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Quality and effectiveness of protein structure models. DIMACS 2006. Anna . Tramontano @uniroma1.it. Molecular function. The paradigm. Molecular structure. Sequence. …. Detecting homology. 3.50. 3.00. 2.50. 2.00. 1.50. 1.00. 0.50. 0.00. 1.0. 0.8. 0.6. 0.4. 0.2. 0.

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slide1

Quality and effectiveness of protein structure models

DIMACS 2006

Anna.Tramontano@uniroma1.it

slide2

Molecular

function

The paradigm

Molecular structure

Sequence

slide3

Detecting homology

slide4

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

1.0

0.8

0.6

0.4

0.2

0

r.m.s.d. = [(1/N)Σ d2]1/2

Proteins evolve

Fraction sequence identity

after structural superposition

Chothia and Lesk, EMBO J., 1986

slide5

AVGIFRAAVCTRGVAKAVDFVP

+

AVGIFRAAVCTRGVAKAVDFVP

| || | | || ||||| ||

AIGIWRSATCTKGVAKA--FVA

Comparative modelling

If the alignment is correct, we can use the Chothia and Lesk relationship to predict the expected quality of the model

slide6

AVGIFRAAVCTRGVAKAVDFVPVESMETTMRSPVFTDNSSPPAVPQSFQVAHLHAPTGSGKSTKVPAAYAAQGYKVLVLNPSVAATLGFGAYMSKAHGIDPNIRTGVRTITTGAPVTYSTYGKFLADGGCSGGAYDIIICDECHSTDSTTILGIGTVLDQAETAGARLVVLATATPPGSVTVPHPNIEEVALSNTGEIPAVGIFRAAVCTRGVAKAVDFVPVESMETTMRSPVFTDNSSPPAVPQSFQVAHLHAPTGSGKSTKVPAAYAAQGYKVLVLNPSVAATLGFGAYMSKAHGIDPNIRTGVRTITTGAPVTYSTYGKFLADGGCSGGAYDIIICDECHSTDSTTILGIGTVLDQAETAGARLVVLATATPPGSVTVPHPNIEEVALSNTGEIP

Fold recognition

Score and select model

Orengo, Curr. Op. Str. Biol, 1994

slide7

AVGIFRAAVCTRGVAKAVDFVP…

AVGIFR

AAVCTR

GVAKAVDF

Fragment based

Bystroff and Baker, JMB, 1998

slide8

AVGIFRAAVCTRGVAKAVDFVP…

AVGIFR

AAVCTR

GVAKAVDF

Fragment based

Bystroff and Baker, JMB, 1998

slide9

AVGIFRAAVCTRGVAKAVDFVP…

AVGIFR

AAVCTR

GVAKAVDF

Fragment based

Bystroff and Baker, JMB, 1998

slide10

AVGIFRAAVCTRGVAKAVDFVP…

AVGIFR

AAVCTR

GVAKAVDF

Fragment based

Bystroff and Baker, JMB, 1998

slide11

AVGIFRAAVCTRGVAKAVDFVP…

AVGIFR

AAVCTR

GVAKAVDF

Fragment based

Score and select model

Bystroff and Baker, JMB, 1998

slide12

AVSRAFT

RAFTAAF

DGHTYIPK

CASP: Critical assessment of techniques for protein structure prediction

The evaluation

Moult et al., Proteins, 1995

slide13

300

250

200

150

100

50

0

30000

25000

70

20000

60

15000

50

10000

40

5000

30

0

20

1

10

2

3

4

5

0

6

Groups

Targets

The evaluation

Models

Tramontano, NSB, 2003

slide14

120,00

110,00

100,00

90,00

casp6

80,00

70,00

casp4

Max P.AL0

60,00

casp5

50,00

40,00

30,00

20,00

0

20

40

60

80

m

CASP4 CASP5CASP6: Best models

The evaluation

Cozzetto and Tramontano, Proteins, 2004

slide15

http://predictioncenter.gov

State of the art

Moult et al., Proteins, 2005.

slide16

http://www.caspur.it/PMDB

State of the art

Castrignano’ et al., NAR, 2006.

slide18

Diffraction data

measurements

Protein

crystallization

Protein

preparation

Phase estimation

Model building

Molecular replacement

slide19

Rotation

search

}

?

Translation

search

Model

Molecular replacement

slide20

ArpWarp

Completely automatic procedure:

CASP Models

MolRep (10x10)

AMoRe. (20)

RefMac (10)

Molecular replacement

slide21

100

80

60

40

?

GDT-TS (distance based measure)

= [NCA(1Å)+NCA (2Å)+NCA (4Å)

+NCA (8Å)]/4

Molecular replacement

Giorgetti et al., Bioinformatics, 2005

slide22

What if we don’t know the quality of the model?

What if we don’t know how to build models?

Molecular replacement

Giorgetti et al., submitted

slide23

ACTFGARTEADEASRTFCGAVHI

GFRLPMNHTYWPLYHMVCS…

Structure factors

Molecular replacement

Giorgetti et al., submitted

slide24

60% success rate

Molecular replacement

slide25

60% success rate

If one of the retrieved models works, the procedure is successful

Molecular replacement

slide26

biological

blood

coagulation

Function prediction

catalityc activity

molecular

extra cellular

cellular

slide27

?

AVSRAFT

RAFTAAF

DGHTYIPK

The experiment

Moult et al., Proteins, 1995

slide28

Scheme of the experiment

Collect known info on targets

Ask people to provide ADDITIONAL information

Compare predictions

Is there a consensus?

Once the structure is known, can we saymore?

Function prediction

slide29

EC Number

Binding

Binding site(s)Residue role(s)

PT modificationsFree text comments

Function prediction

Soro and Tramontano, Proteins 2005

slide30

We had too few predictions per target to derive any sensible conclusion.

However,for the sake of the experiment, we tried to see what we could do and which would be the problems in analysing the data (other than the format)pretending that the numbers were significant.

Function prediction

slide31

Summary table for target T0230

    • Molecular function Unknown / COG annotation: Predicted metal-sulfur cluster biosynthetic enzyme (Group: General function prediction only; Category: Poorly characterized)
  • Predictions:
  • GO number GO name frequency
  • 287 magnesium ion binding1
  • 4176 ATP-dependent peptidase activity1
  • mannose-1-phosphate guanylyltransferase activity 1 1
  • 4672 protein kinase activity1
  • 5094 Rho GDP-dissociation inhibitor activity 1
  • 5554 Molecular function unknown 1 -
  • 6812 PROCESS (1)
  • 6825 PROCESS(1)
  • 8170 N-methyltransferase activity 1
  • 16822 hydrolase activity, acting on acid carbon-carbon bonds 1
  • 46872 metal ion binding1

Function prediction

slide32

Summary table for target T0230

    • Molecular function Unknown / COG annotation: Predicted metal-sulfur cluster biosynthetic enzyme (Group: General function prediction only; Category: Poorly characterized)
  • Predictions:
  • GO number GO name frequency GO Parents
  • 287 magnesium ion binding 1 46872, 43167, 5488
  • 4176 ATP-dependent peptidase activity 1 8233, 16787, 3824
  • mannose-1-phosphate guanylyltransferase
  • activity 1 8905, 16779, 16772, 16740, 3824
  • 4672 protein kinase activity 1 16773, 16772, 16740 (16301), 3824
  • Rho GDP-dissociation inhibitor
  • activity1 1 5092, 5083, 30695, 30234
  • 8170 N-methyltransferase activity 1 8168, 16741, 16740, 3824
  • hydrolase activity, acting on
  • acid carbon-carbon bonds 1 16787, 3824
  • 46872 metal ion binding 1 43167, 5488

Function prediction

slide33

Summary table for target T0230

    • Molecular function Unknown / COG annotation: Predicted metal-sulfur cluster biosynthetic enzyme (Group: General function prediction only; Category: Poorly characterized)
  • Predictions:
  • GO number GO name frequency GO Parents
  • 287 magnesium ion binding 1 46872, 43167, 5488
  • 4176 ATP-dependent peptidase activity1 8233, 16787, 3824
  • mannose-1-phosphate guanylyltransferase
  • activity1 8905, 16779, 16772, 16740, 3824
  • 4672 protein kinase activity1 16773, 16772, 16740 (16301), 3824
  • Rho GDP-dissociation inhibitor
  • activity1 1 5092, 5083, 30695, 30234
  • 8170 N-methyltransferase activity1 8168, 16741, 16740, 3824
  • hydrolase activity, acting on
  • acid carbon-carbon bonds1 16787, 3824
  • 46872 metal ion binding1 43167, 5488

Function prediction

slide34

Summary table for target T0230

    • Molecular function Unknown / COG annotation: Predicted metal-sulfur cluster biosynthetic enzyme (Group: General function prediction only; Category: Poorly characterized)
  • Predictions:
  • GO number GO name frequency GO Parents
  • 287 magnesium ion binding 1 46872, 43167, 5488
  • 4176 ATP-dependent peptidase activity1 8233, 16787, 3824
  • mannose-1-phosphate guanylyltransferase
  • activity1 8905, 16779, 16772, 16740, 3824
  • 4672 protein kinase activity1 16773, 16772, 16740 (16301), 3824
  • Rho GDP-dissociation inhibitor
  • activity1 1 5092, 5083, 30695, 30234
  • 8170 N-methyltransferase activity1 8168, 16741, 16740, 3824
  • hydrolase activity, acting on
  • acid carbon-carbon bonds1 16787, 3824
  • 46872 metal ion binding1 43167, 5488

Function prediction

16787 hydrolase

16740 transferase activity

3824 catalyitic activity

slide35

Results: GO consensus

Function prediction

Soro and Tramontano, Proteins, 2005

slide36

18 months later…

Annotations in DB decreased by 5%

24 new targets were annotated

We looked at methods (abstracts, directly contacting predictors, literature)

Function prediction

slide37

1

1

4

11011

1

10011

10100

2

10001

10000

11100

2

10101

5

11001

2

Function prediction

slide38

18 months later…

4 newly annotated targets had been correctly predicted by at least one method

85% of the consensus non redundant predictions were correct

Function prediction

slide39

Results: GO consensus

Function prediction

Soro and Tramontano, Proteins, 2005

slide40

*

*

Function prediction

*

*

*

*

slide41

CASP is about to start again:

We will start collecting targets next week

There will be a few differences

http://predictioncenter.org

Announcments

slide42

Claudia Bonaccini

Michele Ceriani

Domenico Cozzetto

Emanuela Giombini

Alejandro Giorgetti

Paolo Marcatili

Veronica Morea

Romina Oliva

Massimiliano Orsini

Marialuisa Pellegrini

Domenico Raimondo

Simonetta Soro

Ivano Talamo

Krzysztof Fidelis

Tim Hubbard

Andriy Kryshtafovych

John Moult

Burkhard Rost

Adam Zemla

Structural biologists

Predictors

Acknowledgements

BioSapiens - EU VI Framework

Ministero della Salute

Universita' di Roma

Istituto Pasteur Roma

Facolta' di Medicina

San Paolo

CNR