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Molecular Docking

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Title: PowerPoint Presentation Author: schaft Last modified by: schaft Created Date: 4/25/2003 10:12:16 AM Document presentation format: On-screen Show – PowerPoint PPT presentation

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Title: Molecular Docking


1
Molecular Docking
G. Schaftenaar
2
Docking Challenge
  • Identification of the ligands correct binding
    geometry in the binding site (Binding Mode)
  • Observation
  • Similar ligands can bind at quite different
    orientations in the active site.

3
Two main tasks of Docking Tools
  • Sampling of conformational (Ligand) space
  • Scoring protein-ligand complexes

4
Rigid-body docking algorithms
  • Historically the first approaches. 
  • Protein and ligand fixed.
  • Search for the relative orientation of the two
    molecules with lowest energy.
  • FLOG (Flexible Ligands Oriented on Grid) each
    ligand represented by up to 25 low energy
    conformations.

5
Introducing flexibility Whole molecule docking
  • Monte Carlo methods (MC)
  • Molecular Dynamics (MD)
  • Simulated Annealing (SA)
  • Genetic Algorithms (GA)
  • Available in packages
  • AutoDock (MC,GA,SA)
  • GOLD (GA)
  • Sybyl (MD)

6
Monte Carlo
  • Start with configuration A (energy EA)
  • Make random move to configuration B (energy EB)
  • Accept move when
  • EB lt EA or if
  • EB gt EA except with probability P

7
Molecular Dynamics
  • force-field is used to calculate forces on each
    atom of the simulated system
  • following Newton mechanics, calculate
    accelerations and velocities from the forces.
  • (Force mass times acceleration)
  • The atoms are moved slightly with respect to a
    given time step

8
Simulated Annealing
Finding a global minimium by lowering the
temperature during the Monte Carlo/MD simulation
9
Genetic Algorithms
  • Ligand translation, rotation and configuration
    variables constitute the genes
  • Crossovers mixes ligand variables from parent
    configurations
  • Mutations randomly change variables
  • Natural selection of current generation based on
    fitness
  • Energy scoring function determines fitness

10
Introducing flexibility Fragment Based Methods
  • build small molecules inside defined binding
    sites while maximizing favorable contacts.
  • De Novo methods construct new molecules in the
    site.
  • division into two major groups
  • Incremental construction (FlexX, Dock)
  • Place join.

11
Placing Fragments and Rigid Molecules
  • All rigid-body docking methods have in common
    that superposition of point sets is a fundamental
    sub-problem that has to be solved efficiently
  • Geometric hashing
  • Pose clustering
  • Clique detection

12
Geometric hashing
  • originates from computer vision
  • Given a picture of a scene and a set of objects
    within the picture, both represented by points in
    2d space, the goal is to recognize some of the
    models in the scene

13
(No Transcript)
14
Pose-Clustering
  • For each triangle of receptor compute the
    transformation to each ligand matching triangle.
  • Cluster transformations.
  • Score the results.

15
Clique-Detection
  • Nodes comprise of matches between protein and
    ligand
  • Edges connect distance compatible pairs of nodes
  • In a clique all pair of nodes are connected

16
Scoring Functions
  • Shape Chemical Complementary Scores
  • Empirical Scoring
  • Force Field Scoring
  • Knowledge-based Scoring
  • Consensus Scoring

17
Shape Chemical Complementary Scores
  • Divide accessible protein surface into zones
  • Hydrophobic
  • Hydrogen-bond donating
  • Hydrogen-bond accepting
  • Do the same for the ligand surface
  • Find ligand orientation with best complementarity
    score

18
Empirical Scoring
  • Scoring parameters fit to reproduce
  • Measured binding affinities
  • (FlexX, LUDI, Hammerhead)

19
Empirical scoring
Loss of entropy during binding
Hydrogen-bonding
Ionic interactions
Aromatic interactions
Hydrophobic interactions
20
Force Field Scoring (Dock)
ù
é
B
A
lig
prot
å
å
ú

-

i
ij
ij
E
c
ê
nonbond
ú
6
12
r
r
ê
ë
i
j
ij
ij
  • Nonbonding interactions (ligand-protein)
  • van der Waals
  • -electrostatics
  • Amber force field

21
Knowledge-based Scoring Function
  • Free energies of molecular interactions
  • derived from structural information on
  • Protein-ligand complexes contained in PDB

Boltzmann-Like Statistics of Interatomic Contacts.

22
Distribution of interatomic distances is
converted into energy functions by inverting
Boltzmanns law.
23
Potential of Mean Force (PMF)
(
)
ö
æ
s
ij
r
(
)
(
)

ç
-

seg
i
r
f
T
k
r
A
ln

ç
corr
Vol
B
ij
_
ø
è
(
)
s
ij
r
Number density of atom pairs of type ij at atom
pair distance r
seg
Number density of atom pairs of type ij in
reference sphere with radius R
24
Consensus Scoring
  • Cscore
  • Integrate multiple scoring functions to
  • produce a consensus score that is
  • more accurate than any single function
  • for predicting binding affinity.

25
Virtual screening by Docking
  • Find weak binders in pool of non-binders
  • Many false positives (96-100)
  • Consensus Scoring reduces rate of false positives

26
Concluding remarks
27
Docking programs
  • DOCK
  • FlexX
  • GOLD
  • AutoDOCK
  • Hammerhead
  • FLOG

28
FLEXX
  • Receptor is treated as rigid
  • Incremental construction algorithm
  • Break Ligand up into rigid fragments
  • Dock fragments into pocket of receptor
  • Reassemble ligand from fragments in low
  • energy conformations

29
How DOCK works
  • Generate molecular surface of protein

Cavities in the receptor are used to define
spheres (blue) the centres are potential
locations for ligand atoms.
Sphere centres are matched to ligand atoms,
to determine possible orientations for the
ligand. 104 orientations generated
thioketal in the HIV1-protease active site
30
GOLD (Genetic Optimisation for Ligand Docking)
Performs automated docking with full acyclic
ligand flexibility, partial cyclic ligand
flexibility and partial protein flexibility in
and around active site.
Scoring includes H-bonding term, pairwise
dispersion potential (hydrophobic interactions),
molecular and mechanics term for internal
energy.
  • Analysis shows algorithm more likely to fail if
    ligand is large or highly flexible,
  • and more likely to succeed if ligand is polar
  • The GA is encoded to search for H-bonding
    networks first
  • Fitness function contains a term for dispersive
    interactions but takes no account
  • of desolvation, thus underestimates
    The Hydrophobic Effect
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