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Tuesday September 18th 2012
DEVELOPMENT OF METHODS FOR DOCKING AND
DESIGNING
SMALL MOLECULES WITHIN THE ROSETTA CODE
FRAME...
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
What is structural biology?
ProteinsDNA
Structural Biology is the study of structure and function of
biological molecules ...
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) = ...
Proteins consist of amino acid chains
6
Protein sequence determines
structure7
Protein structure determines function
HIV-1 protease cleaves poly-protein precursors to
form functional proteins
8
Peptide...
Proteins are dynamic
9
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
What is protein modeling?
 Prediction of protein structure from
1. Sequence alone (de novo folding)
HIV-1 PR
Amino Acid S...
What is protein modeling?
 Prediction of protein structure from
2. Sequence similarity (Comparative modeling)
HIV-1 PR Se...
What is ligand docking?
 Prediction of structure of protein/ligand interface
 Prediction of ligand binding affinity
13
+
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
Rosetta protein modeling consists
of sampling and scoring15
RosettaLigand docking consists of
sampling and scoring16
RosettaLigand docking consists of
sampling and scoring17
RosettaLigand docking consists of
sampling and scoring18
RosettaLigand score function
 Knowledge-based score terms
19
Score term
Default
weight
attractive 0.8
repulsive 0.4
solva...
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
21
HIV-1 PR is flexible
Simmerling 2005
22
HIV-1 PR becomes rigid upon
PI binding23
HIV-1 protease mutations
WHO drug resistance
mutations in red
24
Mutation leads to conformational diversity
FDA approved protease inhibitors (PIs)
Tipranavir
Darunavir
Atazanavir
Lopinavir
25
Previous PR/PI ΔΔG
predictions failed
Cheng (2009)
Score Function
Correlation
N=112
Number of non-hydrogen atoms 0.172
X-S...
Defining ΔΔG and ΔΔΔG
27
176 experimental PR/PI ΔΔGs
171 PR template structures28
 176 PR/PI ΔΔGs
 sequence but not structure
 34 sequences
 10...
RosettaLigand PR/PI ΔΔGs predictions
29
0.1 Å 5˚ PI movements
Side chain and ligand rotamer sampling
Minimization of PR si...
Reweighting score terms improves
HIV-1 PR/PI ΔΔG predictions
Score term
Default
weight
Optimized weights
ΔΔG ΔΔΔG
attracti...
Assuming constant unbound ΔG
improves PR/PI ΔΔG predictions
Standard approachConstant unbound approach
31
Correlation plots
Experimental on X Predicted on Y
Default weights:
R=0.16
32
Previous PR/PI ΔΔG
predictions failed
Score Function
Correlation
N=112
Number of non-hydrogen atoms 0.172
X-Score::HPScore...
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
35
Fragment the Ligand Search database for fragments
Assemble rotamer librariesSample from libraries
during docking
Flexibili...
Ligand fragment rotamers allow
efficient flexibility37
Ligand rotamer docking
38
Ligand docking with interface design
A54R
L50Y
C9R
DHT
DHT: Dihydrotestosterone
HisF: imidazole glycerol phosphate synthas...
Fragment based screening can greatly
expand sampling space
Congreve, M. et al. Drug Discov.Today 2003,8, 876-877
Tradition...
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
N...
RosettaLigandDesign
Library of small
molecule fragments
Place fragments in protein binding site
-10
-12
3
-7
-5
Select low...
RosettaLigandDesign
Library of small
molecule fragments
Place fragments in protein binding site
-8
-15
-18
-10
-12
Select ...
Examples of fragments
Carbon
Oxygen Nitrogen
1 connection
2 connections
CH2 connections Ntrp connections
Core fragment
44
Random assembly of fragments
45
Rosetta ligand design in action
46
A. Low-res search for starting fragment
B. Refine (dock) starting fragment
C. Grow smal...
Protein binding sites are complex
Dethiobiotin
(DTB)
Inorganic
phosphate
Mg
Ions ADP
47
Multiple Ligand docking may
capture induced fit effects
Serial Docking
Simultaneous Docking
48
Rosetta multiple ligand docking
49
Outline of presentation
A. What is structural biology?
B. Protein modeling and ligand docking
C. Introduction to Rosetta s...
Binding of HIV-1 protease
inhibitors involves H2O51
Translation of water and PI
52
Rotation of water and PI
53
RMSD measures accuracy of
docked models54
6 Angstrom (Å) RMSD 2 Angstrom (Å) RMSD
6 Angstrom (Å) RMSD 2 Angstrom (Å) RMSD
...
Protein-centric waters improve
HIV-1 protease placement55
Ligand-centric waters improve
CSAR inhibitor placement
 “Community Structure-Activity Resource”
 299 protein/ligand stru...
RMSDs vs Rosetta scores
57
Waters improve docking in non-
crowded interfaces
58
Interface crowdedness correlates
with helpfulness of water docking59
Conclusions
 Binding affinity predictions can be improved by
 Optimizing Rosetta score term weights
 Ignoring the unbou...
Professional acknowledgements
Meiler Lab
Jens Meiler
Kristian Kaufmann
Sam Deluca
Steven Combs
Committee
David Tabb
Richar...
Personal acknowledgments
Church Friends
62
Personal acknowledgements
63
Personal acknowledgements
64
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Docking & Designing Small Molecules within Rosetta Code Framework

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This is the presentation I gave at my PhD defense.

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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

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