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COMPUTER-AIDEDDRUG DESIGNThursday, April 19, 2012Kazi Shefaet Rahmank.s.rahman@gatech.edu
Outline• The pharmaceutical pipeline• Structure-based drug design: docking• Ligand-based drug design: pharmacophore modeli...
What is a drug?               A substance that, when absorbed,                  alters normal bodily function        In ph...
Major Drug Classes Small Molecule                           Protein              VaccineAspirin                           ...
Where do drugs come from?                  Vaccines        Natural                    4%             5%                Bio...
Natural Sources Aspirin is derived   Taxol was discovered      Morphine was purified  from willow bark    in the Pacific y...
Success Rate in Pharma                                      10,000                                      1,000             ...
Compound Libraries     Commercial, government or   10,000 – 10,000,000            academic                 compounds
Target Identification         Farooque & Lee. Annu Rev Physiol 71, 465–487 (2009)
Assay Development         Stockwell. Nature 432, 846-854 (2004)
Ligand Design Pathway   Identify                                         High-          in vitro              Validate   C...
Computer-Aided Drug Design• Enrich existing compound libraries• Reduce amount of chemical waste• Faster progress• Lower co...
Computers in the Ligand Design Pathway    Identify                                          High-          in vitro       ...
Structure-based and Ligand-based                   Receptor Structure?             Known                      Unknown     ...
Video: Molecular Docking using Glide
Protein-Ligand Docking• For a receptor-ligand complex, we want to predict:  1. Preferred orientation (pose)  2. Binding af...
Molecular Representations in Docking1.    Atomic     • Every atom represented     • Usually used with a molecular mechanic...
Search Algorithms: Ligand Flexibility1.    Systematic                                                 •   DOCK            ...
Alchemical Free Energy Calculations                       ΔGI                              Laq + Paq                      ...
Scoring Functions1.     Force-field-based                                                                                 ...
Force-field-based Scoring FunctionAutodock v4.0                Kitchen et al. Nat Rev Drug Discov 3, 935–949 (2004)
Empirical Scoring Functions• LUDI Böhm. J Comput Aided Molec Des 8, 243-256 (1994)• ChemScore Eldridge et al. J Comput Aid...
Knowledge-Based Scoring Functions                  Example: ligand carboxyl O to protein histidine N                  Proc...
Scoring: General Caveats• Ligand flexibility and size   • For rigid molecules, correct pose predicted 90-100% of the time ...
Lead Optimization and de novo Design• Lead Optimization   • Evaluate small changes in structure to distinguish between a μ...
Docking and ADME Evaluation• Absorption, distribution, metabolism and excretion• Concentrate on drug interaction with mode...
Structure-based and Ligand-based                   Receptor Structure?             Known                      Unknown     ...
Lipinski’s Rule of FiveA rule of thumb to evaluate the likely activity of an oral drugcandidate1. ≤5 hydrogen bond donors2...
Pharmacophore modeling• Pharmacophore: Set of features common to all known ligands of a particular target• Methodology:  1...
Pharmacophore-based Virtual Screening• Screen compound library for ligands containing pharmacophore of interest• Methodolo...
Challenges in Pharmacophore Modelling• Modeling ligand flexibility  • Conformers pre-enumerated or generated on-the-fly  •...
Quantitative Structure/Activity Relationships• A QSAR is a mathematical relationship between the geometrical and chemical ...
QSAR Requirements1. Dataset • Experimental measurements of the biological activity of a group of   chemicals2. Descriptors...
Molecular Descriptors in QSAR1.     Constitutional     • Total number of atoms, atoms of a certain type, number of bonds, ...
QSAR Methodology• Thousands of descriptors can be generated for each molecule• Several descriptors will be correlated  • E...
3D QSAR• If the structure of ligands are known, one can map important chemical descriptors into 3D space• This will genera...
Comparative Molecular Field Analysis• Principle: Differences in binding/activity often due to differences in the shape of ...
CoMFA Example                                 3D alignment                                                CoMFA           ...
CADD Lecture
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CADD Lecture

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Slides from a guest lecture on computer-aided drug design in the Spring 2012 Macromolecular Modeling course at Georgia Tech

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CADD Lecture

  1. 1. COMPUTER-AIDEDDRUG DESIGNThursday, April 19, 2012Kazi Shefaet Rahmank.s.rahman@gatech.edu
  2. 2. Outline• The pharmaceutical pipeline• Structure-based drug design: docking• Ligand-based drug design: pharmacophore modeling and QSAR
  3. 3. What is a drug? A substance that, when absorbed, alters normal bodily function In pharmacology: FDA-approved for the diagnosis, treatment, or prevention of disease.
  4. 4. Major Drug Classes Small Molecule Protein VaccineAspirin Insulin Lovastatin Trastuzumab Amprenavir Erythropoietin
  5. 5. Where do drugs come from? Vaccines Natural 4% 5% Biologics 14% Natural Derivatives 23% Synthetic Synthetic 40% Natural Mimics 14% Newnan & Cragg. J Nat Prod 70, 461–477 (2007)
  6. 6. Natural Sources Aspirin is derived Taxol was discovered Morphine was purified from willow bark in the Pacific yew tree from opium poppies
  7. 7. Success Rate in Pharma 10,000 1,000 100 10 1 marketable drug
  8. 8. Compound Libraries Commercial, government or 10,000 – 10,000,000 academic compounds
  9. 9. Target Identification Farooque & Lee. Annu Rev Physiol 71, 465–487 (2009)
  10. 10. Assay Development Stockwell. Nature 432, 846-854 (2004)
  11. 11. Ligand Design Pathway Identify High- in vitro Validate Compound throughput libraries screening in vivo Develop assay Target Discovery Lead Discovery Lead Optimization
  12. 12. Computer-Aided Drug Design• Enrich existing compound libraries• Reduce amount of chemical waste• Faster progress• Lower costs
  13. 13. Computers in the Ligand Design Pathway Identify High- in vitro Validate Compound throughput libraries screening in vivo Develop assay Target Discovery Lead Discovery Lead Optimization Bioinformatics Computer-Aided Drug Design
  14. 14. Structure-based and Ligand-based Receptor Structure? Known Unknown Ligand-Based Design Structure-Based Design e.g. pharmacophore modeling & e.g. Docking QSAR
  15. 15. Video: Molecular Docking using Glide
  16. 16. Protein-Ligand Docking• For a receptor-ligand complex, we want to predict: 1. Preferred orientation (pose) 2. Binding affinity (score)• A docking program has least two functional components 1. Search algorithm 2. Scoring function• Docking can be used for virtual screening, lead optimization, or de novo design of ligands
  17. 17. Molecular Representations in Docking1. Atomic • Every atom represented • Usually used with a molecular mechanics scoring function • Computationally complex2. Surface • Molecules represented by solvent excluded surface • Align points by minimizing surface angle • Commonly used in protein-protein docking3. Grid • Receptor’s energetic contributions stored in grid points • van der Waals, electrostatic, H-bonding terms • May be combined with atomistic representation at binding site surface
  18. 18. Search Algorithms: Ligand Flexibility1. Systematic • DOCK • FlexX • Cycle through values for each degree of freedom • Glide • Quickly leads to combinatorial explosion! • Hammerhead • Often implemented in anchor-and-grow algorithms • FLOG2. Stochastic • AutoDock • MOE-Dock • Make random changes, and evaluate using Monte Carlo or genetic algorithms • GOLD • Tabu search algorithms minimize repetition of dead-ends • PRO_LEADS3. Simulation • DOCK • Glide • Molecular dynamics • MOE-Dock • Energy minimization often used with other search • AutoDock technique • Hammerhead
  19. 19. Alchemical Free Energy Calculations ΔGI Laq + Paq ΔGbind Paq + Lg ΔGII ΔGbind = ΔGI – ΔGII (PL)aq
  20. 20. Scoring Functions1. Force-field-based • D-Score • Quantify sum of receptor-ligand interaction energy and internal ligand • G-Score energy • GOLD • Ligand-receptor potential contains van der Waals and Coulomb • AutoDock electrostatic terms (and H-bonding, in some cases) • DOCK • Limited by lack of solvation and entropic terms2. Empirical • LUDI • Binding energies as sums of uncorrelated terms, similar to but simpler • F-Score than force-field terms • ChemScore • Parameterized to fit regression analysis of experimental data • SCORE • Often contain terms to approximate (de)solvation and entropic penalties • Fresno • X-Score1. Knowledge-based • PMF • Use potentials of mean force derived from libraries of protein-ligand complexes • DrugScore • Computationally simple • SMoG
  21. 21. Force-field-based Scoring FunctionAutodock v4.0 Kitchen et al. Nat Rev Drug Discov 3, 935–949 (2004)
  22. 22. Empirical Scoring Functions• LUDI Böhm. J Comput Aided Molec Des 8, 243-256 (1994)• ChemScore Eldridge et al. J Comput Aided Molec Des 11, 425-444 (1997)
  23. 23. Knowledge-Based Scoring Functions Example: ligand carboxyl O to protein histidine N Procedure: 1. Find all PDB structures with ligand carboxyl O 2. Compute all distances to protein histidine N’s 3. Plot histogram of all O-N distances: p(rO-N) 4. Calculate E(r) using inverse Boltzmann Boltzmann: p(r) ~ exp[ -E(r)/(RT) ] Inverse Boltzmann: E(r) = -RT ln[ p(r) ] Muegge & Martin. J Med Chem 42, 791-804 (1999)
  24. 24. Scoring: General Caveats• Ligand flexibility and size • For rigid molecules, correct pose predicted 90-100% of the time • Drops to 45-80% for molecules with more rotatable bonds and MW• Binding strength • Most strong binders (Kd <100 nM) correctly predicted • Difficult to predict weaker-binding ligands (Kd ~ 1μM)• Binding site • Hydrophobic binding sites yield better results than hydrophilic ones • Placement of water molecules play an important part • Active sites that require a conformational change (induced-fit) fair poorly in rigid protein models • Ideally want to start with holo- structure
  25. 25. Lead Optimization and de novo Design• Lead Optimization • Evaluate small changes in structure to distinguish between a μM and a nM compound • Need highly accurate docking and scoring functions • Typically implemented as an “anchored search” to reduce number of analogues • Can be used to prioritize sites for experimental modification• De novo Design: • Multiple-copy simultaneous search (MCSS): small fragments simultaneously docked, and preferred fragments combined • Difficult to predict synthetic availability of designed molecule
  26. 26. Docking and ADME Evaluation• Absorption, distribution, metabolism and excretion• Concentrate on drug interaction with models of cytochrome P450
  27. 27. Structure-based and Ligand-based Receptor Structure? Known Unknown Ligand-Based Design Structure-Based Design e.g. pharmacophore modeling & e.g. Docking QSAR
  28. 28. Lipinski’s Rule of FiveA rule of thumb to evaluate the likely activity of an oral drugcandidate1. ≤5 hydrogen bond donors2. ≤10 hydrogen-bond acceptors3. MW < 500 Da4. log P ≤ 5 (P = octanol-water partition coefficient)
  29. 29. Pharmacophore modeling• Pharmacophore: Set of features common to all known ligands of a particular target• Methodology: 1. Model conformational space of ligands in training set to simulate flexibility 2. Align generated conformations 3. Extract common features Richmond et al. J Comput Aided Mol Des 20, 567-587 (2006)
  30. 30. Pharmacophore-based Virtual Screening• Screen compound library for ligands containing pharmacophore of interest• Methodology 1. Generate ensemble of conformations for each ligand to be tested 2. Perform pharmacophore pattern matching (“substructure search”) on every conformer • Procedures from graph theory: Ullman, backtracking algorithm, GMA algorithm
  31. 31. Challenges in Pharmacophore Modelling• Modeling ligand flexibility • Conformers pre-enumerated or generated on-the-fly • Systematic torsional grids, genetic algorithms, Monte Carlo• Molecular alignment • Point-based: superimpose pairs of atoms. Anchor points need to be defined • Property-based: use molecular field descriptors to align• Choosing a training set • Choice of training set has big impact on generate pharmacophore model• New research: Pharmacophore-based de novo design
  32. 32. Quantitative Structure/Activity Relationships• A QSAR is a mathematical relationship between the geometrical and chemical characteristics of a molecule and its biological activity• Chemical descriptors are correlated with biological activity in terms of an equation• A valid QSAR should allow prediction of the biological activity of new ligands prior to synthesis and in vitro and in vivo assays
  33. 33. QSAR Requirements1. Dataset • Experimental measurements of the biological activity of a group of chemicals2. Descriptors • Numerical values that encode relevant structure and property data for this group of chemicals3. Statistical methods • To find relationships between these two sets of data
  34. 34. Molecular Descriptors in QSAR1. Constitutional • Total number of atoms, atoms of a certain type, number of bonds, number of rings2. Topological • Molecular shape, degree of branching3. Electronic • Partial atomic charges, dipole moments4. Geometrical • van der Waals volume, molecular surface5. Quantum Mechanical • Total energy, interaction energy between two atoms, nuclear repulsion between atoms6. Physicochemical • Liquid solubility, log P, boiling point
  35. 35. QSAR Methodology• Thousands of descriptors can be generated for each molecule• Several descriptors will be correlated • E.g. MW and boiling point• Statistically analyze descriptors to isolate 3 to 5 independent descriptors that best correlate with biological activity• Use regression methods to express activity in terms of descriptors • BA = a + bX1 + cX2 + …• This model can now be used to predict activity in other test compounds
  36. 36. 3D QSAR• If the structure of ligands are known, one can map important chemical descriptors into 3D space• This will generate a 3D pattern of functionally significant regions of the ligand• Visual identification of regions responsible for (un)favorable interactions
  37. 37. Comparative Molecular Field Analysis• Principle: Differences in binding/activity often due to differences in the shape of the non-covalent fields surrounding the molecule• Methodology: 1. Align all test molecules 2. Place in a 3D grid (2 Å spacing) 3. Measure steric (van der Waals) and electrostatic (Coulomb) energy for each molecule with a probe atom 4. Correlate energies with activity to generate 3D-QSAR 5. Display QSAR as colors and/or contours around molecular structures
  38. 38. CoMFA Example 3D alignment CoMFA QSAR 44 compounds (37 training, 7 test) Shagufta et al. J Mol Model 13, 99-109 (2006)

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