This document discusses molecular docking techniques. It describes two main tasks of docking tools: sampling conformational ligand space and scoring protein-ligand complexes. It outlines early rigid-body docking approaches and introduces flexibility through methods like Monte Carlo, molecular dynamics, and genetic algorithms. It also discusses fragment-based docking and techniques for placing fragments and rigid molecules like geometric hashing, pose clustering, and clique detection. The document compares scoring functions including shape complementarity, empirical, force field, knowledge-based, and consensus scoring. It notes that while docking reliability is limited, it can provide new suggestions and that consensus scoring may reduce false positives.

1. Molecular Docking
G. Schaftenaar

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. • 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.
Rigid-body docking algorithms

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 < EA or if
EB > EA except with probability P:
 
 
kT
E
E
P B
A 

 exp

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

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.  






+


lig
i
prot
j 
ij
i
ij
ij
ij
ij
nonbond
j
r
q
q
r
B
r
A
E c
6
12
Force Field Scoring (Dock)
Nonbonding interactions (ligand-protein):
-van der Waals
-electrostatics
Amber force field

20. Knowledge-based Scoring
Function
Free energies of molecular interactions
derived from structural information on
Protein-ligand complexes contained in PDB
   
 
l
p
ref
l
p F
P
P s
s
b
s
s ,
exp
, 

Boltzmann-Like Statistics of Interatomic
Contacts.

21. Distribution of interatomic distances is converted
into energy functions by inverting Boltzmann’s law.

22. Potential of Mean Force (PMF)
   
 










ij
seg
i
corr
Vol
B
ij
r
r
f
T
k
r
A ij
bulk
s
s
_
ln
 
r
ij
seg
s Number density of atom pairs of type ij
at atom pair distance r
ij
bulk
s Number density of atom pairs of type ij
in reference sphere with radius R

23. Consensus Scoring
Cscore:
Integrate multiple scoring functions to
produce a consensus score that is
more accurate than any single function
for predicting binding affinity.

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

25. Concluding remarks
Although the reliability of docking methods is
not so high, they can provide new suggestions for
protein-ligand interactions that otherwise
may be overlooked
Scoring functions are the Achilles’ heel
of docking programs.
False positives rates can be reduced using several
scoring functions in a consensus-scoring strategy

26. Docking programs
• DOCK
• FlexX
• GOLD
• AutoDOCK
• Hammerhead
• FLOG

27. 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

28. 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.
thioketal in the HIV1-protease active site
Sphere centres are matched to ligand
atoms, to determine possible orientations
for the ligand. 104 orientations generated

29. 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