Post on 19-Dec-2015
EMBIO – Cambridge
Particle Swarm Optimization applied to Automated Docking
Automated docking of a ligand to a macromolecule
Particle Swarm Optimization Multi-objective PSO + Clustering Docking experiments Conclusion
EMBIO – Cambridge
Automated Docking Predict binding of a ligand molecule to
a receptor macromolecule Minimize resulting binding energy
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Energy Evaluation
[Morris et al.]
EMBIO – Cambridge
Autodock 3.05 Determine energies using trilinear
interpolation on precalculated grid maps
Minimize docking energy with various optimization techniques Simulated Annealing Genetic Algorithm with Local Search
Sum of energies is minimized
EMBIO – Cambridge
Particle Swarm Optimization
Multi-dimensional, numerical optimization by a swarm of particles
Each particle has current position ,best position and velocity
Attracted by personal best positionand neighbourhood best position
EMBIO – Cambridge
PSO Algorithm
EMBIO – Cambridge
Clustering Particles are clustered into K separate
swarm
K-means Clustering m data-vectors are clustered into k
clusters Iteratively calculate centroids of each
cluster
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Multiple Objectives Optimize ,
simultaneously Find dominating solutions
Non-Dominated Front
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Clust-MPSO Update personal best position
Each swarm has non-dominated front is dominated if no particle is in Dominated swarms are relocated
Neighbourhood best particle Picked for several iterations
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EMBIO – Cambridge
1hvr Docking
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4cha Docking
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Convergence – 1hvr
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Convergence – 4cha
EMBIO – Cambridge
Conclusions Application of PSO to Automated
Docking Optimization of two objectives Clustering to divide the search space