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In-Silico Screening for Peptides and Non-Peptides: A One-Step Approach

Discover how Fields, XEDs, and FieldPrints revolutionize virtual screening for biologically active molecules without relying on structural comparisons. Learn about innovative methods for exploring molecular properties and improving electrostatic force field accuracy. Join our workshop for insights into virtual screening advancements!

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In-Silico Screening for Peptides and Non-Peptides: A One-Step Approach

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  1. In-silico screening without structuralcomparisons:Peptides to non-peptidesin one stepMaybridge Workshop 23-24 Oct ‘03Bregenz Austria

  2. Cresset Biomolecular Discovery Founded in November 2001 Funding by The Wellcome Trust

  3. Virtual Screening • Virtual screening is the process of trying to find biologically-active molecules using a computer • Protein-based (X-ray, docking) • Need a protein structure • Problems with scoring functions • Ligand-based • Structural similarity • Not specific enough

  4. The Science Problem • The Problem is that: • There is no logical way to change Structural Class and retain Biological Activity • Since we know that: • Different structures can give the same biological effect • Then the Answer is to: • Define what it is that the target actually sees if not structure

  5. Fields, XEDs and FieldPrints • Fields • A new method of describing molecular properties • XEDs • A new molecular modelling approach • FieldPrints • A new virtual screening method

  6. Fields • Chemically different, biologically similar molecules have a similar electron cloud.It is this that is seen by the target • Can we use a representation of that electron cloud to explore molecules’ biological properties? • Fields represent the key binding information contained in the electron cloud

  7. COX-2 Inhibitor

  8. COX-2 Inhibitor

  9. COX-2 Inhibitor

  10. COX-2 Inhibitor

  11. COX-2 Inhibitor R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), J. Comp-Aid. Mol. Design, 9, 33-43.

  12. TheField Templatefor a COX-2 Inhibitor

  13. ACCs get Fields Wrong Without a good description of atoms, the field points are incorrect! Atom-centred charges Fields from ACC’s R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), ’The matching of electrostatic extrema: A useful method in drug design? A study of phosphodiesterase III inhibitors’, J. Comp-Aid. Mol. Design, 9, 33-43.

  14. XEDs make Fields work The Field Points from XED agree well with those obtained from Quantum Mechanics ACCs XEDs Vinter & Trollope 1994 unpublished.

  15. eXtended Electron Distributions The XED force field improves the description of electrostatics by extending electrons away from the nucleus ACCs XEDs J. G. Vinter, (1994) ‘Extended electron distributions applied to the molecular mechanics of intermolecular interactions’, J Comp-Aid Mol Design, 8, 653-668.

  16. XEDs Model Life Better Benzene docked onto Benzene using ACCs X-ray structure of Benzene Benzene docked onto Benzene using XEDs

  17. Aromatic-Aromatic Interactions GSK (SKF) “Azepanone-Based Inhibitors of Human and Rat Cathepsin K”, J. Med. Chem. 2001, Vol. 44, No. 9

  18. Aromatic-Aromatic Interactions

  19. XEDs - Summary A much better treatment of electrostatics • Simplified force field • Hydrogen bonding • Anomeric and gauche effects • Aromatic-aromatic interactions

  20. 1rd7 + 1ra3 Crystal Structures Fields direct ligand binding mode Dihydrofolate Reductase + = + =

  21. Fields - Summary • Protein’s eye view • Represent “electron cloud” NOT structure • Distillate of important binding information Peptide/Steroid/Organic treated identically J. G. Vinter and K. I. Trollope, (1995). ‘Multi-conformational Composite Molecular Fields in the Analysis of Drug Design. Methodology and First Evaluation using 5HT and Histamine Action as examples’, J. Comp-Aid. Mol Design, 9, 297-307.

  22. Virtual Screening with Fields If field points are describing the ‘binding properties’ of molecules: Can they be used for virtual screening? Can we construct a fast & accurate way of searching a Field Database?

  23. 101101100001111011001… ~125,000,000 FieldPrint™ Search Method 0010100100101…

  24. The Database • The current database contains 2,500,000 commercially available compounds • 50 conformations stored for each compound (125,000,000 conformations) • Results consist of similarity score for whole database • Hits can be filtered (e.g. supplier, MW, Lipinski etc.)

  25. Refinement • The FieldPrint search ‘front-loads’ the database • We refine the FieldPrint results by performing true 3D field overlays • Overlays are usually performed on the top ~10-20% of the database (ranked by FieldPrint score) • Results are expressed as a field similarity

  26. The 3D Field Overlay Principle

  27. PPACK D-Phe-Pro-Arg-CH2Cl Fields – Examples PEPTIDE to NON-PEPTIDE

  28. 100 80 60 40 20 0 0 20 40 60 80 100 FieldPrint™ Performance Retrieval of known inhibitors (spikes) from 600,000 compounds Thrombin (49 Spikes) PPACK (D-Phe-Pro-Arg-CH2Cl) % spikes found % ranked database screened

  29. FieldPrint™ - Thrombin Spikes

  30. COX-2 Inhibitors (32 Spikes) 100 80 60 40 100 20 80 0 60 0 20 40 60 80 100 40 20 0 0 20 40 60 80 100 FieldPrint™ Performance (2) Retrieval of known inhibitors (spikes) from 600,000 compounds HIV NNRTI (52 Spikes)

  31. Validation James Black Foundation (JBF) funded by Johnson&Johnson • GPCR target • Exhausted Medicinal Chemistry of current series. Molecule in clinical development • Back-up series required • Two active diverse molecules available for template • 3 Month deadline • Commission mid-August 2002. • Generate and search database. Supply list of compounds by mid-October 2002. • Results returned early December 2002

  32. 100 80 60 40 20 0 0 20 40 60 80 100 FieldPrint™ Validation Collaboration with the James Black Foundation Distilled to 1000 Compounds A GPCR (43 Spikes) Visual inspection to 100 88 Purchased and tested 27 had pKb > 5 (better than 10mM) 4 had pKb > 6 (better than 1mM) No structural similarity to any known actives. MW range 350-600

  33. Intelligent Lead Discovery Change structural class [e.g. peptides to non-peptides, steroids to non-steroids] As well as proteases, kinases (X-ray information) we can; handle poorly defined targets [e.g. GPCRs, Ion Channels] because; no protein data is necessary and minimal ligand 2D data is required

  34. Where can Cresset be used? Fast and flexible lead finding for new programs allowing multiple starting points for medicinal chemistry programs Lead switching on existing programs Patent busting Moving away from ADMET problems Finding back up series

  35. Why should Cresset be used? Diverse Structural Classes with Same Function A B Peptide to non-peptide Cost in Time and Money Significantly faster than conventional routes Cresset could go from A to B in weeks Merck took 3 (?) years with 10 (?) Medicinal Chemists! Much more cost effective than HTS HTS 2,500,000 molecules @ £1 per molecule Cresset distils this to just a few hundred!

  36. Acknowledgements • Cresset • Dr J. G. Vinter • Dr T. J. Cheeseright • Dr M. D. Mackey • Dr Sally Rose (consultant) • James Black Foundation (KCL, JnJ sponsored) • Prof. C. Hunter (Sheffield University) • The Wellcome Trust

  37. Intelligent Lead Discovery

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