Rational Drug Design using Genetic Algorithm
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Transcript of Rational Drug Design using Genetic Algorithm
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Final Year project Rational Drug Design Using Genetic
Algorithm Case of Malaria Disease
Presented ByHassen Mohammed Abdullah Alsafi
International Islamic University Malaysia
Supervision byAssoc.Prof.Imad Fakhri Taha Alshaikhli
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Hassen Alsafi International Islamic University Malaysia
Agenda
• Introduction.
• Problem statement.
• Objectives.
• Proposed methods.
• Findings and Analysis.
• Challenges and Difficulties faced.
• Conclusion and Future work.
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Introduction
How a drug works and how we can expect the body to respond to the
administration of a drug?
Drug design is known as approach uses specifics tools to explore and
search for the best drug candidate.
Drug Compound Protein Medicine
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problem statement
What is the best drug candidate for x disease ?
Drug design and discovery take years for discovering a
new drug and very costly.
Effort to cut down the research timeline and cost by
reducing laboratory experiment use computational
computer modeling.
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Rational drug design approach(rdda)
Foundation of drug design and discovery.
Answer the question , which molecule fit best to the protein active site?
Computational Molecular Docking (CMD)
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Objectives
1. Find and Select the target disease in the
human body.(e.g malaria)
2. Search and choose the best drug candidate.
3. Conduct computational drug design
simulation.
4. Propose some drugs against certain disease
based on results.
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Drug design and development process
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Genetic algorithm flowchart
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Proposed methods
1. Target selection and identification.
1.1 Protein preparation in ADT
2. Drug or ligand identification.
2.1 Ligand preparation in ADT
3. Perform the molecular docking simulation.
4. Techniques used in docking algorithm.
5. Evaluation .
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Methodology
Computational Molecular docking
AutoDock 4.2
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Ligand database Target Protein
Molecular docking
Ligand docked into protein’s active site
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AutoDock 4.2
Automated computational molecular docking
programs .
It is designed to predict how small molecules,
bind to a receptor of known 3D structure.
It uses Genetic Algorithm (GA) .
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AutoDock 4.2
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Methods and materials
1. Target selection and identification.
The protein 3D structured was retrieved form RCSB database.
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Target disease Target protein
Malaria 2GHU.pdb
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Autodock workflow
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Autodock proposed Framework
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Protein databank (pdb)
Molecular protein repository . Contains a tons of protein stored in the
repository. In order to convert the drug compound
from .sdf to pdb <openbabel> software used by the following commend line:
-i: input type(i.e .sdf and pdb) -o: output(convert) type
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Grid file parameters(gfp)
After finish the preparation of protein and
drug , now the task is to precalculate
the grids using the following Linux
commend line:
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autogrid4 –p filename.gpf –l filename.glg
-p: used to specifics the grid parameter file gpf: grid parameters file–i: used as log file output .glg :grid log file
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Grid file parameters(gfp)
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Docking file parameters in adt
Primary goal of AutoDock is to instruct the drug
to move inside the space search grid.
GA selected as search algorithm in the
experiment.
Run the following Linux commend line :
autodock4 –p filename.dpf –l filename.dlg
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Experiment results
Setup the environment
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Equipments used in the experiment
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Tools and materials
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Genetic algorithm in autodock ADT represent chromosome as a vector of
real number .
Quaternion genes
Translation genes
GA features in ADT: 1. Solution space.2. Genetic code (chromosome)3. Genetic operations 4. Fitness function
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Tx Ty Tz Qx Qy Qz Qw R1 Rn
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Results and discussion
Experiment conduct of 3 cases. Case 1 : Default parameters. Case 2 : Parametric study. Case 3: Computational Docking Time (CDT).
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Case 1 : default parameters
Run CMD in 20 drugs compound with 1 target protein.
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Case 1 : default parameters
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[1] Log p: octanol/water partition coefficient
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Case 1 : default parameters
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Case 1 : default parameters
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Case 1 : default parameters
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[1] Log p: octanol/water partition coefficient
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Case 2 : Parametric study
480 samples has been investigated with different parametric value.
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Parameter Value
Pop size(50) 50,100,150
Crossover rate(0.2) 0.2, 0.4, 0.6, and 0.8
Mutation(0.01) 0.01 and 0.02
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Case 2 : Parametric study
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Case 2 : Parametric study
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Case 3 : computational docking time
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Challenges faced
Compiling the python source code under ADT environment.
Installing the openbabel software. Dealing with the bioinformatics tools.
Time given to complete the project. Moving from the old building to the new
building
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Conclusion and future work
Computational molecular docking with GA are crucial tools in RDD.
Using the ADT we can reduce the use of laboratory experiments(but not at all)
RDD helps to reduce the time required to design and discover new drugs .
Future work Further investigation is needed to select
the best potential drug candidate . I propose to deploy the grid computing in
the CMD. 30
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Conclusion and future work
In order to perform the CMD faster and
accurate , the high speed computers is
needed.
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Acknowledgments
Special thanks to My beloved supervisor
Assco.Prof.Dr.Imad Fakhri Taha Alshaikhli
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Thank you for your attention Q & A
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