1. International Chemical Design & Discovery Course 2017
19-Nov-18
Molecular Docking
International Chemical Design & Discovery Course 2017
Sander Nabuurs
ELN some key facts

2. Molecular docking
Molecular docking can be defined as the computational generation and evaluation of the feasible binding geometries of a putative ligand to the receptor of interest.
Molecular docking has one primary requirement: the availability of a 3D structure of the protein receptor.

3. Molecular docking: the questions
In practice molecular docking is generally used to answer two different types of questions:
How does the complex look that is formed by receptor A and compound B?
Which compounds in my compound collection could be active on receptor A?
The latter question is commonly reffered to as ‘virtual screening’.
Compound B
Receptor A +
Complex?
docking
Compound
collection
Receptor A +
Actives?
docking

4. Molecular docking in the pipeline
Target
Discovery
Lead
Discovery
Lead
Optimisation
Pre-Clinical
Development
Clinical
Development
Registration
Marketing &
Sales
RESEARCH
DEVELOPMENT

5. Molecular docking algorithms
Target
protein
The docking problem involves many degrees freedom:
Translational.
Rotational.
Configurational (Ligand + Receptor!)
Since the early eighties several docking algorithms have been devised.
These can be characterized by the number of degrees of freedom that they ignore.
Compound
Docking
program
Target-Compound
complex

6. Docking algorithms Ligand rotations Ligand translations
Ligand flexibility
Receptor flexibility
Rigid body docking
Flexible ligand docking
Induced fit docking
Fully flexible docking

7. Docking tools Dock Autodock FlexX Gold Glide
A few flexible ligand docking programs:
Dock
[Kuntz et al, J Mol Biol, 161: , 1982]
Autodock
[Morris et al, J Comput Chem, 19: , 1994]
FlexX
[Rarey et al, J Mol Biol, 261: , 1996]
Gold
[Jones et al, J Mol Biol, 267: , 1997]
Glide
[Friesner et al, J Med Chem, 47: , 2004]

8. Molecular docking Sampling Scoring
Target
protein
Molecular docking typically consists of two separate stages:
Exploration of conformational and configurational space.
Evaluation of the strength of the receptor-ligand interaction.
Compound
Docking
program
Sampling
Scoring
Target-Compound
complex

9. Site characterization
Molecular docking:
Sampling
Scoring
During the sampling stage two basic tasks have to be accomplished:
Characterization of the receptor binding site.
Positioning of the ligand into the receptor binding site.
Site characterization
Ligand orientation

10. Molecular docking: Sampling Scoring First principles Semi-empirical
Scoring functions can be divided into four major approaches:
First principles
Semi-empirical
Empirical
Knowledge-based

11. Site characterization
Molecular docking
Sampling
Site characterization
Ligand orientation
Scoring
First principles
Semi-empirical
Empirical
Knowledge-based

12. Site characterization
Binding site analysis
Sampling
Site characterization
Prior to ligand placement, most docking programs will create a simplified description of the target binding site.
This is typically done using simple geometry descriptors, like spheres (e.g. DOCK) or triangles (e.g. FlexX).
These geometrical descriptors are usually combined with chemical and electrostatic descriptors to guide ligand placement.
Receptor
Ligand

13. Docking ligands Sampling Ligand orientation
Druglike molecules are flexible: ~70% has between 2 and 8 rotatable bonds. This flexibility has to be considered during docking.
Several approaches to tackle this challenge have been developed:
Ligand fragmentation
FlexX / Dock
Genetic algorithms
Gold
Simulation techniques
Autodock / Glide

14. 1. Ligand fragmentation Sampling Ligand orientation
Fragmentation approaches dissect the ligand into small pieces and use these to rebuild the ligand in the binding pocket.
Fragments are typically small rigid building blocks.
The are two main strategies to handle these fragment in docking:
Place-and–join
Incremental reconstruction

15. 1. Ligand fragmentation: Place-and-join
Sampling
Ligand orientation
The place-and-join approach places all or a subset of the fragment independently in the binding site.
The docked fragments are then reconnected to recreate the complete ligand.
Potential weakness: not every fragment has to be located at a minimum-energy postion.
Ligand
Receptor

16. 1. Ligand fragmentation: Incremental reconstruction
Sampling
Ligand orientation
Anchor fragment
The incremental reconstruction approach typically consist of 3 stages:
Selection of the anchor fragment.
Placement of the anchor fragment.
Incremental growing of the ligand.
It should be noted the choice of anchor fragment can very much influence the end result!
Both FlexX and Dock use this type of approach.
Ligand
Receptor

17. 2. Genetic algorithms Sampling Ligand orientation 1
A well-known implementation of a genetic algorithm in molecular docking is Gold.
In the Gold program two binary strings (chromosomes) are used to describe a docking configuration.
By ‘decoding’ the chromosomes, a ligand can be placed in the binding site.
New poses are generated by ‘evolution’ of the chromosomes.
Torsion angle chromosome 1
Hydrogen bond chromosome

18. 2. Genetic algorithms Sampling Ligand orientation crossover mutation
Initial poses
Scoring
mutation
Docking
Figures borrowed from

19. 3. Simulation techniques
Sampling
Ligand orientation
Simulation methods start with a given conformation and move to configurations with more favourable energies.
Many different simulation techniques have been applied to the docking problem:
Molecular dynamics
Molecular mechanics
Monte Carlo simulations

20. 3. Simulation techniques: Monte carlo
Sampling
Ligand orientation
A Monte Carlo simulation starts with a given configuration A, with an associated energy EA.
Next, a random move is made to a new configuration B with energy EB.
Moves are accepted if EB < EA.
To escape local minima, there is a small chance that a move will be accepted if EB > EA: A B

21. Site characterization
Molecular docking
Sampling
Site characterization
Ligand orientation
Scoring
First principles
Semi-empirical
Empirical
Knowledge-based

22. Scoring methods Scoring
Docking programs generate a large number of different docking poses.
In general one can distinguish two different scenarios:
Many different poses of the same ligand need to be ranked for accuracy.
Different poses of different ligands need to be ranked based on their receptor affinity.
The ideal scoring function works well in both cases... 1 … 5 2 3 4 1 … 5 2 3 4

23. First principles scoring methods
First principles scoring functions generally use a Molecular Mechanics force field.
Such force fields typically contain intra-molecular terms:
Bond lengths
Bond angles
Dihedral terms
And inter-molecular terms:
Van der Waals contacts (non-polar)
Electrostatic interactions (polar)
Ebind = Eintra + Enonpolar + Epolar

24. Semi-empirical scoring methods
Simulation 1
A well-known semi emperical method is the linear interaction energy method (LIE).
LIE is an approach to calculate the binding free energy of a ligand:
ΔGbind = ½<ΔVnonpolar> + α<ΔVpolar>
<ΔV> is the average interaction energy from two MD simulations (time consuming!).
α has to be empirically optimized using known binding data.
Simulation 2

25. Empirical scoring methods
Empirical scoring functions have been developed to score ligands very rapidly.
ΔGbind= ΔG0 +
ΔGpolar · Σ f(Complex) +
ΔGnon-polar · Σ f(Complex) +
ΔGrot · Nrotatable-bonds
ΔG0, ΔGpolar, ΔGnon-polar, and ΔGrot are emperically parametrized weights.
f(Complex) is a penalty function aimed at penalizing any unfavorable interaction geometries.

26. Knowledge-based scoring methods
Instead of reproducing binding energies, knowledge-based scoring functions are usually implemented to reproduce experimental structures.
Complexes are scored using inter-atomic contact preferences.
Scorecomplex = Σ Eij(distance)
Eij(distance) is the protein-ligand atom pair interaction free energy for atoms i and j.
distance
energy

27. Site characterization
Molecular docking
Sampling
Site characterization
Ligand orientation
Scoring
First principles
Semi-empirical
Empirical
Knowledge-based

28. Molecular docking
When applying molecular docking tools in your projects, there are several issues you might want to consider:
Consensus scoring
The role of water
The receptor structure
Possible receptor flexiblity

29. Consensus scoring
Scoring function
Unfortunately, no single scoring function is available today to reliable score putative protein-ligand complexes.
To alleviate this problem, different scoring function are often combined using consensus scoring techniques.
When considered for different targets and compounds consensus scoring techniques typically outperfom the individual scoring functions. 1 2 3 4 5
consensus
best
worst

30. What about the waters? R L R L
As protein receptors in vivo occur in an aqueous environment, water plays an important role in ligand binding.
In some cases, the ligand can be make hydrogen bonds to the receptor via water molecule.
This is extremely difficult to predict using molecular docking techniques!
Should one include ‘bound’ crystal waters when docking into a receptor? R L R L

31. What about the receptor structure?
1 ligand
1 receptor
conformation
The quality of the receptor structure is crucial for the succes of docking experiment.
Even small structural changes can drastically alter the outcome of your docking run.
Despite that most docking programs consider a protein receptor as rigid, they are not!
10 ligands
10 receptor
conformations

32. What about receptor flexibility?
The most natural way to include receptor flexibility is perform a molecular dynamics simulation in water.
In theory, you just place a receptor and ligand in box of water, start an MD simulation and wait, and wait, and wait, and wait, and wait…
However, in practice more efficient tools are required to consider receptor flexibility in molecular docking.

33. Trouw – 3 maart 2009

34. Influenza virus strikes back!
Relenza
Tamiflu
Sialic acid
WT Ki = 1.0
H274Y Ki= 1.9
WT Ki = 1.0
H274Y Ki =265
H274Y
H274Y

35. Docking algorithms Ligand rotations Ligand translations
Ligand flexibility
Receptor flexibility
Rigid body docking
Flexible ligand docking
Induced fit docking
Fully flexible docking

36. + + X Y Induced fit induced fit ‘lock and key’ model
‘induced fit’ model + + X Y
Receptor A
Receptor A
induced fit
Complex A-X
Complex A’-Y

37. Induced fit docking approaches
Sampling
Binding site sampling
Site characterization
Ligand orientation
Scoring
First principles
Semi-empirical
Empirical
Knowledge-based

38. Considering receptor flexibility: Schrödinger IFD
Mutate residues
Generate
complexes
Compound
Glide Docking
Optimize
complexes
MD refinement
Refined
receptor
Target
Generate
complexes
Induced fit
complex
J Med Chem :

39. Considering receptor flexibility: Fleksy
Introduce
flexibility
Generate
complexes
Compound
FlexE docking
Optimize
complexes
MD refinement
Induced fit
complex
Target
J Med Chem : ; J Comp Chem 2012

40. Common docking pitfalls
Most docking algorithms will be more likely to fail if:
The ligand is very large.
The ligand is very flexible.
The ligand is very hydrophobic.
The ligand makes few interactions to the receptor.
The receptor has a dynamic binding site (induced fit).

41. The practical
In the practical this afternoon you will perform a virtual screening experiment on the Estrogen Receptor.
For this you will make use of the MOE program.
You will have a look at:
Docking accuracy
Performance of scoring functions
Consensus scoring
Hit rates
Compound
collection
Receptor ERα +
Actives?
docking