Transcript Document

Molecular Docking
G. Schaftenaar
Docking Challenge
• Identification of the ligand’s correct
binding geometry in the binding site
(Binding Mode)
• Observation:
– Similar ligands can bind at quite
different orientations in the active
site.
Two main tasks of Docking Tools
• Sampling of conformational (Ligand)
space
• Scoring protein-ligand complexes
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.
Introducing flexibility:
Whole molecule docking
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Monte Carlo methods (MC)
Molecular Dynamics (MD)
Simulated Annealing (SA)
Genetic Algorithms (GA)
Available in packages:
AutoDock (MC,GA,SA)
GOLD (GA)
Sybyl (MD)
Monte Carlo
• Start with configuration A (energy EA)
• Make random move to configuration B
(energy EB)
• Accept move when:
EB < EA or if
EB > EA except with probability P:
P  exp EA  EB  kT 
Molecular Dynamics
• force-field is used to calculate forces on
each atom of the simulated system
• following Newton mechanics, calculate
accelerations, velocities and new
coordinates from the forces.
(Force = mass times acceleration)
• The atoms are moved slightly with respect
to a given time step
Simulated Annealing
Finding a global minimium
by lowering the temperature
during the Monte Carlo/MD simulation
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
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.
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
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
Pose-Clustering
• For each triangle of receptor compute
the transformation to each ligand
matching triangle.
• Cluster transformations.
• Score the results.
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
Scoring Functions
• Shape & Chemical Complementary
Scores
• Empirical Scoring
• Force Field Scoring
• Knowledge-based Scoring
• Consensus Scoring
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
Empirical Scoring
Scoring parameters fit to reproduce
Measured binding affinities
(FlexX, LUDI, Hammerhead)
Empirical scoring
DG  DG0 + DGrot  N rot
+ DGhb
+ DGio
 f DR, Da 
Loss of entropy during binding
Hydrogen-bonding
neutral. H bonds
 f DR, Da 
 f DR, Da 
Ionic interactions
ionicint .
+ DGarom
Aromatic interactions
arom.int
+ DGlipo
 f DR, Da 
lipo.cont.
Hydrophobic interactions
Force Field Scoring (Dock)
 Aij Bij
qi q j 

   12  6 + c

r
r
r
i
j 
ij
ij
ij


lig prot
Enonbond
Nonbonding interactions (ligand-protein):
-van der Waals
-electrostatics
Amber force field
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.


P s p , s l  Pref exp  bF s p , s l 
Distribution of interatomic distances is converted
into energy functions by inverting Boltzmann’s law.
F
P(N,O)
Potential of Mean Force (PMF)
ij
 i



s seg
r
Fij r    k BT ln fVol _ corr r  ij 
s bulk 

ij
r 
s seg
ij
s bulk
Number density of atom pairs of type ij
at atom pair distance r
Number density of atom pairs of type ij
in reference sphere with radius R
Consensus Scoring
Cscore:
Integrate multiple scoring functions to
produce a consensus score that is
more accurate than any single function
for predicting binding affinity.
Virtual screening by Docking
• Find weak binders in pool of nonbinders
• Many false positives (96-100%)
• Consensus Scoring reduces rate of
false positives
Concluding remarks
Scoring functions are the Achilles’ heel
of docking programs.
False positives rates can be reduced using several
scoring functions in a consensus-scoring strategy
Although the reliability of docking methods is
not so high, they can provide new suggestions for
protein-ligand interactions that otherwise
may be overlooked