201607 brisbane rost1 evolteaches -...

© Burkhard Rost 1

Evopro

Burkhard Rost TUM Munich

Comp Biol @ INF & IAS & WZWColumbia U NYC - Biochemistry

org

© Burkhard Rost

I. IntroductionEvolution teaches protein prediction

© Burkhard Rost 3

Predict protein function

© Burkhard Rost

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

4

Protein function

© Burkhard Rost 5

www.rostlab.org

© Burkhard Rost 55

Edda Kloppmann

Andrea Schafferhans

Esmeralda Vicedo

Guy Yachdav

Yannick Mahlich

EddaEdda Andrea EAndrea EEsmeraldaEEEsmeraldaEE GuyGuy YannickYannick

IngaWeise

Juan Miguel Cecjuela

Tatyana Goldberg

Tobias Hamp

MaximilianHecht

Thomas Hopf

Tim KarlTimTim Lothar

RichterMaximilianMaximilianTobias MMMTobias MMMJonas

ReebjMichael

BernhoferMichaelMichael

Martin Steinegger

Carsten Uhlig

Inga JJ JonasJonasll Jonas

Ashish Baghudana

pp

Ashish

pp

Ashish

gg

MadhukarSP ShankarMadhukar

Alexandru Buff

Syeda Tanzeem H

Charu

Joel Daon

Christian Dallago

ChristianC

Zosia Gasik

Caroline Gergen

Yulia Gembar-

zhevskaya

Yichun Lin

Maximilian Miller

Dimitrij Nechaev

Jade Martins

p

Dimitrij

Venkata P Satagopam

Sven Punga

Theresa Wirth

Sebastian WilzbachSebastiannSebastiannSebastiann

z

Monika Varshney

© Burkhard Rost

3D structureand function

Epstein & Anfinsen, 1961:sequence uniquely determines structure

INPUT: protein sequence OUTPUT:

6

Goal of protein prediction

© Burkhard Rost

Need to know history to predict!

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

7

Prediction in terms of energy landscapes

© Burkhard Rost 8

Evolution is history!

Chris Sander & Reinhard Schneider 1991 Proteins 9:56-68B Rost 1999 Prot Engin 12:85-94

© Burkhard Rost 9

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

SH3 Src-homology 3 domain one domain of proteins such as Src tyrosine kinase (STK)

© Burkhard Rost 10

Evolution improves prediction

Evolutionary profile implicitly captures history of and individual protein!

fly

chicken

rat

mouse

human

© Burkhard Rost 11

Using evolution to predict structure

Sequence PSI-BLAST Filter

PHDsec1993

B Rost 1996 Meth Enzymol 266:525-539

60% -> 72% /77%

MaxHom

S

© Burkhard Rost

Q9P2H0

Interactions & networks: protein-protein / protein-([DR]NA)LOCtree etc: predict localization Predict enzymatic activity & flexibility Protein disorder Predict membrane regions, epitomes, Improve alignment methods SNP-pipeline: predict nsSNP effectsPredictProtein: web service since 1992 NESG & NYCOMPS: structural genomics

12

Exciting projects

© Burkhard Rost

Machine learning=

understanding in 21st century!

© Burkhard Rost

Machine learning: easy to make believe

© Burkhard Rost

Prediction is the acid test

for understanding

15

© Burkhard Rost

Prediction is the acid test for understanding...

also when prediction based on

machine learning?16

© Burkhard Rost

Machine learning = black magic

17

© Wikipedia© Wikipedia

Unknown artist - User scan of Sadie, Stanley, ed. (1992). The New Grove Dictionary of Opera 2: 132. London: Macmillan

Black box image

is nonsense17

© Wikipedi© Wiki

UUUnnknow(1992L

© Burkhard Rost

complexity matches problem

-> rules not simple

18

© Burkhard Rost

ML extracts truthIF { 1 cross-validation right 2 data set right}

19

© Burkhard Rost 20

Train

Test

Cross-validation: hide data under table

© Burkhard Rost 21

WEKA-like cross-validation

Train

Test

© Burkhard Rost 22

3-way cross-validation

Train

Test

Cross-Train

© Burkhard Rost 23

Family clustering

No two from same group in train & test|cross-train

© Burkhard Rost

Still not enough: exploit “prerelease” data (latest/hottest)

24

© Burkhard Rost 25

Chris Sander

Dana Faber - Harvard

© Burkhard Rost 26

et al

© Burkhard Rost

Prediction is the acid test for understanding

28

© Burkhard Rost

Learning from machine learning

© Burkhard Rost 30

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

Do 6 types of interfaces differ in sequence?

Internal (inter-domain and intra-domain)External homomers (permanent/transient)External heteromers (permanent/transient)

Y Ofran & B Rost (2003) J Mol Biol 325, 377-87

© Burkhard Rost 31

Interface types differ in composition


gp120

CD4


They obviously differ! But, are these differences meaningful?

3Yanay Ofran

© Burkhard Rost

Chi-square test: known problem: small data sets here millions of points

all differences < 10-300

-> SIGNIFICANT

32

Are these differences statistically significant?

Y Ofran & B Rost 2005 unpublished3

Yanay Ofran

© Burkhard Rost

Chi-square test: known problem: small data sets here millions of points

all differences < 10-300

-> SIGNIFICANT

… unfortunately also:proteins [a-b] vs [c-d]1 vs 2 authors random subsets ...

33

Are these differences statistically significant?


Yanay Ofran

© Burkhard Rost 34

Find-self test (statistical significance)


Yanay Ofran

© Burkhard Rost 35

Find-self test on six types of interfaces


gp120

CD4


3Yanay Ofran

© Burkhard Rost 36

Predict PPI interfaces from sequence alone

© Burkhard Rost

Η

Ε

L

>

>

>

pickmaximal

unit=>

currentprediction

J2

inputlayer

first orhidden layer

second oroutput layer

s0 s1 s2J1

:GYIY

DPAVGDPDNGVEP

GTEF:

:GYIY

DPEVGDPTQNIPP

GTKF:

:GYEY

DPAEGDPDNGVKP

GTSF:

:GYEY

DPAEGDPDNGVKP

GTAF:

Alignments

5 . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 5 . .. . . . . . . 2 . . . . . 3 . . . . . .. . . . . . . . . . . . . . . . . 5 . .

. . . . 5 . . . . . . . . . . . . . . .

. . . 5 . . . . . . . . . . . . . . . .

. . 3 . . . . 2 . . . . . . . . . . . .

. . . . 1 . . 2 . . . 2 . . . . . . . .5 . . . . . . . . . . . . . . . . . . .. . . . 5 . . . . . . . . . . . . . . .. . . 5 . . . . . . . . . . . . . . . .. . . . 4 . 1 . . . . . . . . . . . . .. . . . 1 3 . . . 1 . . . . . . . . . .4 . . . . 1 . . . . . . . . . . . . . .. . . . . . . . . . . 4 . 1 . . . . . .. . . 1 . 1 . 1 2 . . . . . . . . . . .. . . 5 . . . . . . . . . . . . . . . .

5 . . . . . . . . . . . . . . . . . . .. . . . . . 5 . . . . . . . . . . . . .. 1 1 . 1 . . 1 1 . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 5 .

GSAPD NTEKQ CVHIR LMYFW

profile table

:GYIY

DPEDGDPDDGVNP

GTDF:

Protein

corresponds to the the 21*3 bits coding for the profile of one residue

37

Alignment information

B Rost (1996) Methods Enzymol 266:525-39B Rost (2001) J Struct Biol 134: 204-18

© Burkhard Rost B Rost (1996) Methods Enzymol 266:525-39B Rost (2001) J Struct Biol 134: 204-18 38

More complex system to predict structure

1996) Methods Enzymol 266:525-39

Sequence PSI-BLAST Filter

PROFsecPROFacc

1999

© Burkhard Rost

1992-now >200 packages in Debian >100k users registered from >110 nations >500 users/day, >12k users/month >57M predictions in 2012

39

PredictProtein

Laszlo Kajan

Guy Yachdav

B Rost & C Sander (1992) Nature 360:540L Kajan, G Yachdav, et al. & B Rost (2013) Biomed Res Int G Yachdav et al & B Rost (2014) NAR 42:W337-43

© Burkhard Rost 40

PP interfaces predicted from sequence

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

InteractSites

4Yanay Ofran

© Burkhard Rost

PPI hot spots?

41

© Burkhard Rost

residues that are essential for protein-protein interactions operational:

• 1. residue in the interface • 2. mutation of the residue knocks out interaction

42

Interaction HOT SPOTS

© Burkhard Rost 43

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

InteractSites

4Yanay Ofran

© Burkhard Rost

Y Ofran and B Rost (2003) FEBS Letters 544: 236-9Y Ofran and B Rost (2007) PLoS Comp Biol 3: e119 44

Strength of prediction reflects reliability?

0.6

0.4

0.9

0.1

weakstrong

© Burkhard Rost 45

Prediction of hot spots for CD4

• alanine scan for V1 domain of CD4 (bound to gp120)(A Ashkenazi et al. & DJ Capon (1990) PNAS87, 7150)

red: observed

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

purple: predicted

Y Ofran & B Rost 2007 PLoS CB 3:e119 Yanay Ofran

method

4Yanay Ofran

© Burkhard Rost 46

Hot spots prediction requires full information

Y Ofran & B Rost 2007 PLoS CB 3:e1194

Yanay Ofran

© Burkhard Rost 47

Connect micro- and macro-level?

micro level:residuesRIGHT: more hotspots

macro level:networks

UP: more partners

4Yanay Ofran

© Burkhard Rost

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

48

Date- and Party-hubs

© Burkhard Rost 49

More hotspots -> more party-hub like!

Non-hubsParty hubsDate hubs

0.0

0.1

0.2

0.3

0.4

0.4

9 26 42

micro: more hotspots

macro: more partners

Y Ofran, A Schlessinger & B Rost (2008) unpublishedA Feiglin, S Ashkenazi, A Schlessinger, B Rost and Y Ofran (2014) Mol Biosyst 10: 787-94 4

Yanay Ofran

© Burkhard Rost

secondary structure prediction

50

© Burkhard Rost

single residues (1. generation)

• Chou-Fasman, GOR 1957-70/8050-55% accuracy

segments (2. generation)

• GORIII 1986-9255-60% accuracy

problems • < 100% argument: 65% max • < 40% argument: strand non-local

51

Secondary structure prediction: 1.+2. Generation

residuesiandi+3

© Burkhard Rost

ACDEFGHIKLMNPQRSTVWY.

H

E

L

D (L)

R (E)

Q (E)

G (E)

F (E)

V (E)

P (E)

A (H)

A (H)

Y (H)

V (E)

K (E)

K (E)

52

Neural Network for secondary structure

© Burkhard Rost 53

NN sec str: training dynamics

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10

Other Strand Helix

time: 1 step = 20,000 training samples

Perfo

rman

ce

Eμ = oiμ − di

μ( )i∑

2

ΔJμ ∝ -∂Eμ{J}∂J

© Burkhard Rost 54

Balanced training: dynamics

0

0.20.40.6

0.81

1 2 3 4 5 6 7 8 9 10

Other Strand Helix

1 2 3 4 5 6 7 8 9 10

1 0.8 0.6 0.4 0.2 0

unbalanced balanced

Eμ = oiμ − di

μ( )i∑

2

ΔJμ ∝ - ∂Eμ{J}∂J

train:

E = oiμ − di

μ( )i∑

μ=α ,β,L∑

2μ

© Burkhard Rost

helix strand otheroverallaccuracymethod

unbalanced 62%comparison:data bankdistribution

comparison:33:33:33balanced 60%

55

full pie: all correctly predicted residues

© Burkhard Rost

Machine learning2nd major challenge: data set preparation

56

© Burkhard Rost

ML 1 - PPIphysical

Protein-ProteinInteractions

© Burkhard Rost 58

Physical interaction NOT association



YES

YES

NO

© Burkhard Rost

Predict PPI:best way: use evolutionary information

© Burkhard Rost 60

PPI pairs more conserved for paralogs than for orthologs

more similarless similar

S Mika & B Rost 2006 PLoS Genetics 2:e29

non-worm

worm

B

A’’ B’’

A’ B’?

?

A

?

© Burkhard Rost

PPI FACT 1:more conserved within than between organisms“paralogs” more conserved than

“orthologs”

61S Mika & B Rost 2006 PLoS Genetics, Vol 2, e29

© Burkhard Rost 62

Order

© Burkhard Rost

Measuring the interaction between A-B twice, results in the same interface?

636Tobias Hamp

Tobias Hamp

© Burkhard Rost T Hamp & B Rost 2012 PLoS Comp Biol 8:e1002623

64

Not homology-based inference, but details!

A B

identical

A-B1 experimental structure 1A-B2 experimental structure 2

identical

A B

interfaces 1 and 2

identical?

6Tobias Hamp

© Burkhard Rost 65

Mostly the same but many differ

T Hamp & B Rost 2012 PLoS Comp Biol 8:e10026236

Tobias Hamp

© Burkhard Rost 66

Many examples for alternative interfaces

T Hamp & B Rost 2012 PLoS Comp Biol 8:e10026236

Tobias Hamp

© Burkhard Rost

FACT 2:Protein-Protein

interaction interfaces vary a LOT LOT LOT

67T Hamp & B Rost 2012 PLoS Comp Biol 8:e1002623

Tobias Hamp 6

Tobias Hamp

© Burkhard Rost

Predicting pairs of protein-protein

interactions (PPIs)

68

© Burkhard Rost

PPIchallenge

machine learningMUCH more

69

© Burkhard Rost 70

Family clustering


© Burkhard Rost 71

Family clustering


B

A’ B’?

A

© Burkhard Rost

case 1: both used before: i.e. training contained A ∧ B NOT interaction AB

case 2: either used for training i.e. train on A | B

case 3: neither A nor B used before

72

PPI sampling needs to consider proteins

Edward Marcotte

Univ Texas Austin

Yungki Park

SUNY Buffalo

Y Park & EM Marcotte (2012) Nature Meth 9: 1134-1136

A B

© Burkhard Rost 73

Reduced performance for new proteins

PIPE2 C1 (AB in training)

C3 (AB NOT in training)

SIGPROD

C1 (AB in training)

C3 (AB NOT in training)

SIGPROD: S Martin, D Roe & JL Faulon (2005) Bioinformatics 21:218-26PIPE2: S Pitre et al & A Golshani (2008) NAR 36:4286-94

T Hamp & B Rost 2015 Bioinformatics 31: 1521-5 7Tobias Hamp

© Burkhard Rost

Sequence similarity only for PPIs,

i.e. positivesNOT enough!

74

Tobias HampTT Hamp & B Rost 2015 Bioinformatics 31: 1521-5

© Burkhard Rost

we HAVE to also consider negative PPIs

for cross-validation

75


© Burkhard Rost

Missing experimental data:

-> take all we have?

NO avoid bias!!

76T Hamp & B Rost 2015 Bioinformatics 31: 1521-5 7Tobias Hamp

© Burkhard Rost

Cross-validation challenge squared

for PPIs

77


© Burkhard Rost

T Hamp & B Rost 2015 Bioinformatics 31: 1945-50T Hamp & B Rost 2015 Bioinformatics 31: 1521-5

78

PPI from sequence through SVM profile kernel

Tobias HampT

• SIGPROD: S Martin, D Roe & JL Faulon (2005) Bioinformatics 21:218-26

• PIPE2: S Pitre et al & A Golshani (2008) NAR 36:4286-94

PIPE2SIGPROD

p4ip4i_filtered

newnew

new new

new

new

C1 proteins have known PPIs C3 not PPI known

© Burkhard Rost 79

Predict subcellular localization: LOCtree 2: 18 classes!

Tatyana Goldberg

T Goldberg, T Hamp & B Rost (2012) submitted

k-mer profile kernel SVM

TatyanaGoldberg

T Goldberg, T HampRost (2012) submit

k-mer profile kernel

SVM

SVM

SVM

Tobias Hamp

© Burkhard Rost

we can predict PPI from sequence

alone!

80T Hamp & B Rost 2015 Bioinformatics 31: 1945-50

Tobias Hamp

© Burkhard Rost

BUT

81

© Burkhard Rost

SVMkernel predict PPI from sequence

But 1:1998-2015

828Tobias Hamp

© Burkhard Rost

SVMkernel predict PPI from sequence

But 2:method 41...

838Tobias Hamp

© Burkhard Rost 84

Positives (A-B) from PDB



© Burkhard Rost

Negatives:not observed

(human ~ 1.9M)

85

© Burkhard Rost

Result: method 41:

very very good

86

© Burkhard Rost

only problem of method 41:

predicts that protein is PDB or SwissProt!

878Tobias Hamp

© Burkhard Rost 88

Draft network of cell-to-cell communication in humanR

IKE

N C

LST

& P

ER

KIN

S

Ro

st L

ab a

t T

UM

& L

CS

B

Jayson Harshbarger

Alistair ForrestPiero CarninciJordan Ramilowski

Edda Kloppmann

Venkata Satagopam

Burkhard RostTatyana

Goldberg

Jordan A. Ramilowski1, Tatyana Goldberg2, Jayson Harschberger1, Edda Kloppmann2, Marina Lizio1, Venkata P. Satagopam3, Masayoshi Itoh1,4, Hideya Kawaji1,4, Piero Carnici1

Burkhard Rost2 & Allistair R.R. Forrest1,5

RIKEN CLST, 2. TUM, 3. LCSB, 4. RIKEN PMI, 5. PERKINS

Nature COMMUNICATIONS 2015 6: 7866

Marina Lizio

Hideya Kawaji

Masayoshi Itoh

C

Jordan A. RamilowskiJ 1, Tatyana Goldberg2, Jayson Harschberger2 1, Edda KlMarina Lizio1, Venkata P. Satagopam3, Masayoshi Itoh3 1,4, Hideya Kawaji1,4, Pi

Burkhard Rost2tt & Allistair R.R. Forrest1,5

RIKEN CLST, 2. TUM, 3. LCSB, 4. RIKEN PMI, 5. PERKINS

Nature COMMUNICATIONS 2015 6: 7866

Receptors cell-type specific

& often evolve before their receptors

© Burkhard Rost 91

Natively unstructured regions: induced fit

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

© Burkhard Rost

CV • BS Chemistry UC Berkeley • MS Physics Univ Wisconsin• PhD Biophysics Univ Wisconsin • PD (CompBiol)Yale• Indiana Univ

Publications (2015/06 GoogleScholar)• > 200 publications • 2x >1,000 • 40x >200 • H-index 78

93

Keith Dunker - Indiana UnivShapers and Shakers

A Keith Dunker Indiana Univ

www.youtube.com

Pedro Romero, Zoran Obradovic, Charles R Kissinger, J Ernest Villafranca, Ethan Garner, Stephen Guilliot, A Keith Dunker (1998) Thousands of proteins likely to have long disordered regions. Pac Symp Biocomput 3, 437-448

© Burkhard Rost 94

Dunker-hypothesis

Residues not visible in 3D structures share disorder

A Keith Dunker Indiana Univ

www.youtube.com

© Burkhard Rost

< 5% helix or strand > 70 residues

J Liu, H Tan & B Rost 2002 J Mol Biol 322:53-64 95

NORS: no regular secondary structure

JinfengLiu

© Burkhard Rost

less than 5% helix or strand over > 70 residues

machine learning:true: all predictions in entire proteomesfalse: the whole PDB

97A Schlessinger, J Liu and B Rost (2007) PLoS Comput Biol 3: e140

Predict NORS (no regular secondary structure)

Avner Schlessinger

© Burkhard Rost 98

More hotspots -> more party-hub like!


0.0

0.1

0.2

0.3

0.4

0.4

9 26 42



Y Ofran, A Schlessinger & B Rost submitted

© Burkhard Rost 99

More unstructured -> more date-hub like!




A Schlessinger, J Liu and B Rost (2007) PLoS Comput Biol 3: e140

© Burkhard Rost 100

Ucon: unstructured regions from contact prediction

A Schlessinger, M Punta and B Rost (2007) Bioinformatics 23: 2376-84Avner SchlessingerMarco Punta

© Burkhard Rost C Schaefer & B Rost 2010 Bioinformatics 26:625-31

Secondary structure (helix, strand)robust under random mutation,

disorder not

101

Christian Schaefer


Eukaryotes dominate disorder (4-10x)

A Schlessinger et al & B Rost 2011 Curr Opin Struc Biol 21:412-8

7-13%

36-43%

Esmeralda Vicedo

Avner Schlessinger

© Burkhard Rost

Avner Schlessinger

E Vicedo, A Schlessinger & B Rost (2015) PLoS One 10:E0133990

habi

tat

evolu

tion

103

Proteome disorder content

more similar to habitat than

family

Esmeralda Vicedo

© Burkhard Rost E Vicedo, Z Gasik, YA Dong, T Goldberg & B Rost (2015) F1000Res 4:1222 104

Quick reaction to heat stress: duplicate chromosome

Chromosomal duplication is a transientevolutionary solution to stress

AH Yona et al & Y Pilpel & O Dahan(2012) PNAS 109:21010-5

Orna Dahan

Yitzhak Pilpel

© Burkhard Rost AH Yona et al & Y Pilpel & O Dahan (2012) PNAS 109:21010-5E Vicedo, Z Gasik, YA Dong, T Goldberg & B Rost (2015) F1000Res 4:1222 105

Quick reaction to heat stress: avoid disorder

Esmeralda Vicedo

Orna Dahan

Yitzhak Pilpel

Zosia Gasik

© Burkhard Rost AH Yona et al & Y Pilpel & O Dahan (2012) PNAS 109:21010-5E Vicedo, Z Gasik, YA Dong, T Goldberg & B Rost (2015) F1000Res 4:1222 106

Quick reaction to heat stress: avoid disorder

Esmeralda Vicedo

Orna Dahan

Yitzhak Pilpel

Zosia Gasik


Structural universe

B Rost 1998 Structure 6:259-263


Close to known structure -> more success

0.0

0.020

0.040

0.060

0.080

0.10

0.12

0 20 40 60 80 100

Succ

ess

(in P

DB/

clon

ed)

PIDE (Percentage pairwise sequence identity)


We cannot only do prokaryotes!


NYCOMPS (New York Consortium On Membrane Protein Structure)

S T R U C T U R A L B I O L O G Y

A peep through anion channelsThe crystal structure of a protein channel provides clues about the mechanisms that control the closure of pores found in the epidermis of plant leaves. Excitingly, the protein channel folds in a way never seen before. See Article p.1074

ARTICLEdoi:10.1038/nature09487

Homologue structure of the SLAC1 anionchannel for closing stomata in leavesYu-hang Chen1,2, Lei Hu3, Marco Punta2,4, Renato Bruni2, Brandan Hillerich2, Brian Kloss2, Burkhard Rost1,2,4, James Love2,Steven A. Siegelbaum3,5,6 & Wayne A. Hendrickson1,2,6,7

Nature 28 Oct 2010 Vol 267 p. 1074-80

a b c d

e f g h

Bacterium reveals how plants work

Wayne Hendrickson Columbia Univ, NYC


NYCOMPS pipeline stages

NYCOMPS ALL 9 PSI membrane

E Kloppmann et al (2012) Curr Op Struc Biol 22:1-7

Wayne Hendrickson

Edda Kloppmann

Marco Punta


11 medically important TMH predicted

OCTN1 Adiponectin receptor 1 MT-ND1

Crohn‘s disease, rheumatoid arthritis

diabetes, obesity, cancer

LHON, MELAS, Alzheimer, Parkinson

© Thomas Hopf - TUM & Debbie Marks - HMS TA Hopf et al & C Sander & DS Marks (2012) Cell 10 May doi: 10.1016

Chris Sander Sloan Kettering NYC

Thomas Hopf TUM Munich

Debora Marks Harvard Medical


Unexpected ... dark proteome

N Perdigao et al & S O’Donoghue (2015) Unexpected features of the dark proteome. PNAS 112: 15898–903

Sean O’Donoghue CSIRO & Garvan Inst

Sydney

© Burkhard Rost

A

A’

similarity > X

117

Homology-based inference

A’ sequence similar enough

to A ->

A’ and A: same function

C Sander & R Schneider 1991 Proteins 9:56-68

TobiasHamp

© Burkhard Rost

Year 2011: 1st Critical Assessment of Function Annotations (CAFA)

• Community effort to measure the current state of the art in function prediction

• Independent assessors, supported by renowned principal investigators

118

Critical Assessment

Tobias Hamp


Course: Protein Prediction II: Function

A

A’

similarity > X

A’ sequence similar enough

to A ->

A’ and A: same function

C Sander & R Schneider 1991 Proteins 9:56-68 Tobias Hamp



3 Teams

Tobias Hamp



Tobias Hamp

Tobias Hamp

© Burkhard Rost

major challenges: • cross-validation • data set preparation

rule-of-thumb:spend 90% of data & validation10% on method, write-up, publication

123

Machine learning IS modern understanding


Evolution teaches protein prediction


got.show: prediction of odds to survive

TUM courseJavaScript + DataMining

Guy Yachdav

Tatyana Goldberg

Christian Dallago

> 10 TV shows > 5 radio shows >600 printed newspapers >1.2 billion potential readers


Thanks & Bye

$$ NIH €€ AvH

Andrea Schafferhans

Lothar Richter

Tim Karl

Laszlo Kajan r

Tatyana Goldberg

Yannick Mahlich

Guy Yachdav

YYMaximilian Hecht

Edda Kloppmann

Marlena Drabik

Chris Schäfer

k Ch i Edda

Yana Bromberg

Rutgers U

Janet Kelso

MPI Leipzig

SeanO’DonoghueCSIRO Sydney

Avner Schlessinger

Mount Sinai

Yanay Ofran

Bar-Ilan U

Michal Linial

Hebrew U

Karima Djabali

TUM

MichalYA Reinhard Schneider

U Luxembourg

Chris Sander

Sloan Kettering

t Karima Lena Rost

BIS

JJStephan KramerMainz U

arg

Tobias Hamp

Mikael Bodén

UQ Brisbane

UQ IMB Brisbane

Nicholas Hamilton

Mark Ragan

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