Docking: placing a ligand into a receptor cavity - Unistrainfochim.u-strasbg.fr/FC/docs/Docking/course-docking.pdf · docking Geometrical Complementarity non-covalent inter-molecular

Docking: placing a ligand into a receptor cavity

Esther Kellenberger

Faculté de PharmacieUMR 7200, Illkirch

Tel: 03 90 24 42 21 e-mail: [email protected]

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docking

Geometrical Complementarity

non-covalent inter-molecular interactions

DockingDocking: : thethe locklock & & keykey principleprinciple

substrate

enzyme

ligand

receptor

3D Protein 2/34introduction scoring conclusion

ThermodynamicsThermodynamics ofof associationassociation

RTGK A

0

exp Δ−=

ΔG0; RT in J/mole

AK =LR[ ]

L[ ] R[ ]

Association constant (equilibrium)

no unit

L

RLR

-4.110-3

-8.210-6

-12.310-9

-16.410-12

-20.410-15

ΔG° (kcal/mol)at 298K

KD (M)

docking3D Protein 3/34introduction scoring conclusion

What is gained/lost upon binding?

Free energy ΔG = ΔH – TΔS

Enthalpy H ~ sum of interactions

Entropy S ~ order

L: ligand, R: recepteur, W: water

ThermodynamicsThermodynamics ofof associationassociation


Thymidylate Synthetase: side-chains rotamersCalmoduline: domain motions

MolecularMolecular flexibilityflexibility: : LockLock & & keykey oror inducedinduced fitfit

Nature 450, 964-972 ( 2007)


Chapter1:

Proteins are folded

biopolymers

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primaryprimary, , secondarysecondary, , tertiarytertiary & & quaternayquaternaystructuresstructures

20 monomers differ by their R group

Polymerisation reaction

http://www.yellowtang.org


secondary structure

tertiary structure

quaternary structure

helixsheet

primary structure amino acids

LimitedLimited numbernumber ofof structural organisations structural organisations for a for a hugehuge varietyvariety ofof functionsfunctions

Membrane proteins

• ~30% of human total genes• Signal transducer, transporter(Na+, proton, glucose), channels

β2 adrenergic receptor stimulated by adrenaline flight or fight"

globular proteins

• hydrosoluble (cytoplasm, blood)• enzyme catalysis, defense (toxin, immunoglobulin), transporter (O2, electrons), motion (actin, myosin), regulation (osmotic protein, gene regulators, hormone) ion storage(ferritin, calmodulin)

Fibers

• auto assembly supramolecules

• collagen (cartilage, bone, teeth), keratin (hair, nail), fibrin(blood clots)

Influenza neuraminidase(H1N1 surface protein)Human collagen

http://www.rcsb.org


ExperimentalExperimental determinationdetermination ofof 3D structure3D structure

X-ray structure of the crystal protein

Quality check: the resolution (Å)

Information about position


ExperimentalExperimental determinationdetermination ofof 3D structure3D structure

NMR structure of the protein in solution

Quality check: quantity/quality/distribution of information

Information about DISTANCE (NOE)ANGLE (coupling constant)RELATIVE POSITION(residual dipolar coupling)


since 1998, maintained by Research Collaboratory for Structural Bioinformatics

European Macromolecular

Structure Database

Protein DataBankJapan

NMR data (since 2006)

Weekly updated, data synchronisation on mirror sites

Data deposit, treatementand distribution

SinceSince 2003: a single archive 2003: a single archive ofofpublic 3D structures public 3D structures ofof biomoleculesbiomolecules


749771388156536NMR

15325315225other(microscopy)

53263332152191746161total

45471241961108642400R-X

totalothersComplexes Nucleic acidsProtein#

Structure Factors available for 34603 entries. NMR constraints for 4189 entries.

PDB PDB statisticsstatistics: : SeptemberSeptember 20082008

Sequence redundancy: 8483 proteins with <30% sequence identity

Structure redundancy : about 700 different folds (ternary structures)


PDB PDB entryentry: format : format andand contentcontent

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

well defined –CONH2

water molecules

Metal ions, cofactors

hydrogen atoms

NMRX-rayMETHOD


TargetingTargeting ProteinProtein by by chemicalschemicals

protein function• biochemical function = interaction with other molecules

• biological function = consequence of these interactions

“druggable” binding sitecavity

• depth

• volume

• lipophilic surface

about 6000 « druggable » sites in PDB protein X-ray structures

http://bioinfo-pharma.u-strasbg.fr/scPDB/

ligandCavity

Binding pocket


Chapter2:

Docking chemicals into

protein cavities

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SemiSemi--flexibleflexible dockingdocking


number of rotatable bonds

conformational search

cpu time

< 25-30

exhaustive

seconds to hours

≥ 3 per amino acids

partial

huge!

feasible Difficult!

Pose = Orientation & conformation

O

NH

ONH

OHO

NH2

OOrientation

• Position of the ligand in the protein

• Rigid body motions • Translations + rotations of

the whole molecule

Conformation• 3D structure of the molecule• Molecular flexiblity

ONH

protein

protein

OHO

NH2

O

search for the best pose search for the best pose of of flexibleflexible ligandligand into into rigidrigid protein siteprotein site


geometrygeometry--basedbased vsvs energyenergy--basedbased algorithmsalgorithms


O

NH

OHO

NH2

O

O

NH

OHO

NH2

O

geometry-baseddeterminist methods

• Protein: feature points in the cavity• Ligand: atoms or feature points• Docking: translations + rotations of

rigid ligand to superpose matching points

energy-basedstochastic methods

• Protein : surface atoms / feature points • Ligand : atoms or feature points• Docking: modification of ligand

position/conformation to optimizean “energy” function that describes molecular interactions

protein protein

ExamplesExamples ofof geometricgeometric algorithmalgorithm


DOCKKuntz, I.D et al. (1982) J. Mol. Biol

• Protein cavity filled withoverlapping spheres(variable radius).

• Feature points: spherecenter colored accordingto physico-chemicalproperties

FlexxM. Rarey et al. (1996) J. Comp.-Aid. Mol. Design

Interaction centers and interaction surfaces identified on both receptor (a) and ligand (b)

• H bond• Salt bridges• Aromatics• methyl-aromatics• amide-aromatics

Cluster of probes• steric (apolar)• Polar

(NH, CO in Surflex, polar in MOE)

Surflex, MOEJain A (2003) J Med Chem

GeometricGeometric matchingmatching ofof trianglestriangles


ligandprotein

Ligand Ligand flexibilityflexibility in in geometricgeometric algorithmsalgorithms


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

energyenergy--basedbased algorithmsalgorithms


Optimization of an energy function

to find stable conformations of the

ligand / receptor complex

energy

conformational spaceO

NH

OHO

NH2

O

O

NH

OHO

NH2

O

ONH

OHO

NH2

O

protein

iterativeiterative generationgeneration ofofpopulations populations ofof conformersconformers


starting population

final population

modified population

modification of conformations

Selection of conformations

based on energy

GeneticGenetic algorithmalgorithm (gold, (gold, autodockautodock))


reproduction

Selection of conformations based

on energy

modification of selection

parameters(soft hard)

Moitessier N (2008) Br J Pharmconvergence

Monte Carlo Monte Carlo (ICM, RosettaLigand)


Selection of conformations

based on energyx cooling cycles

(T decrease)

Bond rotationTranlationRotation

Ei= +36 kcal/mol Ei= 0 kcal/mol

Ef= +10 kcal/molEf= +22 kcal/mol

if Ef < Ei acceptif Ef > Ei metropolis

⎟⎟⎠

⎞⎜⎜⎝⎛− TKB

z⎟⎟⎠

⎞⎜⎜⎝⎛− >

TKEf -Ei

Bexp

accept

Ef= +2 kcal/mol

…n iterations

Chapter3:

Scoring ligand/receptor

interaction

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empiricalempirical vsvs force force fieldfieldscoringscoring functionsfunctions (SF)(SF)


Force Field SFEmpirical SF

selected ligand/receptor

X-ray structures

experimental ΔG

non-bonded interactionsdistance between ligand -receptor pairs of atoms

empirical SF knowledge-based SF

S = Eligand + Ecomplexe

Eligand = internal energy

Ecomplexe =

Prediction of free energy by Empirical SFPrediction 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 MOE affinity affinity dGdG SFSF

fkGhb

hbbind +=d ∑

k: constantf: count of atomic contact

hb fkml

ml +∑ ml fkhh

hh +∑ hh fkhp

hp +∑ hp fkaa

aa∑ aa

hb: H-bond donor – H-bond acceptorml: ionic metal - ligandhh: hydrophobic atom – hydrophobic atomshp: hydrophobic atom – polar atomaa: any atom - any atom

Böhm (1994)

( ) ( ) NROTGAGRfGRfGGG rothb

lipolipoionic

ionichb

hbbind Δ+Δ+ΔΔ+ΔΔΔ+Δ=Δ ∑∑∑ α,0

+5.4 kJ/mol -4.7 kJ/mol -8.3 kJ/mol -0.17 kJ/mol.Å2 +1.4 kJ/mol/rot

H-bond ionic lipophilic rotation

-50 -45 -40 -35 -30 -25 -20-50

-45

-40

-35

-30

-25

-20

ΔGpr

ed, k

J/m

ol

ΔGexp, kJ/mol

Q2LOO = 0.777, spress=3.15 kJ/mol, n=37 r 2pred = 0.698, s = 5.33 kJ/mol, n=16

-55 -50 -45 -40 -35 -30 -25 -20-60

-55

-50

-45

-40

-35

-30

-25

ΔG

pred

, kJ/

mol

ΔGexp, kJ/mol

test

ExempleExemple of empirical SF performance of empirical SF performance


training


StrengthStrength andand weaknessweakness ofof SFSF

• 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, localenvironment of interaction)

• independant of training set

• strongly depends on ligand size• force field accuracy, difficulty to set parameters• no account of entropy• Often includes empirical terms !

Empirical SF

Force Field SF


postpost--processingprocessing output: pose output: pose selectionselection by by Interaction Interaction FingerprintFingerprint analysisanalysis

8 bits / target site residue

1.1. HydrophobiHydrophobicc contact2.2. AromaticAromatic interaction

(Face/Face)

3.3. AromaticAromatic interaction (Face/Edge)

4.4. HH--bondbond (Donor in ligand)

5.5. HH--bondbond (Donor in protein)

6.6. IonicIonic bond (+) in ligand

7.7. IonicIonic bond (+) in protein

8.8. MetalMetalInteraction FingerPrint

AA1 AA2 … AAn10000000 01001000 … 01000100

ligand protein

Marcou, G et al (2007) J Chem Inf Model

conclusion

What can we achieve?

What remains to be improved?

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The state of the artThe 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


The limitationsThe 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

Flexibility of the protein /binding site

Fuzzy scoring functions

no serious problem

a difficult problem

the biggest problem

Documents

Docking: placing a ligand into a receptor cavity - Unistrainfochim.u-strasbg.fr/FC/docs/Docking/course-docking.pdf · docking Geometrical Complementarity non-covalent inter-molecular