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Designing Nuclear Receptor Targeted Arrays July 10, 2003
Nuclear Receptor Gene Family Steroid Receptors ER (  ,  ) estradiol   AR dihydrotestosterone PR progesterone MR aldosterone GR cortisol VDR calcitriol   LXR (  ,  )  oxysterols SF1 (  ,  )  oxysterols  CAR androstanes  PXR pregnanes FXR bile acids Genome C. elegans D. melanogaster H. sapiens No. of NR Genes 270 21 48 Size (Mb) 80 137 3300 C N DBD LBD Orphan Receptors HNF4 (  ,  ) NGFIB (  ,  ) TR2 (  ,  ) COUP (  ,  ) ROR (  ,  ) RevErb (  ,  ) Tlx ERR (  ,  ) GCNF DAX SHP LRH TR (  ,  ) triiodothyronine RAR (  ,  ) retinoic acid RXR (  ,  ) 9-cis retinoic acid PPAR (  ,  )  fatty acids/eicosanoids Non-steroid Receptors
Nuclear Receptor Signaling Nucleus Cytoplasm Nuclear Receptor DNA +/- cortisol aldosterone progesterone testosterone estradiol thyroid hormone vitamin D retinoic acid
Lead Discovery Strategies Low High Knowledge Base Random Screening Targeted Screening Database Mining High Low Compound Diversity Array Synthesis Diverse Arrays Targeted Arrays
NR Chemistry Strategy Array Design molecular descriptors “ NR space” Array Synthesis NR targeted arrays NR compound sets Array Screening screening data virtual screening combichem
NR Array Design Universe of Compounds (Virtual or Real) Nuclear Receptor Ligands Chemistries Universe of Compounds (Virtual or Real) NR Ligands Chemistries
Defining NR Chemical Space DiverseSolutions (DVS) Select Descriptors for a Descriptor Space such that: 1) Maximize dimensionality 2) Minimize axes correlation 3) Separate WDI and NR900 NR900 WDI (42,608 cmpds) WDI (42,608 cmpds) NR900 Apply Basis Set of Descriptors 52 Standard 2D and 3D BCUT Metrics SAVOL Molecular Volume 5 Descriptors measure: 1) Charge 2) Polarizability 3) Molecular Shape 4) Molecular Size
NR Chemical Space NR Chemical Space Training set 900 NR ligands 50  pseudo-3D descriptors Liquid Stores (600k compounds) 70k compounds (E = 12%) Virtual Screen Size Shape (2) Charge Polarizability E = % of compounds in NR space GSK Data World Drug Index Literature
Measures of Array Profiles 0% Virtual Effectiveness for Target Space Poor Coverage in Target Space ~40% Virtual Effectiveness for Target Space Good Coverage in Target Space Collection A Target Space Target Space Descriptor Axis 1 Descriptor Axis 2 Collection B Target Space Target Space Descriptor Axis 1 Descriptor Axis 2
Secondary Amide Array 80 Amines X 80 Acids = 6400 Compounds 67% NR Effective
 
 
Virtual Screening ,[object Object],[object Object]
 
 
 
 
 
[object Object],[object Object],Comparative Screening
[object Object],[object Object],[object Object],Comparative Screening Results
NR-LBD Phylogeny Ligands Known Ligands  Unknown
Summary ,[object Object],[object Object],[object Object]
NR LBD X-ray Crystallography Steroid/Retinoid 1. AR  2. ER  3. GR  4. MR    5. PR  6. RAR  7. TR  8. VDR  Orphans w. Ligands 1. RXR  2. PPAR  3. LXR  4. PXR  5. FXR  6. CAR    7. ERR  8. ROR  Orphans w/o. Ligands 1. COUP  2. DAX    3. GCNF  4. HNF4    5. NGFI-B  6. PNR  7. RevErbA  8. LRH      9. SHP  10. SF1      11. TLX  12. TR2  X-ray structure coordinates  available:    yes    no
Steroid Receptor Focus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Array Design Process ,[object Object],[object Object],[object Object]
Array Design Process ,[object Object],[object Object],[object Object],[object Object],testosterone progesterone cortisol aldosterone
Array Design Process ,[object Object],[object Object],[object Object],[object Object]
Array Design Process ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],ROCS for Shape Similiarity
ROCS for Shape Similarity Shape Match ROCS Crystallized Ligand Virtual Array Shape Score
[object Object],[object Object],[object Object],[object Object],[object Object],OMEGA for Conformations
OMEGA for Conformations OMEGA Ensemble of Conformers
Workflow for ROCS Analysis of AR Ligands   Virtual Array (SMILES) DHT Shape Query CONCORD for 3D coords OMEGA for Conformers Shape Score Shape Match ROCS for Shape Match
ROCS Alignment of AR Antagonists with DHT
Strategy for Virtual High-throughput Docking Large Virtual Array ... Virtual Compounds to be Docked Virtual Compounds to be Synthesized M1 Monomers M2 Monomers M3 Monomers Mx Monomers Enumeration Enumeration Pharmacophore Analysis ROCS Analysis Generation of Conformer Ensemble Dock into Protein Active Site Descriptor Evaluation and Virtual Screening Selection
Array A Array B
Array Design Progress ,[object Object],[object Object],[object Object],[object Object]
Limitations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object]
Acknowledgements Pete Kitrinos Bob Johnson Dean Phelps Melissa Gomez Matt Lochansky Christina Sheedy David Gray Rosemary Sasse Matilde Caivano Vicky Strzelczyk Margaret Clackers Jennifer Brown Ryan Trump Phil Turnbull J.B. Blanc Graham Robinett Peter Brown Bob Wiethe Bill Stuart David Drewry David Jones Andy Noe Bill Hoekstra Tim Willson Frank Schoenen Dudley Rose Donald Lyerly James Ballinger Joyce Turner Brenda Ray Eugene Stewart Mill Lambert Aaron Miller

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Gordon2003

  • 1. Designing Nuclear Receptor Targeted Arrays July 10, 2003
  • 2. Nuclear Receptor Gene Family Steroid Receptors ER (  ,  ) estradiol AR dihydrotestosterone PR progesterone MR aldosterone GR cortisol VDR calcitriol LXR (  ,  ) oxysterols SF1 (  ,  ) oxysterols CAR androstanes PXR pregnanes FXR bile acids Genome C. elegans D. melanogaster H. sapiens No. of NR Genes 270 21 48 Size (Mb) 80 137 3300 C N DBD LBD Orphan Receptors HNF4 (  ,  ) NGFIB (  ,  ) TR2 (  ,  ) COUP (  ,  ) ROR (  ,  ) RevErb (  ,  ) Tlx ERR (  ,  ) GCNF DAX SHP LRH TR (  ,  ) triiodothyronine RAR (  ,  ) retinoic acid RXR (  ,  ) 9-cis retinoic acid PPAR (  ,  ) fatty acids/eicosanoids Non-steroid Receptors
  • 3. Nuclear Receptor Signaling Nucleus Cytoplasm Nuclear Receptor DNA +/- cortisol aldosterone progesterone testosterone estradiol thyroid hormone vitamin D retinoic acid
  • 4. Lead Discovery Strategies Low High Knowledge Base Random Screening Targeted Screening Database Mining High Low Compound Diversity Array Synthesis Diverse Arrays Targeted Arrays
  • 5. NR Chemistry Strategy Array Design molecular descriptors “ NR space” Array Synthesis NR targeted arrays NR compound sets Array Screening screening data virtual screening combichem
  • 6. NR Array Design Universe of Compounds (Virtual or Real) Nuclear Receptor Ligands Chemistries Universe of Compounds (Virtual or Real) NR Ligands Chemistries
  • 7. Defining NR Chemical Space DiverseSolutions (DVS) Select Descriptors for a Descriptor Space such that: 1) Maximize dimensionality 2) Minimize axes correlation 3) Separate WDI and NR900 NR900 WDI (42,608 cmpds) WDI (42,608 cmpds) NR900 Apply Basis Set of Descriptors 52 Standard 2D and 3D BCUT Metrics SAVOL Molecular Volume 5 Descriptors measure: 1) Charge 2) Polarizability 3) Molecular Shape 4) Molecular Size
  • 8. NR Chemical Space NR Chemical Space Training set 900 NR ligands 50 pseudo-3D descriptors Liquid Stores (600k compounds) 70k compounds (E = 12%) Virtual Screen Size Shape (2) Charge Polarizability E = % of compounds in NR space GSK Data World Drug Index Literature
  • 9. Measures of Array Profiles 0% Virtual Effectiveness for Target Space Poor Coverage in Target Space ~40% Virtual Effectiveness for Target Space Good Coverage in Target Space Collection A Target Space Target Space Descriptor Axis 1 Descriptor Axis 2 Collection B Target Space Target Space Descriptor Axis 1 Descriptor Axis 2
  • 10. Secondary Amide Array 80 Amines X 80 Acids = 6400 Compounds 67% NR Effective
  • 11.  
  • 12.  
  • 13.
  • 14.  
  • 15.  
  • 16.  
  • 17.  
  • 18.  
  • 19.
  • 20.
  • 21. NR-LBD Phylogeny Ligands Known Ligands Unknown
  • 22.
  • 23. NR LBD X-ray Crystallography Steroid/Retinoid 1. AR  2. ER  3. GR  4. MR  5. PR  6. RAR  7. TR  8. VDR  Orphans w. Ligands 1. RXR  2. PPAR  3. LXR  4. PXR  5. FXR  6. CAR  7. ERR  8. ROR  Orphans w/o. Ligands 1. COUP  2. DAX  3. GCNF  4. HNF4  5. NGFI-B  6. PNR  7. RevErbA  8. LRH  9. SHP  10. SF1  11. TLX  12. TR2  X-ray structure coordinates available:  yes  no
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30. ROCS for Shape Similarity Shape Match ROCS Crystallized Ligand Virtual Array Shape Score
  • 31.
  • 32. OMEGA for Conformations OMEGA Ensemble of Conformers
  • 33. Workflow for ROCS Analysis of AR Ligands Virtual Array (SMILES) DHT Shape Query CONCORD for 3D coords OMEGA for Conformers Shape Score Shape Match ROCS for Shape Match
  • 34. ROCS Alignment of AR Antagonists with DHT
  • 35. Strategy for Virtual High-throughput Docking Large Virtual Array ... Virtual Compounds to be Docked Virtual Compounds to be Synthesized M1 Monomers M2 Monomers M3 Monomers Mx Monomers Enumeration Enumeration Pharmacophore Analysis ROCS Analysis Generation of Conformer Ensemble Dock into Protein Active Site Descriptor Evaluation and Virtual Screening Selection
  • 37.
  • 38.
  • 39.
  • 40. Acknowledgements Pete Kitrinos Bob Johnson Dean Phelps Melissa Gomez Matt Lochansky Christina Sheedy David Gray Rosemary Sasse Matilde Caivano Vicky Strzelczyk Margaret Clackers Jennifer Brown Ryan Trump Phil Turnbull J.B. Blanc Graham Robinett Peter Brown Bob Wiethe Bill Stuart David Drewry David Jones Andy Noe Bill Hoekstra Tim Willson Frank Schoenen Dudley Rose Donald Lyerly James Ballinger Joyce Turner Brenda Ray Eugene Stewart Mill Lambert Aaron Miller