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Polypharmacology Studied Using Structural Bioinformatics and Systems Biology Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb UCL – December 08, 2010
Big Questions in the Lab ,[object Object],[object Object],[object Object],[object Object],[object Object]
What Really Happens When We Take a Drug? ,[object Object],[object Object],[object Object],Motivation
Why We Think This is Important ,[object Object],[object Object],Motivation
Polypharmacology - One Drug Binds to Multiple Targets ,[object Object],[object Object],[object Object],[object Object],Collins and Workman 2006  Nature Chemical Biology  2 689-700 Motivation
We Have Developed a Theoretical Approach to Address Polypharmacology ,[object Object],[object Object],[object Object],[object Object],[object Object],Our Approach
Our Approach ,[object Object],Our Approach
Which Means … ,[object Object],[object Object],Our Approach
What Have These Off-targets and Networks Told Us So Far? Some Examples… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our Stories
Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8… Testosterone Testosterone Osteoporosis 1AFS, 1I9J .. Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH Viagra Sildenafil citrate ED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. Digoxin Lanoxin Congestive heart failure 1IGJ
Number of released entries Year:
A Quick Aside – RCSB PDB Pharmacology/Drug View 2010-2011 ,[object Object],[object Object],[object Object],[object Object],Mockups of drug view features RCSB PDB Ligand View RCSB PDB Team Drug Name Asp Aspirin Has Bound Drug % Similarity to Drug Molecule 100
A Reverse Engineering Approach to  Drug Discovery Across Gene Families Characterize ligand binding  site of primary target  (Geometric Potential) Identify off-targets by ligand  binding site similarity (Sequence order independent  profile-profile alignment) Extract known drugs  or inhibitors of the  primary and/or off-targets Search for similar small molecules Dock molecules to both  primary and off-targets Statistics analysis  of docking score  correlations … Computational Methodology Xie and Bourne 2009  Bioinformatics 25(12) 305-312
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Characterization of the Ligand Binding Site  - The Geometric Potential Xie and Bourne 2007  BMC Bioinformatics,  8(Suppl 4):S9 Computational Methodology
Discrimination Power of the Geometric Potential ,[object Object],100 0 Geometric Potential Scale Computational Methodology Xie and Bourne 2007  BMC Bioinformatics,  8(Suppl 4):S9
Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm L E R V K D L L E R V K D L Structure A Structure B ,[object Object],[object Object],Xie and Bourne 2008  PNAS , 105(14) 5441 Computational Methodology
Similarity Matrix of Alignment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],f a ,  f b  are the 20 amino acid target frequencies of profile  a  and  b , respectively S a ,  S b  are the PSSM of profile  a  and  b , respectively Computational Methodology Xie and Bourne 2008  PNAS , 105(14) 5441
What Have These Off-targets and Networks Told Us So Far? Some Examples… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our Stories
Selective Estrogen Receptor Modulators (SERM) ,[object Object],[object Object],[object Object],Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
Adverse Effects of SERMs cardiac abnormalities  thromboembolic  disorders ocular toxicities  loss of calcium  homeostatis  ????? Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
Ligand Binding Site Similarity Search On a Proteome Scale ,[object Object],[object Object],[object Object],ER  SERCA Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
Structure and Function of SERCA ,[object Object],[object Object],[object Object],Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
Binding Poses of SERMs in SERCA from Docking Studies ,[object Object],[object Object],6 SERMS A-F (red)  Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
Off-Target of SERMs cardiac abnormalities  thromboembolic  disorders ocular toxicities  loss of calcium  homeostatis  SERCA ! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PLoS Comp. Biol. , 2007 3(11) e217
The Challenge ,[object Object],Side Effects  - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
What Have These Off-targets and Networks Told Us So Far? Some Examples… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our Stories
Nelfinavir  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Possible Nelfinavir Repositioning
Possible Nelfinavir Repositioning
binding site comparison protein ligand docking MD simulation & MM/GBSA Binding free energy calculation structural proteome off-target? network construction  & mapping drug target Clinical Outcomes 1OHR Possible Nelfinavir Repositioning
Binding Site Comparison ,[object Object],[object Object],[object Object],Possible Nelfinavir Repositioning
Enrichment of Protein Kinases in Top Hits ,[object Object],[object Object],[object Object],Possible Nelfinavir Repositioning
Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al.,  Science ,  2002, V298, 1912  Possible Nelfinavir Repositioning
Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of  Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues  H-bond: Met793 with quinazoline N1  H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
Possible Nelfinavir Repositioning
Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning
Inhibition rate of Nelfinavir on EGFR, ErbB2, ErbB4, Akt1, Akt2 Akt3  HTRF® TranscreenerTM ADP Assay is performed for Nelfinavir on 20 μ M by GenScript Results are inconclusive Non-specific aggregation problem? Possible Nelfinavir Repositioning
Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a  known PI3K inhibitor Joell J. Gills et al. Clinic  Cancer Research September 2007 13; 5183  Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest Nelfinavir induces G1 arrest through inhibition of CDK2 activity.  Such inhibition is not caused by inhibition of Akt signaling.  Jiang  W el al. Cancer Res. 2007 67(3) BCR-ABL is a constitutively activated tyrosine kinase   that causes chronic myeloid leukemia (CML) Druker, B.J., et al  New England Journal of Medicine, 2001.  344 (14): p. 1031-1037 Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010.  9 :19 Nelfinavir may inhibit BCR-ABL Possible Nelfinavir Repositioning
Summary  ,[object Object],[object Object],[object Object],[object Object],Possible Nelfinavir Repositioning
What Have These Off-targets and Networks Told Us So Far? Some Examples… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our Stories
The Future as a High Throughput Approach…..
The TB-Drugome ,[object Object],[object Object],[object Object],[object Object],A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol  6(11): e1000976
1. Determine the TB Structural Proteome ,[object Object],284 1, 446 3, 996 2, 266 TB proteome homology models solved structures A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol  6(11): e1000976
2. Determine all Known Drug Binding Sites in the PDB ,[object Object],[object Object],No. of drug binding sites Methotrexate Chenodiol Alitretinoin Conjugated estrogens Darunavir Acarbose A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol  6(11): e1000976
Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of  M.tb  proteins (blue),  and binding sites containing approved drugs (red).
From a Drug Repositioning Perspective ,[object Object],[object Object],No. of potential TB targets raloxifene alitretinoin conjugated estrogens & methotrexate ritonavir testosterone levothyroxine chenodiol A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol  6(11): e1000976
Top 5 Most Highly Connected Drugs Drug Intended targets Indications No. of connections TB proteins levothyroxine transthyretin, thyroid hormone receptor  α  &  β -1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor 14 adenylyl cyclase,  argR , bioD,  CRP/FNR trans. reg .,  ethR ,  glbN , glbO,  kasB ,  lrpA ,  nusA ,  prrA ,  secA1 ,  thyX ,  trans. reg. protein alitretinoin retinoic acid receptor RXR- α ,  β  &  γ , retinoic acid receptor  α ,  β  &  γ -1&2, cellular retinoic acid-binding protein 1&2 cutaneous lesions in patients with Kaposi's sarcoma 13 adenylyl cyclase,  aroG , bioD, bpoC,  CRP/FNR trans. reg. ,  cyp125 ,  embR ,  glbN ,  inhA ,  lppX ,  nusA ,  pknE ,  purN conjugated estrogens estrogen receptor menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure 10 acetylglutamate kinase, adenylyl cyclase,  bphD ,  CRP/FNR trans. reg. ,  cyp121 , cysM,  inhA ,  mscL ,  pknB ,  sigC methotrexate dihydrofolate reductase, serum albumin gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis 10 acetylglutamate kinase,  aroF ,  cmaA2 ,  CRP/FNR trans. reg. ,  cyp121 ,  cyp51 ,  lpd ,  mmaA4 ,  panC ,  usp   raloxifene estrogen receptor, estrogen receptor  β osteoporosis in post-menopausal women 9 adenylyl cyclase,  CRP/FNR   trans. reg.,  deoD,  inhA, pknB ,  pknE ,  Rv1347c ,  secA1, sigC
What Have These Off-targets and Networks Told Us So Far? Some Examples… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our Stories
The Future as a Dynamical Network Approach
Drug Failure - The Torcetrapib Story PLoS Comp Biol  2009  5(5) e1000387
Cholesteryl Ester Transfer Protein  (CETP )  ,[object Object],[object Object],[object Object],HDL LDL CETP CETP inhibitor X Bad Cholesterol Good Cholesterol PLoS Comp Biol  2009  5(5) e1000387 Drug Failure - The Torcetrapib Story
Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets
Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu Roger Chang Bernhard Palsson Jian Wang

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Ucl120810

  • 1. Polypharmacology Studied Using Structural Bioinformatics and Systems Biology Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb UCL – December 08, 2010
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8… Testosterone Testosterone Osteoporosis 1AFS, 1I9J .. Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH Viagra Sildenafil citrate ED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. Digoxin Lanoxin Congestive heart failure 1IGJ
  • 11. Number of released entries Year:
  • 12.
  • 13. A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules Dock molecules to both primary and off-targets Statistics analysis of docking score correlations … Computational Methodology Xie and Bourne 2009 Bioinformatics 25(12) 305-312
  • 14.
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  • 18.
  • 19.
  • 20. Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 29. binding site comparison protein ligand docking MD simulation & MM/GBSA Binding free energy calculation structural proteome off-target? network construction & mapping drug target Clinical Outcomes 1OHR Possible Nelfinavir Repositioning
  • 30.
  • 31.
  • 32. Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al., Science , 2002, V298, 1912 Possible Nelfinavir Repositioning
  • 33. Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
  • 35. Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning
  • 36. Inhibition rate of Nelfinavir on EGFR, ErbB2, ErbB4, Akt1, Akt2 Akt3 HTRF® TranscreenerTM ADP Assay is performed for Nelfinavir on 20 μ M by GenScript Results are inconclusive Non-specific aggregation problem? Possible Nelfinavir Repositioning
  • 37. Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor Joell J. Gills et al. Clinic Cancer Research September 2007 13; 5183 Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest Nelfinavir induces G1 arrest through inhibition of CDK2 activity. Such inhibition is not caused by inhibition of Akt signaling. Jiang W el al. Cancer Res. 2007 67(3) BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML) Druker, B.J., et al New England Journal of Medicine, 2001. 344 (14): p. 1031-1037 Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010. 9 :19 Nelfinavir may inhibit BCR-ABL Possible Nelfinavir Repositioning
  • 38.
  • 39.
  • 40. The Future as a High Throughput Approach…..
  • 41.
  • 42.
  • 43.
  • 44. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
  • 45.
  • 46. Top 5 Most Highly Connected Drugs Drug Intended targets Indications No. of connections TB proteins levothyroxine transthyretin, thyroid hormone receptor α & β -1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor 14 adenylyl cyclase, argR , bioD, CRP/FNR trans. reg ., ethR , glbN , glbO, kasB , lrpA , nusA , prrA , secA1 , thyX , trans. reg. protein alitretinoin retinoic acid receptor RXR- α , β & γ , retinoic acid receptor α , β & γ -1&2, cellular retinoic acid-binding protein 1&2 cutaneous lesions in patients with Kaposi's sarcoma 13 adenylyl cyclase, aroG , bioD, bpoC, CRP/FNR trans. reg. , cyp125 , embR , glbN , inhA , lppX , nusA , pknE , purN conjugated estrogens estrogen receptor menopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure 10 acetylglutamate kinase, adenylyl cyclase, bphD , CRP/FNR trans. reg. , cyp121 , cysM, inhA , mscL , pknB , sigC methotrexate dihydrofolate reductase, serum albumin gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis 10 acetylglutamate kinase, aroF , cmaA2 , CRP/FNR trans. reg. , cyp121 , cyp51 , lpd , mmaA4 , panC , usp raloxifene estrogen receptor, estrogen receptor β osteoporosis in post-menopausal women 9 adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB , pknE , Rv1347c , secA1, sigC
  • 47.
  • 48. The Future as a Dynamical Network Approach
  • 49. Drug Failure - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387
  • 50.
  • 51. Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets
  • 52. Acknowledgements Sarah Kinnings Lei Xie Li Xie http://funsite.sdsc.edu Roger Chang Bernhard Palsson Jian Wang

Notas do Editor

  1. Absorption, distribution, metabolism and excretion
  2. Updated for 2009
  3. P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom
  4. This is great data!
  5. 3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of &gt; 0.7 and a Modpipe quality score of &gt; 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures. 1446 models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF ). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage , and the three individual scores evalue , z-Dope and GA341 . We consider a MPQS of &gt;1.1 as reliable
  6. (nutraceuticals excluded)
  7. Multi-target therapy may be more effective than single-target therapy to treat infectious diseases Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics. GSMN-TB : Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/sbs.surrey.ac.uk/tb) 849 reactions, 739 metabolites, 726 genes Can optimize the model for in vivo growth Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)