Molecular Docking

G. Schaftenaar

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.

Two main tasks of Docking Tools

• Sampling of conformational (Ligand) space

• Scoring protein-ligand complexes

• 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

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)

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:

kTEEP BA exp

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

Simulated Annealing

Finding a global minimiumby lowering the temperatureduring the Monte Carlo/MD simulation

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

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.

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

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

Pose-Clustering

• For each triangle of receptor compute the transformation to each ligand matching triangle.

• Cluster transformations.• Score the results.

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

Scoring Functions

• Shape & Chemical Complementary Scores

• Empirical Scoring• Force Field Scoring• Knowledge-based Scoring• Consensus Scoring

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

Empirical Scoring

Scoring parameters fit to reproduceMeasured binding affinities

(FlexX, LUDI, Hammerhead)

Empirical scoring

rotrot NGGG 0

bondsHneutral

hb RfG.

,

.

,intionic

io RfG

intarom

arom RfG.

,

..

,contlipo

lipo RfG

Loss of entropy during binding

Hydrogen-bonding

Ionic interactions

Aromatic interactions

Hydrophobic interactions

lig

i

prot

j ij

i

ij

ij

ij

ijnonbond

j

r

qq

r

B

r

AE c612

Force Field Scoring (Dock)

Nonbonding interactions (ligand-protein):

-van der Waals -electrostatics

Amber force field

Knowledge-based Scoring Function

Free energies of molecular interactionsderived from structural information onProtein-ligand complexes contained in PDB

lpreflp FPP ,exp,

Boltzmann-Like Statistics of InteratomicContacts.

Distribution of interatomic distances is converted

into energy functions by inverting Boltzmann’s law.

F P(N,O)

Potential of Mean Force (PMF)

ijsegi

corrVolBij

rrfTkrF ij

bulk

_ln

rijseg Number density of atom pairs of type

ijat atom pair distance rij

bulk Number density of atom pairs of type ijin reference sphere with radius R

Consensus Scoring

Cscore:

Integrate multiple scoring functions toproduce a consensus score that ismore accurate than any single

functionfor predicting binding affinity.

Virtual screening by Docking

• Find weak binders in pool of non-binders

• Many false positives (96-100%)• Consensus Scoring reduces rate of

false positives

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 severalscoring functions in a consensus-scoring strategy