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Structure based and ligand based drug designing Slide 1 Structure based and ligand based drug designing Slide 2 Structure based and ligand based drug designing Slide 3 Structure based and ligand based drug designing Slide 4 Structure based and ligand based drug designing Slide 5 Structure based and ligand based drug designing Slide 6 Structure based and ligand based drug designing Slide 7 Structure based and ligand based drug designing Slide 8 Structure based and ligand based drug designing Slide 9 Structure based and ligand based drug designing Slide 10 Structure based and ligand based drug designing Slide 11 Structure based and ligand based drug designing Slide 12 Structure based and ligand based drug designing Slide 13 Structure based and ligand based drug designing Slide 14 Structure based and ligand based drug designing Slide 15 Structure based and ligand based drug designing Slide 16 Structure based and ligand based drug designing Slide 17 Structure based and ligand based drug designing Slide 18 Structure based and ligand based drug designing Slide 19 Structure based and ligand based drug designing Slide 20 Structure based and ligand based drug designing Slide 21 Structure based and ligand based drug designing Slide 22 Structure based and ligand based drug designing Slide 23 Structure based and ligand based drug designing Slide 24 Structure based and ligand based drug designing Slide 25 Structure based and ligand based drug designing Slide 26 Structure based and ligand based drug designing Slide 27 Structure based and ligand based drug designing Slide 28 Structure based and ligand based drug designing Slide 29 Structure based and ligand based drug designing Slide 30 Structure based and ligand based drug designing Slide 31 Structure based and ligand based drug designing Slide 32 Structure based and ligand based drug designing Slide 33
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Structure based and ligand based drug designing

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Computer Aided Drug Design(CADD)

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Structure based and ligand based drug designing

  1. 1. STRUCTURE BASED AND LIGAND BASED DRUG DESIGNING VYSAKH MOHAN M DRUG DESIGNING
  2. 2. INTRODUCTION  CADD STRUCTURE BASED DRUG DESIGNING LIGAND BASED DRUG DESIGNING
  3. 3. STRUCTURE BASED AND LIGAND BASED DRUG DESIGNING STRUCTURE BASED  Don’t know ligands  Know receptor structures LIGAND BASED  Don’t know receptors  Know ligands
  4. 4. STRUCTURE-BASED DRUG DESIGN
  5. 5. STRUCTURE BASED DRUG DESIGNING  Three dimensional structure of the biological target  Obtained through x-ray crystallography or NMT spectroscopy  If experimental structure is not available, create a homology model of the target, based on the experimental structure of a related protein  Various automated computational procedures may be used
  6. 6. STRUCTURE BASED DRUG DESIGNING  Protein structure determination  Docking  Binding free energy  Flexibility of protein-ligand complex  De novo evolution
  7. 7. PROTEIN STUCTURE DETERMINATION  HOMOLOGY MODELING  Fast method to obtain protein structures  To ensure the rationality of modelled structures, checks on stereochemistry, energy profile, residue environments, and structure similarity are needed
  8. 8. PROTEIN STUCTURE DETERMINATION  FOLDING RECOGNITION  Threading  Calculates the probabilities of 3D structures could form by given protein sequences  Both environment of residues interactions and protein surface area are considered in the threading protocol  Structure with highest probability is recommended to construct the protein model
  9. 9. PROTEIN STUCTURE DETERMINATION  Ab initio PROTEIN MODELING  Based on physical principles, residue interaction center and lattice representation of a protein to build the target  Used when other protocols fail to predict and unknown protein structure  Identity and accuracy given by this modelling is lower than others
  10. 10. PROTEIN STUCTURE DETERMINATION  HOT SPOT PREDICTION  One big issue in SBDD is to determine ligand active site  Determined using X-ray crystallography  But not possible for proteins that cannot be crystallised  Several binding site determination methods have been invented
  11. 11. DOCKING  A method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex
  12. 12. save YES NO OK BETTER
  13. 13. DOCKING TOOLS  AUTODOCK  Software AutoDock, developed by Olsen’s Laboratory  A program for docking small flexible ligands into a rigid 3D structure
  14. 14. DOCKING TOOLS  CDOCKER  This protocol is a docking algorithm and retain all the advantages of full ligand flexibility  Uses a sphere to define an active site, so the knowledge of the binding site is not required  CHARMM based docking algorithm
  15. 15. DOCKING TOOLS  FLEXIBLE DOCKING  Retains receptor flexibility during docking of flexible ligands  ChiFlex algorithm  LibDock program  Indicates the binding site where ligand polar and non-polar groups may be bound to the favourable positions of protein
  16. 16. DOCKING TOOLS  LIGAND FIT  A grid-based method for calculating receptor- ligand interaction energies, which is crucial in initial ligand shape match to the receptor binding site  Consists of  Definition of active site  Analysis of ligand conformations  Docking of ligands to a selected site  Scoring of predicted poses
  17. 17. DOCKING TOOLS  TRANSMEMBRANE PROTEIN MODELING  There are many medicines that target transmembrane protein (HER2 and GABA receptor)  Due to difficulties of crystallisation accurately analysing transmembrane protein is difficult  Since there is influence of phospholipid bilayer a membrane force field option can be included
  18. 18. A modeled GABA receptor with membrane force field.
  19. 19. BINDING FREE ENERGY  All the docking protocols discussed above do not include functions for calculating binding free energy energy of binding = energy of complex – energy of ligand – energy of receptor
  20. 20. FLEXIBILITY OF PROTEIN- LIGAND COMPLEX  Flexibility of complex must be studied  The difference in result of flexible docking and LigandFit is due to difference in flexibility of molecules, such that: Flexibility = score of LigandFit – score of flexible docking  The result of molecular simulation is related to flexibility, and a positive relationship can be obtained in flexibility vs. molecular dynamics.
  21. 21. De novo EVOLUTION  After docking program, we can modify ligands by two methods  Based on active site features to identify functional groups that can establish strong interactions with the receptor. Then, functional groups can be linked  Using the original ligand scaffolds to develop derivatives that can complement the receptor
  22. 22. LIGAND-BASED DRUG DESIGN
  23. 23. LIGAND-BASED DRUG DESIGN  Relies on knowledge of other molecules that bind to the biological target of interest  These other molecules may be used to derive a pharmacophore model  Alternatively, a QSAR relationship, in which a correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived  QSAR may be used to predict the activity of new analogues
  24. 24. LIGAND-BASED DRUG DESIGN  Quantitative structure-activity relationship (QSAR)  CoMFA  CoMSIA
  25. 25. QUANTITATIVE STRUCTURE- ACTIVITY RELATIONSHIP  Employs statistics and analytical tools to investigate the relationship between the structures of ligands and their corresponding effects.  Mathematical models are built based on structural parameters to describe  Earlier 2D-QSAR, but 3D-QSAR have been adopted  3D-QSAR methodologies: CoMFA, CoMSIA
  26. 26. CoMFA  Comparative molecular field analysis  Biological activity of a molecule is dependent of the surrounding molecular fields (Steric and electrostatic fields)  Has several problems
  27. 27. CoMSIA  Comparative molecular similarity index analysis  Includes more additional field properties  Steric  Electrostatic  Hydrophobic  Hydrogen bond donor  Hydrogen bond acceptor  Can offer a more accurate structural-activity relationship than CoMFA
  28. 28. CONCLUSION  Molecular simulation has a vital role in drug design and CADD  Fast, efficient and inexpensive tool to  Discover new possible ligands against a macromolecular target  Test library design ideas  Identify most promising scaffolds and R groups prior to synthesis
  29. 29. THANK YOU
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Computer Aided Drug Design(CADD)

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