Protein-ligand docking predicts the binding orientation of one molecule to another to form a stable complex. It finds the best conformation with minimum energy and maximum stability by generating multiple poses and scoring them based on binding affinity. Common docking algorithms like AutoDock and GOLD use search methods like genetic algorithms and simulated annealing to find the best binding pose. They evaluate binding free energy terms like hydrogen bonding, electrostatics, and solvation to score and select conformations.

1. Protein – Ligand Docking
Submitted by:-
Usha
M.Sc.
Bioinformatics

2. Docking
It predicts the best bounding orientation of
one molecule to other to form a stable
complex.

3. Protein ligand docking
• Given a protein structure & predict its ligand binding site.
• Finds the best conformation with minimum energy and maximum
stability.

4. Components of docking software
• Search algorithm
Generates a large number of poses of molecule in binding site.
• Scoring function
Calculate a score or binding affinity for particular pose.

5. Algorithm
• AutoDock
• GOLD

6. AutoDock
• Search methods
Genetic algorithm
 Stimulated annealing
Global – local search method based by Lamarckian genetic algorithm
• Finds the binding free energy for predicting the conformation

7. Genetic algorithm
• Finds the best binding pose
• Ligands are the state variables that correspond to a gene
• An initial population is generated randomly

8. Genetic algorithm
• Crossovers occurs in the individual
• Offspring are mutated randomly
• Selection of the fittest to next generation
• Repeat the generation until some improvement is observed

9. N = (F – E)/ (F - <F>), when F ≠ <F>
Where :
N - number of offspring to be allocated to individual
<F> - mean fitness of individual
F – fitness of worst individual or highest energy in n
generations
E - fitness of individual (energy of protein & ligand)
• F > E or <F>
• If E = F than E < <F>
• If (F – E) > (F - <F>) ; these individual will allocate at least one
offspring.

10. Stimulated annealing
• Start with random or specific pose
• Make random state changes, accepting uphill move with probability
dictated by temperature.
• Reduce temperature after each move
• Stop after temperature gets very small

11. Lamarckian genetic algorithm
Hybrid of genetic algorithm & adaptive local
search is LGA

12. Energy evaluation
ΔG= ΔGvdw + ΔGhbond +ΔGelect + ΔGconfor
+ΔGtor +ΔGsol
ΔGelect – electrostatic forces
ΔGconfor – deviation from covalent bond
ΔGsol – solvation & binding effect
ΔGvdw – vanderwal forces (dispersive/repulsive forces)
ΔGhbond – hydrogen bonding
ΔGtor – torsion angles

13. Observed vs predicted binding free energies

14. GOLD
• Scoring function
Goldscore
Chemscore
• Local scoring
Standard scoring
Local optimization
• Mechanism for protein ligand binding
• Search algorithm
It occur by genetic algorithm

15. Scoring function
• Goldscore
Gold fitness = Shbond-ext + Svdw-ext +Shbond-int +Svdw-int
Shbond – hydrogen bond score due intramolecular force & protein ligand
binding score
Svdw – vanderwal forces score due to intramolecular force and protein
ligand binding score

16. Scoring function
• Chemscore
ΔG = ΔG0 + ΔGhbondShbond + ΔGmetalSmetal + ΔGlipoSlipo +
ΔGrotSrot
ΔGhbondShbond – free energy for binding & score for hydrogen bond
ΔGmetalSmetal - free energy for binding & score for metal interaction
ΔGlipoSlipo - free energy for binding & score for
lipid interaction
ΔGrotHrot - - free energy for binding & score for
rotational movement

17. Tools for protein ligand docking
• HADDOCK
• Cluspro
• PatchDock
• PyDock
• SwissDock