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Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins

Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins. Melissa R. Landon 1 , Jessamin Yu 1 , Spencer C. Thiel 2 , David R. Lancia 2 , Jr., Sandor Vajda 1,3 1 Bioinformatics Graduate Program, Boston University, Boston MA

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Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins

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  1. Identification of “Hot Spots” in Druggable Binding Pockets by Computational Solvent Mapping of Proteins Melissa R. Landon1, Jessamin Yu1, Spencer C. Thiel2, David R. Lancia2, Jr., Sandor Vajda1,3 1Bioinformatics Graduate Program, Boston University, Boston MA 2SolMap Pharmaceuticals, Cambridge MA 3Department of Biomedical Engineering, Boston University, Boston MA

  2. Terms • Druggability: the ability of a protein’s binding pocket(s) to bind lead-like molecules with high affinity • Hot Spots: specific residues within a binding pocket for which ligands display high affinity

  3. Protein mapping and druggability • Observation based on SAR by NMR: • Druggable sites bind a variety of small molecules • Binding of probes is restricted to ligand binding sites • “Hit rate” in mapping is a predictor of druggability • Hajduk PJ, Huth JR, Fesik SW: Druggability indices for protein targets derived from • NMR-based screening data.J Med Chem (2005) 48(7):2518-2525. • Hajduk PJ, Huth JR, Tse C: Predicting protein druggability. Drug Discov Today • (2005) 10(23-24):1675-1682.

  4. CS-Map: Introduction CS-Map is based on an experimental method for ligand binding site identification by the co-crystallization of a protein in multiple organic solvents C. Mattos and D. Ringe. Nature Biotech. 14: 595-599 (1996)

  5. Step 1A: Probe Placement 222 initial probe positions

  6. Steps 1B-2: Rigid Body Search and Minimization • Simplex search • Free energy-based score • Second minimization in CHARMM includes Van der Waal term

  7. Step 3: Clustering of Bound Probes • Interaction-based clustering

  8. Step 4: Creation of Consensus Sites • 5-10 lowest free energy clusters for each probe used

  9. Example 1: Mapping of lysozyme Binding of solvents to lysozyme (Liepinsh & Otting, 1997) NMR data on the binding of methanol, isopropanol, acetone, acetonitrile, t-butanol, urea, DMSO, and methylene chloride Based on observed NOEs: • All ligands bind at site C 9 NOEs: N59 NH, W63 CdH, W63 NeH, I98 CgH, I98 CdH, A107 CdH, W108 CdH, W108 CzH, W108 NeH • In addition to site C, methanol and methylene chloride bind to an internal site • A few week NOEs for isopropanol and acetone show binding at the rim of site C

  10. Dennis, S., Kortvelyesi T., and Vajda. S. Computational mapping identifies the binding sites of organic solvents on proteins. Proc. Natl. Acad. Sci. USA., 99: 4290-4295, 2002. Kortvelyesi, T., Dennis, S., Silberstein, M., Brown III, L., and Vajda, S. Algorithms for computational solvent mapping of proteins. Proteins. 51: 340-351, 2003.

  11. Lowest free energy clusters for eight ligands Methanol Isopropanol Acetone Tert-butanol Urea DMSO Acetonitrile Methylene chloride W108 A107 I98 N59 W63

  12. Subclusters of methanol and isopropanol A107 W108 W108 isopropanol A107 methanol Q57 Q57 I98 I98 N59 W63 W63 N59

  13. Conclusions I: The nature of binding sites • Each ligand binds in several rotational states. • The van der Waals energy is low in each rotational state: a well defined pocket that can burry the ligands and exclude water • The site includes a hydrophobic patch created by hydrophobic side chains • The site also includes several hydrogen bond donor or acceptor groups: • (for lysozyme N59 NH, W62 NeH, W63 NeH, A107 O, and Q57 O)

  14. Example 2: Thermolysin Experimental mapping English, et al. Proteins37, 628-640 (1999) Protein Eng.14, 47-59 (2001). Probes: Isopropanol (IPA) Acetone (ACN) Acetonitrile (CCN) Phenol (IPH) All in the S’1 pocket

  15. Thermolysin – Computational Mapping Consensus sites 1 and 2 Obtained by the CS-Map algorithm Dennis, S., Kortvelyesi T., and Vajda. S. Computational mapping identifies the binding sites of organic solvents on proteins. Proc. Natl. Acad. Sci. USA., 99: 4290-4295, 2002. Kortvelyesi, T., Dennis, S., Silberstein, M., Brown III, L., and Vajda, S. Algorithms for computational solvent mapping of proteins. Proteins. 51: 340-351, 2003.

  16. Comparison of mapping results to contacts in the PDB

  17. Hydrogen bonds in thermolysin

  18. Textbook-type representation of H-Bonds

  19. Why does CS-Map give better results than earlier methods ? • Improved sampling of the regions of interest • A scoring potential that accounts for desolvation • Clusters are ranked, not individual conformations • Consensus site: The binding of different solvents reduces • the probability of finding false positives

  20. Detection of Hot Spots within Druggable Binding Pockets by CS-Map • Purpose of study: To determine the predictive power of CS-Map toward the identification of hot spots within a binding pocket • Comparisons are based on known ligand interactions and NMR data

  21. Part 1: Identification of hot spots in peptide binding pocket of Renin • Major target for the treatment of hypertension • Over 25 years of research into small molecule inhibitors • Most inhibitors are peptidomimetics • Novartis in Phase III trials of Aliskiren, a novel non-peptidomimetic renin inhibitor http://www.merck.com/mmhe/sec03/ch022/ch022a.html

  22. Part 1: Identification of hot spots in peptide binding pocket of Renin • First orally available inhibitor, Aliskiren, binds in a different • conformation than peptidomimetic inhibitors • -Wood, JM. et. al. Biochem. Biohphys. Res. Commun. 308(4): 698-705 (2003 • Used the GOLD algorithm for docking • -Verdonk, M.L, et. al. Proteins. 52:609-623 (2003)

  23. Identification of Peptide Binding Pocket by CSMap 1RNE 1BIL 1BIM 1HRN 2REN Top two consensus sites for each structure are located in the binding pocket

  24. S2 S1’ S4 S3SP S1 S3 S2’ Identification of Preferred Binding Modality of Aliskiren using CSMap

  25. S1 S2 S3SP S1 S1 S2’ S2 S4 S3 S1’ CS-Map Based Identification of Hot Spots in Peptide Binding Pocket of Renin • Atom-Based Interactions calculated using HBPlus • I.K. McDonald and J.M.Thornton. J. Mol. Biol. 238:777-793 (1994) • Pearson Correlation between Probes & Aliskiren = .73 • Pearson Correlation between Probes & Peptidomimetic = .17

  26. Conclusions IV Conclusions: Part 1 • Mapping results indicate the druggable pockets in the renin active site • Pockets S2 and S4 are not “hot spots” and should not be targeted. • The most important pockets are S1 and S3 • Pockets S1’ and S2’ are of intermediate importance, but contribute to the • binding. • Some of these regions, primarily S2’, is not utilized by Aliskiren, suggesting • that a higher affinity drug may be developed.

  27. Ketopantoate Reductase *Figures reproduced from Ciulli, et. al. • NMR studies of E.coli Ketopantoate Reductase using NADPH fragments and co-factor analogues revealed two hot spots located on opposite ends of the NADPH binding region • -Ciulli, et. al. J.Med. Chem. 2006 Vol. 49 • Mutational analysis of residues on opposite ends of the binding region, R31 and N98, confirmed these results.

  28. N98 R31 Mapping Results: Ketopantoate Reductase CS-Map Results (% Interaction/Residue): Red: 4.15/residue Green: 4.13/residue White: 2.52/residue Blue: 4.63/residue • Mapping analysis of three structures, PDB IDs 1YJQ, 1YON, 1KS9, yielded hot spots on either end of the NADPH binding region, in agreement with the experimental study

  29. Part 2: Hot Spot Identification for Proteins used in NMR druggability study NMR study published by Hajduk, et. al. J. Med Chem. 2005 *Verified by structural and/or NMR data

  30. Example from Study: FK-506 Binding Protein Important Target for Immunosuppression CS-Map Results Correspond to NMR data Shuker, S.B., et. al. Science 274(5292): 1531-4 (1996)

  31. Comparison of Residue Interactions between CS-Map Probes and Bound Ligands

  32. Conclusions: Part 2 • CS-Map is capable of determining hot spots within binding pockets of druggable proteins, supported both by NMR and structural data

  33. General Conclusions and Future Directions • The computational prediction of residues important for ligand binding is crucial to structure-based drug design efforts, as well as providing further insight into protein-ligand interactions. • Future work will focus on the use of CS-Map derived data to predict hot spots on proteins for which no experimental binding data exists, namely to build pharmacophore models of ligand interactions and to predict hydrogen bonding patterns.

  34. The Vajda Group: Melissa Landon Karl Clodfelter Jessamin Yu Spencer Thiel David Lancia, Jr. SolMap Pharmaceuticals: Frank Guarnieri Patrick Devaney This work was funded by National Institutes of Health SolMap Pharmaceuticals Many Thanks

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