Electrostatics: Semi-Coulombic Approximation

1
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
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
Computational Mapping Step 1 Placing the probes
4
Step 2 Move the probes around to find binding
positions
5
Step 3 Remove high energy clusters of the ligand
6
Step 4 Repeat mapping with a number of fragments
7
Step 5 Combine fragment into potential ligand
molecules
8
Why does CS-Map give better results than earlier
methods ?

• Comparison to
• Geometric Flood-fill, PASS
• Energetic QsiteFinder, PocketFinder
• Mapping/Docking GRID, MCSS

• 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

9
Comparison of the Locus technology with
Computational Solvent Mapping
Property Computational Solvent Mapping Locus Core Technology
Sampling method Multistart 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 Consensus sites
CPU time About 1 hour About 7 days
10
Structure and hot spots of PPAR-g
C2
C2
P3
P2
P1
P3
C1
P4
C1
P4
E2
E1
Unbound structure Structure
with farglitazar (1fm9)
11
Structure and hot spots of PPAR-g
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
Current work A prototype fragment library
13
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.