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Target Based Rational Drug Design

Target Based Rational Drug Design

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Target Based Rational Drug Design

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  1. Target Based Rational Drug Design Objectives: Current drug discovery efforts are based on rationally identifying chemical compounds that will bind to therapeutic target molecules such as proteins.  These efforts are based on the 3D structure of the target molecule and use a variety of computer-based molecular modeling techniques to exploit the 3D structural information.  Upon completion of these lectures, the student will be familiar with molecular modeling approaches that are used in target based drug design and how they facilitate bringing drugs to market.

  2. Goals of target-based drug design • Understand atomic details of drug binding strength and specificity 2) Identify or create novel molecules to bind to a selected target and elicit a biological response via de novo drug design or database searching techniques 3) Optimize the therapeutic index of an already available drug or lead compound Computer-Aided Drug Design (CADD)

  3. Molecular Modeling 3D representation of molecules based on graphic or mathematical representations of chemical structures Qualitative versus quantitative models Relate 3D structure/conformation to energy via a mathematical equation

  4. Quantum mechanics versus molecular mechanics Quantum mechanicsi) treat electrons explicitlyii) limited to 100 atoms Molecular mechanicsi) atom is smallest particle ii) every atom interacts with every other atom iii) allows systems containing 10s of thousands to millions of atoms to be studied

  5. Molecular mechanics(empirical energy function) Determine total potential energy, Vtotal, as a function of conformation Internal energybondsvalence anglesdihedrals External energy (nonbond, interaction)van der Waals (VDW, LJ)electrostatic

  6. Potential energy vs. bond length Similar equation for valence angles:

  7. How can a mathematical model be related to the type of chemical being studied? Parameters: single bond: bo of 1.54 Å,Kb of 100 kcal/mole double bond: bo of 1.34 Å, Kb of 150 kcal/mole triple bond: bo of 1.20 Å, Kb of 200 kcal/mole

  8. Potential energy vs. dihedral angle

  9. Example: dihedral energy for a double bond

  10. External energy vs. distance

  11. Treatment of hydrogen bonding via partial atomic charges, where the sum of the electrostatic interactions between each atom (i.e. O with H, O with N, C with H and C with N) yields an interaction energy appropriate for a hydrogen bond (~-1 to -5 kcal/mol).

  12. van der Waals energy vs. distance

  13. Total potential energy for rotation of the O-C-O-H dihedral in acetic acid

  14. Energy Minimization Force vs bond length

  15. Hypothetical energy surface for conformational changes in a protein Acetic acid: 1 degree of freedom leading to 2 conformations Proteins: ~10 conformations/amino acid leading to 10n conformations where n is the number of amino acids. Note global minimum vs. local energy minima Therefore: require methods to more rigorously sample conformational space that allow energy barriers to be overcome

  16. Schematic diagram of a molecular dynamics simulation

  17. Information required for calculation of energy, for minimization and for molecular dynamics Energy 1) atom positions Energy minimization 1) atom positions 2) forces Molecular dynamics simulation 1) atom positions 2) forces 3) atomic velocities: assign randomly to correspond to desired temperature

  18. Computer-Aided Rational Drug Design:Lead Identification • Dock known chemicals from an in silico database into the receptor target site (database searching or screening). 2) Design of novel compounds to fit into receptor target site (de novo design).

  19. Pharmacophore Geometric arrangement of types of functional groups that are required for “activity” to the active site of HIV protease

  20. Pharmacophore identification:1) receptor analysis

  21. Pharmacophore identification:2) identify functional group3) determine distances

  22. Final Pharmacophore Many molecular could fulfill the above pharmacophore: Identify such molecules via database screening or de novo design

  23. Grid • a) Prepare a 3D lattice of grid points encompassing the binding site • b) Determine interaction energy of different types of functional groups with binding site at each grid point. • c) Select favorable interaction sites • d) Determine relative spatial orientations of the selected interaction sites • Types of functional groups and their positions yields a pharmacophore. Grid may also be used to calculate interaction energies of ligands with a receptor.

  24. Database searching Identify novel lead compounds from databases of known compounds Chemical databasesCambridge Structural DatabaseChemical AbstractsNational Cancer InstitutePharmaceutical CompaniesSoP Computer-Aided Drug Design Center

  25. Docking 1) Determine solvent accessible (Connelly) surface of the binding pocket by rolling a sphere the size of a water molecule along the surface

  26. Docking 2) Generate "negative" image of receptor based on spheres that is complementary to the receptor surface generated in step 1 3) Determine sphere-sphere distances of the negative receptor image 4) Convert sphere-sphere distances to possible atom-atom distances 5) Compare atom-atom distances with actual atom distances of chemical compounds in a database 6) Select ligands with greatest overlap for further studies (perform conformational search and energy minimization to obtain low energy structure of ligand in binding site). 7) Calculate ligand-receptor interaction energies (via GRID, see below) of selected ligands

  27. De Novo Drug Design: Groupbuild Build novel compounds that are complementary to a target binding site on a protein via “random” combination of small molecular fragments. Example fragments: formic acid, formaldehyde, formamide, amine, benzene, cyclohexane, cyclopentane, ethane, ethylene, water, methanol, methane, sulfone, thiophene

  28. Groupbuild Structure Generation 1) Define scoring grids for the binding siteElectrostaticVDW (steric) 2) Structure generationa) Predock a "core" (starting) fragment into the binding siteb) Structure build up (extension of compound)c) Randomly select one of top 25% of the new structuresd) Iterate over steps B and C (i.e. keep building) until termination criteria are fulfilled 3) Selection of built structures for synthesis and analysis

  29. Groupbuild build-up procedure

  30. Analyses of built structures • Visual examination of structures for chemical feasibility B) Identify specific positions of certain functional groups etc. that my be related to a known pharmacophore C) Database screening for similar compounds in chemical databases (avoid synthesis!).

  31. Limitation of database screening and de novo design Rigid geometry of receptor and ligand Ligands often treated as flexible Multiple conformations of receptor can partially overcome rigid representation Inherent assumptions and simplifications in molecular mechanics

  32. Target-based lead compound optimization:Free energy perturbation calculations

  33. Steps involved in drug-receptor interactions 1) diffusion controlled encounter rate 2) initial Michaelis complex 3) desolvation of both inhibitor and binding site 4) conformational changes of both inhibitor and binding site upon binding 5) correct orientation between drug and receptor binding site All the above cannot be calculated, therefore, need a trick to make the problem computational feasible

  34. Thermodynamic cycle applied to relative binding of two inhibitors

  35. How is an alchemical perturbation performed? Single bond: bo of 1.54 Å, K of 100 kcal/mole Double bond: bo of 1.34 Å, K of 150 kcal/mole Gradually change the equilibrium and force constants. For example the equilibrium bond length of 1.54 goes to 1.34 Å in 10 steps of 0.02 Å and the force constant of 100 goes to 200 kcal/mole in 10 steps of 10 kcal/mole. Sum over the “change” in energy in each step to get the total free energy difference between C-C and C=C.

  36. Data for binding to a hypothetical receptor G = Geq,2 – Geq,1 = Greceptor - Gsolution

  37. Free Energy Component Analysis Since energy calculations involve contributions from every atom in the system, the individual contributions of functional groups on the drug to drug-receptor interactions or solvation energies can be estimated. This allows for a detailed understanding of the relationship of structural changes to changes in binding to be determined. From this information atomic details of drug solubility and drug-receptor interactions are obtained that can be used to better interpret experimental data and make predictions on how the change a drug's structure to improve it's biological activity.

  38. Lead compound optimization via free energy perturbation calculation Improved interpretation of experimental data allowing for rational design of compounds Solvation vs. receptor binding Contributions of amino acids and/or drug functional groups to binding Prediction of modifications to improve activity