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

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

S. Vajda, 2005

Computational Mapping Step 1: Placing the probes

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Step 2: Move the probes around to find binding positions

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Step 3: Remove high energy clusters of the ligand

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Step 4: Repeat mapping with a number of fragments

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Step 5: Combine fragment into potential ligand molecules

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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

reducesthe probability of finding false positives

Comparison to:

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

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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

S. Vajda, 2005

Unbound structure Structure with farglitazar (1fm9)

C2

C1

P2P3

P4

C2

C1

E1

P1P3

P4

E2

Structure and “hot spots” of PPAR-

S. Vajda, 2005

Structure and “hot spots” of PPAR-

Site Description

P1 Head group of agonists, interacting with helix H12

P2 Overlapping the middle of agonists

P3 Upper distal end of the binding site, reached only by the partial agonist

P4 Hydrophobic pocket close to the entrance

B Surface pocket in the back, overlapping the dimerization region

F Surface pocket of unknown role

C1 Surface pocket, possibly contributing to cofactor binding

C2 Overlapping with the binding site of the co-activator peptide SRC-1

E1 Pocket defined by the lower ends of helices H3, H7, and H10

E2 Putative ligand entrance between H2’ and the -sheet

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.

S. Vajda, 2005

Current work: A prototype fragment library

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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 DennisDr. Tamas Kortvelyesi Shu-Hsien SheuKarl Clodfelter

Dr. Dagmar Ringe (Brandeis University)

National Institute of Health National Institute of Environmental HealthNational Science FoundationSolMap Pharmaceuticals, Inc.