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Drug discovery  take years to decade for discovering a new drug and very costly
Effort  to cut down the research timeline and cost by reducing wet-lab experiment  use computer modeling

Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.

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  1. 1. MOLECULAR DOCKINGSaramita De ChakravartiComputational Biology LaboratoryS V Chembiotech, Bangaloresaramita16@chembiotech.com1Molecular Docking by Saramita Chakravarti
  2. 2. Introduction• Drug discovery  take years to decade fordiscovering a new drug and very costly• Effort  to cut down the research timeline andcost by reducing wet-lab experiment  usecomputer modeling2Molecular Docking by SaramitaChakravarti
  3. 3. Drug discoveryChemical + biological system  desired response?3Molecular Docking by SaramitaChakravarti
  4. 4. TRADITIONAL DRUG DESIGNLead generation:Natural ligand / ScreeningBiological TestingSynthesis of New CompoundsDrug Design CycleDrug Design CycleIf promisingPre-Clinical Studies4Molecular Docking by Saramita Chakravarti
  5. 5. Finding lead compound• A lead compound is a small molecule that serves as the startingpoint for an optimization involving many small molecules thatare closely related in structure to the lead compound• Many organizations maintain databases of chemicalcompounds• Some of these are publically accessible others are proprietary• Databases contain an extremely large number of compounds(ACS data bases contains 10 million compounds)• 3D databases have information about chemical and geometricalfeatures» Hydrogen bond donors» Hydrogen bond acceptors» Positive Charge Centers» Aromatic ring centers» Hydrophobic centers5Molecular Docking by SaramitaChakravarti
  6. 6. Finding lead compound• There are two approaches to this problem–A computer program AutoDock (or similarversion Affinity (accelrys)) can be used tosearch a database by generating “fit” betweenmolecule and the receptor–Alternatively one can search 3Dpharmacophore6Molecular Docking by SaramitaChakravarti
  7. 7. Structure based drug design• Drug design and development• Structure based drug design exploits the 3Dstructure of the target or a pharmacophore–Find a molecule which would be expected tointeract with the receptor. (Searching a data base)–Design entirely a new molecule from“SCRATCH” (de novo drug/ligand design)• In this context bioinformatics andchemoinformatics play a crucial role7Molecular Docking by SaramitaChakravarti
  8. 8. Structure-based Drug Design (SBDD)Molecular Biology & Protein Chemistry3D Structure Determination of Targetand Target-Ligand ComplexModellingStructure Analysisand Compound DesignBiological TestingSynthesis of New CompoundsIf promisingPre-ClinicalStudiesDrug Design CycleDrug Design CycleNatural ligand / Screening8Molecular Docking by Saramita Chakravarti
  9. 9. Structure based drug design• SBDD:• drug targets (usually proteins)• binding of ligands to the target (docking)↓“rational” drug design(benefits = saved time and $$)9Molecular Docking by SaramitaChakravarti
  10. 10. Select and Purify thetarget proteinModel inhibitorwithcomputationaltoolsSynthesis, Evaluatepreclinical, clinical,invitro, invivo, cells,animals, & humansDrugSchematics for structure based drug designSchematics for structure based drug designObtain knowninhibitorX-Ray structuraldetermination of nativeproteinX-Ray structuraldetermination ofinhibitor complexDetermine IC50Structure Based Drug Design have the potential to shave off years and millions of dollars10Molecular Docking by SaramitaChakravarti
  11. 11. Working at the intersection• Structural Biology• Biochemistry• Medicinal Chemistry• Toxicology• Pharmacology• Biophysical Chemistry• Natural Products Chemistry• Chemical Ecology• Information Technology11Molecular Docking by SaramitaChakravarti
  12. 12. Molecular docking-definition• It is a process by which two molecules areput together in 3 Dimension• Best ways to put two molecules together• Using molecular modeling and computationalchemistry tools12Molecular Docking by SaramitaChakravarti
  13. 13. Molecular docking• Docking used for finding binding modes ofprotein with ligands/inhibitors• In molecular docking, we attempt to predict thestructure of the intermolecular complex formedbetween two or more molecules• Docking algorithms are able to generate a largenumber of possible structures• We use force field based strategy to carry outdocking13Molecular Docking by SaramitaChakravarti
  14. 14. Oxygen transport molecule (101M) withsurface and myoglobin ligand14Molecular Docking by SaramitaChakravarti
  15. 15. Influenza virus b/beijing/1/87 neuraminidasecomplexed with zanamivir15Molecular Docking by SaramitaChakravarti
  16. 16. Influenza virus b/beijing/1/87 neuraminidasecomplexed with zanamivir16Molecular Docking by SaramitaChakravarti
  17. 17. Plasma alpha antithrombin-iii and pentasaccharideprotein with heparin ligand17Molecular Docking by SaramitaChakravarti
  18. 18. Steps of molecular docking• Three steps(1) Definition of the structure of the targetmolecule(2) Location of the binding site(3) Determination of the binding mode18Molecular Docking by SaramitaChakravarti
  19. 19. Best ways to put two molecules together–Need to quantify or rank solutions–Scoring function or force field–Experimental structure may be amongst oneof several predicted solutions-Need a Search method19Molecular Docking by SaramitaChakravarti
  20. 20. Questions• Search–What is it?–When/why and which search?• Scoring–What is it?• Dimensionality–Why is this important?20Molecular Docking by SaramitaChakravarti
  21. 21. Spectrum of search• Local– Molecular Mechanics• Short - Medium– Monte Carlo Simulated Annealing– Brownian Dynamics– Molecular Dynamics• Global– Docking21Molecular Docking by SaramitaChakravarti
  22. 22. Details of searchLevel-of-Detail• Atom types• Terms of force field– Bond stretching– Bond-angle bending– Torsional potentials– Polarizability terms– Implicit solvation22Molecular Docking by SaramitaChakravarti
  23. 23. Kinds of searchSystematic• Exhaustive• Deterministic• Dependent on granularity of sampling• Feasible only for low-dimensionalproblems• DOF, 6D search23Molecular Docking by SaramitaChakravarti
  24. 24. Kinds of searchStochastic• Random• Outcome varies• Repeat to improve chances of success• Feasible for higher-dimensional problems• AutoDock, < ~40D search24Molecular Docking by SaramitaChakravarti
  25. 25. Stochastic search methods• Simulated Annealing (SA)• Evolutionary Algorithms (EA)–Genetic Algorithm (GA)• Others–Tabu Search (TS)• Hybrid Global-Local Search–Lamarckian GA (LGA)25Molecular Docking by SaramitaChakravarti
  26. 26. Simulated annealing• One copy of the ligand (Population = 1)• Starts from a random or specificpostion/orientation/conformation (=state)• Constant temperature annealing cycle(Accepted & Rejected Moves)• Temperature reduced before next cycle• Stops at maximum cycles26Molecular Docking by SaramitaChakravarti
  27. 27. Search parametersSimulated Annealing• Initial temperature (K)• Temperature reduction factor (K-1cycle)• Termination criteria:– accepted moves– rejected moves– cycles27Molecular Docking by SaramitaChakravarti
  28. 28. Genetic function algorithm• Start with a random population (50-200)• Perform Crossover (Sex, two parents -> 2children) and Mutation (Cosmic rays, oneindividual gives 1 mutant child)• Compute fitness of each individual• Proportional Selection & Elitism• New Generation begins if total energyevals or maximum generations reached28Molecular Docking by SaramitaChakravarti
  29. 29. Search parameters• Population size• Crossover rate• Mutation rate• Local search–energy evals• Termination criteria–energy evals–generations29Molecular Docking by SaramitaChakravarti
  30. 30. Dimensionality of molecular docking• Degrees of Freedom (DOF)• Position or Translation–(x,y,z) = 3• Orientation or Quaternion–(qx, qy, qz, qw) = 4• Rotatable Bonds or Torsions–(tor1, tor2, … torn) = n• Total DOF, or Dimensionality,D = 3 + 4 + n 30Molecular Docking by SaramitaChakravarti
  31. 31. Docking scoreDGbinding = DGvdW + DGelec + DGhbond + DGdesolv+ DGtorsDGvdW12-6 Lennard-Jones potential• DGelecCoulombic with Solmajer-dielectric• DGhbond12-10 Potential with Goodford Directionality• DGdesolvStouten Pairwise Atomic Solvation Parameters• DGtorsNumber of rotatable bonds31Molecular Docking by SaramitaChakravarti
  32. 32. Molecular mechanics: theory• Considering the simple harmonicapproximation, the potentialenergy of molecules is given byV= VBond+ VAngle + VTorsion + Vvdw +Velec+ Vop• VBond = ∑1/2Kr (rij-r0)2• Where Kr is the stretching forceconstant• VAngle =∑1/2Kθ (θijk-θ0)2• Where Kθ is the bending forceconstant• VTorsion =∑V/2 (1+ Cos n(ϕ+ϕ0))• Where V is the barrier to rotation,ϕ is torsional angle32Molecular Docking by SaramitaChakravarti
  33. 33. Molecular mechanics: Theory• Lennard-Jones type of 6-12 potential is used todescribe non-bonded and weak interaction• Vvdw= ∑(Aij/rij12-Bij/rij6)• Simple Columbic potential is used to describeelectrostatic interaction• Velec=∑(qiqj/εrij)• Out of plane bending/deformation is describedby the following expression• Vop= 0.5 Kop δ233Molecular Docking by SaramitaChakravarti
  34. 34. 34Molecular Docking by SaramitaChakravarti
  35. 35. The forcefield• The purpose of a forcefield is to describe the potentialenergy surface of entire classes of molecules withreasonable accuracy• In a sense, the forcefield extrapolates from theempirical data of the small set of models used toparameterize it, a larger set of related models• Some forcefields aim for high accuracy for a limited setof elements, thus enabling good predictions of manymolecular properties• Others aim for the broadest possible coverage of theperiodic table, with necessarily lower accuracy35Molecular Docking by SaramitaChakravarti
  36. 36. Components of a forcefield• The forcefield contains all the necessary elements forcalculations of energy and force:– A list of forcefield types– A list of partial charges• Forcefield-typing rules– Functional forms for the components of the energyexpression• Parameters for the function terms– For some forcefields, rules for generating parameters thathave not been explicitly defined– For some forcefields, a way of assigning functional formsand parameters36Molecular Docking by SaramitaChakravarti
  37. 37. The energy expression37Molecular Docking by SaramitaChakravarti
  38. 38. Valence interactions• The energy of valence interactions is generally accounted forby diagonal terms:– bond stretching (bond)– valence angle bending (angle)– dihedral angle torsion (torsion)– inversion, also called out-of-plane interactions (oop)terms, which are part of nearly all forcefields for covalentsystems– A Urey-Bradley (UB) term may be used to account forinteractions between atom pairs involved in 1-3configurations (i.e., atoms bound to a common atom)• Evalence=Ebond + Eangle + Etorsion+ Eoop + EUB38Molecular Docking by SaramitaChakravarti
  39. 39. Non-bond interactions• The energy of interactions between non-bondedatoms is accounted for by• van der Waals (vdW)• electrostatic (Coulomb)• hydrogen bond (hbond) terms in some olderforcefields• Enon-bond=EvdW + ECoulomb + Ehbond39Molecular Docking by SaramitaChakravarti
  40. 40. Molecular dynamics (MD)simulations• A deterministic method basedon the solution of Newton’sequation of motionFi = miaifor the ithparticle; theacceleration at each step iscalculated from the negativegradient of the overallpotential, usingFi = - grad Vi - = - ∇ ViVi = Sk(energies ofinteractions between i and allother residues k locatedwithin a cutoff distance of Rcfrom i) 40Molecular Docking by SaramitaChakravarti
  41. 41. Classical molecular dynamics• Constituent molecules obeyclassical laws of motion• In MD simulation, we have to solveNewtons equation of motion• Force calculation is the timeconsuming part of the simulation• MD simulation can be performed invarious ensembles• NVT, NPT and NVE are theensembles widely used in the MDsimulations• Both quantum and classicalpotentials can be used to performMD simulation 41Molecular Docking by SaramitaChakravarti
  42. 42. Calculation of interaction energy• MM total energy can be used to get interactionenergy of the ligands with biomolecules• In order to compute the interaction energy,calculations have to be performed for thebiomolecule, ligands and the biomolecule-ligandadduct using the same force field• Eint= Ecomplex - {Ebiomolecule+Eligand}42Molecular Docking by SaramitaChakravarti
  43. 43. Integration of equation of motionand time step• A key parameter in the integration algorithm is theintegration time step• The time step is related to molecular vibration• The main limitation imposed by the highest-frequencymotion• The vibrational period must be split into at least 8-10segments for models to satisfy the Verlet algorithm thatthe velocities and accelerations are constant over time stepused• In most organic models, the highest vibrational frequencyis that of C-H stretching, whose period is of the order of10-14s (10fs). Therefore integration step should be 0.5-1 fs43Molecular Docking by SaramitaChakravarti
  44. 44. Stages and duration in MDsimulation• Dynamics simulations are usually carried out in twostages, equilibration and data collection• The purpose of the equilibration is to prepare the systemso that it comes to the most probable configurationconsistent with the target temperature and pressure• For large system, the equilibration takes long timebecause of the vast conformational space it has to search• The best way to judge whether a model has equilibratedis to plot various thermodynamic quantities such asenergy, temperature, pressure versus time• When equilibrated, the system fluctuate around theiraverage44Molecular Docking by SaramitaChakravarti
  45. 45. Durations of some real moleculareventsEvent Approximate durationBond stretching 1-20 fsElastic domain modes 100 fs to several psWater reorientation 4 psInter-domain bending 10 ps-100 nsGlobular protein tumbling 1-10 nsAromatic ring flipping 100 µs to several secondsAllosteric shifts 2 µs to several secondsLocal denaturation 1 ms to several seconds45Molecular Docking by SaramitaChakravarti
  46. 46. Free energy simulations• Ability to predict binding energy• Free energy perturbation andthermodynamic integration• Computational demand and issues relatedto sampling prevent this technique inprobing structure based drug design• Free Energy equation46Molecular Docking by SaramitaChakravarti
  47. 47. De nova design of inhibitor for HIV-I protease• An impressive example of the applicationof SBDD is was the design of the HIV-Iprotease inhibitor47Molecular Docking by SaramitaChakravarti
  48. 48. De nova design• It is a member of the aspartyl protease familywith the two active sites• Structure has tetra coordinated water moleculestat accepted two hydrogen bond from thebackbone amide hydrogens of isoleucine in theflaps• Two hydrogen bonds to the carbonyl oxygens ofthe inhibitor48Molecular Docking by SaramitaChakravarti
  49. 49. Application of structure based drugdesign: HIV protease inhibitors• The starting point is the series of X-ray structures of the enzyme andenzyme-inhibitor complex• The enzyme is made up of two equalhalves• HIV protease is a symmetricalmolecule with two equal halves andan active site near its center likebutterfly• For most such symmetricalmolecules, both halves have a"business area," or active site, thatcarries out the enzymes job• But HIV protease has only one suchactive site in the center of themolecule where the two halves meet 49Molecular Docking by SaramitaChakravarti
  50. 50. Structure based drug design: HIVprotease inhibitors• The single active site was plugged with a smallmolecule so that it is possible shut down the wholeenzyme and theoretically stop the virus spread inthe body• Several Inhibitors have been designed based on–Peptidic inhibitor–Peptidomemitic compounds–Non-peptide inhibitors• Further work has demonstrated the success of thisapproach 50Molecular Docking by SaramitaChakravarti
  51. 51. Some examples• Ritonavir (trade name Norvir) is one of a classof anti-HIV drugs called protease inhibitors• Saquinavir• Indinavir is another example of very potentpeptidomimetic compound discovered using theelements of 3D structure and Structure ActivityRelationship (SAR)51Molecular Docking by SaramitaChakravarti
  52. 52. De nova design…• The first step was a 3D database search ofa subset of the Cambridge StructuralDatabase• The pharmacophore for this searchcomprised of two hydrophobic groups anda hydrogen bond donor or acceptor• The hydrophobic groups were intented tobind to the catalytic asp residues52Molecular Docking by SaramitaChakravarti
  53. 53. De nova design…• The search yielded the hit which containeddesired element of the pharmacophore but it alsohad oxygen that could replace the bound watermolecules• The benzene ring in the original compound waschanged to a cyclohexanone, which was able toposition substituents in a more fitting manner• The DuPont Merck group had explored a seriesof peptide based diols that were potent inhibitorsbut with poor oral bioavailability53Molecular Docking by SaramitaChakravarti
  54. 54. De nova design• They have retained the diol functionality andexpanded the six me member ring to a sevenmembered diol• The ketone was changed to cyclic urea toenhance the hydrogen bonding to the flaps andto help synthesis• The compound chosen further studies includingclinical trails was p-hydroxymethylbenzylderivative54Molecular Docking by SaramitaChakravarti
  55. 55. P1’P1H-bond donor or acceptor3.5-6.5Å 3.5-6.5Å8.5-12ÅSymmetric diol docked intoHIV active site3D pharmacophore3D hitInitialidea forinhibitorExpand ring to give dioland incorporate ureaStereochemistry requiredfor optimal bindingFinal Molecule selectedfor clinical Trials55Molecular Docking by SaramitaChakravarti
  56. 56. Host-Guest Interactions withCollagen: As moleculesDominated by Geometrical factors andSolvent Accessible Volumes56Molecular Docking by SaramitaChakravarti
  57. 57. Energy minimized structure of 24-mercollagen triple helix57Molecular Docking by SaramitaChakravarti
  58. 58. Aspargine of T.Helixand gallic acidAspartic acid ofT.Helix and catechinComplex Formation of poly phenols atvarious collagen sitesLysine of T.Helix andepigallocatechingallate58Molecular Docking by SaramitaChakravarti
  59. 59. Binding Sites intriple helixBinding Energy (Kcal/mol)Gallic acid(Gal)Catechin (Cat)Epigallocatechingallate(EGCG)Pentagalloylglucose (PGG)9thresidue Serof C-chain (α2) 16.5 22.5 35.2 56.66thresidue Hypof A-chain (α1) 14.5 20.8 34.5 48.412thresidue Lysof B-chain (α1) 19.2 23.8 37.9 41.121stresidue Aspof A-chain (α1) 18.4 20.0 38.2 59.817thresidue Asnof C-chain (α2) 14.1 23.7 34.3 52.8Binding energies different complexesBinding energies different complexesbetween polyphenols and triple helixbetween polyphenols and triple helix59Molecular Docking by SaramitaChakravarti
  60. 60. Interfacial interacting volume Vs BindingInterfacial interacting volume Vs Bindingenergy of the collagen-poly phenol complexenergy of the collagen-poly phenol complexInteracting Interfacial Volume (Å3)60Molecular Docking by SaramitaChakravarti
  61. 61. Effective solvent inaccessible contact volumeEffective solvent inaccessible contact volumeVs Binding energy of the collagen-poly phenolVs Binding energy of the collagen-poly phenolcomplexcomplexInset: effective solvent inaccessible contact surface area Vs Binding energy of the complex61Molecular Docking by SaramitaChakravarti
  62. 62. Plot of inverse of interacting interfacial volumePlot of inverse of interacting interfacial volume(1/Int.Vol.) Vs inverse of binding energy(1/B.E) of the(1/Int.Vol.) Vs inverse of binding energy(1/B.E) of thecomplexescomplexes62Molecular Docking by SaramitaChakravarti
  63. 63. Acknowledgement• Mr. R. Parthasarathi• Mr. B. Madhan• Mr. J. Padmanabhan• Mr. M. Elango• Mr. S. Sundar Raman• Mr. R. Vijayraj• CSIR & DST, GOI• MD, S V Chembiotech.63Molecular Docking by SaramitaChakravarti
  64. 64. Big Thank YouOthers have done the work. Somehave used the work. I havespoken only on behalf of theirbehalf.64Molecular Docking by SaramitaChakravarti