CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Virtual Screening at the post-genomic era Dr. Didier ROGNAN Bioinformatic Group UMR CNRS 7081 Illkirch, France

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Virtual screening: Definition Searching electronic databases (2D, 3D) for molecules fitting: a pharmacophore an active site Walters et al. Drug Discovery Today 1998, 3, Schneider et al., Drug Discovery Today 2002, 7,

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Sci Scientific reasons ntific reasons 1.Increasing number of interesting macromolecular targets (500  10,000) 2.Increasing number of protein 3-D structures (X-ray, NMR) 3.Better knowledge of protein-ligand interactions 4.Dévelopement of chem- and bio-informatic methods 5.Increasing computing facilities Economic reasons 1.High cost of high-througput screening (HTS): 0.2 € /molecule 2.Increase the ratio ions Applications 1.Identifying the very first ligands of orphan targets 2.Identifying/optimizing new chemical scaffolds Importance of virtual screening # of active molecules (hits) # of tested molecules

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Protein-based virtual screening 2. Evaluation « Scoring » Mol #  G bind Database (3-D) 1. Orientation « docking » Target-Ligand Complex Target (3D !!) Hit list

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking Goal Quickly find (1-2 min./molécule)  the orientation of the ligand in the active site  the protein-bound conformation Méthods Orientation  Surface complementarity  Complementarity of intermolecular interactions Conformational freedom  Incremental construction  Conformational sampling (MC, GA, SA) Abagyan et al. Curr. Opin. Struct. Biol. 2001, 5,

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking : Orientation Surface-based orientation (e.g. DOCK) 2. Molecular surface (active site) 3. Filling the surface by overlapping spheres 4. Matching sphere centers with atoms 1. 3D structure

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking : Orientation interactions-based orientation (e.g. FlexX) -Statistical rules for locating ligand atoms -Overall placement of a base fragment by triangulation

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking: Ligand flexibility - by preselecting several conformers/molecules - by incremental construction Termination adding the 2ndadding the 1st peripheral fragment peripheral fragment Reading preferred torsion values Selecting the « best » Ligand Fragment decomposition base fragment

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE - by a genetic algorithm (e.g. Gold) Initial population Selection of parents Genetic operators Selection of children New population Convergence test size Parent Score A2.5 B5.0 C1.5 D1.0 B A C D Survival rate gene: x,y,z coords. tors. angles orientation … crossing over mutation New generation crossing over rate mutation rate # of evolutions Chromosome = Ligand (orientation, conformation) Docking: Ligand flexibility

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking Accuracy Analysing 100 high-resolution PDB complexes Paul,N. and Rognan, D. Proteins, in press Finding a reliable pose out of a set of solutions is feasible !

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Docking Accuracy Analysing 100 high-resolution PDB complexes Paul,N. and Rognan, D. Proteins, in press Ranking the most reliable solution at the top of the list is still an issue !

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Source of Docking Errors Nature of the active site (flat vs. cavity) Missed influence of water Ligand flexibility Inaccuracy of the scoring function Unusual binding mode/interactions Inadequate set of protein coordinates Wrong atom typing Impossible Difficult Easy

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Scoring Thermodynamic Methods: FEP, TI (2) Force-fields (10-100) QSAR, 3D-QSAR (100-1,000) Empirical scoring functions (>100,000) # of molecules Error, kJ/mol Accuracy ,

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Scoring First-principle methods: sum of physically meaningfull terms Regression-based free energy approximations: sum of regression-weighted terms Potential of mean forces distance-dependent atom pair-weighted Helmotz free energies Gohlke et al. Curr. Opin. Struct. Biol. 2001, 11,

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Empirical Scoring function Constant H-bond term g 1 (  r) =       )/0.4-r(1 1 Å 0.65 r if Å 0.65 r Å 0.25 if 0.25År if    g 2 (  ) =       0 30)/50-α ( 1 1 º80 α if 80º α 30º if º03α    f(r) =       0 R1)/3.-r(1 1 R2 r if R2r R1 if R1r if    lipophilic term buried-polar repulsive term rotational term desolvation term Fresno Rognan et al. (1999) J. Med. Chem., 42,

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Scoring Accuracy Current accuracy: 5-10 kJ/mol (1-2 pK unit) Weak point of all docking programs Entropic contributions are difficult to handle ! ! Way-around: use of consensus scoring functions

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Isis/Base 2-D Fingerprint Full database Filtering Chemical reactivty pharmacokinétics Drug-likeness Filtered database 2D  3D Hydrogens Ionisation 3-D Database Library set-up

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Applications High-resolution X-ray structures (enzymes) TargetLigands BaseHit Reference Rate CD4-gp120inhibitors 150, % Li et al., PNAS (1997) gp41inhibitors 20, % Debnath et al., J. Med. Chem. (1999) FTinhibitors 219, % Perola et al., J. Med. Chem (2000) kinesininhibitors 20, % Hopkins et al., Biochemistry (2000) HIV1 Tar-Tatinhibitors 153, % Filikov et al., JCAMD (2000) gp41inhibitors 20, % Debnath et al., J. Med. Chem Bcl-2inhibitors 207, % Enyedi et al., J. Med. Chem (2001) HCA-IIinhibitors 90, % Grüneberg et al., Angew. (2001) RARagonists 250, % Shapira et al., BMC Struct. Biol. (2001) TPIinhibiteurs 108, % Joubert et al., Proteins (2001) ER  antagonists 1,500, % Shapira et al. IBM Sys. J. (2001) FT: farnesyltransférase, HCA: human carbonic anhydrase, RAR: retonic acid receptor, ER: Estrogen receptor, TPI: triosephosphate isomerase, PEP: phosphoenolpyruvate

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Conclusions What is possible ? Discriminate true hits from random ligands Enriching a reduced library by a factor 20 Retrieving about 50% of all true hits Prioritizing ligands for synthesis and experimental screening Using virtual screening for lead finding What remains to improve ? Predicting the exact orientation Predicting the absolute binding free energy Discriminating true hits from “similar inactives“ Catching all hits Using virtual screening for lead optimization Throughput (100K mols/day  1M/day ?) Pre and post-processing of vHTS

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE Virtual screening at the genomic scale Primary Sequence 3-D Model virtual Hits True Hits Sélectivity Affinity ADME/Tox GPCR-Gen vHTS Validation Available analogues Focussed Libraries vs. Enzymes (PDB library) vs. RCPGs (RCPG library) e-Libraries “Bioinfo” (350,000) “RCPG” ( 30,000) “Endo” ( 2,000) Optimisation RCPGs of the human genome

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE molecules virtual Library (10 8 conformations) 10 5 (10 6 conformations) ADME/Tox Similarité 2-D Conformations 3-D Similarity 3-D Docking Scoring expt. Validation True hits Virtual screening: Tomorrow