Protein structure prediction: the customer view [email protected].

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Transcript of Protein structure prediction: the customer view [email protected].

Page 1: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.
Page 2: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Protein structure

prediction: the customer view

[email protected]

Page 3: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Protein structure prediction:why

Protein No.

NOS1_RABIT 6 A K A T I L Y A T E T G K S Q A Y A KNOS3_HUMAN 7 A K A T I L Y G S E T G R A Q S Y A QNOS_RHOPR 8 A K A T I L F A T E T G K S E M Y A RNOS_ANOST 9 A K A T V L Y A T E T G R S E Q Y A RNOS_LYMST 10 A K C S I F Y A T E T G R S E R F A RNCPR_HUMAN 11 A N I I V F Y G S Q T G T A E E F A NNCPR_CANTR 12 A N T L L L F G S Q T G T A E D Y A N

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Page 4: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Predicting:• Expected quality of a model (QMode 1)• Expected error on residue Cα (QMode 2)

You may submit your quality assessment prediction in one of the two different modes:QMODE 1 :   global model quality score (MQS - one number for a model)QMODE 2 :   MQS and error estimate on per-residue basis.

Quoting the CASP web page:

Protein structure Quality prediction:The casp initiative

Page 5: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Target xx PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target xx GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Protein structure Quality prediction:The casp initiative

Page 6: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Target xx PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target xx GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Pearson correlation

By target

Protein structure Quality prediction:The casp initiative

Page 7: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Target xx PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy PredModel serv1_1 N1Model serv1_2 N2…………. …Model serv1_5 ……Model serv3_4 …….

Target xx GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Target yy GDTModel serv1_1 G1Model serv1_2 G2…………. …Model serv1_5 ……Model serv3_4 …….

Pearson correlation

Global

Protein structure Quality prediction:The casp initiative

Page 8: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

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Cozzetto et al., Proteins 2007

Protein structure Quality prediction:The casp initiative

Page 9: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Protein structure modelling:A digression

Protein No.FLAV_CLOBE 1 A . . . I V Y W S G T G N T E K M A ECYSJ_THIRO 2 A . I T I L F G S Q T G N A K A V A E

Page 10: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Protein structure modelling:Expected accuracy

Cozzetto and Tramontano, Proteins 2004

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Maistas: taking splicing into account

Protein No.FLAV_CLOBE 1 A . . . I V Y W S G T G N T E K M A ECYSJ_THIRO 2 A . I T I L F G S Q T G N A K A V A E

Protein No.FLAV_CLOBE 1 A . . . I V Y W S G T G N T E K M A E

Page 12: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

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Maistas: taking splicing into account

http://www.bioinformatica.crs4.org

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Maistas: taking splicing into account

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ANTIBODIES: A different story

Page 15: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

C

N

N C

H3 H1

H2L2

L1L3

V L

H2C

H1C

V H

H3C

V L

V H

H1C

H2C

H3C

C LC L

Antibody

Antigen binding site

SS

ANTIBODIES: A different story

Page 16: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

ANTIBODIES: A different story

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91 92 93 94 95 96 90 * *Y Q S L P Y Q

91 92 93 94 95 96 90 * *W T Y P L I Q

95Pro

90Gln

94Pro

ANTIBODIES: A different story

Chothia et al., Nature 1989

Page 18: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Canonical structures for the ‘torso’ of H3:94R – 101D

94 non R or 101 non D

103

94101

103

94101

Morea et al., JMB., 1998

ANTIBODIES: A different story

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target sequence

BLAST

Align

VL templateTL

Build framework

ANTIBODIES: A different story

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ANTIBODIES: A different story

target sequence

Build framework

Align

BLAST

VL templateTL

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target sequence

BLAST

Align

template

Build framework

ANTIBODIES: A different story

Ab VL sequence Ab VH sequence

“BLAST”

VL templateTL

VH templateTH

“Align”

TL=TH?

Fit conserved interface

Build template

Build framework

Page 22: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

“Align”

Ab VL sequence Ab VH sequence

“BLAST”

VL templateTL

VH templateTH

TL=TH?

Fit conserved interface

Build template

Build framework

ANTIBODIES: A different story

Page 23: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Taking the frameworks from different structures introduces errors

One might be better off selecting the same template, at the cost of loosing in sequence identity

ANTIBODIES: A different story

Page 24: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

Taking the loops from different structures introduces errors

One might be better off selecting a template with the right CS, at the cost of loosing in sequence identity

ANTIBODIES: A different story

Page 25: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

•Same antibody•Same antibody and canonical structures •Same canonical structures•Best Vl and Vh

ANTIBODIES: A different story

Page 26: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

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QuickTime™ and aTIFF (LZW) decompressor

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QuickTime™ and aTIFF (LZW) decompressor

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QuickTime™ and aTIFF (LZW) decompressor

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Page 28: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

QuickTime™ and aTIFF (LZW) decompressor

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ANTIBODIES: A different story

?

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ANTIBODIES: A different story

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ANTIBODIES: A different story

AVACFATGAFGTARASDFEARTASADFAERAYHGTARYAPLSVNTERAT…..

ADFAERAYLDFNMRSYPDFHGRTYAEFKLLSY

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ANTIBODIES: A different story

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ANTIBODIES: A different story

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ANTIBODIES: A different story

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ANTIBODIES: A different story

ANTIBODIES: A different story

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ANTIBODIES: A different story

PDB

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ANTIBODIES: A different story

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ANTIBODIES: A different story

Page 39: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

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QuickTime™ and aTIFF (LZW) decompressor

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QuickTime™ and aTIFF (LZW) decompressor

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ANTIBODIES: A different story

?

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acknowledgements

Giuliana BrunettiEnrico Capobianco Simone CarcangiuAlberto de la FuenteMatteo Floris Elisabetta MarrasJoël MasciocchiElisabetta MuscasMassimiliano OrsiniEnrico PieroniFrédéric ReinierPatricia Rodriguez Tome’Alphonse Thanaraj ThangavelMaria Valentini

Tiziana CastrignanòP. D’Onorio De Meo Danilo Carrabino

Domenico Cozzetto Enrico FerraroFabrizio Ferre’Emanuela GiombiniAlejandro Giorgetti Paolo MarcatiliDomenico RaimondoStefania Bosi

Claudia BertonatiAlessandra GodiMichele CerianiRomina OlivaClaudia BonacciniMarialuisa Pellegrini Simonetta Soro

EU Biosapiens Institut Pasteur-Cenci

HFPRegione Sardegna

EU Biosapiens Institut Pasteur-Cenci

HFPRegione Sardegna

Page 42: Protein structure prediction: the customer view Anna.Tramontano@uniroma1.it.

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8

8th

Cagliari, SardiniaItaly

Sometimes early December 2008