1. 3D Structures of Biological Macromolecules Part 5 Protein Structure Prediction - II Jürgen Sühnel jsuehnel@imb-jena.de Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material: http://www.imb-jena.de/www_bioc/3D/

2. Molecular Mechanics (Force Field) http://cmm.info.nih.gov/modeling/guide_documents/molecular_mechanics_document.html

3. How Do We Get the Parameters ? Experimental Data (Examples: Geometrical Parameters) Quantum-chemical Calculations (Examples: Charges)

4. Quantum Chemistry

5. Quantum Chemistry

7. Geometry Optimization

8. Optimization Methods

9. Optimization Methods

10. Optimization Methods – Steepest Descent Selection of an initial point x0 Determination of direction and step size for calculating the next point

11. Optimization Methods – Conjugate Gradients Method

12. Optimization Methods – Newton-Raphson Methods g -. gradient h - Hessian

13. Molecular Dynamics

14. Simulation of Protein Folding – Molecular Dynamics AMBER GROMOS CHARMM TINKER

15. 1HGV, extended structure 1HGV, actual structure 1HGV, 61% helix, 1.928 ns 1HGV, 75% helix, 3.428 ns Molecular Dynamics Simulation Protein Capsid Of Filamentous Bacteriophage Ph75 From Thermus Thermophilus Images created using VMD (Visual Molecular Dynamics) (HUMPHREY, W., DALKE, A. and SCHULTEN, K., 1996.VMD - Visual Molecular Dynamics. Journal Molecular Graphics,14, pp33-38).

16. Molecular Dynamics Packages amber.scripps.edu

17. Molecular Dynamics Packages www.igc.ethz.ch/gromos/

18. Molecular Dynamics Packages www.charmm.org

19. Molecular Dynamics Packages dasher.wustl.edu/tinker/

20. Visualizing and Analyzing Molecular Dynamics Simulations www.ks.uiuc.edu/Research/vmd/

21. Folding Surface for Lysozyme Dobson, Sali, Karplus, Angew. Chem. Int. Ed.1998, 37, 868.

22. Protein Folding States Dobson, Sali, Karplus, Angew. Chem. Int. Ed.1998, 37, 868.

23. Monitoring Protein Folding by Experimental Methods Dobson, Sali, Karplus, Angew. Chem. Int. Ed.1998, 37, 868.

24. Monitoring Protein Folding by Experimental Methods Paxco, Dobson, Curr. Opin. Struct. Biol.1996, 6, 630.

25. Protein Folding by Molecular Dynamics

26. Protein Folding by Molecular Dynamics

27. Protein Folding by Molecular Dynamics Villin headpiece domain (PDB code: 1vii) Actin binding site highlighted 36 amino acids

28. Protein Folding by Molecular Dynamics

29. Protein Folding by Molecular Dynamics

30. Protein Folding by Molecular Dynamics

31. Radius of Gyration The radius of gyration Rgis defined by the root-mean-square distance between all atoms in a molecule and the centroid. In a globular protein the radius of gyration Rg can be predicted with reasonable accuracy from the relationship Rg(pred) = 2.2 N 0.38 where N is the number of amino acids.

32. Protein Folding by Molecular Dynamics

33. Protein Folding by Molecular Dynamics

34. Statistical Potentials wij(r) – interaction free energy ij(r) - pair density * - reference pair density at infinite separation Statistical potentials can be determined by simply counting interactions of a specific type in a dataset of experimental structures. The distance dependence may or may not be taken into account. If not, the interaction free energy is usuallycalled a contact potential. It represents an average over distances shorter than some cutoff distance rc. Thomas, Dill, J. Mol. Biol.1996, 257, 457-469

35. Lattice Folding

36. Lattice Algorithm • Red = hydrophobic, Blue = hydrophilic • If Red is near empty space E = E+1 • If Blue is near empty space E = E-1 • If Red is near another Red E = E-1 • If Blue is near another Blue E = E+0 • If Blue is near Red E = E+0

37. Ab Initio Protein Structure Prediction http://rosettadesign.med.unc.edu/

38. Ab Inition Protein Structure Prediction - Rosetta Structure representation: Only main-chain heavy atoms and Cbeta-atom of sidechains are taken into account, Bond lengths and bond angles are held constant and correspond to the alanine geometry. The only remaining geometrical variables are the backbone torsion angles. Structure generation: Generation of fragment libraries from experimental structures (3 and 9 amino acids). Splicing together fragments of proteins of known structure with similar sequences. The conformational space defined by these fragments is then searched by a Monte Carlo procedure with an energy function that favors compact structures with paired beta-strands and buried hydrophobic amino acids. A total of 1000 independent simulations are carried out (starting from different random number seeds) for each query sequence. The resulting structures are clustered. Initial evaluation by the scoring function Low-scoring conformations are identified by simulated annealing with a move set that involves replacing the torsion angles of a segment of the chain with a related amino acid sequence. Further evaluation by

39. Protein Backbone Torsion Angles and Ramachandran Plot

40. Bayesian Statistics Bayesian statistical methods differ from other types of statistics by the use of conditional probabilities. Bayes Theorem P(A|B) = [P(B|A) x P(A)] / P(B)

41. ROSETTA Results Simons, Strauss, Baker. J. Mol. Biol.2001, 306, 1191-1199.

42. Computational Thermostabilization

43. Computational Thermostabilization Prediction of stabile mutations with Rosetta Design

44. Computational Thermostabilization PDB code: 1ox7 Cytosine deaminase (CD) catalyzes the deamination of cytosine (converts cytosine to uracil) and is only present in prokaryotes and fungi, where it is a member of the pyrimidine salvage pathway. The enzyme is of interest both for antimicrobial drug design and gene therapy applications against tumors.

45. Computational Thermostabilization

46. Computational Thermostabilization

47. Computational Thermostabilization Superposition of double and triple mutant structures (PDB codes: 1ysb, 1ysd) A23L I140L V108I

48. N Comparing Protein Structures • The RMSD is a measure to quantify structural similarity • Requires 2 superimposed structures (designated here as “a” & “b”) • N = number of atoms being compared RMSD = S (xai - xbi)2+(yai - ybi)2+(zai - zbi)2

49. Comparing Protein Structures http://wishart.biology.ualberta.ca/SuperPose/

50. Comparing Protein Structures http://www.ebi.ac.uk/DaliLite/