Virtual Screening. Anna Linusson. Department of Chemistry Umeå University Sweden

Transcription

1 Virtual Screening Anna Linusson Department of Chemistry Umeå University Sweden

2 Virtual screening N N NH 2 N Cl N N H H H N N N S N HN N H S N N S S N N

3 Free energy of binding G RT ln K A H T S 0 G 0 0 R' L' aq ( 0 Raq 0 Laq ) Experimentally determined inhibition constants =10-2 to M, i.e. 10 pm to10 mm; pki= 2 to 8 (at T=298 K) A one order of magnitude change corresponds to 5.7 kj/mol or 1.36 kcal/mol (1kcal = 4.18 kj)

4 Virtual screening Development Validation Preparation Execution Evaluation H 6 (Embelin) H

5 utline Methodology Ligand-based Structure-based What can we expect? Virtual screening vs. HTS Case study Take home message

6 Virtual screening + Enthalpy Ionic interactions Hydrogen bonds Dipole-dipole moments Aromatic interactions van der Waal interactions Binding entropy loss of conformational flexibility Solvation/desolvation Flexibility (degrees of freedom)

7 Virtual screening H Similarity principle 6 (Embelin) H Receptor-based approach A plethora of methods/approaches to choose from Case dependent Don t trust retrospective studies

8 What do I know? Drug Discovery and Development Years First patent application Clinical trial application Product licence application Drug Discovery Target and lead identification Lead optimisation Drug Development Development Concept testing for launch Launch Product life cycle support Clinical Development Phase I people Phase II people Phase III 500-5,000 people Phase IV studies continue Toxicology and pharmacokinetic studies (absorption, distribution, metabolism, excretion) No. of compounds Up to 10, Pharmaceutical and analytical development Process chemistry and manufacturing Registration and regulatory affairs Sales and marketing (preparation, promotion, advertising and selling) Cost $3 million $225 million $450 million

9 Virtual screening - methodology Ligand-based Structure-based Demands a (set of) known ligands/active molecule/s These molecules may be structurally similar or different Validated inactive molecules may also be of use Demands a 3D-structure of the target receptor (protein) These may be crystal structures, NMR structures or homology models Several structures of the same target bound to different ligands is an advantage H 6 (Embelin) H Read more: Taboureau,. et al. Chemistry & Biology, 2012, 19, Klebe, DDT, 2006, 11: Shoichet, Nature, 2004, 432:

10 Virtual screening generic procedures Development Validation Preparation Execution Evaluation Source of chemicals (e.g. commercial) Selection of known ligand/s and/or target/s Preparation of chemical structures Chemicals and ligands: 2D, 2D->3D Macromolecules: X-ray, NMR, homology models Tautomers Protonation states Stereochemistry Conformations Experimental evaluation Confirmation of hits Biologically Chemical structure Follow up

11 Ligand-based virtual screening Prerequisite Screening methods ne or a set of known active molecules Descriptor-based Shape-based H 6 (Embelin) H Pharmacophore-based... Unsupervised (similarity search) Supervised (regression models)

12 Descriptor-based screening Based on the assumption that a similarity in compound physicochemical descriptors (i.e. physicochemical properties) will lead to a similar biological action Preparation Execution Calculation of descriptors Fingerprints Physical properties 3D-GRID descriptors Selection of similarity measure (Tanimoto) Creation of a quantitative structureactivity relationship Linear regression model Non-linear regression model Rule-based model Assessment of model quality Applicability domain Estimation of similarity Prediction of the biological activity of the compound library structures using the QSAR model Evaluation Use the ranking list from execution Cut offs Selection criteria (diversity) Additional filtering Clustering

13 Shape-based screening The goal is to find compounds similar in 3D-shape to the query molecule(s) Preparation Generation of 3D structures (Alignment of structures) Calculating volumes Creation of shape-query (Combine with electrostatics) Selection of similarity measure Evaluation Execution Screening of the compound database (3D-structures) based on overlapping volumes Multiple shape queries A good overlap will be ranked high Prioritization of hit compounds Cut offs Chemical features Combination of shape queries Read more: Kirchmair et al, J. Chem. Inf. Model. 2009, 49,

14 Pharmacophore-based screening A pharmacophore is an abstract (3D) description of chemical features in a molecule, necessary for biological recognition Preparation Align analogues Generation of low-energy 3D structures of the active molecules Alignment of structures Identification of 2-4 common features Creation of 3D-pharmacophore Assign pharmacophore features to a search chemicals Select measure Create pharmacophore Pseudo-receptor

15 Pharmacophore-based screening Execution Evaluation Fit molecules (i.e. the pharmacophore features) in the database to the generated pharmacophore A good match is ranked high Prioritization of hit compounds What about diversity among the hits? Many different pharmacophores Read more: Kim et al., Expert pin. Drug Discov., 2010, 5(3),

16 Structure-based virtual screening Prerequisite Structure of known target Molecular Docking Rigid receptor - flexible ligands Soft receptor - flexible ligands Multiple receptors Induced-fit docking

17 Docking-based virtual screening Preparation Preparation of protein structure(s) Addition of hydrogens ptimization of H-bond networks Treatment of water Protonation state and tautomers Definition of binding site ptional settings depending on docking software Inclusion of water Movement of side-chains Energy minimization of resulting complexes Re-docking (and crossdocking) of the crystal structure ligand may be guide the choice of docking software Structure-based virtual screening

18 Docking-based virtual screening Execution Generation of molecule conformations Generation of docking-poses for each molecule Judgment of clashes and interactions between ligand and protein Depending on docking software addition and removal of water molecules in the binding site docking filtering (removal unwanted molecules based on e.g. absence of specific interactions with the protein) Structure-based virtual screening

19 Docking-based virtual screening Evaluation Scoring of docking poses using one scoring function Scoring using several scoring functions (consensus scoring or fused) Selection of a subset of molecules using a simple scoring function and re-scoring with a computational intensive function Choice of scoring function may be guided by the scoring of known active and inactive molecules Biological evaluation Example of a simple scoring function that estimates the free energy of binding ( G bind ): G bind = G solv + G int + G conf + G hbond + G elect

20 Account for (some) flexibility Soft receptors Multiple receptors Mimics movement by varying the Lennard-Jones potential of the receptor atoms (i.e. the distance at which atoms clash) Allowing for more clashes Variable binding site size Crystal structures of the same protein with different conformations induced by different ligands Conformation ensembles of a protein generated by MD-simulations Manual alterations of proteins structures (e.g. conformations of flexible loops) Ligated complexes usually performs better than apo-structures The complex with the largest ligand is usually a good choice

21 Account for (some) flexibility Induced-fit docking Movements or position of hydrogen atoms attached to hetero atoms (, N or S) Flips of histidine, aspargine and glutamine side chains (i.e. tautomers) Allowing for movements of userspecified receptor side chain and/or backbone atoms. The entire complex is optimized (e.g. the protein is forced to adopt to the ligand) Demands a scoring function that can account for protein internal energy changes Further reading: Kokh et al.,, Comput. Mol. Sci., 2011, 1,

22 Water To include or not to include? Crystal structure can guide for important waters Highly conserved waters can be included The benefit of included waters is questionable If any then case dependentc NB: It is not questionable that water is important Tools to predict important waters Docking tools to account for waters on/off

23 Using multiple methods Compound Library Fast: Rigid/shape based filtering, Fast: Pharmacophore/substructure searches, Fast: High-throughput docking ex. RCS, FRED Slow: Flexible docking Fast: Rescoring, Slow: Visual Inspection Biological evaluation ex. GLD, Glide, FlexX,...

24 Which method/approach should I use? How to best use existing information about ligands and targets? What is your goal? Combining methods is often beneficial Case dependent Don t trust retrospective studies Nicholls, JCAMD, 2008, 22: Warren et al., JCM, 2006, 49:

25 What can we expect? Scoring functions cannot predict binding affinities Marsden et al. BC, 2004, 2: Enyedy and Egan, JCAMD, 2008, 22:

26 What can we expect? G RT ln K A H T Sources of errors in virtual screening Limitations in fundamental knowledge Assumptions made (flexibility, water, simplifications) Bad science (pre-treatments, statistics, parameters, validations) S 0 Knowledge-based driven approach 31 pitfalls: Agrafiotis and co-workers, JCIM, 2012, 52: Pitfalls and traps: Hawkin et al., JCAMD, 2008, 22: Shoichet, Nature, 2004, 432: Klebe, DDT, 2006, 11:

27 What can we expect? Enrichment. Increase the likelihood of finding promising hits. Hit rates: To what good? Recover false negatives from HTS and disclose false positives When only a smaller library can be screened Virtual screening and HTS are complementary methods Ferreria et al., JCM, 2010, 53: Polgár et al. Comb. Chem. High Throughput Screening, 2011, 14,

28 utline Methodology Ligand-based Structure-based What can we expect? Virtual screening vs. HTS Case study Take home message

29 ADP-ribosyltransferases Post-translational modification NAD + as cosubstrate Mono- or Polyribosylation Writers in epigenetics The nomenclature... ARTD: diphteria toxin-like ADP-ribosyltransferase also known as PARP: poly ADP-ribose polymerase

30 Docking-based virtual screening...to find new small molecules that bind to ADP-ribosyltransferases...to develop selective ligands for different ADP-ribosyltransferases...to use the molecules as chemical tools in the elucidation of biological functions Andersson et al., JCM, 2012, 55:

31 Virtual screening What do we know? And what do we want? PARP14 PARP15

32 Virtual screening methodology Preparation Re-docking and cross-docking of crystal structures Evaluation of docking tools and settings Compound Database Chemicals In house compound collection Protonation states, tautomers and stereoisomers Determination of features and property criteria Filtering of compound collection Target/s Proteins Investigation of D-loop conformations of all crystalized PARPs Selection of protein structures Selection of conformers Protein preparation (hydrogens, tautomers, protonation states) Definition of binding site Known binders Collection of list known binders based on crystal structures and/or experimental assays Protonation states, tautomers and stereoisomers

33 Virtual screening methodology Execution Evaluation

34 Docking score resemblance Compounds were selected based on scoring similarities to known binders Previously known binders Compounds selected for testing

35 Selection of compounds for wet lab 64 were chosen by our scoring method and 64 (47) were selected solely based on Glidescore 111 molecules were selected for biological testing No enzymatic assay available! Thermal shift assay Senisterra et al. Mol. BioSyst., 2009, 5,

36 Hits for PARP14 and/or PARP15 ΔT m of degrees Identified by scoring resemblance Identified by Glidescore

37 Multiple protein structures Hits were identified from screening against all protein structures 63% of the hits were identified from PARPs with unaltered D-loop Three hits were exclusively identified from structures with truncated loop (including the two best hits)

38 Selectivity profile All 111 compounds experimentally tested for binding to PARP1 PARP15 PARP14 PARP1

39 Hit validation A16 (E) NH 2 N H H A16 (Z) H 2 N N H H

40 Hit validation Isothermal titration calorimetry A16 (Z) A16 (E) H 2 N N H H NH 2 N H H K d = 7.6 μm K d = 11.2 μm

41 Crystallization of A16 and PARP14 Z-isomer A16 H 2 N N H H Resolution 1.9 Å E-isomer A16 NH 2 N H H Resolution 2.8 Å

42 Binding mode of A16 to PARP14 Crystal structure of A16 (E and Z) Docking pose of A16(E) 2 1

43 Case summary 16 binders (8 different structural classes) => hit rate of 14 % ur scoring method found structurally diverse compounds Both isomers of A16 binds to PARP14 (~10μM) Crystal structures of the A16 isomers confirmed docking poses The virtual screen provided good starting points to develop selective inhibitors

44 Take home message Virtual screening increases the likelihood of finding hits in a set Virtual screening is a knowledge-based method use available information Don t trust retrospective studies Virtual screening is a complementary method to HTS

45 Acknowledgements Department of Chemisty, UmU Linusson s Lab David Andersson Lotta Berg Brijesh Kumar Mishra Cecilia Lindgren Urszula Uciechowska Cecilia Engdahl Elofsson s Lab Sara Spjut Anders Lindgren Rémi Caraballo Wittung-Stafshede s Lab Pernilla Wittung-Stafshede Moritz Niemiec Laboratories for Chemical Biology Umeå (LCBU) Karolinska institutet Schüler s Lab Tobias Karlberg Torun Ekblad Ann-Gerd Thorsell Johan Weigelt Financial Support The Swedish Research Council Umeå University Swedish Foundation for Strategic Research

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