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STRUCTURE BASED
AND LIGAND
BASED DRUG
DESIGNING
VYSAKH MOHAN M
DRUG DESIGNING
INTRODUCTION
 CADD
STRUCTURE BASED DRUG DESIGNING
LIGAND BASED DRUG DESIGNING
STRUCTURE BASED AND
LIGAND BASED DRUG
DESIGNING
STRUCTURE
BASED
 Don’t know ligands
 Know receptor
structures
LIGAND BASED
 Don’t know receptors
 Know ligands
STRUCTURE-BASED
DRUG DESIGN
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
STRUCTURE BASED DRUG DESIGNING
 Protein structure determination
 Docking
 Binding free energy
 Flexibility of protein-ligand complex
 De novo evolution
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
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
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
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
DOCKING
 A method which predicts the preferred
orientation of one molecule to a second when
bound to each other to form a stable complex
save
YES
NO
OK
BETTER
DOCKING TOOLS
 AUTODOCK
 Software AutoDock, developed by Olsen’s
Laboratory
 A program for docking small flexible ligands into a
rigid 3D structure
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
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
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
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
A modeled GABA receptor with membrane force field.
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
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.
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
LIGAND-BASED
DRUG DESIGN
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
LIGAND-BASED DRUG DESIGN
 Quantitative structure-activity relationship (QSAR)
 CoMFA
 CoMSIA
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
CoMFA
 Comparative molecular field analysis
 Biological activity of a molecule is dependent of
the surrounding molecular fields (Steric and
electrostatic fields)
 Has several problems
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
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
THANK YOU

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Structure based and ligand based drug designing

  • 1. STRUCTURE BASED AND LIGAND BASED DRUG DESIGNING VYSAKH MOHAN M DRUG DESIGNING
  • 2. INTRODUCTION  CADD STRUCTURE BASED DRUG DESIGNING LIGAND BASED DRUG DESIGNING
  • 3.
  • 4. STRUCTURE BASED AND LIGAND BASED DRUG DESIGNING STRUCTURE BASED  Don’t know ligands  Know receptor structures LIGAND BASED  Don’t know receptors  Know ligands
  • 5.
  • 7. 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
  • 8. STRUCTURE BASED DRUG DESIGNING  Protein structure determination  Docking  Binding free energy  Flexibility of protein-ligand complex  De novo evolution
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. DOCKING  A method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex
  • 15. DOCKING TOOLS  AUTODOCK  Software AutoDock, developed by Olsen’s Laboratory  A program for docking small flexible ligands into a rigid 3D structure
  • 16.
  • 17. 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
  • 18. 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
  • 19. 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
  • 20.
  • 21. 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
  • 22. A modeled GABA receptor with membrane force field.
  • 23. 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
  • 24. 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.
  • 25. 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
  • 27. 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
  • 28. LIGAND-BASED DRUG DESIGN  Quantitative structure-activity relationship (QSAR)  CoMFA  CoMSIA
  • 29. 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
  • 30. CoMFA  Comparative molecular field analysis  Biological activity of a molecule is dependent of the surrounding molecular fields (Steric and electrostatic fields)  Has several problems
  • 31. 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
  • 32. 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