secondary structure prediction cb1 sec1 lecture: protein prediction 1 - protein ... · 2014. 5....

© Burkhard Rost (TUM Munich) /991

title: Secondary structure predictionshort title: cb1_sec1

lecture: Protein Prediction 1 - Protein structure Computational Biology 1 TUM summer 2014

Wednesday May 21, 2014


Announcements

Videos: YouTube / www.rostlab.orgTHANKS : Tim Karl + Jonas ReebSpecial lectures:• Apr 15 - Andrea Schafferhans

No lecture:• Apr 17/22 Easter• May 01 Thu May day• May 06 Tue Student assembly• May 29 Thu Ascension day• Jun 03 Tue no room• Jun 10 Tue Whitsun holidays• Jun 19 Thu Corpus Christi

LAST lecture: July 1Examen: July 8 • Makeup: Oct 21 - morning

CONTACT: Lothar Richter [email protected]

2

TimKarl

LotharRichter

JonasReeb


http://www.rostlab.org

http://www.rostlab.org

mailto:[email protected]

mailto:[email protected]


Recap: secondary structure

prediction

3Wednesday May 21, 2014


Goal of structure prediction

Epstein & Anfinsen, 1961: sequence uniquely determines structure

• INPUT: sequence

3D structureand function

• OUTPUT:



Zones

5

Day

light

Zon

e

Twili

ght Z

one

Mid

nigh

t Zon

eprofile - profile

sequence - profilesequence - sequence

sequ

ence

sim

ilar

->st

ruct

ure s

imila

r

B Rost (1997) Fold Des 2:S19-24B Rost (1999) Protein Eng 12:85-94



Comparative modeling applicable to about 1/3 of all proteins



CASP

Protein Structure Prediction

Only homology modeling goodNo general prediction of 3D from sequence, yetImportant improvements in many fields!

© Burkhard Rost (Columbia New York)



L Pauling & RB Corey (1953) PNAS 39:247-252L Pauling, RB Corey & HR Branson (1951) PNAS 37:205-234W Kabsch & C Sander (1983) Biopolymers 22:2577-2637

DSSP

Pauling’s H-bond pattern used in DSSP



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



1D: secondarystructureprediction



Words

11

Secondary structure prediction2ndary structure prediction2D prediction



Close Homology (Sequence Id. > 60% Psi-Blast Eval < 10-20)

Distant Homology (Domain, Motif)

Machine Learning (NN, SVM)

Protein Space:

X=Positive Y=Negative

Protein function classification

© Kaz Wrzeszczynski: Thesis

W



Coverage of structure space



Secondary structure prediction

14

DSSP secondary assignment has 8 “states”

H = HelixG = 310 helixI = Pi helixE = Extended (strand)B = beta-bridge, single strand residueT = Turn, i.e. one turn of helixS = bent“ “ = loop



Goal of secondary structure prediction

LEDKSPDHNPTGID

AKGKPMDRNFTGRNHPPKDSS

AAQVKDALTK

LEQWGTLAQLRAIWEQELTDFPEFLTMMARQETWLGWLTI

helix strand

loop

LAVIGVLMKW

FVFLMIEKIYHKLT

DIRVGLTYYIAQ

VNTFVGTFAAVAHAL

15W Kabsch & C Sander (1985) Identical pentapetides with different backbones. Nature 317:207



??

???

How pentapeptides occur in 2 states?



L Pauling & RB Corey (1953) PNAS 39:247-252L Pauling, RB Corey & HR Branson (1951) PNAS 37:205-234W Kabsch & C Sander (1983) Biopolymers 22:2577-2637

DSSP

Pauling’s H-bond pattern used in DSSP



Secondary structure prediction methods

18

L Pauling, RB Corey and HR Branson (1951) Two Hydrogen-Bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-211.L Pauling, RB Corey and HR Branson (1951) The Structure of Proteins: Two Hydrogen-bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-234.AG Szent-Györgyi & C Cohen (1957) Role of proline in polypeptide chain configuration of proteins. Science 126:697.some are more equal than others ...



Sec str pred methods: single residues

19

Pauling, RB Corey and HR Branson (1951) Two Hydrogen-Bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-211.L Pauling, RB Corey and HR Branson (1951) The Structure of Proteins: Two Hydrogen-bonded Helical Configurations of the Polypeptide Chain. PNAS 37:205-234.AG Szent-Györgyi & C Cohen (1957) Role of proline in polypeptide chain configuration of proteins. Science 126:697.MF Perutz, MG Rossmann, AF Cullis, G Muirhead, G Will and AT North (1960) Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5 Å resolution, obtained by X-ray analysis. Nature 185:416-422.JC Kendrew, RE Dickerson, BE Strandberg, RJ Hart, DR Davies and DC Phillips (1960) Structure of myoglobin: a three-dimensional Fourier synthesis at 2 Å resolution. Nature 185:422-427.



Simple prediction: frequency

First step (Szent-Györgyi)Proline breaks a helixHelices span several turns, i.e. >4 residues-> identify helices/non-helices

20

Proline bends main chain




First step (Szent-Györgyi)Proline breaks a helixHelices span several turns, i.e. >4 residues-> identify helices/non-helices

from Proline to odds for all ....




from Proline to odds for all

22

....,....1....,....2....QEKSPREVTMKKGDILTLLNSTNK E..E EEEEEE

AA D E G I K L M N P Q R S T V

E 1 1 3 1 1 1

L 1 1 1 4 1 1 1 1 2 1




23

single residues (1. generation)• Chou-Fasman, GOR 1957-70/80

Robson B & Pain RH (1971) Analysis of the Code Relating Sequence to Conformation in Proteins: Possible Implications for the Mechanism of Formation of Helical Regions. J. Mol. Biol. 58:237-259.Chou PY & Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:211-215.Garnier J, Osguthorpe DJ and Robson B (1978) Analysis of the accuracy and Implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120.



how to assess performance?

problem 1: where to get secondary structure from?



how to assess performance?

problem 2: how to measure?



Secondary structure prediction accuracy

26

• Q3 : three-state per-residue accuracy

number of correctly predicted residues in states helix, strand, otherQ3= ---------------------------------------------------------------------------- number of residues in protein

Schulz GE & Schirmer RH (1979) Prediction of secondary structure from the amino acid sequence. In: (eds). Principles of protein structure. Berlin: Springer-Verlag, pp 108-130.




27


published: 63% accuracy




Secondary Structure Assignment: DSSP

Dictionary of protein Secondary Structure for ProteinsASSESSING secondary structure prediction

28

Wolfgang Kabsch & Chris Sander (1983) Biopolymers 22:2577-637

Wolfgang KabschChris Sander




29


50-55% accuracy (assessed in 1994)




2nd Generation:what would you do?



Secondary Structure Prediction: Segment 83

Dictionary of protein Secondary Structure for Proteins

31

Wolfgang Kabsch & Chris Sander (1983) Biopolymers 22:2577-637W Kabsch & C Sander (1985) Identical pentapetides with different backbones. Nature 317:207W Kabsch & C Sander (1983) Segment 83 unpublished



Secondary structure prediction: 1.+2. Generation

32


50-55% accuracy segments (2. generation)• GORIII 1986-92

• Gibrat J-F, Garnier J and Robson B (1987) Further developments of protein secondary structure prediction using information theory. New parameters and consideration of residue pairs. J. Mol. Biol. 198:425-443.

• Biou V, Gibrat JF, Levin JM, Robson B and Garnier J (1988) Secondary structure prediction: combination of three different methods. Prot. Engin. 2:185-191.

• Garnier J & Robson B (1989) The GOR method for predicting secondary structure in proteins. In: D. FG (eds). Prediction of protein structure and the principles of protein conformation. New York: Plenum Press, pp 417-465.




33


50-55% accuracy (Q3) segments (2. generation)• GORIII 1986-92

55-60% Q3• Gibrat J-F, Garnier J and Robson B (1987) Further developments of protein

secondary structure prediction using information theory. New parameters and consideration of residue pairs. J. Mol. Biol. 198:425-443.

• Biou V, Gibrat JF, Levin JM, Robson B and Garnier J (1988) Secondary structure prediction: combination of three different methods. Prot. Engin. 2:185-191.

• Garnier J & Robson B (1989) The GOR method for predicting secondary structure in proteins. In: D. FG (eds). Prediction of protein structure and the principles of protein conformation. New York: Plenum Press, pp 417-465.




34


50-55% accuracy

segments (2. generation)• GORIII 1986-92

55-60% accuracy

problems• < 100% they said: 65% max



Helix formation is local

residuesiandi+3

THYROID hormone receptor (2nll)



β-sheet formation is NOT local

Erabutoxin β (3ebx)Wednesday May 21, 2014



37


50-55% accuracy


55-60% accuracy


• < 40% they said: strand non-local




38


50-55% accuracy


55-60% accuracy



• short segments



SEQ! KELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVNDRQGFVPAAYVKKLDOBS! EEEE E E E EEEEEE EEEEEE EEEEEEHHHEEEE

TYP! EHHHH EE EEEE EE HHHEE EEEHH

Problems of secondary structure predictions (before 1994)



INSERT:concept of neural

networks



J11

J12

1

1

1

0

out0 = in1J11 in2J12 +

out = tanh (out0)

Simple Neural Network

Simple neural network



10

Training a neural network 1



10

Errare = (out net - out want) 2

.

1

- 121-1-2

out

in




Error

Junctions

10

01

11

11




10

01

11

11

.

1

- 121-1-2

out

in

10

01

01

12

10

01

- 1

1

12+?




Neural networks classify points



Simple Neural NetworkWith Hidden Layer

outi = f ij2 J ⋅ f jk

1 Jk∑ ⋅ kin⎛

⎝⎜

⎞

⎠⎟

j∑

⎛

⎝⎜⎜

⎞

⎠⎟⎟

Simple neural network with hidden layer



Principles of networks: input -> output

two steps:1. linear: sum over all input × connection2. non-linear: sigmoid trigger, i.e., project sum onto 0-1

.

:ACACC:

1.0

0input to unit

(=sum)

Σconnectionij*inputjstep 1:

step 2:

outp

utfr

om u

nit

inpu

t = 3

adj

acen

t res

idue

s in

pro

tein

seq

uenc

e

outp

ut =

sec

onda

ry s

truct

ure

stat

e of

cen

tral r

esid

ue

α

L

s1s2s3

Jdecision line

sum

result: < decision line



outi = ∑i=1

Nin+1

Jij inj

inj value of input unit j ; outi value of output unit i ; Jij connection between input unit j and output unit i

E = ∑i=1

Nout

(outi - desi)2

outi value of output unit i ; desi secondary structure stateobserved for central amino acid for output unit i (e.g. fora helix: des1=1, des2=0, des3=0)

Principles of neural networks: error

• output:

• error:

• free variables: connections { J }• goal:

representation of set of examples (training set) for which the mapping input->output is known, i.e., the secondary structure state of the central residue has been observed by the network



Principles of neural networks: training

∆Jij(t+1) = - ε ∂E(t)∂Jij(t) + α ∆Jij(t-1)

where ∂E/∂J is the derivative of the error with respect tothe network connection; t is the algorithmic time given bythe presentation of one example; ε determines the stepwidth of the change (learning strength, typically some0.01); α gives the contribution of the momentum term(∆J(t-1) , typically some 0.2), which permits uphill moves

Error

{ J }

training = change of connections {J} such that E decreasessimplest procedure:• gradient descent



Effect of over-training: theory

100

50

0Training time

over-train



0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

num

ber o

f cor

rect

clas

sific

atio

ns p

er ex

ampl

e

0 5 10 15 20 25

number of cycles

ratio for training set

ratio for testing set

Effect of over-training: practice



RETURN:secondary structure prediction



Secondary structure predictions of 1. and 2. generation

54


50-55% accuracy


55-60% accuracy



• short segments



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)

Neural Network for secondary structure



helix strand otheroverallaccuracymethod

unbalanced 62%

NN predicts secondary structure

56

neural network




unbalanced 62%


57

neural network




unbalanced 62%


57

neural network

... and developer believes that application of machine learning is all the intelligence he will ever need...



NN sec str: training dynamicsOther Strand Helix

time: 1 step = 20,000 training samples

Perfo

rman

ce

Eµ = oiµ − di

µ( )i∑

2

ΔJµ ∝ - ∂Eµ{J}∂J



NN sec str: training dynamics

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1Other Strand Helix

time: 1 step = 20,000 training samples

Perfo

rman

ce

Eµ = oiµ − di

µ( )i∑

2





unbalanced 62%neural network


59

full pie: all correctly predicted residues




unbalanced 62%comparison:data bankdistribution


60

neural network






comparison:33:33:33


61

neural network




Eµ = oiµ − di

µ( )i∑

2


normal training

Balanced training



E = oiµ − di

µ( )i∑

µ=α ,β,L∑

2

Eµ = oiµ − di

µ( )i∑

2


normal training

balanced training

Balanced training



Balanced training: dynamics

64

Other Strand Helix

10.80.60.40.20

unbalanced balancedEµ = oi

µ − diµ( )

i∑

2


train:

E = oiµ − di

µ( )i∑

µ=α ,β,L∑

2µ



Balanced training: dynamics

64

1 2 3 4 5 6 7 8 9 100

0.20.40.60.8

1Other Strand Helix

1 2 3 4 5 6 7 8 9 10

10.80.60.40.20

unbalanced balancedEµ = oi

µ − diµ( )

i∑

2


train:

E = oiµ − di

µ( )i∑

µ=α ,β,L∑

2µ





comparison:33:33:33balanced 60%

65




Neural networks DO improve if developer does something more

than dream the machine learning

dream...66




67


50-55% accuracy


55-60% accuracy



• short segments



β-sheet formation is NOT local

Erabutoxin β (3ebx)Wednesday May 21, 2014


Conclusion:not all sound

explanations are right!




70


50-55% accuracy


55-60% accuracy



• short segments



Bad segment prediction

HHHHHHHHHEEEEE

HHHHEEE

HHHHHHHEEEEE

1st level

2nd level

comparison:observed:

71

SEQ! KELVLALYDYQEKSPREVTMKKGDILTLLNSTNKDWWKVEVNDRQGFVPAAYVKKLDOBS! EEEE E E E EEEEEE EEEEEE EEEEEEHHHEEEE

TYP! EHHHH EE EEEE EE HHHEE EEEHH



Select samples at random

72

∆Jij(t+1) = - ε ∂E(t)∂Jij(t) + α ∆Jij(t-1)

where ∂E/∂J is the derivative of the error with respect tothe network connection; t is the algorithmic time given bythe presentation of one example; ε determines the stepwidth of the change (learning strength, typically some0.01); α gives the contribution of the momentum term(∆J(t-1) , typically some 0.2), which permits uphill moves

Error

{ J }



Local correlations in reality

residuesiandi+3

Erabutoxin β (3ebx)



??

???

How to get those into the prediction?



H

E

L

V (E)

P (E)

A (H)

PHDsec:

structure-to-structure

PHDsec: structure-to-structure network

75B Rost (1996) Methods Enzymol 266:525-39Wednesday May 21, 2014


Better segment prediction

HHHHHHHHHEEEEE

HHHHEEE

HHHHHHHEEEEE

1st level

2nd level

comparison:observed:



.

0

200

400

600

800

1000

1200

0 10 20 30 40 50

Num

ber o

f seg

men

ts

Segment length

0

5

10

15

20

25

25 30 35 40 45 50

DSSPPHD

-800

-600

-400

-200

0

200

400

600

800

0 2 4 6 8 10

helixstrandloop

Diff

eren

ce in

num

ber

of o

bser

ved

- pre

dict

ed se

gmen

tsSegment length

A B

Better prediction of segment lengths



Structure-to-structure network: Invented?

79

N Qian & TJ Sejnowski (1988) Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202:865-884.



Other ideas

More output units, e.g. instead of central residue: take central 31. 9 output units2. average output -> 3 unitsoutput back into neural networks:Gianluca Pollastri, Dariusz Przybylski, B Rost and Pierre Baldi (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins: Structure, Function, and Bioinformatics 47:228-235.



Other ideas

output back into neural networks:

81

Gianluca Pollastri, Dariusz Przybylski, B Rost and Pierre Baldi (2002) Proteins 47:228-235: Fig. 1

idea: P Frasconi & M Gori (1996) IEEE Trans Neural netw 7:1521-5



STILL ONLY 60+ε% accuracy.

How to improve beyond that?



How to get more data into it?



How to get more data into it?

83

?Wednesday May 21, 2014


Evolution has it!

.

0

20

40

60

80

100

0 50 100 150 200 250

Perc

enta

ge se

quen

ce id

entit

y

Number of residues aligned

Sequence identityimplies structural

similarity !

Don't know region

84

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



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)

86



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



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

PHD: Neural network & evolutionary information

88B Rost & C Sander (1993) PNAS 90:7558-62B Rost (1996) Methods Enzymol 266:525-39



25%

80

100%

number of residues alignedSequ

ence

iden

tity

filterMaxHom

sequencedata bank

protein Aprotein B

:protein N

protein Aprotein C

:protein M

MaxHom

BLAST

11

22

33

ext ractal ignment

PHD

U

From sequence to profile



P H D s e c

H

L

E

4+1""""""

20444

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

H

L

E

0.5

0.1

0.4percentage of each amino acid in proteinlength of protein (≤60, ≤120, ≤240, >240)distance: centre, N-term (≤40,≤30,≤20,≤10)distance: centre, C-term (≤40,≤30,≤20,≤10)

input global in sequence

input local in sequence

localalign-ment13

adjacentresidues

:::AAAAA.LLLLIIAAGCCSGVV:::

globalstatist.wholeprotein

%AALength∆ N-term∆ C-term

A C L I G S V ins del cons100 0 0 0 0 0 0 0 0 1.17100 0 0 0 0 0 0 33 0 0.42 0 0 100 0 0 0 0 0 33 0.92 0 0 33 66 0 0 0 0 0 0.74 66 0 0 0 33 0 0 0 0 1.17 0 66 0 0 0 33 0 0 0 0.74 0 0 0 33 0 0 66 0 0 0.48

first levelsequence-to- structure

second levelstructure-to- structure

PHDsec: more details



Jury

centre of mass = jury over 1-4

architecture 3architecture 4

singlenetworkvs.jurydecision

architecture 2architecture 1



PROFsec: Evolutionary information + more

B Rost (2001) J Struct Biol 134, 204-18 92Wednesday May 21, 2014


HEADER CYTOSKELETONCOMPND ALPHA SPECTRIN (SH3 DOMAIN) �SOURCE CHICKEN (GALLUS GALLUS) BRAINAUTHOR M.NOBLE,R.PAUPTIT,A.MUSACCHIO,M.SARASTE

Spectrin homology domain (SH3)

59%65%

72%



Prediction accuracy varies!

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Num

ber o

f pro

tein

cha

ins

Per-residue accuracy (Q3)

<Q3>=72.3% ; sigma=10.5%

1spf

1bct

1stu

3ifm

1psm



Stronger predictions more accurate!

.

0

20

40

60

80

100

0

20

40

60

80

100

3 4 5 6 7 8 9

Q per protein3 fit: Q3fit = 21 + 8.7 * Q

3

Q3 p

er p

rote

in

Reliability index averaged over protein

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)

H=0.5E=0.4L=0.1

H=0.8E=0.1L=0.1

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Num

ber o

f pro

tein

cha

ins

Per-residue accuracy (Q3)

<Q3>=72.3% ; sigma=10.5%

1spf

1bct

1stu

3ifm

1psm



Correct prediction of correctly predicted residues

.

70

75

80

85

90

95

100

0 20 40 60 80 100

PHDsec

PHDacc

PHDhtm

70

75

80

85

90

95

100RI=9

RI=0RI=9

RI=0

RI=9

RI=4

7

over

all p

er-re

sidue

acc

urac

y

percentage of resdidues predicted96



BAD errors are frequent!

0

50

100

150

200

250

300

350

0 10 20 30 40

Num

ber o

f pro

tein

cha

ins

BAD error (H for E, or E for H)

<BAD>=4.0% ; sigma=5.9%

0

5

10

15

20

0 20 40 60 80 100Pe

rcen

tage

of e

rrors

Cumulative percentage of protein chains



False prediction for engineered proteins!

GB1: IgG-binding domain of protein G (CHAMELEON) Kim & Berg, Nature, 366, 267-270, 1993

....,....1....,....2....,....3....,....4....,....5....,..AA TTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKDSSP EEEEEEE EEEEEEEEE HHHHHHHHHHHHHHHHH EEEEEEE EEEEEEEE

PHD 30 EEEEEE E EEHHHHHHHHHHHHHHEEE EEEEEE EEEEEPHD no EEEEEE EEEEEHHHHHHHHHHHHHHHH EEEEE EEEEEE

AATAEKVFKQY AWTVEKAFKTFPHD 30 EEEEEE EEEEEEE HHHHHHHHHEEE EEEE EEEEEEPHD no EEEEEE EEEEEEHHHHHHHHHHHHHHH EEEEE EEEEEE

EWTYDDATKTF AWTVEKAFKTFPHD 30 EEEEEE EEE EHHHHHHHHHHHHHHHH EEEEE EEEEEEPHD no EEEEEE E E EHHHHHHHHHHHHHHHH HHHHHHH EEEEE

AWTVEKAFKTF HHHHH



Lecture plan (CB1: Structure)-generic01: 2014/04/08 Tue: sorry02: 2014/04/10 Thu: welcome: who we are03: 2014/04/15 Tue: Intro I - acids/structure (Andrea Schafferhans)04: 2014/04/17 Thu: SKIP: Easter vacation05: 2014/04/22 Tue: SKIP: Easter vacation06: 2014/04/24 Thu: Intro 2 - domains07: 2014/04/29 Tue: Intro 3 - 3D comparisons08: 2014/05/01 Thu: SKIP: “May day” - (NOT to be confused with “m’aidez”)09: 2014/05/06 Tue: SKIP: student assembly (SVV)10: 2014/05/08 Thu: Alignment 111: 2014/05/13 Tue: Alignment 2 12: 2014/05/15 Thu: Comparative modeling 113: 2014/05/20 Tue: Secondary structure prediction 114: 2014/05/22 Thu: Secondary structure prediction 215: 2014/05/27 Tue: 1D: Secondary structure prediction 116: 2014/05/29 Thu: SKIP: holiday (Ascension Day)17: 2014/06/03 Tue: SKIP: no room 18: 2014/06/05 Thu: 1D: Secondary structure prediction 219: 2014/06/10 Tue: SKIP: Whitsun holidays20: 2014/06/12 Thu: 1D: Transmembrane helix prediction21: 2014/06/17 Tue: Nobel prize symposium22: 2014/06/19 Thu: SKIP: Corpus Christi (Fronleichnam)23: 2014/06/24 Tue: 1D: Transmembrane strand prediction, solvent accessibility24: 2014/06/26 Thu: 2D prediction25: 2014/07/01 Tue: 3D prediction/wrap up26: 2014/07/03 Thu: wrap up again27: 2014/07/08 Tue: examen, no lecture28: 2014/07/10 Thu: no lecture


secondary structure prediction cb1 sec1 lecture: protein prediction 1 - protein ... · 2014. 5....

Documents