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

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

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

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

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

Pearson correlation

Global

Cozzetto et al., Proteins 2007

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

Protein structure modelling:Expected accuracy

Cozzetto and Tramontano, Proteins 2004

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

QuickTime™ and aTIFF (Uncompressed) decompressor

http://www.bioinformatica.crs4.org

ANTIBODIES: A different story

C LC L

Antibody

Antigen binding site

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

Chothia et al., Nature 1989

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

94 non R or 101 non D

Morea et al., JMB., 1998

target sequence

VL templateTL

Build framework

target sequence

Build framework

VL templateTL

target sequence

template

Build framework

Ab VL sequence Ab VH sequence

“BLAST”

VL templateTL

VH templateTH

“Align”

TL=TH?

Fit conserved interface

Build template

Build framework

“Align”

Ab VL sequence Ab VH sequence

“BLAST”

VL templateTL

VH templateTH

TL=TH?

Fit conserved interface

Build template

Build framework

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

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

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

AVACFATGAFGTARASDFEARTASADFAERAYHGTARYAPLSVNTERAT…..

ADFAERAYLDFNMRSYPDFHGRTYAEFKLLSY

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

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Cagliari, SardiniaItaly

Sometimes early December 2008

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

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