo•••••o•ooo•oo•••oo••o

align pre-dicted andknownstructure(s)

Project known 3D structureonto 1D

Predict 1D structure from sequence

input:sequence

generatesequencealignment

predict 1Dstructure

-----EEEEE----EEEEEE-----oooo•o•o•o•ooooo•ooooo•oo

-----EEEEE----EEEEEE-----

oooo•o•o•o•ooooo•ooooo•oo

-----EEEEE-----EEHHHH----o•oo•••••o•ooo•oo•••oo••o

note: exposed = oburied = •

.

55

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85

55

60

65

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75

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85

302520151050

Perc

enta

ge of

thre

e-st

ate p

airw

ise

per-

resi

due i

dent

ity (Q

3)

Percentage of pairwise sequence identity

55

60

65

70

75

80

85

55

60

65

70

75

80

85

302520151050Percentage of pairwise sequence identity

Perc

enta

ge of

two-

stat

e pai

rwis

epe

r-re

sidu

e ide

ntity

(Q2)

0

100

200

0 5 10 15 20 25 30N

umbe

r of p

airs

Percentage of pairwise sequence identity



Fold recognition without folds: AGAPE

1D predictionerrors correlate!

D Przybylski & B Rost 2004 J Mol Biol 341, 255-269Thursday June 9, 2011


Fold recognition without folds: AGAPE

1D predictionerrors correlate!

Fold recognition withoutbetter than with folds



AGAPE

Aligning GenerAlized ProfilEs



Announcements

Videos: SciVee www.rostlab.orgTHANKS : Tim Karl + Haitham Sohby NO lectures: ?LAST lecture: Jul 7Examen: Jul 12 (?), 10:30 (likely this room)

• Makeup: likely: October 13 - morning

CONTACT: Marlena Drabik [email protected]






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