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Protein structure prediction: the customer view

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Presentation on theme: "Protein structure prediction: the customer view"— Presentation transcript:

1

2 Protein structure prediction: the customer view

3 Protein structure prediction:
why

4 Predicting: Expected quality of a model (QMode 1)
Protein structure Quality prediction: The casp initiative Predicting: Expected quality of a model (QMode 1) Expected error on residue Cα (QMode 2) Quoting the CASP web page: 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.

5 Protein structure Quality prediction:
The casp initiative Target xx Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target xx GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … …. Target yy Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target yy GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … ….

6 By target Protein structure Quality prediction: Target xx Pred
The casp initiative Target xx Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target xx GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … …. Pearson correlation Target yy Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target yy GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … …. By target

7 Global Protein structure Quality prediction: Target xx Pred
The casp initiative Target xx Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target xx GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … …. Target yy Pred Model serv1_1 N1 Model serv1_2 N2 …………. … Model serv1_5 … … Model serv3_4 … …. Target yy GDT Model serv1_1 G1 Model serv1_2 G2 …………. … Model serv1_5 … … Model serv3_4 … …. Global Pearson correlation

8 Cozzetto et al., Proteins 2007
Protein structure Quality prediction: The casp initiative Cozzetto et al., Proteins 2007

9 Protein structure modelling:
A digression …

10 Cozzetto and Tramontano, Proteins 2004
Protein structure modelling: Expected accuracy Cozzetto and Tramontano, Proteins 2004

11 Maistas: taking splicing into account

12 Maistas: http://www.bioinformatica.crs4.org
taking splicing into account

13 Maistas: taking splicing into account Maistas output
Server output are three-dimensional coordinates in PDB format for each modelled peptide and a table describing results of the structural analysis. Then all models are analyzed in terms of their suitability to exist in the monomeric state. When model check!! warning appears in the output report we cannot exclude the possibility that they take some other multimeric state for stabilizing. Moreover, as a model is built, splicing isoform exonic coordinates are mapped on it. A PyMol script is created too, so the user can easily visualize exon mapping on protein model with different colours.

14 ANTIBODIES: A different story

15 Antibody Antigen binding site ANTIBODIES: A different story N C H3 H1
. ANTIBODIES: A different story C N H3 H1 H2 L2 L1 L3 V L H Antibody Antigen binding site SS

16 ANTIBODIES: A different story

17 ANTIBODIES: 94 95 Pro 90 Gln Chothia et al., Nature 1989
A different story 95 Pro 90 Gln 94 * * * * Y Q S L P Y Q W T Y P L I Q Chothia et al., Nature 1989

18 Canonical structures for the ‘torso’ of H3:
ANTIBODIES: A different story Canonical structures for the ‘torso’ of H3: 94R – 101D 101 101 94 94 103 103 94 non R or 101 non D Morea et al., JMB., 1998

19 ANTIBODIES: target sequence BLAST VL template TL Align Build framework
A different story target sequence BLAST VL template TL Align Build framework

20 ANTIBODIES: target sequence BLAST VL template TL Align Build framework
A different story target sequence BLAST VL template TL Align Build framework

21 ANTIBODIES: Ab VL sequence Ab VH sequence target sequence “BLAST”
A different story Ab VL sequence Ab VH sequence target sequence “BLAST” BLAST VL template TL VH template TH template “Align” Align TL=TH? Fit conserved interface Build template Build framework Build framework

22 ANTIBODIES: Ab VL sequence Ab VH sequence “BLAST” VL template TL
A different story Ab VL sequence Ab VH sequence “BLAST” VL template TL VH template TH “Align” TL=TH? Fit conserved interface Build template Build framework

23 Taking the frameworks from different structures introduces errors
ANTIBODIES: A different story 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

24 Taking the loops from different structures introduces errors
ANTIBODIES: A different story 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

25 Same antibody and canonical structures Same canonical structures
ANTIBODIES: A different story Same antibody Same antibody and canonical structures Same canonical structures Best Vl and Vh

26

27

28

29 ANTIBODIES: A different story ?

30 ANTIBODIES: A different story

31 ADFAERAY LDFNMRSY PDFHGRTY AEFKLLSY
ANTIBODIES: A different story AVACFATG AFGTARAS DFEARTAS ADFAERAY HGTARYAP LSVNTERAT ….. ADFAERAY LDFNMRSY PDFHGRTY AEFKLLSY

32 ANTIBODIES: A different story

33 ANTIBODIES: A different story

34 ANTIBODIES: A different story

35 ANTIBODIES: A different story ANTIBODIES: A different story

36 ANTIBODIES: A different story PDB

37 ANTIBODIES: A different story

38 ANTIBODIES: A different story

39

40 ANTIBODIES: A different story ?

41 Institut Pasteur-Cenci
acknowledgements Giuliana Brunetti Enrico Capobianco Simone Carcangiu Alberto de la Fuente Matteo Floris Elisabetta Marras Joël Masciocchi Elisabetta Muscas Massimiliano Orsini Enrico Pieroni Frédéric Reinier Patricia Rodriguez Tome’ Alphonse Thanaraj Thangavel Maria Valentini Tiziana Castrignanò P. D’Onorio De Meo Danilo Carrabino Domenico Cozzetto Enrico Ferraro Fabrizio Ferre’ Emanuela Giombini Alejandro Giorgetti Paolo Marcatili Domenico Raimondo Stefania Bosi Claudia Bertonati Alessandra Godi Michele Ceriani Romina Oliva Claudia Bonaccini Marialuisa Pellegrini Simonetta Soro EU Biosapiens Institut Pasteur-Cenci HFP Regione Sardegna

42 Advertisements:

43 Sometimes early December 2008
Advertisements: 8th Cagliari, Sardinia Italy Sometimes early December 2008 8

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