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Docking & Designing Small Molecules within Rosetta Code Framework

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Docking & Designing Small Molecules within Rosetta Code Framework

  1. 1. Tuesday September 18th 2012 DEVELOPMENT OF METHODS FOR DOCKING AND DESIGNING SMALL MOLECULES WITHIN THE ROSETTA CODE FRAMEWORK A doctoral dissertation defense presented by GORDON HOWARD LEMMON ROSETTA
  2. 2. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 2
  3. 3. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 3
  4. 4. What is structural biology? ProteinsDNA Structural Biology is the study of structure and function of biological molecules such as DNA, RNA, and proteins 4
  5. 5. How big are proteins? 5 Water 1.51 Å HH O Amprenavir ~17 Å 72 atoms HIV-1 Protease (PR) ~54 Å 3163 atoms 1 Angstrom (Å) = 1 ten millionth of a millimeter
  6. 6. Proteins consist of amino acid chains 6
  7. 7. Protein sequence determines structure7
  8. 8. Protein structure determines function HIV-1 protease cleaves poly-protein precursors to form functional proteins 8 Peptide chain HIV-1 protease
  9. 9. Proteins are dynamic 9
  10. 10. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 10
  11. 11. What is protein modeling?  Prediction of protein structure from 1. Sequence alone (de novo folding) HIV-1 PR Amino Acid Sequence ANPCCSNPCQNRGECMSTGFDQ YKCDCTRTGFYGENCTTPEFLTRI KLLLKPTPNTVHYILTHFKGVWNIV NNIPFLRSLIMKYVLTSRSYLIDSP PTYNVHYGYKSWEAFSNLSYYTR ALPPVADDCPTPMGVKGNKELPD SKEVLEKVLLRREFIPDPQGSNM MFAFF… 11
  12. 12. What is protein modeling?  Prediction of protein structure from 2. Sequence similarity (Comparative modeling) HIV-1 PR Sequence PQITLWKRPLVTIRIGGQL KEALLDTGADDTVLEEMN LPGRWKPKMIGGIGGFIK VRQYDQIPIEICGHKAIGT VLVGPTPTNVIGRNLLTQI GCTLNF… HIV-2 PR HIV-1 PR 12 +
  13. 13. What is ligand docking?  Prediction of structure of protein/ligand interface  Prediction of ligand binding affinity 13 +
  14. 14. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 14
  15. 15. Rosetta protein modeling consists of sampling and scoring15
  16. 16. RosettaLigand docking consists of sampling and scoring16
  17. 17. RosettaLigand docking consists of sampling and scoring17
  18. 18. RosettaLigand docking consists of sampling and scoring18
  19. 19. RosettaLigand score function  Knowledge-based score terms 19 Score term Default weight attractive 0.8 repulsive 0.4 solvation 0.6 dunbrack 0.4 pair 0.8 hbond_lr_bb 2.0 hbond_bb_sc 2.0 hbond_sc 2.0
  20. 20. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 20
  21. 21. 21
  22. 22. HIV-1 PR is flexible Simmerling 2005 22
  23. 23. HIV-1 PR becomes rigid upon PI binding23
  24. 24. HIV-1 protease mutations WHO drug resistance mutations in red 24 Mutation leads to conformational diversity
  25. 25. FDA approved protease inhibitors (PIs) Tipranavir Darunavir Atazanavir Lopinavir 25
  26. 26. Previous PR/PI ΔΔG predictions failed Cheng (2009) Score Function Correlation N=112 Number of non-hydrogen atoms 0.172 X-Score (HPScore) 0.341 SYBYL (ChemScore) 0.276 DS (PMF04) 0.183 DrugScore (PairSurf) 0.225 AutoDock 0.38 Jenwitheesuk E Samudrala R. (2003) 26 Experimental vs Predicted HIV-1 PR ΔΔG
  27. 27. Defining ΔΔG and ΔΔΔG 27
  28. 28. 176 experimental PR/PI ΔΔGs 171 PR template structures28  176 PR/PI ΔΔGs  sequence but not structure  34 sequences  10 distinct protease inhibitors  171 PR structures represent PR flexibility
  29. 29. RosettaLigand PR/PI ΔΔGs predictions 29 0.1 Å 5˚ PI movements Side chain and ligand rotamer sampling Minimization of PR side chain and PI torsion angles MC Accept Minimize Backbone torsion angles Energy filter Random 5 Å Translation complete rotation of PI 171 PR template structures 176 Sequence/PI pairs 10 Rosetta relaxed models per input (300,960 models) 30,096 Rosetta inputs 1000 RosettaLigand docked models per relaxed model (300,960,000 docked models) Top 10% of models by total score for each Sequence/PI pair Top models by interface score for each Sequence/PI pair RosettaLigand DockingPR/PI ΔΔGs prediction workflow x6
  30. 30. Reweighting score terms improves HIV-1 PR/PI ΔΔG predictions Score term Default weight Optimized weights ΔΔG ΔΔΔG attractive 0.8 0.71 0.31 repulsive 0.4 -0.01 0.17 solvation 0.6 0.68 0.15 dunbrack 0.4 0.29 0.43 pair 0.8 0.80 0.80 hbond_lr_bb 2.0 0.85 0.11 hbond_bb_sc 2.0 0.09 -0.20 hbond_sc 2.0 -0.35 1.71 CORRELATIONS (R) 0.16 0.38 0.51 30
  31. 31. Assuming constant unbound ΔG improves PR/PI ΔΔG predictions Standard approachConstant unbound approach 31
  32. 32. Correlation plots Experimental on X Predicted on Y Default weights: R=0.16 32
  33. 33. Previous PR/PI ΔΔG predictions failed Score Function Correlation N=112 Number of non-hydrogen atoms 0.172 X-Score::HPScore 0.341 SYBYL::ChemScore 0.276 DS::PMF04 0.183 DrugScorePDB::PairSurf 0.225 AutoDock 0.38 RosettaLigand 0.71 33 Experimental vs Predicted HIV-1 PR ΔΔG
  34. 34. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 34
  35. 35. 35
  36. 36. Fragment the Ligand Search database for fragments Assemble rotamer librariesSample from libraries during docking Flexibility through fragments 36
  37. 37. Ligand fragment rotamers allow efficient flexibility37
  38. 38. Ligand rotamer docking 38
  39. 39. Ligand docking with interface design A54R L50Y C9R DHT DHT: Dihydrotestosterone HisF: imidazole glycerol phosphate synthase HisF DHT Enlarged prostate gland prostate cancer RosettaLigand prediction 39
  40. 40. Fragment based screening can greatly expand sampling space Congreve, M. et al. Drug Discov.Today 2003,8, 876-877 Traditional Screening Fragment based screening 40
  41. 41. Common drug based Fragments Hartshorn M.J. Murray C.W.et.al. J. Med. Chem. 2005 48 403-413 H N N N N N N H N N S O O NH2 NH NH2 O N H OH OH N H N N NH N O N N NH O 41
  42. 42. RosettaLigandDesign Library of small molecule fragments Place fragments in protein binding site -10 -12 3 -7 -5 Select low energy models for refinement Dock ligand with flexible protein side-chains and backbone 42
  43. 43. RosettaLigandDesign Library of small molecule fragments Place fragments in protein binding site -8 -15 -18 -10 -12 Select low energy models for refinement Dock ligand with flexible protein side-chains and backbone 43
  44. 44. Examples of fragments Carbon Oxygen Nitrogen 1 connection 2 connections CH2 connections Ntrp connections Core fragment 44
  45. 45. Random assembly of fragments 45
  46. 46. Rosetta ligand design in action 46 A. Low-res search for starting fragment B. Refine (dock) starting fragment C. Grow small-molecule using fragment library D. Refine (dock) 2-fragment complex E. Grow small-molecule using fragment library F. Refine (dock) 3-fragment complex G. Add Hydrogens to unsatisfied connection points
  47. 47. Protein binding sites are complex Dethiobiotin (DTB) Inorganic phosphate Mg Ions ADP 47
  48. 48. Multiple Ligand docking may capture induced fit effects Serial Docking Simultaneous Docking 48
  49. 49. Rosetta multiple ligand docking 49
  50. 50. Outline of presentation A. What is structural biology? B. Protein modeling and ligand docking C. Introduction to Rosetta software D. HIV-1 PR/PI binding affinity prediction E. Rosetta software development F. Ligand docking with waters using improved Rosetta ligand docking code 50
  51. 51. Binding of HIV-1 protease inhibitors involves H2O51
  52. 52. Translation of water and PI 52
  53. 53. Rotation of water and PI 53
  54. 54. RMSD measures accuracy of docked models54 6 Angstrom (Å) RMSD 2 Angstrom (Å) RMSD 6 Angstrom (Å) RMSD 2 Angstrom (Å) RMSD “Root mean square deviation”
  55. 55. Protein-centric waters improve HIV-1 protease placement55
  56. 56. Ligand-centric waters improve CSAR inhibitor placement  “Community Structure-Activity Resource”  299 protein/ligand structures with interface waters 56
  57. 57. RMSDs vs Rosetta scores 57
  58. 58. Waters improve docking in non- crowded interfaces 58
  59. 59. Interface crowdedness correlates with helpfulness of water docking59
  60. 60. Conclusions  Binding affinity predictions can be improved by  Optimizing Rosetta score term weights  Ignoring the unbound state  New RosettaLigand code allows  Multiple ligand docking  Fragment based rotamers for greater flexibility  Fragment based design of ligands  Docking with waters helps in spacious binding cavities, hurts in crowded binding cavities 60
  61. 61. Professional acknowledgements Meiler Lab Jens Meiler Kristian Kaufmann Sam Deluca Steven Combs Committee David Tabb Richard DAquila Brian Bachmann Jarrod Smith Molecular Biophysics Training Grant (NIH) RosettaCommons 61
  62. 62. Personal acknowledgments Church Friends 62
  63. 63. Personal acknowledgements 63
  64. 64. Personal acknowledgements 64

Editor's Notes

  • Do NOT explain!
  • The meilerlab focuses on proteins…There are 1000s of different proteins that all have a unique role to play – these include proteins that form muscle, hair, and skin, to proteins that perform chemical reactions, forming and breaking chemical bonds.
  • Explain here that most drugs that you pick up at the pharmacy work by binding to specific proteins.Proteins are very large. How is a molecule this large constructed?
  • How are molecules as large as proteins created?
  • This protein has 198 amino acids – it is actually two chains of 99 AA eachHow can the sequence determine something as complex as 3-D structure? It has to do with the way that amino acids interact with each other.
  • Sequence determines structure, which determines function.These mature proteins plays a role in the activity of the HIV virus
  • Determining sequence is easy, determining structure is hard. If we can predict structure we can understand function.
  • Using EXPERIMENTAL structures as comparison
  • Structure means the position of the small molecule with respect to the ligand.Predicting binding affinity is more difficult.If we can predict ligand binding affinity, then we can make predictions about how tight a potential drug will bind to its target and how specific that binding will be.
  • Point out that this is
  • The lowest scoring model we predict will be closest to the true position that the small molecule will assume.
  • I’ve talked about H-bonding but there are many terms and each has a default weight.
  • Mutations lead to drug resistance. WHO keeps track of these mutations…
  • Medicine: As HIV-1 PR mutates, a patient being treated with one of these PRs stops responding to treatment. So they are switched to a different PR.
  • Experimental vs Predicted!
  • dG = Gibbs free energy.ddG = Binding affinity. Relative binding affinity w/respect to mutation.
  • Explain the hypothesis about effect of mutation on flexible vs. rigid structure.
  • Experimental vs Predicted!
  • Explain that the ligand moves as well. This is very important!
  • The idea is that instead of screening libraries of millions of larger compounds, one could screen libraries of several hundred fragments for several independent fragments, then link these together.
  • for example a protein binding pocket can have…
  • Induced-fit means that the protein changes its shape as it interacts with the small molecule.Enzymes that catalyze chemical reactions, either creating or breaking bonds are good examples.
  • RMSD is an average distance over all pairs of atoms.
  • Talk about how important these results are for PI development
  • RMSD on X axis and Rosetta Interface Score on Y axisWith water we are consistently producing low scoring models below 2 A RMSD
  • ×