Docking: Modeling the structure of the ligand protein complex

Transcription

1 1/41 Docking: Modeling the structure of the ligand protein complex G. Marcou 1, E. Kellenberger 2 (author) 1 Faculté de Chimie, UMR Faculté de Pharmacie, UMR 7200, Illkirch

2 introduction 3D Protein docking scoring conclusion 2/41 Docking: the lock & key principle Geometrical Complementarity substrate enzyme non-covalent inter-molecular interactions receptor ligand

3 introduction 3D Protein docking scoring conclusion 3/41 Thermodynamics of the association L R LR Association constant (equilibrium) K D (M) ΔG (kcal/mol) at 298K K A LR L R G RT 0 K A exp no unit G 0 ; RT in J/mole

4 introduction 3D Protein docking scoring conclusion 4/41 Thermodynamics of association Free energy ΔG = ΔH TΔS Enthalpy H Entropy S ~ sum of interactions ~ order What is gained/lost upon binding? L: ligand, R: recepteur, W: water

5 introduction 3D Protein docking scoring conclusion 5/41 Molecular flexibility: Lock & key or induced fit Thymidylate Synthetase: side-chains rotamers Calmoduline: domain motions Nature 450, ( 2007)

6 6/41 Chapter1: Proteins are folded biopolymers

7 introduction 3D Protein docking scoring conclusion 7/41 primary, secondary, tertiary & quaternay structures Polymerisation reaction primary structure amino acids secondary structure sheet helix tertiary structure quaternary structure 20 monomers differ by their R group

8 introduction 3D Protein docking scoring conclusion 8/41 Limited number of structural organisations Human collagen for a huge variety of functions Influenza neuraminidase (H1N1 surface protein) β2 adrenergic receptor stimulated by adrenaline flight or fight" Fibers auto assembly supramolecules collagen (cartilage, bone, teeth), keratin (hair, nail), fibrin (blood clots) globular proteins hydrosoluble (cytoplasm, blood) enzyme catalysis, defense (toxin, immunoglobulin), transporter (O2, electrons), motion (actin, myosin), regulation (osmotic protein, gene regulators, hormone) ion storage (ferritin, calmodulin) Membrane proteins ~30% of human total genes Signal transducer, transporter (Na+, proton, glucose), channels

9 introduction 3D Protein docking scoring conclusion 9/41 Experimental determination of protein 3D structure X-ray structure of the crystal protein Information about position Quality check: the resolution (Å)

10 introduction 3D Protein docking scoring conclusion 10/41 Experimental determination of protein 3D structure NMR structure of the protein in solution Quality check: quantity/quality/distribution of information Information about DISTANCE (NOE) ANGLE (coupling constant) RELATIVE POSITION (residual dipolar coupling)

11 introduction 3D Protein docking scoring conclusion 11/41 Since 2003: a single archive of public 3D structures of biomolecules since 1998, maintained by Research Collaboratory for Structural Bioinformatics Weekly updated, data synchronisation on mirror sites European Macromolecular Structure Database Protein DataBank Japan Data deposit, treatement and distribution NMR data (since 2006)

12 introduction 3D Protein docking scoring conclusion 12/41 PDB statistics: August 2013 # Proteins Nucleic acids Complexes Other biopolymers Total X-RAY NMR other methods (microscopy, neutron diffraction) total Structure Factors available for entries. NMR constraints for 7237 entries. Sequence redundancy: sequence clusters (>30% sequence identity) Structure redundancy : about 2626 different folds (ternary structures, CATH)

13 introduction 3D Protein docking scoring conclusion 13/41 PDB entry: format and content Format: Flat file organized into tagged fields Header: information Connexion table: atom section only (element: C, O, N, S, H) no explicit bonds for standard amino acids Incomplete description of protein structure METHOD X-ray NMR hydrogen atoms well defined CONH 2 water molecules Metal ions, cofactors

14 introduction 3D Protein docking scoring conclusion 14/41 Protein preparation is very important a Prediction of protein-ligand complex: comparison with X-ray co-crystal structure (PDB entry: 1mtw). a Docking of the native ligand in the binding site leads to a wrong solution b protein : orange surface docking solution : atom-colored sticks reference ligand : green sticks c b, c Inclusion of the crystallograpically related protein residues results in an acceptable solution additional protein chains : green surface or line Nissink et al (2002) Proteins: Structure, Function, and Bioinformatics 49:

15 introduction 3D Protein docking scoring conclusion 15/41 PDB structure can contain errors; exemple of Ligand protein clashes examples of crystal structures with very short contacts between ligand and protein atoms a a PDB entry 1srf, dc-c=2.2å b PDB entry 8gch, dc-o=2.0 Å. ligand and protein representation - residues: capped sticks if steric clash, lines otherwise - atoms: balls if steric clash, not represented otherwise b Nissink et al (2002) Proteins: Structure, Function, and Bioinformatics 49:

16 16/41 Chapter2: Docking chemicals into proteins

17 introduction 3D Protein docking scoring conclusion 17/41 Docking into a protein pocket / cavity Scan the entire protein surface? not applicable to most docking projects (computational cost) not relevant: docking only efficient for druggable sites What is a protein binding site suitable for docking? important features are depth/enclosure volume lipophilic surface shallow indentations pocket cavity binding site

18 introduction 3D Protein docking scoring conclusion 18/41 The identification of pockets and cavities in apo-protein in holo-protein using geometrical methods based on the ligand ligand pocket binding site cavity 8080 «druggable» sites in PDB protein X-ray structures in

19 introduction 3D Protein docking scoring conclusion 19/41 The representation of pockets and cavities The size and the complexity of the protein prevent the direct use of its atomic coordinates. In docking, the structural information is reduced. The available space for the ligand is caracterized by geometric descriptors combined with physicochemical descriptors. grid points surface points or vectors pharmacophoric points or vectors

20 introduction 3D Protein docking scoring conclusion 20/41 In docking, the protein representation is the negative image of the binding site, colored with the properties of the residues flanking the pocket (or cavity). set of NH, C=O and CH 4 probes computed by surflex v2.4 the ligand binding site the ligand binding pocket Example of the human estrogen receptor α in the inactivated state (PDB code: 3ert)

21 introduction 3D Protein docking scoring conclusion 21/41 other examples of representation DOCK Kuntz, I.D et al. (1982) J. Mol. Biol Flexx M. Rarey et al. (1996) J. Comp.-Aid. Mol. Design Protein cavity filled with overlapping spheres (variable radius). Feature points: sphere center colored according to physico-chemical properties Interaction centers and interaction surfaces identified on both receptor (a) and ligand (b) H bond Salt bridges Aromatics methyl-aromatics amide-aromatics

22 introduction 3D Protein docking scoring conclusion 22/41 Docking a flexible ligand into a rigid protein H Stavudine (dtt) N O N H CONH 2 O H N OH N H CONHtBu Saquinavir (Roche, RO ) Saquinavir (Roche, RO ) Efavirenz (DMP 266) < exhaustive seconds to hours number of rotatable bonds conformational search cpu time of conf. search HIV protease (PDB code: 1aaq) 3 per amino acids partial huge!

23 introduction 3D Protein docking scoring conclusion 23/41 The search for the ligand best pose Pose = Orientation & conformation O OH Orientation O H N O Position of the ligand in the NH 2 protein Rigid body motions Translations + rotations of the whole molecule protein Conformation 3D structure of the molecule Molecular flexibility O O H HNN O OH O NH 2 protein

24 introduction 3D Protein docking scoring conclusion 24/41 Handling of ligand flexibility: three determinist approaches Search: translations + rotations of rigid ligand conformers or rigid ligand fragments Systematic sampling of torsion angles: 1. Library of conformers rigid docking of all conformers, individually 2. Molecule fragmentation The molecule is fragmented Each fragment is individually docked Applied in de novo design of new ligands 1 3 The 200 low energy 3D conformers of halopteridol generated by omega v (Openeye, Santa Fe) incremental construction (i.e., anchor-and-growth) OH NH+ protein Cl 3. Incremental construction First placement of a base fragment Molecule is built incrementally Outcome depends on the selection and placement of the base fragment protein Cl protein OH OH NH+ NH + O F

25 introduction 3D Protein docking scoring conclusion 25/41 Algorithmic: Geometric matching of triangles ligand protein Other common algorithms: geometric hashing, clique detection

26 introduction 3D Protein docking scoring conclusion 26/41 Handling of ligand flexibility: the stochastic approaches Search: iterative and random modifications of ligand position and conformation the best solution is selected at each stage using a scoring function Docking : an optimization procedure, where the energy function describes molecular interactions Example of ligand numerical representation for conformational sampling Cl OH NH + O F modification of rotation/translation torsion angle values Cl OH NH + O F t x t y t z r x r y r z σ x Χ 1 Χ 2 Χ n t x t y t z r x r y r z σ x Χ 1 Χ 2 Χ n The position of the ligand is encoded by three translations ( tx, ty, and tz) and three rotations (rx, ry and rz, σx defining the rotation axis). The conformation of the ligand is encoded by the torsion angles of its rotatable bonds

27 introduction 3D Protein docking scoring conclusion 27/41 the Monte-Carlo/simulated annealing algorithm step: i < N T(i)=0.995 i T o X steps Initial state Acceptance Test if E f < E i new pose is accepted if E f > E i calculate probability P Perform move Protein/ligand interaction mapping Ef -Ei kt P = exp k: boltzman constant T: temperature Compare P with random number h yes yes, replace state Evaluate E(x) Better energy no acceptance test no, restore previous state if h < P if h > P new pose accepted restart based on last accepted pose Temperature T decreased in defined number of cooling cycles: T high T low broad region of space is sampled space is explored locally Multiple independent runs are required for convergence (10-50)

28 max number of generations introduction 3D Protein docking scoring conclusion 28/41 the genetic algorithm (GA) initial population parents size selection Parent Score A 2.5 B 5.0 C 1.5 D 1.0 A D B C Genetic operators crossover Crossover rate offsprig mutation Mutation rate Selection fitness score (phenotype) new population Survival rate Gold program,

29 29/41 Chapter3: Scoring ligand/receptor interaction

30 introduction 3D Protein docking scoring conclusion 30/41 empirical scoring functions vs force field Empirical SF Force Field SF selected ligand/receptor S = E ligand + E complexe E ligand = internal energy E complexe = X-ray structures non-bonded interactions distance between ligand - receptor pairs of atoms empirical SF knowledge-based SF experimental ΔG

31 introduction 3D Protein docking scoring conclusion 31/41 Prediction of free energy by Empirical SF Score G = k i * F i F i : function which describes protein ligand interaction - H-bond, salt bridge - Van der Waals interactions computed using geometry predicted by docking k i : constant adjusted using a training set composed of ligand-protein complexes for which - 3D structure is known - experimental value of free energy G is available

32 introduction 3D Protein docking scoring conclusion 32/41 Böhm J. Comp. Aid Mol. Des (1994) ΔG= ΔGo + ΔG hb h-bonds f(δr, Δα) + Δ G ionic ionic int. f(δr, Δα) + Δ G lipo A lipo + Δ G rot N rot Δ Go: Δ G hb : f(δr, Δα): Δ G ionic : Δ G lipo : A lipo : Δ G rot : N rot : constant term entropy lost contribution of one perfect H-bond penalty for bad geometry ionic contribution lipophilic contribution lipophilic contact surface lost of free energy due to internal rotation number of rotatable bonds frozen upon receptor binding

33 introduction 3D Protein docking scoring conclusion 33/41 Empirical statistical functions or «knowledged-based» functions g 2 xy : probability to find 2 atoms distant from r Å (wo unit) obtained by statistical count using a training set of X-ray structures of PDB complexes (distribution of distances between type x atoms and type y atoms) w 2 score mean-force Potential, logarithmic function of g 2 xy sum of w 2 (rij) values for each our i (ligand) j (protein) atoms pair based on the 3D structure of the complexe: Score = i j w 2 (r ij ) w 2 (r) = -RT ln g xy2 (r) g 2 (r) r ij Distance r R : rare gaz constant, T : temperature w 2 (r) Grzybowski et al Acc. Chem. Res

34 introduction 3D Protein docking scoring conclusion 34/41 Strength and weakness of SF Empirical SF Force Field SF fast calculation adaptable to custom target training set (incompleteness, inaccuracy of data) binding mode (if few polar interactions, underestimation of score) missing penalty (steric clashes, polar/apolar match, internal ligand energy, lost of entropy, geometry of directional interaction, local environment of interaction) independant of training set strongly depends on ligand size force field accuracy, difficulty to set parameters no account of entropy includes empirical terms!

35 introduction 3D Protein docking scoring conclusion 35/41 post-processing output: pose selection by Interaction Fingerprint analysis Intermolecular interactions between ligand and protein encoded in a binary string 8 bits / protein site residue description of hydrophobic contacts, aromatic interactions, H-bonds, ionic bonds, bond to metal Similarity of binding to a given protein evaluated by Tanimoto coefficient Successful applications in pose prediction, compound ranking and scaffold hopping Requires a reference IFP! Interaction FingerPrint AA 1 AA 2 AA n Marcou, G et al (2007) J Chem Inf Model

36 36/41 conclusion What are the available tools? What can we achieve? What remains to be improved?

37 introduction 3D Protein docking scoring conclusion 37/41 In 2013, 50 reported docking programs (Swiss Institute of Bioinformatics) the representation of the protein cavity Regular (grid) or irregular (set of probes) ensemble of geometrical objects Object: Point, vector or more complex geometrical object Properties assigned to object (pharmacophoric feature, atom type, potential..) handling of ligand flexibility Determinist approach: systematic conformational exploration, and rigid-body motion Stochastic approach: random modification of ligand structure to optimize of a function the algorithmic Determinist approach: geometric matching (triplet matching, geometric hashing, clique detection) Stochastic approach: Monte-Carlo/Simulated annealing, evolutionary algorithms, swarm intelligence methods scoring function: various empirical scoring functions, force field

38 introduction 3D Protein docking scoring conclusion 38/41 The state of the art docking accuracy (Warren et al Proteins, Kellenberger et al Proteins) many programs able to reproduce x-ray conformation but performance is highly dependend on the studied protein cpu time few seconds to several minutes to dock one compound app. screening rate: 1,500 compounds/day/processor hit rate (true positives in hit list) in screening by high throughput docking ~ 50 % retrospective studies from 10% to 30% in prospective studies using X-ray structures lower rates for docking using homology models

39 introduction 3D Protein docking scoring conclusion 39/41 The limitations Pre-processing of the protein which ionisation state for his, arg, lys, asp, glu? addition of missing hydrogens, optimization of hydrogen bonds network selection of bound water molecules? Pre-processing of the ligands which protonation state? which tautomeric state? no serious problem Flexibility of the ligand Flexibility of the protein /binding site Fuzzy scoring functions a difficult problem the biggest problem

40 introduction 3D Protein docking scoring conclusion 40/41 Details in algorithms and methods Recommanded readings Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: An overview of search algorithms and a guide of scoring functions. Proteins: Structure, Function and Bioinformatics (2002), 47, Kitchen D, Decornez H, Fun JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Drug Discovery (2004), 3, Online course by Lydia E. Kavraki Examples of docking tools, classified by algorithms Taylor RD, Jewsbury PJ, Essex JW. A review of protein-small molecule docking methods. Journal of computer-aided Molecular Design (2004), 3, Evaluation of docking/scoring, perspectives Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR. Towards the development of universal, fast and accurate docking/scoring methods: a long way to go. Britsh Journal of Pharmacology (2008), 153, S7-26.

41 introduction 3D Protein docking scoring conclusion 41/41 Popular docking softwares Freely available AUTODOCK (the Scripps research institute) autodock.scripps.edu Commercial Surflex (Tripos/Certara) Gold (Cambridge Crystallographic Data Center) FRED (Openeye) LeadIt/FlexX (BioSolveIT) Glide (Schrödinger) Dock (UCSF) dock.compbio.ucsf.edu LigandFit (Accelrys) MOE-dock (Chemical Computing Group)