Target Based Rational Drug Design
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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) 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)
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
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
Determine total potential energy, Vtotal, as a function of conformation
Internal energybondsvalence anglesdihedrals
(nonbond, interaction)van der Waals (VDW, LJ)electrostatic
Similar equation for valence angles:
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
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).
Force vs bond length
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
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
2) Design of novel compounds to fit into receptor target site (de novo design).
Geometric arrangement of types of functional groups that are required for “activity” to the active site of HIV protease
Many molecular could fulfill the above pharmacophore: Identify such molecules via database screening or de novo design
Identify novel lead compounds from databases of known compounds
Chemical databasesCambridge Structural DatabaseChemical AbstractsNational Cancer InstitutePharmaceutical CompaniesSoP Computer-Aided Drug Design Center
1) Determine solvent accessible (Connelly) surface of the binding pocket by rolling a sphere the size of a water molecule along the surface
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
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
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
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!).
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
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
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.
G = Geq,2 – Geq,1 = Greceptor - Gsolution
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.
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