1. Protein modelling ● Protein structure is the key to understanding protein function ● Protein structure ● Topics in modelling and computational methods – Comparative/homology modelling – Fold recognition – Fold prediction – Dynamics of proteins

2. Motivation ● Protein structure determines protein function ● For the majority of proteins the structure is not known

3. Correlation structure & sequence ● Chothia & Lesk (1986): Correlation between structural divergence and sequence similarity Fold space Time Fold 1 Fold 2 Evolution

4. Comparative/homology modelling Template sequence Template structure Target sequence Alignment Model

5. The crucial importance of the alignment ● An alignment defines structurally equivalent positions! Template sequence Template structure Target sequence Alignment Model

6. Steps in comparative modelling ● Find suitable template(s) ● Build alignment between target and template(s) ● Build model(s) – Replace sidechains – Resolve conflicts in the structure – Model loops (regions without an alignment) ● Evaluate and select model(s)

7. State of the art in homology modelling ● Template search – (iterative) sequence database searches (PSIBLAST) ● Alignment step – multiple alignment of close to fairly distant homologues ● Modelling step – rigid body assembly – segment matching – satisfaction of spatial constraints

8. Modelling by spatial restraints ● Generate many constraints: – Homology derived constraints ● Distances and angles between aligned positions should be similar – Stereochemical constraints ● Bond lengths, bond angles, dihedral angles, nonbonded atom-atom contacts ● Model derived by minimizing restraints Modeller: Sali & Blundell (1993)

9. Loop modelling ● Exposed loop regions usually more variable than protein core ● Often very important for protein function ● Loops longer than 5 residues difficult to built ● Mini-protein folding problem

10. Model evaluation ● Check of stereochemistry – bond lengths & angles, peptide bond planarity, side- chain ring planarity, chirality, torsion angles, clashes ● Check of spatial features – hydrophobic core, solvent accessibility, distribution of charged groups, atom-atom-distances, atomic volumes, main-chain hydrogen bonding ● 3D profiles/mean force potentials – residue environment

11. Knowledge-based mean force potentials Melo & Feytmanns (1997) ● Compute typical atomic/residue environments based on known protein structures

12. ● Sequence from different species ● Is binding to ligand conserved? Modelling a transcription factor

13. Ligand binding domain hydrogen bonds to ligand homo-serine lactone moiety binding acyl moiety binding

14. DNA binding domain Linker DNA binding domain

15. Template Target Variable loops New Loop MODELLER output

16. Ligand binding pocket

17. Errors in comparative modelling Marti-Renom et al. (2000) a)Side chain packing b)Distortions and shifts c)Loops d)Misalignments e)Incorrect template Template Model True structure

18. Modelling accuracy Marti-Renom et al. (2000)

19. Applications of homology modelling Marti-Renom et al. (2000)

20. Structural genomics ● Post-genomics: – many new sequences, no function ● Aim: a structure for every protein ● High-throughput structure determination – robotics – standard protocols for cloning/expression/crystallization

21. Structural coverage Vitkup et al. (2001) high quality models Complete models Total = 43 %

22. Target selection

23. Protein modelling ● Protein structure is the key to understanding protein function ● Protein structure ● Topics in modelling and computational methods – Comparative/homology modelling – Fold recognition – Fold prediction – Dynamics of proteins

24. Fold recognition ● Structure is more conserved than sequence Limit of sequence similarity searches Structural similarity Fold space Target Protein structures

25. Fold recognition / Threading ● Is a sequence compatible with a structure? ● The idea: evolutionary related proteins share common folding motifs ● Contact matrix = motif ● Mean-force potentials to score every contact ● Optimize alignment to minimize pseudo-energy

26. Protein modelling ● Protein structure is the key to understanding protein function ● Protein structure ● Topics in modelling and computational methods – Comparative/homology modelling – Fold recognition – Fold prediction – Dynamics of proteins

27. Fold prediction – Rosetta method ● Knowledge based scoring function P(structure) * P(sequence|structure) P(sequence) P(structure|sequence) = P(structure) = probability of a protein-like structure (no clashes, globular shape) P(sequence|structure) = f(residue contacts in native structures) Simons et al. (1997) Bayes' law: protein-like structures sequence consistent local structure near-native structures

28. Environment specific scoring function ● Environment E i specific interactions ● Environment – defined by the number of neighbours – implicitely distinguishes between buried and exposed residues i i<j cf. mean force potential Simons et al. (1997)

29. Collection of putative backbone conformations Protein sequence Library of small segments sequencesstructures... For each window of 9 residues: lookup 25 closest (sequence) neighbours in library... Simons et al. (1997)

30. MC-SA optimization Simons et al. (1997) ● for each random position – pick a random neighbour – replace backbone conformation – calculate probability of new structure ● MC: Monte-Carlo – accept up-hill moves with a certain probability ● SA: simulated annealing – first allow many changes, later less changes

31. Results ● Small molecules: ok ● Proteins with mostly α-helices: ok ● Proteins with mostly β-sheets: not so ok Simons et al. (1997)

32. Dynamics of proteins ● Protein structure is the key to understanding protein function ● Protein structure ● Topics in modelling and computational methods – Comparative/homology modelling – Fold recognition – Fold prediction – Dynamics of proteins

33. Dynamics of proteins ● Local Motions (0.01 to 5 Å, 10 -15 to 10 -1 s) – Atomic fluctuations – Sidechain Motions – Loop Motions ● Rigid Body Motions (1 to 10Å, 10 -9 to 1s) – Helix Motions – Domain Motions (hinge bending) – Subunit motions ● Large-Scale Motions (> 5Å, 10 -7 to 10 4 s) – Helix coil transitions – Dissociation/Association – Folding and Unfolding

34. Molecular dynamics/molecular modelling ● Molecular mechanics ● Normal mode analysis ● Quantum mechanical simulations ●...

35. Molecular mechanics ● Atom representation – sphere – charge – topology ● Forces – Bonded interactions – Non-bonded interactions ● Electrostatic interactions ● Van-der-Waals interactions – Forcefields: AMBER, GROMOS,... ● Newton's law of mechanics http://cmm.info.nih.gov/modeling/guide_documents/molecular_mechanics_document.html

36. Molecular mechanics ● Molecular mechanics simulations take long! – because of the size of the system ● Proteins are large ● Water molecules to consider solvent effects ● 10.000 to millions of atoms – because of the number of iterations ● update atom positions according to time-scale of fastest fluctuations: bond vibrations ca. 1 fs ● movements of interest frequently have long time-scale, e.g. folding ● 1s => 10 15 iterations!

37. Benefit of simulations ● Result is an ensemble of structures – Time-averaged statistical quantities – e.g., relative free energies of different conformations ● Protein engineering – e.g., relative free energies of different mutants ● Physical accuracy of models? – chemical reactions? – cutoff and long-range interactions? – dielectric constant? movie from: C. Letner, G. Alter Journal of Molecular Structure (Theochem) 368 (1996) 205–212

38. The end Proteins are beautiful! www.holmgroup.org