1. S. Vajda, 2005 Computational mapping of proteins for fragment based drug design Sandor Vajda, Spencer Thiel, Michael Silberstein, Melissa Landon, and David Lancia Boston University, Boston, MA & SolMap Pharmaceuticals, Cambridge MA

2. S. Vajda, 2005 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. Silberstein, M., Dennis, S., Brown III, L., Kortvelyesi, T., Clodfelter, K., and Vajda, S. Identification of substrate binding sites in enzymes by computational solvent mapping, J. Molec. Biol. 332, 1095-1113, 2003. Mattos C, Ringe D: Locating and characterizing binding sites on proteins. Nat. Biotechnol. (1996) 14(5):595-599. 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. Small molecule binding druggability of the binding site

3. S. Vajda, 2005 Computational Mapping Step 1: Placing the probes

4. S. Vajda, 2005 Step 2: Move the probes around to find binding positions

5. S. Vajda, 2005 Step 3: Remove high energy clusters of the ligand

6. S. Vajda, 2005 Step 4: Repeat mapping with a number of fragments

7. S. Vajda, 2005 Step 5: Combine fragment into potential ligand molecules

8. S. Vajda, 2005 Why does CS-Map give better results than earlier methods ? Properties: 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 Comparison to: Geometric: Flood-fill, PASS Energetic: QsiteFinder, PocketFinder Mapping/Docking: GRID, MCSS

9. S. Vajda, 2005 Comparison of the Locus technology with Computational Solvent Mapping PropertyComputational Solvent Mapping Locus Core Technology Sampling methodMultistart nonlinear simplex, off-grid Grand Canonical Monte Carlo on a grid Solvation representation Continuum Electrostatics (GBSA) None; simulations in water are run separately, and water-filled sites are removed Binding free energy evaluation Empirical (no configurational entropy) For gas-phase within the accuracy limits of the Grand Canonical Monte Carlo sampling Criterion for retaining a probe Low Boltzmann-averaged free energy of the corresponding probe clusters Probe remains bound to the protein after transition from liquid to gas phase Predicted druggable binding sites Consensus sites CPU timeAbout 1 hourAbout 7 days

10. S. Vajda, 2005 Unbound structure Structure with farglitazar (1fm9) C2 C1 P2 P3 P4 C2 C1 E1 P1 P3 P4 E2 Structure and “hot spots” of PPAR- 

11. S. Vajda, 2005 Structure and “hot spots” of PPAR-  H12 Sheu, S-H., Kaya, T., Waxman D. J., and Vajda, S. Exploring the binding site structure of the PPAR-g ligand binding domain by computational solvent mapping. Biochemistry, 44, 1193-1209, 2005.

12. S. Vajda, 2005 Current work: A prototype fragment library

13. S. Vajda, 2005 Credits Poster: Hot spots in the binding site of renin Vajda, S. and Guarnieri, F. Characterization of protein-ligand interaction sites using experimental and computational methods. Current Opinion in Drug Design and Development. In press (May 2006). Dr. Sheldon Dennis Dr. Tamas Kortvelyesi Shu-Hsien Sheu Karl Clodfelter Dr. Dagmar Ringe (Brandeis University) National Institute of Health National Institute of Environmental Health National Science Foundation SolMap Pharmaceuticals, Inc.