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Workshop031211

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Presentation on March 12, 2011 at the Skaggs School of Pharmacy and Pharmaceutical Sciences (UCSD) during the Workshop in Allosteric and Orthosteric Ligands in Drug Action

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Workshop031211

  1. 1. Mining databases for understanding target recognition Philip E. Bourne 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  2. 2. Mining databases for understanding target recognition – well the PDB anyway Philip E. Bourne 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  3. 3. “ If you remember 3 things from a lecture a week later it was a good lecture” from Ten Simple Rules for Making Good Oral Presentations PLoS Comp Biol 2007 3(4): e77 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  4. 4. 1. The PDB services almost 200,000 scientists per month, but you are special – take advantage of this offer 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  5. 5. “ I want to review all multimeric quaternary complexes in the PDB that may be of interest in the understanding of allosteric mechanisms exhibited by such complexes” Jean-Pierre Changeux
  6. 6. 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  7. 7. Its not as easy as it sounds.. Group II Chaperonins - Open and Closed Conformation <ul><li>all chains are sequence identical within one chaperone and 95% similar between the two PDB entries </li></ul>3KFK 3KFB
  8. 8. Identify biological relatedness even if quaternary structures show variability
  9. 9. Methodology <ul><li>Take all chains from a PDB95 sequence cluster </li></ul><ul><li>Fetch the (1st) Biological Assembly (BA) for the PDB ID of the chain </li></ul><ul><li>Align the whole BAs against each other using CE-CP </li></ul>Prlic et al 2010 Bioinformatics 10.1093/bioinformatics/btq572
  10. 10. Our scores allow to pick out unusual ones: 1Y01 Z-score: 6.23 Coverage 1:22 Coverage 2:59 TM-Score: 0.56 4HHB (self) Z-score: 8.49 Coverage 1:100 Coverage 2:100 TM-Score: 1.0 2W72 Distal site hemoglobin mutant AHSP bound to Fe(II) alpha-hemoglobin Z-score: 7.02 Coverage 1:76 Coverage 2:100 TM-Score: 0.98
  11. 11. Long Term Goal <ul><li>Characterize all quaternary structures found in the PDB according to level of structural similarity </li></ul><ul><li>Where structural similarity exists classify according to ligands bound </li></ul><ul><li>Characterize those with drug binding sites at the subunit boundaries </li></ul><ul><li>Better characterize allosteric mechanisms associated with quaternary structures </li></ul>03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  12. 12. 2. We have research tools, not part of the PDB (yet), which are important for discovering and characterizing protein receptors 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  13. 13. <ul><li>Initially assign C  atom with a value that is the distance to the environmental boundary </li></ul><ul><li>Update the value with those of surrounding C  atoms dependent on distances and orientation – atoms within a 10A radius define i </li></ul><ul><li>Conceptually similar to hydrophobicity </li></ul><ul><li>or electrostatic potential that is </li></ul><ul><li>dependant on both global and local </li></ul><ul><li>environments </li></ul>Characterization of the Ligand Binding Site - The Geometric Potential Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
  14. 14. Discrimination Power of the Geometric Potential <ul><li>Geometric potential can distinguish binding and non-binding sites </li></ul>100 0 Geometric Potential Scale Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
  15. 15. Search for Similar Ligand Binding Sites L E R V K D L L E R V K D L Structure A Structure B <ul><li>Build an associated graph from the graph representations of two structures being compared. Each of the nodes is assigned with a weight from the similarity matrix </li></ul><ul><li>The maximum-weight clique corresponds to the optimum alignment of the two structures </li></ul>Xie and Bourne 2008 PNAS , 105(14) 5441 http://funsite.sdsc.edu
  16. 16. 3. We can undertake high-throughput hypothesis generation for protein-drug interactions on a proteome-wide scale 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  17. 17. The TB-Drugome <ul><li>Determine the TB structural proteome </li></ul><ul><li>Determine all known drug binding sites from the PDB </li></ul><ul><li>Determine which of the sites found in 2 exist in 1 </li></ul><ul><li>Call the result the TB-drugome </li></ul>Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976 Workshop in Allosteric and Orthosteric Ligands in Drug Action 03/12/11
  18. 18. 1. Determine the TB Structural Proteome <ul><li>High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3% </li></ul>284 1, 446 3, 996 2, 266 TB proteome homology models solved structures 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  19. 19. 2. Determine all Known Drug Binding Sites in the PDB <ul><li>Searched the PDB for protein crystal structures bound with FDA-approved drugs </li></ul><ul><li>268 drugs bound in a total of 931 binding sites </li></ul>No. of drug binding sites Methotrexate Chenodiol Alitretinoin Conjugated estrogens Darunavir Acarbose 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
  20. 20. 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).
  21. 21. From a Drug Repositioning Perspective <ul><li>Similarities between drug binding sites and TB proteins are found for 61/268 drugs </li></ul><ul><li>41 of these drugs could potentially inhibit more than one TB protein </li></ul>No. of potential TB targets raloxifene alitretinoin conjugated estrogens & methotrexate ritonavir testosterone levothyroxine chenodiol 03/12/11
  22. 22. Top 5 Most Highly Connected Drugs 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action 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
  23. 23. Acknowledgements Funding Agencies: NSF, NIGMS, DOE, NLM, NCI, NCRR, NIBIB, NINDS, NIDDK 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action

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