Virtual Screening: How Are We Doing?

Transcription

1 Virtual Screening: How Are We Doing? Mark E. Snow, James Dunbar, Lakshmi Narasimhan, Jack A. Bikker, Dan Ortwine, Christopher Whitehead, Yiannis Kaznessis, Dave Moreland, Christine Humblet Pfizer Global Research & Development Ann Arbor, MI Spring 2001 ACS National Meeting, San Diego

2 Compound DBs Virtual libraries Collected synthetic ideas Virtual Screening Prioritized list of compounds for - screening - array synthesis - individual synthesis

3 Compound DBs Virtual libraries Collected synthetic ideas Virtual Screening Prioritized list of compounds for - screening - array synthesis - individual synthesis Docking and Scoring vs. Protein Structure 3D DB searching with pharmacophore query 2D DB searching with statistical models Physical Properties filters, ADME/Tox models

4 Virtual Screening Statistical Compound-Based Methods Docking and Scoring Knowledge of protein structure not required Requires 3D structure of protein target (x-ray, nmr, or very good homology model)

5 Virtual Screening Statistical Compound-Based Methods Docking and Scoring Screening data required to build and train model No screening data required

6 Virtual Screening Statistical Compound-Based Methods Docking and Scoring Excellent at finding compounds with structural similarities to known actives May have poorer overall enrichment rates, but be better at finding novel structural types

7 AGDOCK Autodock DOCK FlexX GOLD Virtual Screening: Docking Tools Gehlhaar, D. K., et al Chem. Bio. 1995, 2, 317. Goodsell, D.S., et al, Proteins: Struct., Funct. Genet., 1990, 8, 195. Ewing, T.J.A. and Kuntz, I.D., J. Comput. Chem. 1997, 18,1175. Rarey, M., et al, J. Mol. Biol. 1996, 261, 470. Jones G., et al, J. Mol. Biol., 1997, 267, 727. Computer-Assisted Drug Discovery

8 FlexX scoring function* FlexX score = HB_term + electrostatic_term + lipophilic term + NROT term + constant. * Bohm, H.-J. J. Comput.-Aided Mol. Design (1994) 8, Computer-Assisted Drug Discovery

9 Docking vs. Scoring Docking: Selecting the correct binding mode, from among the alternatives, for a given compound. Scoring: Comparing the relative affinities of different compounds against a common protein target. A good function for Docking may not necessarily be a good function for scoring.

10 * 300 MHz R Computer-Assisted Drug Discovery Docking and Scoring: Computational throughput is still an issue, but not like it used to be: FlexX 1-2 cpu min/compound* cfx (combinatorial arrays) - considerably faster

11 Protein 1 (a short-chain dehydrogenase) log (IC 50 ) FlexX Score Computer-Assisted Drug Discovery

12 Enrichment Study Test database: actives from mass screen + ~3000 control compounds (diverse subset of corporate database, cluster plates ). Rank compounds in test database by docking score. If you select compounds based on their scores, how rapidly so you select the actives relative to the other compounds? How does this compare to random?

13 Protein 1 (a short-chain dehydrogenase) percent of actives found percent of DB searched Computer-Assisted Drug Discovery

14 Protein 2 (a kinase) log (IC 50 ) FlexX Score

15 Protein 2 (a kinase) percent of actives found percent of DB searched

16 Protein 3 (a serine protease) log (IC 50 ) FlexX Score Computer-Assisted Drug Discovery

17 Protein 3 (a serine protease) percent of actives found percent of DB searched Computer-Assisted Drug Discovery

18 Protein 8 (an aspartyl protease) log (IC 50 ) FlexX score

19 Protein 8 (an aspartyl protease) percent of actives found percent of DB searched Computer-Assisted Drug Discovery

20 Scoring Functions GOLD score = H_bond_energy (complex) + steric_energy (complex) + internal_energy (ligand) * Jones, G., et al J. Mol. Biol. (1997) 267, DSCORE = DOCK energy score (grid-based, AMBER forcefield, VDW + electrostatic terms). * Meng, E.C., et al J. Comput. Chem. (1992)

21 Scoring Functions FlexX score = HB_term + electrostatic_term + lipophilic term + NROT term + constant. * Bohm, H.-J. J. Comput.-Aided Mol. Design (1994) 8, PSCORE = Potential of Mean Force, sum of pairs of atoms. * Muegge, I. & Martin, Y.C. J. Med. Chem. (1999) 42,

22 Correlation Between Scoring Functions and IC 50 s Protein 1, 68 active compounds. Binding is in correct mode for the seven compounds for which we have crystal structures log (IC 50 ) FlexX PSCORE GSCORE log (IC 50 ) FlexX PSCORE GSCORE 1.00

23 Scoring Functions The GOLD and DOCK functions consists primarily of Molecular Mechanics enthalpies. The FlexX and PMF functions attempt to include entropic terms (both configurational and desolvation) either explicitly (FlexX) or implicitly (PMF).

24 Protein 4 (a short-chain dehydrogenase - no cofactor) percent of actives found percent of DB searched Computer-Assisted Drug Discovery

25 Protein 4 (a short-chain dehydrogenase) no cofactor with cofactor percent of actives found percent of DB searched Computer-Assisted Drug Discovery

26 Protein 4 (a short-chain dehydrogenase) -5 FlexX (with cofactor) FlexX (no cofactor) rule of five compliant not compliant

27 FlexX scores - correlation between proteins FlexX (protein 2) FlexX (protein 3) FlexX (protein 5) rule of five compliant not compliant FlexX (protein 6) FlexX (protein 4)

28 FlexX scores - correlation between proteins (45 mass screen hits vs. protein 4) protein 4 protein 4(apo) protein 5 protein 6 protein 3 protein 2 protein 7 protein protein 4 (apo) protein protein protein protein 2.82

29 Take-home points A scoring function optimized for docking (comparing binding modes of a given compound) is not necessarily optimized for scoring (comparing strength of binding between compounds). Scoring functions which include entropic terms appear to be better scorers than those that look like molecular mechanics enthalpies. Computer-Assisted Drug Discovery

30 Take-home points A modest correlation between docking score and log(ic 50 ) does not preclude the efficient identification of actives embedded in a larger DB. Computer-Assisted Drug Discovery

31 Take-home points Virtual screening using docking methods works reasonably well for some proteins and very poorly for others. It helps to have crystal structures with bound ligand(s).

32 Take-home points FlexX scores are correlated across different (unrelated) protein targets! Some compounds score well regardless of the protein to which they are docked.

33 Take-home points There is still room to improve scoring functions.

34 Thank you Jim Dunbar Lakshmi Narasimhan Jack Bikker Dan Ortwine Chris Whitehead Yiannis Kaznessis Dave Moreland Christine Humblet Dave Lowis (Tripos) Steve Burkett (Tripos)