Molecular Docking G. Schaftenaar
Dec 18, 2015
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
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