Rational Drug Design using Genetic Algorithm

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Rational Drug Design using Genetic Algorithm

  1. 1. Final Year project Rational Drug Design Using Genetic Algorithm Case of Malaria Disease Supervision by Assoc.Prof.Imad Fakhri Taha Alshaikhli Presented By Hassen Mohammed Abdullah AlsafiInternational Islamic University Malaysia
  2. 2. Agenda • Introduction. • Problem statement. • Objectives. • Proposed methods. • Findings and Analysis. • Challenges and Difficulties faced. • Conclusion and Future work. 2Hassen Alsafi International Islamic University Malaysia 1
  3. 3. 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 CompoundHassen Alsafi Protein Medicine 3 3 International Islamic university Malaysia
  4. 4. 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. 4 Hassen Alsafi International Islamic University Malaysia 4
  5. 5. 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) 5 Hassen Alsafi International Islamic University Malaysia 5
  6. 6. Objectives1. 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. 6 Hassen Alsafi International Islamic University Malaysia 6
  7. 7. Drug design and development process 7Hassen Alsafi International Islamic University Malaysia 7
  8. 8. Genetic algorithm flowchart 8 Hassen Alsafi International Islamic University Malaysia 11
  9. 9. Proposed methods1. Target selection and identification. 1.1 Protein preparation in ADT1. Drug or ligand identification. 2.1 Ligand preparation in ADT1. Perform the molecular docking simulation.2. Techniques used in docking algorithm.3. Evaluation . 9 Hassen Alsafi International Islamic University Malaysia 9
  10. 10. MethodologyComputational Molecular docking Ligand database Target ProteinAutoDock 4.2 Molecular docking Ligand docked into protein’s active site 10 Hassen Alsafi International Islamic University Malaysia 10
  11. 11. 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) . 11 Hassen Alsafi International Islamic University Malaysia 11
  12. 12. AutoDock 4.2 12 Hassen Alsafi International Islamic University Malaysia 15
  13. 13. Methods and materials1. Target selection and identification . Target disease Target protein Malaria 2GHU.pdb The protein 3D structured was retrieved form RCSB database. 13 Hassen Alsafi International Islamic University Malaysia 13
  14. 14. Autodock workflow 14 Hassen Alsafi International Islamic University Malaysia 14
  15. 15. Autodock proposed Framework 15 Hassen Alsafi International Islamic University Malaysia 19
  16. 16. 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 16 Hassen Alsafi International Islamic University Malaysia 16
  17. 17. 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: 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 17 Hassen Alsafi International Islamic University Malaysia 17
  18. 18. Grid file parameters(gfp) 18 Hassen Alsafi International Islamic University Malaysia 18
  19. 19. 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 19 Hassen Alsafi International Islamic University Malaysia 19
  20. 20. Experiment results Setup the environment 20 Hassen Alsafi International Islamic University Malaysia 20
  21. 21. Equipments used in the experiment 21 Hassen Alsafi International Islamic University Malaysia 21
  22. 22. Tools and materials 22 Hassen Alsafi International Islamic University Malaysia 17
  23. 23. Genetic algorithm in autodock ADT represent chromosome as a vector of real number . Quaternion genes Tx Ty Tz Qx Qy Qz Qw R1 Rn Translation genesGA features in ADT:1. Solution space.2. Genetic code (chromosome)3. Genetic operations4. Fitness function 23 Hassen Alsafi International Islamic University Malaysia 18
  24. 24. Results and discussion Experiment conduct of 3 cases. Case 1 : Default parameters. Case 2 : Parametric study. Case 3: Computational Docking Time (CDT). 24 Hassen Alsafi International Islamic University Malaysia 19
  25. 25. Case 1 : default parameters Run CMD in 20 drugs compound with 1 target protein. 25 Hassen Alsafi International Islamic University Malaysia 20
  26. 26. [1] Log p: octanol/water partition coefficientCase 1 : default parameters 26 Hassen Alsafi International Islamic University Malaysia 21
  27. 27. Case 1 : default parameters 27 Hassen Alsafi International Islamic University Malaysia 22
  28. 28. Case 1 : default parameters 28 Hassen Alsafi International Islamic University Malaysia 23
  29. 29. [1] Log p: octanol/water partition coefficientCase 1 : default parameters 29 Hassen Alsafi International Islamic University Malaysia 24
  30. 30. Case 2 : Parametric study 480 samples has been investigated with different parametric value. 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 30 Hassen Alsafi International Islamic University Malaysia 25
  31. 31. Case 2 : Parametric study 31 Hassen Alsafi International Islamic University Malaysia 26
  32. 32. Case 2 : Parametric study 32 Hassen Alsafi International Islamic University Malaysia 27
  33. 33. se 3 : computational docking time 33 Hassen Alsafi International Islamic University Malaysia 28
  34. 34. 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  34 Hassen Alsafi International Islamic University Malaysia 29
  35. 35. 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. 35 Hassen Alsafi International Islamic University Malaysia 30
  36. 36. Conclusion and future work In order to perform the CMD faster and accurate , the high speed computers is needed. 36 Hassen Alsafi International Islamic University Malaysia 31
  37. 37. Acknowledgments Special thanks to My beloved supervisor Assco.Prof.Dr.Imad Fakhri Taha Alshaikhli 37 Hassen Alsafi International Islamic University Malaysia 32
  38. 38. Thank you for your attention Q&A 38Hassen Alsafi International Islamic University Malaysia 33

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