Dockcrunch and beyond the future of receptor based virtual screening
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DockCrunch and Beyond... The future of receptor-based virtual screening. Bohdan Waszkowycz, Tim Perkins & Jin Li Protherics Molecular Design Ltd Macclesfield, UK. Outline. Structure-based virtual screening an achievable (and possibly useful) tool for drug discovery

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Dockcrunch and beyond the future of receptor based virtual screening

DockCrunch and Beyond...The future of receptor-based virtual screening

Bohdan Waszkowycz, Tim Perkins & Jin Li

Protherics Molecular Design LtdMacclesfield, UK


Outline

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)


Virtual screening

receptor

structure

molecular docking

Virtual Screening

compound

collections

virtual

libraries

computational

screening

targeted

selection

screen smaller focused libraries


Why use molecular docking

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


Dockcrunch

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


Oestradiol oestrogen receptor complex

Oestradiol:Oestrogen Receptor Complex


Dockedenergy profiles

Agonist Receptor

Antagonist receptor

DockedEnergy Profiles

  • Achieve good separation in terms of predicted binding affinity


Dockcrunch results

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


Since dockcrunch

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


Dockcrunch and beyond the future of receptor based virtual screening

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


Pro leads docking

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


Example of grid definition

Example of Grid Definition

cAMP-dependent kinase (1YDS)

contact surface coloured by lipophilicity


Docking throughput

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


Data analysis and subset selection

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


Achieving better selection

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


Enrichment rates effect of different selection criteria for er set for recovery of seeded compounds

Enrichment RatesEffect of different selection criteria for ER set for recovery of seeded compounds


Requirements for analysis package

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


Propertyviewer

PropertyViewer


Approach to analysis

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


Approach to analysis1

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


Dockedenergy vs size

DockedEnergy vs Size


Complementarity space er and fxa datasets

Complementarity SpaceER and FXa datasets


Addressing more difficult cases cox2

Addressing More Difficult Cases - COX2

Knowns show clustering in property space despite modest DockedEnergy


Improvements in docking function

Improvements in Docking Function

original docking function

some misdocked knowns

new docking function

more consistent docking

+ve shift in random energies


Comparison of filters in subset selection

Comparison of filters in subset selection

87% pass

2D filters

37% pass

energy filters

  • Initial filtering to ~10%

    • energy filters

    • complementarity

    • 2D properties

  • Selection of final ~1% subset

    • 3D structural features

    • preferred binding motifs

    • 2D/3D diversity

43%

22%

2%

12%

1%

9%

0%

22% pass

complementarity filters


Conclusions

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


Future directions for vs

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


Acknowledgements

Tim Perkins Martin Harrison

Richard SykesCarol Baxter

Richard HallChris Murray

David FrenkelJin Li

David Sheppard

Thanks to:

SGI, MSI, MDL, VA Linux

http://www.protherics.com/crunch/

Acknowledgements


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