Receptor-based Virtual Screening of Very Large Chemical Datasets

Transcription

1 Receptor-based Virtual Screening of Very Large Chemical Datasets Bohdan Waszkowycz, Tim Perkins, Carol Baxter, Jin Li and John Liebeschuetz Protherics Molecular Design Ltd Macclesfield, UK

2 Virtual Screening Computational compound selection for assay/synthesis Benefits focused subsets with enhanced hit rates analysis before assay established evaluate virtual combinatorial libraries before synthesis

3 Virtual Screening Methodologies 2-D similarity to known ligands low chance of novelty 3-D pharmacophore search imitates known binding mode limited steric constraints receptor-based docking 3-D structure allows open-ended query requires effective scoring procedures

4 PRO_LEADS Flexible-ligand docking software Empirical scoring function: ChemScore hydrogen bonding; lipophilic; flexibility calibrated against X-ray ligand protein complexes Direct estimate of free energy of binding Tabu searching algorithm

5 Docking Validation 86% correct over 70 protein ligand complexes Raloxifene in 1err r.m.s. error 0.77 Å docked solution green; X-ray orientation grey

6 Virtual Screening Experiment DockCrunch: dock 1 million ACD-SC compounds into estrogen receptor DockCrunch Objectives demonstrate technical practicability recover known active compounds identify novel ligands Collaboration with SGI

7 Estrogen Receptor Conformations Agonist (1ere) Antagonist (1err)

8 ACD-SC Preparation Strip counter-ions Add hydrogen atoms Check/fix valency problems Protonate as physiological ph Calculate 2-D properties Convert to 3-D with MSI Converter

9 2-D Property Selection Property Min Max Molecular weight log P -8 7 Rotatable bonds 0 10 Chiral carbon atoms 0 3 Hydrogen-bond acceptors 0 8 Hydrogen-bond donors 0 8 Heavy atoms 11

10 DockCrunch Experiment 1.1 million compounds Spiked with known agonists/antagonists Dock against both receptor forms 64-processor Origin 2000 at SGI 30 s per ligand 6 days per million ligands

11 Results and Analysis: Aims Navigate and visualize 1.1M dockings Identify known ligands Select subset for bioassay objective selection criteria docked energy plus complementarity identify novel ligands

12 Docked-Energy Profiles Agonist Receptor Antagonist receptor ACD-SC Agonists 25 ACD-SC Antagonists Relative Frequency (%) Relative Frequency (%) Docked Energy (kj/mol) Docked Energy (kj/mol)

13 Antagonist Enrichment Rates Energy Cutoff % total n Rate none kj/mol kj/mol kj/mol

14 Selectivity Docked Energy in Antagonist ER (kj/mol) ACD-SC Agonists Antagonists Docked Energy in Agonist ER (kj/mol)

15 Receptor-derived Descriptors for Post Processing Docked energy & component energies can over-predict random compounds Receptor-ligand complementarity quality of steric complementarity polar-lipophilic surface area mismatch 3-D similarity to known ligands in terms of ligand to receptor contacts

16 Complementarity Space Polar-lipophilic Clash Area (Ų) Low-energy ACD-SC Agonists Steric Penalty

17 PropertyViewer Interactive analysis Multipleproperty filters Coupled 3-D views Graphing

18 Agonist Selection Process Selection Criteria n None 1,152,379 Docked Energy 73,961 Energy components 12,265 Surface complementarity D properties (non-steroidal) D similarity 293

19 Sample Virtual Hits O O O N O H H H

20 Validated ER Hits from DockCrunch Approximately 100 hits were proposed for purchase. 37 virtual hits (non-steroidal) were actually purchased and assayed (Panlabs) 7 with Ki between 100 nm and 1 µm 12 with Ki between 10 and 100 nm 2 with Ki < 10 nm 10 drug-like, predicted poor binders were also purchased and assayed as negative control. No actives found at 10 µm.

21 Characteristics of ER Hits MACCS 2D similarity to the reference structures ER Hits Ki (nm) Oestradiol DiethylStil. Tamoxifen Raloxifen PMD PMD PMD PMD PMD PMD PMD PMD PMD PMD PMD PMD

22 Summary of Other Virtual Screening Datasets of 10,000 screened against Thrombin and Xa. ~85% recovery of seeded known inhibitors, in top 1000 scoring compounds. 264 non-amidines selected for testing from Thrombin hit list, after post screen filtering. 8 hits with K i < 30 µm (3%). Several hits with good looking modes of binding and scope for rapid SAR investigation via combinatorial chemistry - PRO_SELECT.

23 Linux Implementation of PRO_LEADS Linux Based System open, stable widely supported and used 100 P3 750 MHz Processor Farm - Costeffective solution to large scale computing requirement Estimated time to run DockCrunch: 3 Days per receptor

24 Conclusions Docking very large databases both practicable and useful find known and novel hits Need good filtering protocols using energetic and structural criteria Fast and accurate receptor based virtual screening a reality john.liebeschuetz@protherics.com

25 Acknowledgements Bohdan Waszkowycz Tim Perkins Carol Baxter Richard Sykes Chris Murray Jin Li SGI MSI