What is Protein-Ligand Docking?

Transcription

1 MOLECULAR DOCKING

2 Definition: What is Protein-Ligand Docking? Computationally predict the structures of protein-ligand complexes from their conformations and orientations. The orientation that maximizes the interaction reveals the most accurate structure of the complex. Importance of complexes - structure -> function 2

3 What is Docking? Given two molecules find their correct association: T + = 3

4 3-D Representation of a Protein Binding Site Distances between binding groups in Angstroms and the type of interaction 4 is searchable

5 General Protein Ligand Binding Ligand - Molecule that binds with a protein - DNA, drug lead compounds, etc. Protein active site(s) - Allosteric binding - Competitive binding Function of binding interaction - Natural and artificial 5

6 Issues Involved in Docking Protein Structure and Active Site - Assumed knowledge (PDBs, comparative modeling etc.) - PROCAT database: 3d enzyme active site templates Ligand Structure - Pharmacophore (base fragment) in potential drug compound - well known groups Rigid vs. Flexible - In solution or in vacum - Structure fixed, partly fixed, modeling of flexibility 6

7 Algorithmic Approaches to Docking Qualitative Geometric Shape complementarity and fitting Quantitative Energy calculations Determine global minimum energy Free energy measure Hybrid Geometric and energy complementarity 2 phase process: soft and hard docking 7

8

9 It involves: Docking uses a search and score method Finding useful ways of representing the molecules and molecular properties. Exploration of the configuration spaces available for interaction between ligand and receptor. Evaluate and rank configurations using a scoring system, in this case the binding energy However, since it is difficult to evaluate the binding energy because the binding sites may not be easily accessible, the binding energy is modeled as follows: G bind= Gvdw + Ghbond + Gelect + G conform+ G tor + G sol

10 Docking Strategy PDB files Surface Representation Patch Detection Matching Patches Scoring & Filtering Candidate complexes 10

11

12 Adding angles in Cartesian space x = r cos y = r sin converting internal motion to Cartesian motion y (x,y ) (x,y) x' = r cos ( = r (cos cos sin sin = ( r cos cos r sin sin = x cos y sin r x y' = r sin ( = r (sin cos sin cos = ( r sin cos r cos sin = y cos x sin in matrix notation... x' y' cos sin sin x cos y rotation matrix

13 A 3D rotation matrix Is the product of 2D rotation matrices. cos sin 0 cos 0 sin cos cos sin cos sin cos sin cos cos sin sin sin 0 cos sin 0 cos

14 Kinds of Kinds of search:systematic : search Anchor and grow (or) incremental construction algorithm It tries to explore all the degrees of freedom in a molecule, but ultimately face the problem of combinatorial explosion So ligands are often incrementally grown into active sites Exhaustive Deterministic Dependent on granularity of sampling Feasible only for low-dimensional problems DOF, 6D search DOCK (incremental) FlexX (incremental) Glide (incremental)

15 Kinds of search:stochastic Kinds of:::: search Monte Carlo (MC) methods and Evolutionary algorithm It works by making random changes to either a single ligand or a population of ligands Novel ligand is evaluated by pre defined probability function In Tabu search, to accept the novel molecule, it calculates RMSD between current moecular coordinates and every molecule s previously recorded conformation Random Outcome varies Repeat to improve chances of success Feasible for higher-dimensional problems Simulated Annealing (SA) AutoDock (MC/SA,GA/LGA) GOLD (GA) Evolutionary Algorithms (EA) Genetic Algorithm (GA)/Tabu Search (TS) Hybrid Global-Local Search/Lamarckian GA (LGA)

16 Kinds of Search:DETERMINISTIC Energy Minimization methods and Molecular Dynamics simulations Molecular Dynamics simulations are often unable to cross highenergy barriers within feasible simulation time periods, they might accommodate ligands in local minima of the energy surface so, an attempt is made to simulate different parts of a proteinligand system at different temperatures Energy minimization is rarely used as stand alone search techniques, as only local energy minima can be reached DOCK Glide AutoDock

17 Random/stochastic AutoDock (MC) MOE-Dock (MC,TS) GOLD (GA) PRO_LEADS (TS) Systematic DOCK (incremental) FlexX (incremental) Glide (incremental) Hammerhead (incremental) Simulation/Deterministic DOCK Glide MOE-Dock AutoDock Hammerhead

18 SCORING FUNCTIONS:FORCE FIELD BASED SCORING Quantifying the sum of two energies Receptor ligand interaction energy and internal ligand energy Most scoring functions consider a single protein conformation to omit the internal protein energy, which simplifies the scoring Force field scoring functions varies based on different force field parameter sets For E.g.: G Score Tripos force field AutoDock AMBER force field

19 SCORING FUNCTIONS:EMPIRICAL SCORING Based on binding energies and/or conformations It is designed based on idea that binding energies can be approximated by a sum of individual uncorrelated terms The coefficients are obtained from regression analysis using experimentally determined binding energies and X ray structural information Disadvantage it depends on the molecular data sets used to perform regression analysis

20 KNOWLEDGE BASED SCORING It reproduce experimental structures rather than binding energies Protein ligand complex is modelled using relatively simple atomic interaction pair potentials A number of atom type interactions are defined depending on their molecular environment The main attraction is computational simplicity which permits efficient screening of large compound databases Disadvantage derivation is essentially based on information implicitly encoded in limited sets of protein ligand complex

21

22

23 Scoring in Ligand-Protein Docking Potential Energy Description: 23

24

25

26 Type of Scoring Functions FORCE FIELD BASED D-Score G-Score Gold Score Autodock Dock EMPIRICAL Ludi F-Score Chem Score KNOWLEDGE BASED PMF Drug Score CONSENSUS CSCORE X-Score

27 AutoDock

28 Cerius2/LigScore vdw 6-9 C+pol buried polar surface in attractive protein ligand complex Totpol2 square of buried polar surface in attractive repulsive protein ligand complex Cerius2/PLP Cerius2/PMF

29 Cerius2/ LUDI

30 SYBYL/F-Score

31 SYBYL/G-Score:

32 SYBYL/D-Score: SYBYL/ChemScore:

33 DrugScore: X-Score:

34 DOCKING SCORE It based on the bonded and non bonded interactions of ligand binding site Bonded Interactions: Non Bonded Interactions:

35 BINDING AFFINITY The interaction of most ligands with their binding sites can be characterized in terms of a binding affinity The free energy of binding (ΔG) is related to binding affinity by The equilibrium equation is : Where ΔG is Gibb s free energy, R is gas constant, T is temperatures and K is equilibrium constant E is enzyme and I is inhibitor

36

37 172 Protein ligand complexes chosen based on resolution (better than 2.5 Å) 172 Conformational sampling procedure 100 passes 100 Protein Ligand complex has: 43 different proteins Molecular weight KDa Rotatable single bond (ligand) 0 20

38 The selection of suitable sample for study It is done by AutoDock (Genetic Algorithm) Parameter: For best fitting, Translation, rotation, and torsions are set to 0.5 Å, 15, and15, respectively The size of the docking box is 30 Å X 30 Å X 30 Å

39 Screening parameter: RMSD 0-15 Å Distinctive conformational clusters Docked conformation should close to experimental conformation (RMSD 2.0 Å) ga-num-generations determines the quality of the sample runs per complex

40 Force Field based scoring: AutoDock, G-Score, D-Score Empirical scoring: LigScore, PLP,LUDI, F-Score, ChemScore, X-Score Knowledge Based scoring: PMF, DrugScore

41 Success rates of 11 scoring functions under different rmsd criteria

42

43

44

45

46

47

48

49

50

51

52

53 AUTODOCK Simulated Annealing Based on temperature effects Start with high temperature and global search Lower temperature local search Genetic Algorithm Charles Darwin s Theory of Evolution Genotype Phenotype Lamarckian Algorithm ( Jean Baptiste de Lamarck) Phenotype Genotype

54 Search parameters Population size Crossover rate Mutation rate Local search energy evals Termination criteria energy evals generations

55 Genetic function algorithm Start with a random population (50-200) Perform Crossover (Sex, two parents -> 2 children) and Mutation (Cosmic rays, one individual gives 1 mutant child) Compute fitness of each individual Proportional Selection & Elitism New Generation begins if total energy evals or maximum generations reached

56

57

58

59 Dimensionality of molecular docking Degrees of Freedom (DOF) Position or Translation (x,y,z) = 3 Orientation or Quaternion (qx, qy, qz, qw) = 4 Rotatable Bonds or Torsions (tor 1, tor 2, tor n ) = n Total DOF, or Dimensionality, D = n

60 Docking Preparation Grid AutoDock uses gridbased docking Ligand-protein interaction energies are pre-calculated and then used as a look-up table during simulation Grid maps are constructed based on atoms of interest in ligand (here CANOSH)

61

62 Initial X Ray crystallographic positions of protein and ligand (SYBYL)

63 Simulated annealing One copy of the ligand (Population = 1) Starts from a random or specific postion/orientation/conformation (=state) Constant temperature annealing cycle (Accepted & Rejected Moves) Temperature reduced before next cycle Stops at maximum cycles

64 Docking Simulated Annealing Runs = 100 Cycles = 50 Initial Temp (RT) = 1,000 Temp reduction factor =.95 Linear temperature reduction Translation reduction factor = 1 Quaternion reduction factor = 1 Torsional reduction factor = 1 # rotatable bonds = 12 Initial coordinates = Random Initial quaternion = Random Initial dihedrals = Random Translation step = 2.0 Å Quaternion step = 50 deg Torsion step = 50 deg Results: 100 different clusters Energy range: to +64,000 Conformation #81: Conformation #67: Conformation #68: Lowest energy conf not close to position but similar to original Conf #67 closest to position and conformation of original ligand; higher energy Conf #68 close to position but not conformation of original ligand; not as high energy

65 Original ligand conf SA conformation #67 Close up of previous (SYBYL)

66 Original ligand conf Best GA conf Best LGA conf Best SA conf Best LS conf (SYBYL)

67 GOLD (CCDC, Cambridge, UK) Flexible docking: match protein and ligand hydrogen bond fitting points optimizes the poses using a Genetic algorithm Flexible rings by flipping ring corners Locally flexible protein: polar hydrogens allowed to move Water switched on and off to maximize interactions SF: GOLDfitness score

68 FRED (OpenEye, Santa Fe, CA, USA) Rigid body docking using a shape-based approach: random generation of poses within active site use of Gaussian functions to represent atoms Use of Gaussian docking functions (combines overlap between ligand with protein atoms and area intersection) and a Quasi-Newton rigid body optimization algorithm to place ligand and select poses Uses Rigid protein SF : ChemScore; Emperical FF

69 FlexX/FlexE (BioSolveIt, Sankt Augustin, Germany) Flexible docking: incremental construction for the ligand combined with a matching of ligand groups to protein interaction types multiple conformations for rings Rigid of flexible protein: all atom representation composite structures assessed (FlexE) water considered using the particle concept (waters placed before docking and only kept during the docking run if favourable interactions are created) SF : Flex X SF;Emperical FF

70 FLEX-X The general schema Ligand conformational flexibility Modeling Receptor-ligand interactions Scoring function Algorithm Base selection Base placement Incremental construction

71 Scoring function Estimates the free binding energy in the complex match score contact score The function is additive in the ligand atoms.

72 Ligand fragmentation Good results are produced if the added fragments are small Every fragment, except for the base fragment consist of only one component.

73

74 DOCK 6.0( (UCSF,CA,USA) Rigid body docking using a clique matching algorithm Flexible ligand using an incremental construction algorithm combined with a simplex minimizer Flexible protein: -negative image of the active site using spheres -use of precomputed grids based on AMBER intermolecular energy and GB/SA(Generalised Born/surface Area) solvation energy -protein flexibility considered using combined grids SF: Dockscore; Amber Force Field

75 DOCK as an Example DOCK works in 5 steps: Step 1 Start with 3D coordinates of target receptor Step 2 Generate molecular surface for receptor Step 3 Generate spheres to fill the active site of the receptor: The spheres become potential locations for ligand atoms Step 4 Matching: Sphere centers are then matched to the ligand atoms, to determine possible orientations for the ligand Step 5 Scoring: Find the top scoring orientation 75

76 DOCK as an Example 4 5 Three scoring schemes: Shape scoring, Electrostatic scoring and Force-field scoring Image 5 is a comparison of the top scoring orientation of the molecule thioketal with the orientation found in the crystal structure

77 The DOCK Algorithm Two steps in rigid ligand mode: Orienting the putative ligand in the site Guided by matching distances, between predefined site points on the target to interatomic distances of the ligand. The RT matrix is used for the transform of the ligand. Scoring the resulting orientation Each orientation is scored for each quality fit. The process is repeated a user-defined number of orientations or maximum orientations 77

78 Site Points Generation in DOCK Program SPHGEN identifies the active site, and other sites of interest. Each invagination is characterized by a set of overlapping spheres. For receptors, a negative image of the surface invaginations is created; For a ligand, the program creates a positive image of the entire molecule. 78

79 The Matching Can be directed by 2 additional features: Chemical matching - labeling the site points such that only particular atom types are allowed to be matched to them. Critical cluster - subsets of interest can be defined as critical clusters, so that at least one member of them will be part of any accepted ligand match. Increase in efficiency and speed due to elimination of potentially less promising orientations! 79

80 Define the target binding site points. O O S S N H N N N H N N N F F Match the distances. 3. Calculate the transformation matrix for the orientation. F H N N O S 4. Dock the molecule. O S H N N N F 5. Score the fit. 80

81 DOCK

82

83 Pharmacophore-Based Docking 83

84 Pharmacophore-based Docking Basic idea: Appropriate spatial disposition of a small number of functional groups in a molecule is sufficient for achieving a desired biological effect. The ensemble formation will be guided by these functional groups. 84

85 Pharmacophore Fingerprint Pharmacophore fingerprint - a set of pharmacophore features and their relative position. Typical pharmacophore features: Hydrogen-bond donors and acceptors Positive and negative ionizable atoms/groups Hydrophobes and ring centroids Implemented in DOCK Hydrogen-bond donors Hydrogen-bond acceptors Dual hydrogen-bond donor and acceptor 5 or 6 membered ring centroids 85

86 Pharmacophore DOCK Prepare target structure Save the best scoring conformer for each molecule Generate a set of chemically labeled site points Score all conformers Orientations tries >MAX Yes No Read a 3D pharmacophore from the database Compare distances between pharmacophore points and site points to determine an orientation matrix Yes No Orientations tries >MAX Use the transformation matrix to dock all conformers associated with the pharmacophore Yes Match? No 86

87 Advantages of Pharmacophore-based Docking Rapid elimination of ligands containing functional groups which would interfere with binding. Speed increase over docking of individual molecules. More information pertaining to the entire molecule is retained (no rigid portions). Chemical matching and critical clusters are encouraged. 87

88 Limitations of Pharmacophore-based Searching A limited subset of key interactions (typically 4-6) which must be extracted from the target site with dozens of potential interactions. Complex queries are extremely slow. The majority of the information contained in the target structure is not considered during the search. There is no scoring function beyond the binary (match/no match). Any steric or electronic constraints imposed by the target, but not defined by the target are ignored. 88

89 Conformational Ensembles Docking Observations: 1. Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation. 2. Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies. 89

90 Conformational Ensembles Docking Observations: 1. Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation. 2. Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies. 90

91 Overview of the Ligand Ensemble Method 91

92 Disadvantages of Conformational Ensemble Docking Loss of information when the orientations are guided only by a subset of the atoms in molecule. Orientations may be missed because potential distance matches from non-rigid portions of the molecule are not considered. The ensemble method will fail for ligands that lack internally rigid atoms. The use of chemical matching and critical clusters is limited. 92

93 ACTIVE SITE IDENTIFICATION PROGRAMS CASTP: XSITE: Voidoo: APROPOS: apropos/apropos_descrip.html CANGAROO: Surfnet: PASS: Active site templates for Enzymes

94 Protein Ligand Docking Programs AutoDock GOLD FLEXX GLIDE ICM Dock

95 Protein protein Docking Programs ZDOCK : HEX : GRAMM : ICM : CLUSPRO : KORDO : MOLFIT : ort//molfit/