Rational Drug Design using Genetic Algorithm

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

http://images.google.com/imgres?imgurl=www.molsoft.com/images/ligand.gif&imgrefurl=http://www.molsoft.com/icmpages/icmlite.htm&h=656&w=700&prev=/images?q=ligand&svnum=10&hl=en&lr=&ie=UTF-8&oe=UTF-8


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


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


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


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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[1] Log p: octanol/water partition coefficient



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[1] Log p: octanol/water partition coefficient


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 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


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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Technology

Rational Drug Design using Genetic Algorithm