1. DockCrunch and Beyond... The future of receptor-based virtual screening
Bohdan Waszkowycz, Tim Perkins & Jin Li
Protherics Molecular Design Ltd Macclesfield, UK

2. Outline Structure-based virtual screening
an achievable (and possibly useful) tool for drug discovery
the DockCrunch validation study
Protherics’ experience since DockCrunch
methods: making VS a routine task
analysis: getting the most from your data
the future (and beyond)

3. Virtual Screening compound collections virtual libraries computational
receptor
structure
molecular docking
targeted
selection
screen smaller focused libraries

4. Why Use Molecular Docking?
Most detailed representation of binding site
overcomes simplifications of pharmacophores
identify both conservative and novel solutions
impetus for de novo design/optimisation
Broad range of analyses applicable
diverse scoring/selection criteria
Quality/throughput of available methods
good enough, despite technical limitations

5. DockCrunch Validation study for large-scale virtual screening
flexible ligand/rigid receptor docking
PRO_LEADS docking code using ChemScore scoring function
1.1M druglike ACD-SC compounds
dock versus oestrogen receptor (agonist and antagonist structures)
collaboration with SGI

6. Oestradiol:Oestrogen Receptor Complex

7. DockedEnergy Profiles
Agonist Receptor
Antagonist receptor
Achieve good separation in terms of predicted binding affinity

8. DockCrunch Results Demonstrated technical feasibility
1.1M cpds docked in 6 days/64 processor Origin
implemented automated pre- and post-processing
Demonstrated potential for lead identification
successful discrimination of seeded known hits
activity for 21 out of 37 assayed compounds
ER binding affinities to 7nM Ki
novel non-steroidal chemistries

9. Since DockCrunch... VS established as a routine CAMD task:
2.2M structures docked in DockCrunch
1.5M docked versus in-house target
2.5M docked to date in external contracts
project 1: 0.25M Dec 2000
project 2: 0.25M Jan 2001
project 3: 1M Feb 2001
project 4: 1M March-April 2001
project 5: 0.5M to do in May...
diverse targets/databases/project objectives

10. Virtual Screening within Prometheus
Database preparation
e.g. salt removal, protonation
Virtual
databases
Commercial
databases
Database pre-filtering
select drug-like profile
Receptor
structure
Receptor-ligand docking
predict binding mode/affinity
Analysis
graphical browsing,
subset selection

11. PRO_LEADS Docking Tabu search + extended ChemScore function
robust prediction of binding free energy
85% success rate achieved across diverse test set
Pre-calculated grids for energies/neighbour lists
defines extent of binding site
automatically/graphically defined
Selection of PRO_LEADS docking protocol
use standard protocol across all receptors
specific constraints or modified energy terms available if desired

12. Example of Grid Definition
cAMP-dependent kinase (1YDS)
contact surface coloured by lipophilicity

13. Docking Throughput Standard protocols take 1–5 mins/ligand
e.g. typical VS run at ~4 min for 3M tabu steps
250k cpds/week on 100 processor Linux cluster (VA Linux 750MHz PIII)
PLUNDER script for parallelization
automatic processing of ligand batches
balances processor workload
works across heterogeneous architectures
supplies running time statistics
handles hardware failures

14. Data Analysis and Subset Selection
Intrinsic problems of scoring functions:
cannot parameterize all critical interactions
try to take account of induced fit effects
calibrated only versus good binders
ignore co-operativity in binding
When applied to random datasets:
predicted affinity typically normal distributed
overestimates binding affinity of random set
 energy alone not ideal for subset selection

15. Achieving Better Selection
Need to supplement scoring function
consensus scoring schemes
Explore more fundamental descriptors of receptor:ligand complementarity
capture characteristics of diverse receptor types
assess deficiencies of existing scoring functions
use as simple filters or as pseudo energy terms

16. Enrichment Rates Effect of different selection criteria for ER set for recovery of seeded compounds

17. Requirements for Analysis Package
VS generates huge data output
want to be able to browse through entire dataset
Real-time navigation of large datasets
graphing property distributions
selections based on property filters
browsing of 3D models within selections
initiating additional property calculations
data transformations
writing subset/reports

18. PropertyViewer

19. Approach to Analysis 1. Preliminary exploration
browse property distributions
comparisons with known ligands
2. Initial elimination of poor structures
DockedEnergy, component energies
DE corrected for size/functionality
receptor:ligand steric complementarity
polar/lipophilic surface complementarity

20. Approach to Analysis 3. Further filtering  define focused subsets
tighter 2D property filters
clustering by 2D chemistry
presence of key 3D binding interactions
specific H-bonds, specific lipo contacts, pocket occupancy, volume overlap with reference ligand/fragment, etc
similarity/diversity of 3D binding mode
3D similarity descriptors
final ranking by DockedEnergy or hybrid energy/complementarity scoring function

21. DockedEnergy vs Size

22. Complementarity Space ER and FXa datasets

23. Addressing More Difficult Cases - COX2
Knowns show clustering in property space despite modest DockedEnergy

24. Improvements in Docking Function
original docking function
some misdocked knowns
new docking function
more consistent docking
+ve shift in random energies

25. Comparison of filters in subset selection
87% pass
2D filters
Initial filtering to ~10%
energy filters
complementarity
2D properties
Selection of final ~1% subset
3D structural features
preferred binding motifs
2D/3D diversity
37% pass
energy filters
43%
22% 2%
12% 1% 9% 0%
22% pass
complementarity filters

26. Conclusions Established VS as a routine CAMD task
focused software development
achieved success in drug discovery projects
VS is more than a black box
data mining is worthwhile
explore receptor-ligand complementarity to achieve good subset selection and point towards better scoring functions

27. Future Directions for VS
Exploit expanding computing resource
improved docking/scoring functions
improved receptor representations
Broader application of VS
evaluation of drugability of early targets
screening of very large virtual libraries
routine screening across protein families
DMPK issues

28. Acknowledgements Tim Perkins Martin Harrison
Richard Sykes Carol Baxter
Richard Hall Chris Murray
David Frenkel Jin Li
David Sheppard
Thanks to:
SGI, MSI, MDL, VA Linux