Ev olut ion teaches predic ting protein func tionSH3 Src-homology 3 domain one domain of proteins such as Src tyrosine kinase (STK) ... Road to predicting protein-protein partners

InCoB, Hong Kong, Aug 29, 2007 © Burkhard Rost (Columbia New York) /70

Evolution teaches predicting protein function

Burkhard RostCUBIC, NESG, C2B2

Dept. Biochemistry & Mol. Biophysics Columbia University

http://www.rostlab.org/Google “rost”1


I. Introduction:protein function

evolution

2


Protein function

Intuitive but not well-defined:• chemical how atom bound?• biochemical transferase• cellular (kinase) cell cycle• developmental time, regulatory• physiological related to disease• genetic dominant/recessive

Protein function as action: Function =

anything that happens to or through a protein


Our goals

Predict protein function from sequence + structure• Where?

nuclear/cytoplasmic/extra-cellular/mitochondrial/other, membrane/not/which, nuclear matrix, ER/Golgi/vesicle?

• What?protein-protein, protein-DNA, protein-small substrate, “is enzyme”, “is cell-cycle control protein”, “SNP deleterious?”

• When?pathways

Predict protein structure: focus on aspects relevant for function


Sarah Gilman

5


Gap sequence/annotation grows!

>7 million protein sequences known by fall 2006>100 million“gene”

sequences


Less than 25% have some annotation

B Rost, Nair, Liu, Wrzeszczynski & Ofran 2003 CMLS 60 2637-50

coverage of homology transfer

< 10-25%

we clearly need something more!


Prediction in terms of energy landscapes









Need to know history to predict!

Point mutationBinding (Substrate/Protein)Environmental change (DNA close/pH)


Evolution is history!

B Rost 1999 Prot Engin 12:85-94


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

SH3Src-homology 3 domainone domain of proteins such asSrc tyrosine kinase (STK)


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

SH3Src-homology 3 domainone domain of proteins such asSrc tyrosine kinase (STK)


Evolution improves prediction

Evolutionary profile implicitly captures history of and individual protein!


Evolution improves prediction

Evolutionary profile implicitly captures history of and individual protein!

fly

chicken

rat

mouse

human


Using evolution to predict structure

Sequence PSI-BLAST Filter

PHDsec1993

B Rost 1996 Meth Enzymol 266:525-539

60%->72%/77%

MaxHom

SWIS

S-PROT


II. Focus:Predict physical protein-protein interactions

13


Physical interaction NOT association

PD Kwong, R Wyatt, J Robinson, RW Sweet, J Sodroski & WA Hendrickson (1998) Nature 393, 648-659.PD Kwong, R Wyatt, S Majeed, J Robinson, RW Sweet, J Sodroski & WA Hendrickson (2000) Structure 8, 1329-1339.

gp120

CD4

antibody-1antibody-2

HIV gp120 / CD4 / FAB

YES

YES

NO


1999: Want to predict protein-protein partners

Simple method failed fully to do this, problem:too many false positives


Road to predicting protein-protein partners

Implement simple method to do this failed entirely: too many false positives

Reduce false positives:

predict surface residues (PROFacc, 1999) note: 1/2 of residues -> 1/4 of false positives!

predict residues in external interfaces (ISIS, 2004)


Sarah Gilman

17


Different interfaces = different physics?

PD Kwong, R Wyatt, J Robinson, RW Sweet, J Sodroski & WA Hendrickson (1998) Nature 393, 648-659.PD Kwong, R Wyatt, S Majeed, J Robinson, RW Sweet, J Sodroski & WA Hendrickson (2000) Structure 8, 1329-1339.

gp120

CD4

antibody-1antibody-2

HIV gp120 / CD4 / FAB


Find-self test on six types of interfaces

Y Ofran & B Rost 2003 J Mol Biol 325:377-87


Strength of prediction reflects reliability?

0.6

0.4

0.9

0.1

weakstrong


PP interfaces predicted from sequence

Very strong =

hot spots?

Y Ofran & B Rost 2003 FEBS Lett 544:236-9Y Ofran & B Rost 2007 Bioinformatics e13-16


Prediction of hot spots for CD4

• alanine scan for V1 domain of CD4 (bound to gp120)(A Ashkenazi et al. & DJ Capon (1990) PNAS 87, 7150)red: observed

Y Ofran & B Rost 2007 PLoS CB 3, e119


Prediction of hot spots for CD4

• alanine scan for V1 domain of CD4 (bound to gp120)(A Ashkenazi et al. & DJ Capon (1990) PNAS 87, 7150)red: observed

• structure:PD Kwong et al. & WA Hendrickson (2000) Structure 8, 1329-1339.

purple: predicted(Y Ofran & B Rost (2006) ISIS submitted)



Hot spots reliably predicted from sequence!

worst:~60%right

hottest of hot = no error!



Predict protein-protein binding partners

Reducing false positives:predict surface residues (PROFacc, 1999)


predict residues saturated internally (PROFcon, 2004)

localization (e.g. only all nuclear, LOCtree, 2004)







predict residues in protein-substrate interfaces (active)








predict protein domains/improve alignments








predict protein domains/improve alignments

Put it all together and predict binding partners


III. In passing:Predict subcellular

localization

28


Predict sub-cellular localization

Homology• sequence similarity

• text similarity

Motifs

De novo• structure

• sequence

KDEL


Sarah Gilman

30


Hierarchical prediction system

B Alberts et al. 1994 The Cell Garland R Nair & B Rost 2005 JMB 348:85-100


Complete map of localization

R Nair & B Rost 2005 JMB 348:85-100


Sarah Gilman

33


Protein-protein interactions across compartments

34

Extra-cellular

Cytoplasm Organelles Mitochondria

Nuclear TMtransmembrane

Extra-cellular

Cytoplasm

Organelles

Mitochondria

Nuclear

TMtransmembrane


PiNat (Protein Interaction Network analysis tool)

35Y Ofran G Yachdav, E Mozes, T Soong, R Nair, B Rost al. 2006 Bioinformatics 15:22 e402-7

Q9P2H0

ADD


IV. In passing:Model organisms pose problems for protein-protein interactions

36


Sarah Gilman

37


A B

A’ ? B’

similarity > X similarity > X

Can we transfer binding through homology?

38

Obviously, otherwise no value in model organisms ...

©Sven Mika & Burkhard Rost (Columbia New York)S Mika & B Rost 2006 PLoS Genetics, Vol 2, e29


A B

A’ B’

similarity > X

Inter and Intra-species the same?

39

Worm?

A’’

similarity > X

B’’ Human?

©Sven Mika & Burkhard Rost (Columbia New York)


Much better intra-species

©Sven Mika & Burkhard Rost (Columbia New York)S Mika & B Rost 2006 PLoS Genetics, Vol 2, e29


Model organisms pose problems for protein-protein interactions

41S Mika & B Rost 2006 PLoS Genetics, Vol 2, e29


V. In passing:micro->macro

Connect molecular and systems biology

42


Sarah Gilman

43


Connect micro- and macro-level

44

micro level:residuesRIGHT: more hotspots

macro level:networks

UP: more partners


Date- and Party-hubs

45

Hubs: promiscuous proteins

Date/Party hubsNotation introduced by Marc VidalJD Han et al. & M Vidal 2004 Nature 430:88-93

• Date hubs interactions at different times/same location?• Party hubs interactions at same time/different location


More hotspots -> more party-hub like!

46

Non-hubsParty hubsDate hubs

00.10.20.30.40.5

9 26 42

micro: more hotspots

macro: more partners

Y Ofran, A Schlessinger & B Rost submitted


Natively unstructured regions: induced fit

HJ Dyson & PE Wright 2005 Nat Rev Mol Cell Biol 6:197-208


More unstructured -> more date-hub like!

48

Non-hubsParty hubsDate hubs

micro: more hotspots

macro: more partners

Y Ofran, A Schlessinger & B Rost 2007 PLoS CB, 3:e140


VI. In passing:Structural Genomics

49


Structural genomics: 1 structure / family for all

50B Rost 1998 Structure 6:259-263


Speeding up structure determination

• today: more structures in 27 days than in first 27 years

• 8% ‘new sequence-structure family’

51

>$15

0M/y

ear

BIG4


3D modules

52

Multiple 3D alignment identifies consensus secondary structure

© C

hris

tine

Oren

go


Computational biology & bioinformatics @NESG


Ta-Tsen Soong

Eyal MozesSara Gilman


How many clusters?16,000 MANY MOREProkaryotes only suffices?YES Only for now

55


Structural genomics successful?

VERY MUCH AND INCREASINGLY MORE

SO

56


SG contributes significantly to novel!

57

Every fourth novel protein from 4 US SG centers

J Liu, GT Montelione & B Rost 2007 Nat Biotech 25:849-851


Structural genomics very effective

58J Liu, GT Montelione & B Rost 2007 Nat Biotech 25:849-851


Structure provides insight into function

59


Structure reveals function

60

Claudia Bertonati, Sharon Goldsmith-Fischman & Barry Honig, unpublished


Ta-Tsen Soong

Eyal MozesSara Gilman


N’

C’

core/1k8w[251-312]

Common 3D motif/fold

62


Sequence-based annotation


Topology

64

241 3 5

A

A

core/1k8w[251-312]

241 3 5

A

B

INS2

B

single-I

INS1

241 3 5

A

D

2

multi

INS2

INS1’

624

1 3 5

A

INS3

C

single-II

B

INS2

INS1

most similar?


2eve 2ar1 1j2b

Different groups

65

conserved not conservedConSurf F Glaser et al. & N Ben-Tal 2003 Bioinformatics 19:163-4


3D reveals: PUA differs from ASCH!

66

241 3 5

A

A

core/1k8w[251-312]

241 3 5

A

B

INS2

B

single-I

INS1

241 3 5

A

D

2

multi

INS2

INS1’

624

1 3 5

A

INS3

C

single-II

B

INS2

INS1

PUAASCHdiffer!


Other highlights

D Przybylski & B Rost 2007 NAR 35:2238-2246

The FANTOM Consortium 2005 Science 309:1559-1563

A Schlessinger + PLoS CB 2007 3: e140 + Bioinformatics in press + to submit

Y Bromberg& B Rost 2007 NAR 35:3823-3835


Conclusions

68

Transient protein-protein interfaces specific -> specific prediction very accurate

Localization predicted at levels of accuracy similar to high-throughput experiments

Evolution provides key for de novo prediction of (protein) function

NO key without SWISS-PROT/TrEMBL


Thanksgiving


Thanksgiving

Sarah Gilman


X=http://www.rostlab.orgPredictProtein PP X/predictprotein/

META-PP X/meta/submit_meta.htmlEVA X/eva/services: X/services/

LOCtree X/services/loctree/PredictNLS X/predictNLS/NORSp X/services/norsp/DSSPcont X/services/dsspcont/NLProt X/services/nlprot/CHOP/CHOPnet X/services/chop/ISIS X/services/isis/

databases: X/db/PEP X/db/PEP/CellCycleDB X/db/cellcycledb/NMPdb X/db/nmpdb/

Documents

Ev olut ion teaches predic ting protein func tionSH3 Src-homology 3 domain one domain of proteins such as Src tyrosine kinase (STK) ... Road to predicting protein-protein partners