Download Diapositive 1

1/34
Docking: placing a ligand
into a receptor cavity
Esther Kellenberger
Faculté de Pharmacie
UMR 7200, Illkirch
Tel: 03 90 24 42 21
e-mail: [email protected]
introduction
3D Protein
docking
scoring
conclusion
Docking: the lock & key principle
Geometrical Complementarity
substrate
enzyme
non-covalent inter-molecular interactions
receptor
ligand
2/34
introduction
3D Protein
docking
scoring
3/34
conclusion
Thermodynamics of association
L
LR
R
Association constant (equilibrium)
LR

K A  LR
no unit
G
K A  exp 
RT
G0; RT in J/mole
0
KD (M)
ΔG° (kcal/mol)
10-15
-20.4
10-12
-16.4
10-9
-12.3
10-6
-8.2
10-3
-4.1
at 298K
introduction
3D Protein
docking
scoring
conclusion
4/34
Thermodynamics of association
Free energy
ΔG = ΔH – TΔS
Enthalpy H
~ sum of interactions
Entropy S
~ order
What is gained/lost upon binding?
L: ligand, R: recepteur, W: water
introduction
3D Protein
docking
scoring
conclusion
5/34
Molecular flexibility:
Lock & key or induced fit
Calmoduline: domain motions
Thymidylate Synthetase: side-chains rotamers
Nature 450, 964-972 ( 2007)
6/34
Chapter1:
Proteins are folded
biopolymers
introduction
3D Protein
docking
scoring
conclusion
7/34
primary, secondary, tertiary & quaternay
structures
Polymerisation reaction
primary structure
amino acids
secondary
structure
sheet
helix
tertiary
structure
quaternary
structure
20 monomers differ by their R group
http://www.yellowtang.org
introduction
3D Protein
docking
scoring
conclusion
8/34
Limited number of structural organisations
for a huge variety of functions
Human collagen
Influenza neuraminidase
(H1N1 surface protein)
β2 adrenergic receptor stimulated
by adrenaline  flight or fight"
Fibers
globular proteins
Membrane proteins
• auto assembly
 supramolecules
• collagen (cartilage, bone,
teeth), keratin (hair, nail), fibrin
(blood clots)
• hydrosoluble (cytoplasm, blood)
• enzyme catalysis, defense (toxin,
immunoglobulin), transporter (O2,
electrons), motion (actin, myosin),
regulation (osmotic protein, gene
regulators, hormone) ion storage
(ferritin, calmodulin)
• ~30% of human total genes
• Signal transducer, transporter
(Na+, proton, glucose), channels
http://www.rcsb.org
introduction
3D Protein
docking
scoring
conclusion
9/34
Experimental determination of 3D structure
X-ray structure of the crystal protein
Information about position
Quality check:
the resolution (Å)
introduction
3D Protein
docking
scoring
conclusion
10/34
Experimental determination of 3D structure
NMR structure of the protein in solution
Information about
DISTANCE (NOE)
ANGLE (coupling constant)
RELATIVE POSITION
Quality check:
quantity/quality/distribution of information
(residual dipolar coupling)
introduction
3D Protein
docking
scoring
conclusion
11/34
Since 2003: a single archive of
public 3D structures of biomolecules
Weekly updated, data synchronisation on mirror sites
since 1998, maintained by
Research Collaboratory for
Structural Bioinformatics
European
Macromolecular
Structure Database
Protein DataBank
Japan
Data deposit, treatement
and distribution
NMR data (since 2006)
introduction
3D Protein
docking
scoring
12/34
conclusion
PDB statistics: September 2008
Protein
Nucleic acids
Complexes
others
total
R-X
42400
1086
1961
24
45471
NMR
6536
815
138
7
7497
other
225
15
53
2
153
46161
1917
2152
33
53263
#
(microscopy)
total
Structure Factors available for 34603 entries.
NMR constraints for 4189 entries.
Sequence redundancy: 8483 proteins with <30% sequence identity
Structure redundancy : about 700 different folds (ternary structures)
introduction
3D Protein
docking
scoring
conclusion
PDB entry: format and content
Format: Flat file organized into tagged fields
• Header:
information
• Connexion table:
atom section only (element: C, O, N, S, H),
no explicit bonds for standard amino acids
Incomplete description of protein structure
METHOD
X-ray
NMR
hydrogen atoms








well defined –CONH2
water molecules
Metal ions, cofactors
13/34
introduction
3D Protein
docking
scoring
conclusion
Targeting Protein by chemicals
protein function
• biochemical function = interaction with other molecules
• biological function = consequence of these interactions
“druggable” binding site
cavity
• depth
• volume
ligand
• lipophilic surface
Cavity
Binding
pocket
about 6000 « druggable » sites in PDB protein X-ray structures
http://bioinfo-pharma.u-strasbg.fr/scPDB/
14/34
15/34
Chapter2:
Docking chemicals into
protein cavities
introduction
3D Protein
docking
scoring
conclusion
Semi-flexible docking
Difficult!
feasible
< 25-30
number of rotatable bonds
 3 per amino acids
 exhaustive
conformational search
partial
seconds to hours
cpu time
huge!
16/34
introduction
3D Protein
docking
scoring
conclusion
17/34
search for the best pose
of flexible ligand into rigid protein site
Pose = Orientation & conformation
O
Orientation
•
OH
H
N
O
O
NH2
Position of the ligand in the
protein
•
Rigid body motions
•
Translations + rotations of
the whole molecule
protein
Conformation
•
3D structure of the molecule
•
Molecular flexiblity
O
O
H
HN
N
O
OH
OH
O
O
NH
NH22
protein
introduction
3D Protein
docking
scoring
18/34
conclusion
geometry-based vs energy-based algorithms
geometry-based
O
HN
O
determinist methods
energy-based
OH
O
O
NH2
HN
protein
stochastic methods
O
OH
O
NH2
protein
•
Protein: feature points in the cavity
•
Protein : surface atoms / feature points
•
Ligand: atoms or feature points
•
Ligand : atoms or feature points
•
Docking: translations + rotations of
•
Docking: modification of ligand
rigid ligand to superpose matching
position/conformation to optimize
points
an “energy” function that describes
molecular interactions
introduction
3D Protein
docking
scoring
conclusion
19/34
Examples of geometric algorithm
DOCK
Surflex, MOE
Flexx
Kuntz, I.D et al. (1982) J. Mol. Biol
Jain A (2003) J Med Chem
M. Rarey et al. (1996) J. Comp.-Aid. Mol. Design
•
•
Protein cavity filled with
overlapping spheres
(variable radius).
Feature points: sphere
center colored according
to physico-chemical
properties
Cluster of probes
•
•
steric (apolar)
Polar
(NH, CO in Surflex,
polar in MOE)
Interaction centers and interaction
surfaces identified on both
receptor (a) and ligand (b)
•
H bond
•
•
•
•
Salt bridges
Aromatics
methyl-aromatics
amide-aromatics
introduction
3D Protein
docking
scoring
conclusion
Geometric matching of triangles
ligand
protein
20/34
introduction
3D Protein
docking
scoring
conclusion
21/34
Ligand flexibility in geometric algorithms
Incremental construction
(FlexX, DOCK, Surflex)
Library of conformers
•
•
rigid docking of all conformers (DOCK, MOE)
conformer generation usually not included in the docking tool
•
in MOE-Dock, the conformational search is coupled to docking, using a library
of preferred torsion values for rotatable bonds
introduction
3D Protein
docking
scoring
22/34
conclusion
energy-based algorithms
Optimization of an energy function to
O
O
find stable conformations of the ligand
H
N
OH
O
NH2
/ receptor complex
energy
OH
O
O
HN
O
NH2
O
O
conformational space
H
N
OH
O
NH2
protein
introduction
3D Protein
docking
scoring
conclusion
iterative generation of
populations of conformers
starting population
modified population
final population
modification of
conformations
Selection of
conformations
based on
energy
23/34
introduction
3D Protein
docking
scoring
conclusion
24/34
Genetic algorithm (gold, autodock)
reproduction
modification of
selection
parameters
(soft  hard)
convergence
Selection of
conformations based
on energy
Moitessier N (2008) Br J Pharm
introduction
3D Protein
docking
scoring
25/34
conclusion
Monte Carlo (ICM, RosettaLigand)
Ei= +36 kcal/mol
Ef= +22 kcal/mol
Ei= 0 kcal/mol
Ef= +10 kcal/mol
Bond rotation
Tranlation
Rotation
Selection of
conformations
based on energy
x cooling cycles
(T decrease)
…
n iterations
Ef= +2 kcal/mol
if Ef < Ei
if Ef > Ei
accept
metropolis
 Ef -Ei 
exp  
 >z
 K BT 
accept
26/34
Chapter3:
Scoring ligand/receptor
interaction
introduction
3D Protein
docking
scoring
conclusion
empirical vs force field
scoring functions (SF)
Empirical SF
selected ligand/receptor
Force Field SF
S = Eligand + Ecomplexe
Eligand = internal energy
Ecomplexe =
X-ray structures
non-bonded interactions
distance between ligand experimental ΔG
receptor pairs of atoms
empirical SF
knowledge-based SF
27/34
introduction
3D Protein
docking
scoring
conclusion
28/34
Prediction of free energy by Empirical SF
S ≈ ∆G =  ki * Fi
Fi : function which describes protein – ligand interaction (H-bond, salt bridge..)
computed using geometry predicted by docking
ki : constant adjusted using the training set
MOE affinity dG SF
dGbind  khb  fhb + kml  fml + khh  fhh + khp  fhp + kaa  faa
hb
ml
k: constant
f: count of atomic contact
hh
hp
aa
hb: H-bond donor – H-bond acceptor
ml: ionic metal - ligand
hh: hydrophobic atom – hydrophobic atoms
hp: hydrophic atom – polar atom
aa: any atom - any atom
introduction
3D Protein
docking
scoring
29/34
conclusion
Exemple of empirical SF performance
H-bond
Böhm (1994)
ionic
rotation
lipophilic
Gbind  G0 + Ghb  f R,   + Gionic  f R  + Glipo  Alipo + Grot NROT
hb
ionic
hb
-8.3 kJ/mol
+5.4 kJ/mol -4.7 kJ/mol
-0.17 kJ/mol.Å2
+1.4 kJ/mol/rot
-20
-25
Q2LOO = 0.777, spress=3.15 kJ/mol, n=37
-30
-35
-40
training
-45
-50
-50
r
2
pred =
0.698, s = 5.33 kJ/mol, n=16
-30
-45
-40
-35
-30
Gexp, kJ/mol
-25
-20
Gpred, kJ/mol
Gpred, kJ/mol
-25
-35
-40
-45
-50
test
-55
-60
-55
-50
-45
-40
Gexp, kJ/mol
-35
-30
-25
-20
introduction
3D Protein
docking
scoring
conclusion
30/34
Strength and weakness of SF
Empirical SF
•
•
fast calculation
adaptable to custom target
•
•
•
training set (incompleteness, inaccuracy of data)
binding mode (if few polar interactions, underestimation of score)
missing penalty (steric clashes, polar/apolar match, internal ligand
energy, lost of entropy, geometry of directional interaction, local
environment of interaction)
Force Field SF
•
independant of training set
•
•
•
•
strongly depends on ligand size
force field accuracy, difficulty to set parameters
no account of entropy
Often includes empirical terms !
introduction
3D Protein
docking
scoring
conclusion
31/34
post-processing output: pose selection by
Interaction Fingerprint analysis
ligand
protein
8 bits / target site residue
1. Hydrophobic contact
2. Aromatic interaction
(Face/Face)
3. Aromatic interaction
(Face/Edge)
Interaction FingerPrint
AA1
AA2
… AAn
10000000 01001000 … 01000100
4.
5.
6.
7.
8.
H-bond (Donor in ligand)
H-bond (Donor in protein)
Ionic bond (+) in ligand
Ionic bond (+) in protein
Metal
Marcou, G et al (2007) J Chem Inf Model
32/34
conclusion
What can we achieve?
What remains to be improved?
introduction
3D Protein
docking
scoring
conclusion
33/34
The state of the art
• docking accuracy
(Warren et al. 2006 Proteins, Kellenberger et al. 2004 Proteins)
• many programs able to reproduce x-ray conformation
• but performance is highly dependend on the studied protein
• cpu time:
• few seconds to several minutes to dock one compound
• app. screening rate: 1,500 compounds/day/processor
• hit rate (true positives in hit list) in screening by high throughput docking
• ~ 50 % retrospective studies
• from 10% to 30% in prospective studies using X-ray structures
• lower rates for docking using homology models
introduction
3D Protein
docking
scoring
conclusion
34/34
The limitations
Pre-processing of the protein
Lacking hydrogens, hydrogen bonds networks, protonation states of
his, lys, asp, glu
Water molecule(s) involved in the binding mode
Pre-processing of the ligands
Protonation states, tautomers
Flexibility of the ligand
no serious problem
Flexibility of the protein /binding site
a difficult problem
Fuzzy scoring functions
the biggest problem