docking algorithms pavithra
TRANSCRIPT
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MOLECULAR RECOGNITION AND
DOCKING ALGORITHM
Natasja Brooijmans & Irwin Kuntz
Pavithra.K.B
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INTRODUCTION
Binding between two molecules such that
their orientation maximizes the interaction
Earlier use
Protein-ligand complex
To find and optimize lead compounds
Docking program- key parts
Search
Scoring
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1. Protein-Protein Docking: Both molecules are rigid Interaction produces no change in conformation Similar to lock-and key model
2. Protein-Ligand Docking: Ligand is flexible but the receptor protein is rigid
Interaction produces conformational changes inligand
Categories of docking
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1. Protein-Protein Docking
2. Protein-Ligand Docking
optimized
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It involves:
Finding useful ways of representing the molecules and molecularproperties.
Exploration of the configuration spaces available for interactionbetween ligand and receptor. Evaluate and rank configurations using a scoring system, in this
case the binding energy
However, since it is difficult to evaluate the binding energy because the
binding sites may not be easily accessible, the binding energy is modeledas follows:
G bind= Gvdw+ Ghbond + Gelect+ G conform+ G tor + Gsol
Docking uses a search and scoremethod
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Flexibility Algorithms
Ligand FlexibilityAlgorithm
IncrementalConstruction
Monte carloAlgorithms
EvolutionaryAlgorithms
MolecularDynamicsMethod
Receptor FlexibilityAlgorithm
ConformerLibraries
Monte carlomethods
MolecularDynamicsMethod
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Algorithms
Systematic searches
Incremental construction
Stochastic searches
Montecarlo
Evolutionary Algorithm
Genetic Algorithm and LamarckianGA
Deterministic searches Energy minimization methods
Molecular dynamics simulations
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Incremental Construction
Anchor and grow method
Divide a ligandrigid and flexible.
Rotatable bonds Takes lowest n posenext level- Greedy
algorithm.
DOCK 4.0, FlexX Hammerhead- fragments- database screening.
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Monte Carlo Algorithm
Ligand is considered as whole
Random changes- translation, rotation & torsion.
Each move- structure is minimized
First cycle- high temperature
(Simulated annealing MC)- Autodock
Metropolis selection criterion= Local energy
minimization+ surface based solvationenergy+entropy calculation
Structure accepted or rejected. Prodock
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Simulated Annealing Algorithm modeled after the cooling of a solution to form glass,
though its better explained by crystal formation Given a long enough cooling time, molecules will relax into their
lowest energy state to form the largest crystals
Quick cooling - highly disordered system Slow cooling - highly ordered crystal, with each molecule in its
lowest energy state Algorithm simulates either linear or proportional slow cooling
At high temperatures, many higher energy solutions willbe accepted; at low temps., majority of probabilisticmoves rejected
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Evolutionary Algorithm
Stochastic method similar to MC methods
Finds global energy minimum
Fittest individuals carried- next generation
Random or biased mutation- genetic diversity
& prevent premature convergence
Cross over permitted in Genetic Algorithm
GOLD
AutodockGA and LGA (Lamarckian)
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How Genetic Algorithms Work -
A Simple Example
1 1 1 1 0 0
0 0 0 0 0 1
1 0 0 0 0 1
0 0 0 0 0 0
Initial population ofbinary creatures
having 6 genes Each gene has two
different alleles, eithera 0 or a 1
Three operators:crossover, mutationand selection
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Selection
1 1 1 1 0 0
0 0 0 0 0 1
1 0 0 0 0 1
0 0 0 0 0 0
Selection based on afitness function f(x)
This operator choosesthose individuals withthe lowest values
Those with highervalues chosen with avery low probability
20
13
48
52
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Crossover
0 0 0 1 0 0
1 1 1 0 0 1
1 1 1 1 0 1
0 0 0 0 0 0
1 1 1 1 0 0
0 0 0 0 0 1
1 1 1 1 0 0
0 0 0 0 0 1
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Mutation
0 0 1 1 0 0
1 1 1 0 1 1
1 1 1 1 0 1
0 0 1 0 1 0
0 0 0 1 0 0
1 1 1 0 0 1
1 1 1 1 0 1
0 0 0 0 0 0
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Replacement
Lower scoring individuals createmore offspring, higher scoringones create fewer or none at all
Offspring replace parentalgeneration
Cycle of selection, mutation,crossover and replacementrepeated
0 0 1 1 0 0
1 1 1 0 1 1
1 1 1 1 0 1
0 0 1 0 1 0
15 1
9 1
22 0
1 2
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MOLECULAR DYNAMICS METHODS
Cannot cross energy barriers
Easily trappedlocal minimauneven
potential energy
Two Approaches
Temperature ofthe simulation
Manipulatepotential energy
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SCORING FUNCTIONS
Accurate Binding free energy- time consuming
MM-PBSA & ES/IS methods
First-principles Methods DOCK & Autodock - Coulomb and
Van der waals force field
EUDOC- force field function- without grid
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Semi-empirical methods
Aqvist and coworkers- The linear interaction
energy (LIE)
Kuntz group developed SDOCK combines the van der Waals force field score
and the generalized Born-surface area (GB-SA)
Surface area
term-favors
VDW
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Empirical methods
Logical extension of Structure Activity
Relationship
First empirical scoring function LUDI
Reduction in
rotational
entropy
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The Application
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HIV-1 Protease and AHA006
HIV-1 Protease in complex with the cyclicsulfamide inhibitor, AHA006
Source: Protein Data Bank Authors: K. Backbro, T. Unge Exp. Method: X-ray Diffraction (2 res.) Primary Citation: Backbro et al, J Med Chem
40 pp. 898 (1997) Polymer Chains: A, B; Residues: 198; Atoms:
1632
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Protein (HIV-1 Protease)
Ligand(AHA006)
(Source: PDB)
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HIV-1 Protease dimer
(Rasmol)
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(SYBYL)
Initial X-Raycrystallographicpositions of protein
and ligand
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Docking Preparation Ligand
Assign charges Define rotatable bonds
Rename aromatic carbons Merge non-polar hydrogens Write .pdbq ligand file
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Docking Preparation Protein
Add essential hydrogens Load charges
Merge lone-pairs Add solvation parameters Write .pdbqt protein file
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AutoDock uses grid-
based docking
Ligand-proteininteraction energies
are pre-calculated and
then used as a look-uptable during
simulation
Grid maps areconstructed based onatoms of interest inligand (here CANOSH)
Docking Preparation Grid
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(AutoDockTools)
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(SYBYL)
Original ligand confSA conformation #67
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Original ligand confSA conformation #67
(SYBYL)
Close-up of previous
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