The Importance of Protein Flexibility in Protein-Ligand Docking

1. The Importance of Protein Flexibility in Protein-Ligand Docking Jennifer Metzger October 15th, 2008

2. Introduction Methods Results Discussion Talk Outline • Introduction • Motivation • My Task • Methods • Preparation of molecules • AutoGrid • AutoDock • Results • Streptavidin + Biotin • MDM2 + Diz • Discussion 1 Jennifer Metzger Saarland University

3. Introduction Methods Results Discussion Motivation Why are so many people interested in protein-ligand docking? 2 Jennifer Metzger Saarland University

4. Introduction Methods Results Discussion Interactions Protein-ligand interactions have important role in cellular processes: • Signal transduction • Immune response • Energy generation • DNA repair • Apoptosis http://porpax.bio.miami.edu/~cmallery/150/memb/c11x10hormone-receptors2.jpg 3 Jennifer Metzger Saarland University

5. Introduction Methods Results Discussion Docking Definition: Docking tries to find the energetically most feasible three dimensional arrangement of two molecules in close contact with each other. Shortly, answers two questions: • What does complex look like? • What energy necessary to disrupt complex? 4 Jennifer Metzger Saarland University

6. Introduction Methods Results Discussion Docking => Plays essential role in: • Study of macromolecular structure and interactions • Rational drug design 5 Jennifer Metzger Saarland University

7. Introduction Methods Results Discussion Induced Fit Model • Modification to lock-key model • Binding of ligand causes change in shape of protein and ligand • Results in proper alignment http://www.chemeddl.org/collections/TSTS/Gellman/Gellmanpg9-12/LockandKey.html http://neurobio.drexel.edu/GalloWeb/loudon_enzymes.htm 6 Jennifer Metzger Saarland University

8. Introduction Methods Results Discussion My Task Show that accounting for protein flexibility is crucial in protein-ligand docking when the ligand binds to the protein surface instead of a deep binding pocket 7 Jennifer Metzger Saarland University

9. Introduction Methods Results Discussion Streptavidin+Biotin Streptavidin: • Homotetramer • Isolated from bacterium Streptomyces avidinii • Each monomer binds one molecule of vitamin biotin non-covalently Complex: • Exceptionally high affinity (Ka ~ 1013 M-1) • One of strongest known non-covalent interaction • Basis for many important biotechnological applications 8 Jennifer Metzger Saarland University

10. Introduction Methods Results Discussion MDM2 MDM2 (Mouse double minute protein 2): • 491 amino acids • Important negative regulator of tumour suppressor p53 • Represses transcriptional activity • Carries nuclear export signal • Accelerates destruction within proteasome • Part of p53 auto-regulatory feedback loop • Transcription activated by p53 • Various mechanisms allow p53 to escape inhibition • Increased levels in several human tumour types => Important drug target in anticancer therapy 9 Jennifer Metzger Saarland University

11. Introduction Methods Results Discussion MDM2+Diz Diz: • Benzodiazepinedione antagonist of HDM2-p53 interaction • Increases transcription of p53 target genes • Decrease proliferation of tumour cells expressing wild-type p53 10 Jennifer Metzger Saarland University

12. Introduction Methods Results Discussion Method My Task: Show importance of protein flexibility in protein-ligand dockingby using two test systems Two different approaches: • Using AutoDock4 • Using molecular dynamics (MD) snapshots 11 Jennifer Metzger Saarland University

13. Introduction Methods Results Discussion First Example Test system: Streptavidin + Biotin Three different experiments are performed: • Docking to bound protein structure without flexibility (Re-docking) • Docking to unbound protein structure with(out) flexibility (Apo-docking) 12 Jennifer Metzger Saarland University

14. Introduction Methods Results Discussion Second Example Test system: MDM2 + Diz Four different experiments are performed: • Re-docking to bound protein structure • Apo-docking to unbound protein structure with and without flexibility • Docking into molecular dynamics (MD) snapshots 13 Jennifer Metzger Saarland University

15. Introduction Methods Results Discussion Scheme One experiment consists of: • Preparation of Ligand and Protein • Pre-computation of AutoGrid maps • Perform docking with AutoDock • Analyzing AutoDock results Jennifer Metzger Saarland University 14

16. Introduction Methods Results Discussion PDB Files Following crystal structures from PDB where used: 1swb apo Streptavidin 1mk5 Streptavidin + Biotin 1z1m apo MDM2 1t4e MDM2 + Diz 15 Jennifer Metzger Saarland University

17. Introduction Methods Results Discussion PDB files are not perfect • Missing atoms • Added water • More than one molecule • Chain breaks • Alternate locations => Need to be corrected before usage 16 Jennifer Metzger Saarland University

18. Introduction Methods Results Discussion Ligand • Add all hydrogen atoms • Add charges • Check whether total charge per residue is integer • Detect aromatic carbons • Assign ‚AutoDock type‘ to atoms (Only AutoDock4) • Choose rotatable bonds 17 Jennifer Metzger Saarland University

19. Introduction Methods Results Discussion Protein • Add all hydrogen atoms • Add charges • Check whether total charge per residue is integer • Assign ‚AutoDock type‘ to atoms (Only AutoDock4) • Choose flexible residues (Only AutoDock4) • Assign solvation parameters 18 Jennifer Metzger Saarland University

20. Introduction Methods Results Discussion Grid Maps • AutoGrid computes grid maps needed by AutoDock • One map for each atom type in ligand and moving part of protein + electrostatics map • Interactions of atom probe with protein atoms are pre-computed on grids • Trilinear interpolation used to compute score of candidate ligand conformation http://autodock.scripps.edu/faqs-help/tutorial/using-autodock-4-with-autodocktools/UsingAutoDock4WithADT.ppt.Handouts3pp.v3.pdf 19 Jennifer Metzger Saarland University

21. Introduction Methods Results Discussion Short Repetition A docking algorithm needs: • Search method • Scoring function http://irafm.osu.cz/en/c43_developing-new-evolutionary-algorithms-for-global-optimization-in-fuzzy-models/ 20 Jennifer Metzger Saarland University

22. Introduction Methods Results Discussion Search Methods Four methods are available in AutoDock: • Simulated Annealing • Genetic Algorithm • Local Search • Lamarckian Genetic Algorithm 21 Jennifer Metzger Saarland University

23. Introduction Methods Results Discussion Genetic Algorithm In our case: • Individuals represent ligand conformations • Genes correspond to state variables • State variables describe translation, orientation, and torsion angles of ligand => Ligand’s state corresponds to genotype atomic coordinates correspond to phenotype 22 Jennifer Metzger Saarland University

24. Introduction Methods Results Discussion Genetic Algorithm • Start with a random population 23 Jennifer Metzger Saarland University

25. Introduction Methods Results Discussion Genetic Algorithm • Start with a random population • Perform genetic operations • Mapping and Fitness evaluation • Selection • Crossover • Mutation • Elitism 24 Jennifer Metzger Saarland University

26. Introduction Methods Results Discussion Genetic Algorithm • Start with a random population • Perform genetic operations • Mapping and Fitness evaluation • Selection • Crossover • Mutation • Elitism • Until one termination criteria is met • Maximum number of generations • Maximum number of evaluations 25 Jennifer Metzger Saarland University

27. Introduction Methods Results Discussion LamarckianGenetic Algorithm • Genetic algorithm that mimic Lamarckian evolution • Environmental adaptation of phenotype become heritable traits • Additional local search • Morris, G. M., Goodsell, D.S. et al. Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Computational Chemistry (1998) 26 Jennifer Metzger Saarland University

28. Introduction Methods Results Discussion Scoring Functions Empirical Free Energy Function of AutoDock3: 27 Jennifer Metzger Saarland University

29. Introduction Methods Results Discussion Scoring Functions Semiempirical Free Energy Force Field of AutoDock4: Huey, R., Morris, G. M., et al. Semiempirical Free Energy Force Field with Charge-Based Desolvation J. Computational Chemistry (2007) 28 Jennifer Metzger Saarland University

30. Introduction Methods Results Discussion Scoring Functions Semiempirical Free Energy Force Field of AutoDock4: 29 Jennifer Metzger Saarland University

31. Introduction Methods Results Discussion Streptavidin + Biotin Apo Streptavidin Biotin Bound Streptavidin 30 Jennifer Metzger Saarland University

32. Introduction Methods Results Discussion Docking with AutoDock3 • Rmsd in all trials below 1 • Only small changes in rmsd => Complex conformation could be obtained without protein flexibility 31 Jennifer Metzger Saarland University

33. Introduction Methods Results Discussion Docking with AutoDock3 • Score of re-docking conformations always better than apo-docking • Only very small changes in score 32 Jennifer Metzger Saarland University

34. Introduction Methods Results Discussion Docking with AutoDock4 • Outlier in higher ranks • Small changes in lower ranks 33 Jennifer Metzger Saarland University

35. Introduction Methods Results Discussion Docking with AutoDock4 • To many flexible residues result in worse rmsd 34 Jennifer Metzger Saarland University

36. Introduction Methods Results Discussion Docking with AutoDock4 • One flexible residue results in better energy than inflexible apo-docking • More flexible residues lead to better energies and to larger changes => In this case flexibility does not help so much 35 Jennifer Metzger Saarland University

37. Introduction Methods Results Discussion MDM2 + Diz Bound MDM2 Diz 36 Jennifer Metzger Saarland University

38. Introduction Methods Results Discussion MDM2 + Diz Apo MDM2 Diz 37 Jennifer Metzger Saarland University

39. Introduction Methods Results Discussion Docking with AutoDock3 • Very small changes at re-docking • Small changes at apo-docking • Rmsd of apo-docking considerably worse than of re-docking 38 Jennifer Metzger Saarland University

40. Introduction Methods Results Discussion Docking with AutoDock4 • Similar rmsd as inflexible apo-docking for 3R and 6R • A bit better for 1R 39 Jennifer Metzger Saarland University

41. Introduction Methods Results Discussion Docking with AutoDock3 • Best rmsd of snapshots mostly better than best rmsd of apo-docking • Gets comparable or better results than with rigid protein => In this case flexibility improve results 40 Jennifer Metzger Saarland University

42. Introduction Methods Results Discussion Docking with AutoDock3 Best rmsd docking result of snapshot 25 Native MDM2-Diz complex 41 Jennifer Metzger Saarland University

43. Introduction Methods Results Discussion Discussion • Protein flexibility is very helpful when binding pocket is not present in apo structure • To use AutoDock4 protein flexibility, residues in pocket have to be known • Docking to snapshots is better approach • Molecular dynamics simulations help to find pocket what results in better rmsd • Native complex not necessarily determined after MD snapshot approach => Docking into MD snapshots is a promising approach which builds starting point for further investigations 42 Jennifer Metzger Saarland University

44. Introduction Methods Results Discussion Most proteins do not match rigid lock-key model Inclusion of protein flexibility in protein-ligand docking can be crucial Docking into molecular dynamics snapshots detects binding pockets Promising approach for e.g. structure-based drug design Summary 43 Jennifer Metzger Saarland University

45. References Streptavidin+Biotin • Freitag, S., Trong, I.L., et al. Structural studies of the streptavidin binding loop. Protein Sci. (1997), 6: 1157-1166 MDM2 • Uhrinova, S., Uhrin, D., et al. Structure of free MDM2 N-terminal domain reveals conformational adjustments that accompany p53-binding. J Mol. Biol. (2005), 350: 587-598 AutoDock+AutoGrid • http://autodock.scripps.edu/ • Morris, G. M., Goodsell, D.S. et al. Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Computational Chemistry (1998), 19: 1639-1662. • Huey, R., Morris, G. M., et al. Semiempirical Free Energy Force Field with Charge-Based Desolvation J. Computational Chemistry (2007), 28: 1145-1152. Approach • Eyrisch, S., Helms, V. Transient Pockets on Protein Surfaces Involved in Protein-Protein Interaction. J. Med. Chem. (2007), 50: 3457-3464 Jennifer Metzger Saarland University