In-silico screening without structural comparisons: Peptides to non-peptides in one step Maybridge Workshop 23-24 Oct ‘03 Bregenz Austria

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  • In-silico screening without structural comparisons:Peptides to non-peptidesin one step

    Maybridge Workshop 23-24 Oct 03Bregenz Austria

  • Founded in November 2001Funding by The Wellcome TrustCresset Biomolecular Discovery

  • Virtual ScreeningVirtual screening is the process of trying to find biologically-active molecules using a computer

    Protein-based (X-ray, docking)Need a protein structureProblems with scoring functions

    Ligand-basedStructural similarityNot specific enough

  • The Science ProblemThe 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

  • Fields, XEDs and FieldPrintsFieldsA new method of describing molecular properties

    XEDsA new molecular modelling approach

    FieldPrintsA new virtual screening method

  • FieldsChemically 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

  • COX-2 Inhibitor

  • COX-2 Inhibitor

  • COX-2 Inhibitor

  • COX-2 Inhibitor

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

  • The Field Template for a COX-2 Inhibitor

  • ACCs get Fields WrongWithout a good description of atoms, the field points are incorrect!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.Atom-centred chargesFields from ACCs

  • XEDs make Fields workThe Field Points from XED agree well with those obtained from Quantum MechanicsVinter & Trollope 1994 unpublished. ACCsXEDs

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

  • XEDs Model Life BetterX-ray structure of BenzeneBenzene docked onto Benzene using XEDsBenzene docked onto Benzene using ACCs

  • Aromatic-Aromatic InteractionsGSK (SKF) Azepanone-Based Inhibitors of Human and Rat Cathepsin K, J. Med. Chem. 2001, Vol. 44, No. 9

  • Aromatic-Aromatic Interactions

  • XEDs - SummaryA much better treatment of electrostatics

    Simplified force field

    Hydrogen bonding

    Anomeric and gauche effects

    Aromatic-aromatic interactions

  • +==+Fields direct ligand binding modeDihydrofolate Reductase

  • Fields - SummaryProteins eye view

    Represent electron cloud NOT structure

    Distillate of important binding informationPeptide/Steroid/Organictreated 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.

  • Virtual Screening with FieldsIf 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?

  • FieldPrint Search Method0010100100101

  • FieldPrintTM LimitationsFields are conformation dependent

    Need to populate database with conformations, not moleculesNeed to search with a specific conformationThrowing away some information (eg chirality)

  • Conformation SearchPop 2D to 3DTwist bond searchMinimisation of all found conformationsFiltered using 1.5 RMSD6 Kcal mol-1 cut-offKeep 50 conformationsRings not flexed & amides forced trans

  • The DatabaseThe current database contains 2,500,000 commercially available compounds50 conformations stored for each compound (125,000,000 conformations)Results consist of similarity score for whole databaseHits can be filtered (e.g. supplier, MW, Lipinski etc.)

  • RefinementThe 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

  • The 3D Field Overlay Principle

  • Fields ExamplesPEPTIDE to NON-PEPTIDE

  • FieldPrint PerformanceThrombin (49 Spikes) PPACK (D-Phe-Pro-Arg-CH2Cl)

    Retrieval of known inhibitors (spikes) from 600,000 compounds% spikesfound% ranked database screened

  • FieldPrint - Thrombin Spikes

  • FieldPrint Performance (2)COX-2 Inhibitors (32 Spikes)HIV NNRTI (52 Spikes)Retrieval of known inhibitors (spikes) from 600,000 compounds

  • ValidationJames Black Foundation (JBF) funded by Johnson&Johnson

    GPCR targetExhausted Medicinal Chemistry of current series. Molecule in clinical developmentBack-up series requiredTwo active diverse molecules available for template

    3 Month deadlineCommission mid-August 2002.Generate and search database. Supply list of compounds by mid-October 2002.Results returned early December 2002

  • FieldPrint ValidationA GPCR (43 Spikes)Distilled to 1000 CompoundsVisual inspection to 10088 Purchased and tested27 had pKb > 5 (better than 10mM)4 had pKb > 6 (better than 1mM)No structural similarity to any known actives.MW range 350-600Collaboration with the James Black Foundation

  • 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 necessaryandminimal ligand 2D data is required

  • 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

  • Why should Cresset be used?BADiverse Structural Classes with Same FunctionPeptide to non-peptideMuch more cost effective than HTSHTS 2,500,000 molecules @ 1 per molecule Cresset distils this to just a few hundred!Significantly faster than conventional routesCresset could go from A to B in weeksMerck took 3 (?) years with 10 (?) Medicinal Chemists!Cost in Time and Money

  • AcknowledgementsCressetDr J. G. VinterDr T. J. CheeserightDr M. D. MackeyDr Sally Rose (consultant)

    James Black Foundation (KCL, JnJ sponsored)

    Prof. C. Hunter (Sheffield University)

    The Wellcome Trust

  • Intelligent Lead Discovery