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SWISS-MODEL: Giving the proteome a third dimension. Torsten Schwede Fortaleza, Brasil 1. August 2006 Schweizerisches Institut.

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1 SWISS-MODEL: Giving the proteome a third dimension. Torsten Schwede Fortaleza, Brasil 1. August 2006 Schweizerisches Institut für Bioinformatik Institut Suisse de Bioinformatique Istituto Svizzero di Bioinformatica Swiss Institute of Bioinformatics

2 Introduction

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4 Same "Sequence" - still different... We need to understand structure and function of proteins.

5 (Source: PDB) Public Database content Experimentally determined protein structures (PDB) '874

6 Public Database content Experimentally known protein structures (PDB)

7 Public Database content Annotated Protein Sequences: Swiss-Prot

8  No experimental structure for most protein sequences Public Database content Protein Sequences translated from DNA: trEMBL (Sources: PDB, EBI, SIB)

9 The protein sequence contains all information needed to create a correctly folded protein (Anfinsen principle). Can we model protein structures from their protein sequences? Many proteins fold spontaneously to their native structure Protein folding is relatively fast (nsec – sec) Chaperones speed up folding, but do not alter the structure MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK DEAEKLFNQD VDAAVRGILR NAKLKPVYDS LDAVRRCALI NMVFQMGETG VAGFTNSLRM LQQKRWDEAA VNLAKSRWYN QTPNRAKRVI TTFRTGTWDA YKNL Protein Folding

10 Comparative protein structure modelling

11

12 René Magritte. The Human Condition Oil on canvas.

13 Known Structures (Templates) Target Sequence Template Selection Alignment Template - Target Structure modeling Structure Evaluation & Assessment Homology Model(s) Comparative Protein Structure Modelling

14 Why do we need automated expert systems for comparative protein modelling? Too many new sequences, too many new structures Objectivity and Reproducibility: remove individual human bias; predictable reliability of results Improve methods and algorithms  Easy access for “end users”

15 User s... To: From: Karin Schwarz Dear Mr. Schwede, I am looking for firms, which needs also models with a high from 168 cm. Do you need someone? Best regards K. Schwarz

16 ExPASY Biozentrum, SIB (Basel) & NCI (ABBC) PDB Database -> SMTL SwissProt/TrEMBL RCSB EBI NR / Blast NCBI How to automate protein modeling? Target Sequence Known Structures (Templates) Template Selection Alignment Template - Target Structure modeling Structure Evaluation & Assessment Homology Model(s) 3D - Model requests: 2005: >148’000 requests ~ 400 models per work day ~ one model every 4 minutes SWISS-MODEL Server 1. M.C. Peitsch and C.V. Jongeneel (1993) Int. Immunol. 5, Peitsch MC (1995) Bio/Technology 13: Guex N and Peitsch MC (1997) Electrophoresis 18: Schwede T, Kopp J, Guex N, Peitsch MC (2003) Nucleic Acids Research 31,

17 SWISS-MODEL Workspace Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22,

18 SWISS-MODEL Template Library Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22,

19 [ ] Collaboration with Nicolas Guex (GSK) DeepView - Swiss-PdbViewer Guex N and Peitsch MC (1997) Electrophoresis 18:

20 SwissProt / TrEMBL SWISS-MODEL Repository Oracle Control DB Quality ok? PDB/SMTL Sequence updates Template updates SWISS-MODEL Server Update Model? SWISS-MODEL Repository mySQL DB / WEB Image Rendering Server Coordinate Repository Server Computational Services e.g. Anolea quality control Reverse Proxy Services CRC64 SWISS-MODEL Repository Kopp J, and Schwede T (2004). Nucleic Acids Research 32, D230-D234.

21 SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34, D315-D318.

22 SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34, D315-D318.

23 SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34, D315-D318.

24 Evaluation of Model Accuracy

25 SIV Model based on: 1BL3 (C) HIV-1 Integrase core domain Experimental structure:1C6V (C) SIV Integrase core domain Seq. Identity:61 % “... a model must be wrong, in some respects -- else it would be the thing itself. The trick is to see... where it is right.” Evaluation of model accuracy Henry A. Bent "Uses (and Abuses) of Models in Teaching Chemistry," J. Chem. Ed , 774.

26 Evaluation of Model Accuracy CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction EVA Evaluation of Automatic protein structure prediction (Koh et al. (2003) Nucleic Acids Res. 31, ) 3D - Crunch Experiment Very Large Scale Protein Modelling Project (1998)

27 Weekly PDB released target sequences MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK EVA Server Prediction Servers e.g. SWISS-MODEL 1 Evaluation of automated protein structure prediction (Koh et al. (2003) Nucleic Acids Res. 31, ) 2 Predicted structure Evaluation of prediction accuracy 3 Does the model have the correct fold? What would have been the best template? What would have been the best alignment? How difficult was the modeling? Alignment accuracy Overall accuracy (RMSD) core regions loop regions side chain rotamer Stereo-chemical quality of the model MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK

28 Evaluation of Automated protein structure prediction [ Data source: EVA-CM ] The analysis of ~1 year of models for all PDB released structures is necessary for a statistically relevant comparison of modelling methods. Evaluation of 246 weekly PDB releases (20'108 models)

29 Evaluation of Automated protein structure prediction Evaluation of 246 weekly PDB releases (20'108 models) [ Data source: EVA-CM ] SWISS-MODEL has the lowest cumulative average global RMSD of the servers in the comparison.

30 Evaluation of Automated protein structure prediction [ Data source: EVA-CM ] The lower RMSD of SWISS-MODEL is related to a lower coverage in sequence space. Evaluation of 246 weekly PDB releases (20'108 models)

31 Save Zone Twilight Zone Midnight Zone Evaluation of Model Accuracy [ Data source: EVA-CM ]

32 Applications of comparative modelling Possible applications of comparative models depend on the expected accuracy. (Kopp & Schwede, Pharmacogenomics, 2004 )

33 Application Examples

34 Structural Assessment of Sequence Variations Comparative modeling allows for detailed structure-based assessment of sequence variations. Mutations, ns-cSNPs, alternative splicing, iso- forms and other sequence variations may be responsible for functional variations and involved in rare inherited diseases, individual differences in drug response, and susceptibility for common diseases. (Wattenhofer, et. al. J. Mol. Med ) Applications: Rational design of Functional Mutations Comparative modeling allows for designing specific site directed mutations to study functional role of individual residues or structural features. Example: Design of partial and complete deletion mutations of the plug domain of Sec61p to study its functional role. Conclusion: The plug domain of Sec61p is non-essential, but influences topogenesis and Sec61 assembly. Junne, Schwede, Goder & Spiess (2006) Molecular Cell, in press

35 Application: Models in structure based drug discovery Can homology models be used in structure based drug discovery when no experimental protein structures are available? How do errors and inaccuracies of the homology models affect the subsequent molecular modeling of protein-ligand interaction? Estimating relative ligand binding free energies using MD-GBSA 500 ps MD Simulation for conformational sampling (MM-GBSA) ∆G bind = + - Collaboration with Markus Meuwly (Computational Chemistry, Uni Basel) & Vincent Zoete (SIB Lausanne) Thorsteinsdottir HB, Schwede T, Zoete V, and Meuwly M (2006). Proteins (in press).

36 Example: 16 HIV-I protease inhibitor complexes Validation: Correlation between experimental and calculated relative binding free energies Application: Systematic analysis of the influence of typical modeling errors. Application: Models in structure based drug discovery Reference 1HVI relative  G calculated Reference Model: All ligands computed in 1HVI as reference structure HM based on EIAV Rotamer model relative  G calculated Twilight Zone Model: based on Equine infectious anemia virus protease (1FMB) sharing 32% seq. id. RMSD (5 Å of ligand) 1.30 Å SCWRL Rotamer Model: Rotamers of reference structure remodelled using SCWRL RMSD (5 Å of ligand) 1.26 Å 1 SCWRL3.0 (A. A. Canutescu, A. A. Shelenkov, and R. L. Dunbrack, Jr. Protein Science 12, (2003). Thorsteinsdottir HB, Schwede T, Zoete V, and Meuwly M (2006). Proteins (in press).

37 Summary and Outlook

38 Summary and outlook Today's Bioinformatics view is still largely (DNA)- sequence centric. Understanding macromolecular function in detail requires knowledge of its 3D structure. 10 years ago, for the vast the majority of proteins no information about its 3-dimensional structure was available. Today, structural genomics and comparative modelling are complementing each other in exploring the structural space. Within the next decade, 3D information will become available for the majority of all globular protein domains. > 90 % of this structural information will be based on comparative models. SWISS-MODEL aims at providing a fully integrated protein-centric view of all available structural information on a given protein.

39 SWISS-MODEL is accessible freely at Expasy: SWISS-MODEL SWISS-MODEL Server: DeepView (Swiss-PdbViewer) SWISS-MODEL Workspace: SWISS-MODEL Repository:

40 Biozentrum & SIB Basel Hólmfríður B. Þorsteinsdóttir Lorenza Bordoli Jürgen Kopp Michael Podvinec James Battey Konstantin Arnold Rainer Pöhlmann Roger Jenni Robert Gaisbauer Mihaela Zavolan Martin Spiess University of Basel Markus Meuwly Novartis Basel Manuel Peitsch SIB - Swiss Institute of Bioinformatics (LS & GE) Amos Bairoch Elisabeth Gasteiger Ernest Feytmans Olivier Michielin Vincent Zoete Victor Jongeneel Jacques Rougemont NCI Frederick Jack Collins Karol Miaskiweicz Robert W. Lebherz GSK R&D Nicolas Guex Alexander Diemand Financial support: EBI - European Bioinformatics Institute Kim Henrick Rolf Apweiler Nicola Moulder Ujjwal Das Pontificia Universidad Católica de Chile Francisco Melo Acknowledgements

41 Biozentrum & SIB Basel Hólmfríður B. Þorsteinsdóttir Lorenza Bordoli Jürgen Kopp Michael Podvinec James Battey Konstantin Arnold Rainer Pöhlmann Roger Jenni Robert Gaisbauer Mihaela Zavolan Martin Spiess University of Basel Markus Meuwly Novartis Basel Manuel Peitsch SIB - Swiss Institute of Bioinformatics (LS & GE) Amos Bairoch Elisabeth Gasteiger Ernest Feytmans Olivier Michielin Vincent Zoete Victor Jongeneel Jacques Rougemont NCI Frederick Jack Collins Karol Miaskiweicz Robert W. Lebherz GSK R&D Nicolas Guex Alexander Diemand Financial support: EBI - European Bioinformatics Institute Kim Henrick Rolf Apweiler Nicola Moulder Ujjwal Das Pontificia Universidad Católica de Chile Francisco Melo Acknowledgements


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