1. Molecular Docking

2. 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.

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 E A)
• Make random move to configuration B
(energy EB)
• Accept move when:
EB < EA or if
EB > EA except with probability P:
P = exp( − [ E A − E B ] kT )

7. 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

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

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
∆G = ∆G0 + ∆Grot × N rot Loss of entropy during binding
+ ∆Ghb ∑ f (∆R, ∆α ) Hydrogen-bonding
neutral. H −bonds
+ ∆Gio ∑ f (∆R, ∆α ) Ionic interactions
ionic −int .
+ ∆Garom ∑ f (∆R, ∆α ) Aromatic interactions
arom.int
+ ∆Glipo ∑ f (∆R, ∆α ) Hydrophobic interactions
lipo.cont.

20. Force Field Scoring (Dock)
Aij Bij
lig prot
qi q j 
Enonbond = ∑∑ 12 − 6 +c 
i j  ij
r rij r 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.
[
P ( p , σ l )= Pref exp − βF ( p , σ l )
σ σ ]

22. Distribution of interatomic distances is converted
into energy functions by inverting Boltzmann’s law.
F P(N,O)

23. Potential of Mean Force (PMF)
 i σ seg (r ) 
ij
Fij (r ) = −kB T ln  fVol _corr (r ) ij


 σ bulk  
σ ij
seg
(r ) Number density of atom pairs of type ij
at atom pair distance r
σ ij
bulk
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
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