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Structure based computer aided drug design


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A lecture on Structure based computer-aided drug design

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Structure based computer aided drug design

  1. 1. Prof. Thanh N. Truong University of Utah Astonis LLC Institute of Computational Science and Technology
  2. 2. The Drug Discovery Process Drug Target Identification Target Validation Lead Identification Lead Optimization Pre-clinical & Clinical Development It takes about 15 years and costs around 880 millions USD, ~10,000 compounds (DiMasi et al. 2003; Dickson & Gagnon 2004) to develop a new drug. FDA Review
  3. 3. 25,000 metabolite
  4. 4. Genomics Facts Around 99% of our genes have counterparts in mice Our genetic overlap with chimpanzees is about 97.5% The genetic difference between one person and another is less than 0.1 % But because only a few regions of DNA actively encode life functions, the real difference between one person and another is only 0.0003 % It is becoming increasingly evident that the complexity of biological systems lies at the level of the proteins, and that genomics alone will not suffice to understand these systems.
  5. 5. Structure-based Computer-Aided Drug Design Drug Target Identification Target Validation Shorten development time to Lead Identification Lead Identification Lead Optimization Known 3D structure PDB databank Pre-clinical & Clinical Development FDA Review Unknown 3D structure for the target protein Homology modeling or Protein Structure Prediction Reduce cost MD simulations Past Successes 1. 2. 3. HIV protease inhibitor amprenavir (Agenerase) from Vertex & GSK (Kim et al. 1995) HIV: nelfinavir (Viracept) by Pfizer (& Agouron) (Greer et al. 1994) Influenza neuraminidase inhibitor zanamivir (Relenza) by GSK (Schindler 2000) Target Model Docking Simulation Cluster Analysis Scoring Analysis Trajectories
  6. 6. Homology Modeling Prediction of protein tertiary structure from the known sequence
  7. 7. Docking The Problem: Determine the optimal binding structure of a ligand (a drug candidate, a small molecule) to a receptor (a drug target, a protein or DNA) and quantify the strength of the ligand-receptor interaction. 1. 2. 3. 4. 5. Where the ligand will bind? How will it bind? How strong? Why? What make a ligand binds to the receptor better than the others? 6. ????
  8. 8. The Challenge Ligand and receptor are conformational flexible. Receptor may have more than one possible binding site. Weak short-range Interactions: hydrogen bonds, salt bridges, hydrophobic contacts, electrostatics, van der Walls repulsions Surface complementary. Binding affinity is the difference to the uncomplexed state – solvation and desolvation play important role. Binding affinity describes an ensemble of complexes not a single one. Orientation of Ligand Bound waters Flexibility of residues in the binding site Large protein conformation change
  9. 9. Binding Affinity Association equilibrium constant [ RL ]s Ka = [ R ]s [ L ]s b ∆Gg + ∆Gsolv ( R ) ∆Gsolv ( L ) Free energy of binding: b ∆Gs = ∆H − T ∆S = − RT ln K a Enthalpy Entropy + From the thermodynamic cycle: b b ∆Gs = ∆Gg + ∆Gsolv ( RL ) − {∆Gsolv ( R ) + ∆Gsolv ( L )} b ∆Gs ∆Gsolv ( RL )
  10. 10. Docking Process Descriptions of the receptor 3D structure, binding site and ligand Sampling of the configuration space of the binding complex Multiple binding configurations for a single protein structure and a ligand Evaluating free energy of binding for scoring Local/global minimum Ensemble of protein structures and/or multiple ligands
  11. 11. Description of Receptor 3D Structure Known 3D protein structures from Protein Data Bank (PDB) ( Locations of hydrogen atoms, bound water molecules, and metal ions are either not known or highly uncertain. Identities and locations of some heavy atoms (e.g., ~1/6 of N/O of Asn & Gln, and N/C of His incorrectly assigned in PDB; up to 0.5 Å uncertainty in position) Conformational flexibility of proteins is not known Homology models from highly similar sequences with known structures Critical analysis of the receptor structure before docking is needed: resolution, missing residues, bound waters and ions, protonation states, etc.
  12. 12. Descriptions of Binding Site Known binding site – PDB database has about 6000 proteinligand complexes Atomistic based o Receptor atomic coordinates and location of a binding box Descriptor based o o o o Surface Volume Points & distances, bond vectors grid and various properties such as electrostatic potential, hydrophobic moment, polar, nonpolar, atom types, etc Unknown binding site Blind docking with the binding box cover the entire receptor – computationally expensive Better method for finding potential binding sites is needed
  13. 13. Ligand Chemical Space National Cancer Institute (NCI) public database ( About 250 K 3D structures ZINC public database ( About 8 million 3D structures PubChem public database ( About 19 million entries (but no 3D structures) Cambridge Structure database (CSD) About 3 million crystal structures Chemical Abstract Service (CAS) and SciFinder Several other smaller databases … Atomic partial charges from MM force fields or MO calculations must be added to each molecule for evaluation of the score function
  14. 14. Different Approaches in Docking Complete conformation and configuration space are too large. Different approaches were developed for effective sampling of the receptor-ligand configuration space. Automated Manual Descriptor Matching Simulation-based • Use pattern-recognizing geometric methods to match ligand and receptor site descriptors • Ligand flexibility is limited • Receptor is rigid • Accuracy is not very good – not discriminative • Fast • Use simulation methods to sample the local configuration space: MC-Simulated Annealing, Genetic Algorithm. Must run an ensemble of starting orientations for accurate statistics • Ligand and protein flexibility can be considered • Free energy of binding is evaluated • Accuracy is good • Time consuming • Grid map is often used to speed up energy evaluations User interactive force feedbacks through haptic devices Focus
  15. 15. MC-Simulated Annealing Method Randomly change the receptor flexible residues, ligand position, orientation, and/or conformation Evaluate the new energy, Enew YES Enew < Eold ? NO Accept the new move with P = exp{-∆E/kbT} Accept the new move Enew Eold Reduce the temperature NO Naccept or reject > Nlimit YES Done
  16. 16. Genetic Algorithm Darwin Theory of Evolution Living organisms Made up of cells Has the same set of chromosomes (DNA) Genome: A set of all chromosomes Chromosome consists of genes Genotype: A particular set of genes Each gene encodes a protein (a trait) Each gene has a location in the chromosome (locus) Reproduction by cross-over and mutation
  17. 17. Genetic Algorithm for Docking Gene 1 Gene 2 Gene 3 x1 y1 z1 φ1 ψ1 ω1 τ1 τ2 τ3 τ4 Position Orientation Chromosome 1 Torsional angles x2 y2 z2 φ2 ψ2 ω2 τ1’ τ2’ τ3’ τ4’ Chromosome 2 A chromosome is a possible solution: binding position, orientation, and values of all rotatable torsional angles Fitness Test Translates genotypes to phenotypes (receptor-ligand complex structures) for binding free energy evaluation. A cell is a set of possible solutions, i.e. chromosomes. Typical population = 100-200 Select best parents Those with large negative ∆G binding Generate new generation Migration: Move the best genes to the next generation Cross-over: Exchange a set of genes from one parent chromosome to another. Typical cross-over rate = 80-90% Mutation: Randomly change a value of a gene, i.e. position, orientation, or torsional values. Typical mutation rate = 0.5-1%
  18. 18. Two-point Cross-over Operator x1 y1 z1 φ1 ψ1 ω1 τ1 τ2 τ3 τ4 Parent 1 Swap positions x2 y2 z2 φ2 ψ2 ω2 τ1’ τ2’ τ3’ τ4’ Parent 2 x2 y2 z2 φ1 ψ1 ω1 τ1 τ2 τ3 τ4 Child 1 x1 y1 z1 φ2 ψ2 ω2 τ1’ τ2’ τ3’ τ4’ Child 2
  19. 19. Lamarkian Genetic Algorithm -- AutoDock Environmental adaptation of an individual’s phenotypic characteristics acquired during lifetime can become heritable traits Survival of the fittest. 1. Mutation and cross-breeding to generate new genotype generate new possible ligand binding configuration 2. Transfer to phenotype to evaluate fitness forming receptor-ligand configuration. Environmental adaption 3. Adapt to the local environment to improve fitness local minimization. 4. Transfer back to genotype for future generations save the optimized ligand binding configuration for future generations. Genetic Algorithm – Local Search Morris et al., J. Comp. Chem. 1998, 19, 1639 Transfer to genotype for future generations, i.e. heritable traits. GA LS
  20. 20. Scoring Functions Force Field based function Focus GOAL: Fast & Accurate Experimentally observed complex •Score = -∆Gbinding •Has physical basis •Fast with pre-computed grid Multivariate regression fit physically motivated structural functions to experimentally known complexes with measured binding affinity -Score Empirical function Knowledge-based function Statistical pair potential derived from known complex structures Descriptor based function Based on chemical properties, pharmacaphore, contact, shape complementary Complex configurations
  21. 21. Force Field Based Scoring Function b Score = −∆Gs b ∆Gs = Cvdw ∗ ∆Gvdw + Cele ∗ ∆Gele + Chb ∗ ∆Ghb + Ctor ∗ ∆Gtor + Csolv ∗ ∆Gsolv Coefficients are empirically determined using linear regression analysis from a set of protein-ligand complexes from LPDB with known experimental binding constants.
  22. 22. Analyses Energy histogram Clustering analysis Distribution of binding energies average binding energy Distribution of binding modes different binding sites and ligand binding orientations
  23. 23. Docking with Science Community Laboratory Identify a target Millions of molecules from ZINC database Docking simulation with AutoDock-Vina Rank according to binding energy