1. MOLECULAR DOCKING V. Subramanian Chemical Laboratory
Central Leather Research Institute Adyar, Chennai

2. Introduction
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

3. Chemical + biological system  desired response?
Drug discovery
Chemical + biological system  desired response?

4. Natural ligand / Screening
TRADITIONAL DRUG DESIGN
Lead generation:
Natural ligand / Screening
Biological Testing
Drug Design Cycle
If promising
Synthesis of New Compounds
Pre-Clinical Studies

5. Finding lead compound
A lead compound is a small molecule that serves as the starting point for an optimization involving many small molecules that are closely related in structure to the lead compound
Many organizations maintain databases of chemical compounds
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 geometrical features
Hydrogen bond donors
Hydrogen bond acceptors
Positive Charge Centers
Aromatic ring centers
Hydrophobic centers

6. Finding lead compound There are two approaches to this problem
A computer program AutoDock (or similar version Affinity (accelrys)) can be used to search a database by generating “fit” between molecule and the receptor
Alternatively one can search 3D pharmacophore

7. Structure based drug design
Drug design and development
Structure based drug design exploits the 3D structure of the target or a pharmacophore
Find a molecule which would be expected to interact with the receptor. (Searching a data base)
Design entirely a new molecule from “SCRATCH” (de novo drug/ligand design)
In this context bioinformatics and chemoinformatics play a crucial role

8. Structure-based Drug Design (SBDD) Natural ligand / Screening
Molecular Biology & Protein Chemistry
3D Structure Determination of Target
and Target-Ligand Complex
Modelling
Drug Design Cycle
Structure Analysis
and Compound Design
Biological Testing
If promising
Synthesis of New Compounds
Pre-Clinical
Studies

9. Structure based drug design
SBDD:
drug targets (usually proteins)
binding of ligands to the target (docking) ↓
“rational” drug design
(benefits = saved time and $$)

10. Schematics for structure based drug design
Select and Purify the target protein
Obtain known
inhibitor
X-Ray structural determination of native protein
X-Ray structural determination of inhibitor complex
Synthesis, Evaluate preclinical, clinical, invitro, invivo, cells, animals, & humans
Model inhibitor with computational tools
Determine IC50
Drug
Structure Based Drug Design have the potential to shave off years and millions of dollars

11. Working at the intersection
Structural Biology
Biochemistry
Medicinal Chemistry
Toxicology
Pharmacology
Biophysical Chemistry
Natural Products Chemistry
Chemical Ecology
Information Technology

12. Molecular docking-definition
It is a process by which two molecules are put together in 3 Dimension
Best ways to put two molecules together
Using molecular modeling and computational chemistry tools

13. Molecular docking
Docking used for finding binding modes of protein with ligands/inhibitors
In molecular docking, we attempt to predict the structure of the intermolecular complex formed between two or more molecules
Docking algorithms are able to generate a large number of possible structures
We use force field based strategy to carry out docking

14. Oxygen transport molecule (101M) with surface and myoglobin ligand

15. Influenza virus b/beijing/1/87 neuraminidase complexed with zanamivir

16. Influenza virus b/beijing/1/87 neuraminidase complexed with zanamivir

17. Plasma alpha antithrombin-iii and pentasaccharide protein with heparin ligand

18. Steps of molecular docking
Three steps
Definition of the structure of the target molecule
(2) Location of the binding site
(3) Determination of the binding mode

19. Best ways to put two molecules together
Need to quantify or rank solutions
Scoring function or force field
Experimental structure may be amongst one of several predicted solutions
-Need a Search method

20. Questions Search Scoring Dimensionality What is it?
When/why and which search?
Scoring
Dimensionality
Why is this important?

21. Spectrum of search Local Short - Medium Global Molecular Mechanics
Monte Carlo Simulated Annealing
Brownian Dynamics
Molecular Dynamics
Global
Docking

22. Details of search Level-of-Detail Atom types Terms of force field
Bond stretching
Bond-angle bending
Torsional potentials
Polarizability terms
Implicit solvation

23. Kinds of search Systematic Exhaustive Deterministic
Dependent on granularity of sampling
Feasible only for low-dimensional problems
DOF, 6D search

24. Kinds of search Stochastic Random Outcome varies
Repeat to improve chances of success
Feasible for higher-dimensional problems
AutoDock, < ~40D search

25. Stochastic search methods
Simulated Annealing (SA)
Evolutionary Algorithms (EA)
Genetic Algorithm (GA)
Others
Tabu Search (TS)
Hybrid Global-Local Search
Lamarckian GA (LGA)

26. Simulated annealing One copy of the ligand (Population = 1)
Starts from a random or specific postion/orientation/conformation (=state)
Constant temperature annealing cycle (Accepted & Rejected Moves)
Temperature reduced before next cycle
Stops at maximum cycles

27. Search parameters Simulated Annealing Initial temperature (K)
Temperature reduction factor (K-1cycle)
Termination criteria:
accepted moves
rejected moves
cycles

28. Genetic function algorithm
Start with a random population (50-200)
Perform Crossover (Sex, two parents -> 2 children) and Mutation (Cosmic rays, one individual gives 1 mutant child)
Compute fitness of each individual
Proportional Selection & Elitism
New Generation begins if total energy evals or maximum generations reached

29. Search parameters Mutation rate Population size Crossover rate
Local search
energy evals
Termination criteria
generations

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 = n

31. DGbinding = DGvdW + DGelec + DGhbond + DGdesolv + DGtors
Docking score
DGbinding = DGvdW + DGelec + DGhbond + DGdesolv + DGtors
DGvdW
12-6 Lennard-Jones potential
DGelec
Coulombic with Solmajer-dielectric
DGhbond
12-10 Potential with Goodford Directionality
DGdesolv
Stouten Pairwise Atomic Solvation Parameters
DGtors
Number of rotatable bonds

32. Molecular mechanics: theory
Considering the simple harmonic approximation, the potential energy of molecules is given by
V= VBond+ VAngle + VTorsion + Vvdw + Velec+ Vop
VBond = 1/2Kr (rij-r0)2
Where Kr is the stretching force constant
VAngle =1/2K (ijk-0)2
Where K is the bending force constant
VTorsion =V/2 (1+ Cos n(+0))
Where V is the barrier to rotation,  is torsional angle

33. Molecular mechanics: Theory
Lennard-Jones type of 6-12 potential is used to describe non-bonded and weak interaction
Vvdw= (Aij/rij12-Bij/rij6)
Simple Columbic potential is used to describe electrostatic interaction
Velec=(qiqj/rij)
Out of plane bending/deformation is described by the following expression
Vop= 0.5 Kop 2

35. The forcefield
The purpose of a forcefield is to describe the potential energy surface of entire classes of molecules with reasonable accuracy
In a sense, the forcefield extrapolates from the empirical data of the small set of models used to parameterize it, a larger set of related models
Some forcefields aim for high accuracy for a limited set of elements, thus enabling good predictions of many molecular properties
Others aim for the broadest possible coverage of the periodic table, with necessarily lower accuracy

36. Components of a forcefield
The forcefield contains all the necessary elements for calculations of energy and force:
A list of forcefield types
A list of partial charges
Forcefield-typing rules
Functional forms for the components of the energy expression
Parameters for the function terms
For some forcefields, rules for generating parameters that have not been explicitly defined
For some forcefields, a way of assigning functional forms and parameters

37. The energy expression

38. Valence interactions
The energy of valence interactions is generally accounted for by 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 covalent systems
A Urey-Bradley (UB) term may be used to account for interactions between atom pairs involved in 1-3 configurations (i.e., atoms bound to a common atom)
Evalence=Ebond + Eangle + Etorsion + Eoop + EUB

39. Non-bond interactions
The energy of interactions between non-bonded atoms is accounted for by
van der Waals (vdW)
electrostatic (Coulomb)
hydrogen bond (hbond) terms in some older forcefields
Enon-bond=EvdW + ECoulomb + Ehbond

40. Molecular dynamics (MD) simulations
A deterministic method based on the solution of Newton’s equation of motion
Fi = mi ai
for the ith particle; the acceleration at each step is calculated from the negative gradient of the overall potential, using
Fi = - grad Vi - = -  Vi
Vi = Sk(energies of interactions between i and all other residues k located within a cutoff distance of Rc from i)

41. Classical molecular dynamics
Constituent molecules obey classical laws of motion
In MD simulation, we have to solve Newton's equation of motion
Force calculation is the time consuming part of the simulation
MD simulation can be performed in various ensembles
NVT, NPT and NVE are the ensembles widely used in the MD simulations
Both quantum and classical potentials can be used to perform MD simulation

42. Calculation of interaction energy
MM total energy can be used to get interaction energy of the ligands with biomolecules
In order to compute the interaction energy, calculations have to be performed for the biomolecule, ligands and the biomolecule-ligand adduct using the same force field
Eint= Ecomplex - {Ebiomolecule+Eligand}

43. Integration of equation of motion and time step
A key parameter in the integration algorithm is the integration time step
The time step is related to molecular vibration
The main limitation imposed by the highest-frequency motion
The vibrational period must be split into at least 8-10 segments for models to satisfy the Verlet algorithm that the velocities and accelerations are constant over time step used
In most organic models, the highest vibrational frequency is that of C-H stretching, whose period is of the order of s (10fs). Therefore integration step should be fs

44. Stages and duration in MD simulation
Dynamics simulations are usually carried out in two stages, equilibration and data collection
The purpose of the equilibration is to prepare the system so that it comes to the most probable configuration consistent with the target temperature and pressure
For large system, the equilibration takes long time because of the vast conformational space it has to search
The best way to judge whether a model has equilibrated is to plot various thermodynamic quantities such as energy, temperature, pressure versus time
When equilibrated, the system fluctuate around their average

45. Durations of some real molecular events
Approximate duration
Bond stretching  
1-20 fs  
Elastic domain modes  
100 fs to several ps  
Water reorientation  
4 ps  
Inter-domain bending 
10 ps-100 ns  
Globular protein tumbling 
1-10 ns  
Aromatic ring flipping  
100 µs to several seconds  
Allosteric shifts
2 µs to several seconds  
Local denaturation 
1 ms to several seconds  

46. Free energy simulations
Ability to predict binding energy
Free energy perturbation and thermodynamic integration
Computational demand and issues related to sampling prevent this technique in probing structure based drug design
Free Energy equation

47. De nova design of inhibitor for HIV-I protease
An impressive example of the application of SBDD is was the design of the HIV-I protease inhibitor

48. De nova design
It is a member of the aspartyl protease family with the two active sites
Structure has tetra coordinated water molecules tat accepted two hydrogen bond from the backbone amide hydrogens of isoleucine in the flaps
Two hydrogen bonds to the carbonyl oxygens of the inhibitor

49. Application of structure based drug design: HIV protease inhibitors
The starting point is the series of X-ray structures of the enzyme and enzyme-inhibitor complex
The enzyme is made up of two equal halves
HIV protease is a symmetrical molecule with two equal halves and an active site near its center like butterfly
For most such symmetrical molecules, both halves have a "business area," or active site, that carries out the enzyme's job
But HIV protease has only one such active site in the center of the molecule where the two halves meet

50. Structure based drug design: HIV protease inhibitors
The single active site was plugged with a small molecule so that it is possible shut down the whole enzyme and theoretically stop the virus' spread in the body
Several Inhibitors have been designed based on
Peptidic inhibitor
Peptidomemitic compounds
Non-peptide inhibitors
Further work has demonstrated the success of this approach

51. Some examples
Ritonavir (trade name Norvir) is one of a class of anti-HIV drugs called protease inhibitors
Saquinavir
Indinavir is another example of very potent peptidomimetic compound discovered using the elements of 3D structure and Structure Activity Relationship (SAR)

52. De nova design…
The first step was a 3D database search of a subset of the Cambridge Structural Database
The pharmacophore for this search comprised of two hydrophobic groups and a hydrogen bond donor or acceptor
The hydrophobic groups were intented to bind to the catalytic asp residues

53. De nova design…
The search yielded the hit which contained desired element of the pharmacophore but it also had oxygen that could replace the bound water molecules
The benzene ring in the original compound was changed to a cyclohexanone, which was able to position substituents in a more fitting manner
The DuPont Merck group had explored a series of peptide based diols that were potent inhibitors but with poor oral bioavailability

54. De nova design
They have retained the diol functionality and expanded the six me member ring to a seven membered diol
The ketone was changed to cyclic urea to enhance the hydrogen bonding to the flaps and to help synthesis
The compound chosen further studies including clinical trails was p-hydroxymethylbenzyl derivative

55. Final Molecule selected for clinical Trials
P1’ P1
H-bond donor or acceptor Å
8.5-12Å
3D hit
3D pharmacophore
Symmetric diol docked into HIV active site
Initial idea for inhibitor
Final Molecule selected for clinical Trials
Stereochemistry required for optimal binding
Expand ring to give diol and incorporate urea

56. Host-Guest Interactions with Collagen: As molecules
Dominated by Geometrical factors and Solvent Accessible Volumes

57. Energy minimized structure of 24-mer collagen triple helix

58. Complex Formation of poly phenols at various collagen sites
Aspargine of T.Helix and gallic acid
Aspartic acid of T.Helix and catechin
Lysine of T.Helix and epigallocatechingallate

59. Binding energies different complexes between polyphenols and triple helix
Binding Sites in triple helix
Binding Energy (Kcal/mol)
Gallic acid (Gal)
Catechin (Cat)
Epigallocatechingallate (EGCG)
Pentagalloyl glucose (PGG)
9th residue Ser of C-chain (α2)
16.5
22.5
35.2
56.6
6th residue Hyp of A-chain (α1)
14.5
20.8
34.5
48.4
12th residue Lys of B-chain (α1)
19.2
23.8
37.9
41.1
21st residue Asp of A-chain (α1)
18.4
20.0
38.2
59.8
17th residue Asn of C-chain (α2)
14.1
23.7
34.3
52.8

60. Interfacial interacting volume Vs Binding energy of the collagen-poly phenol complex
Interacting Interfacial Volume (Å3)

61. Effective solvent inaccessible contact volume Vs Binding energy of the collagen-poly phenol complex
Inset: effective solvent inaccessible contact surface area Vs Binding energy of the complex

62. Plot of inverse of interacting interfacial volume (1/Int. Vol
Plot of inverse of interacting interfacial volume (1/Int.Vol.) Vs inverse of binding energy(1/B.E) of the complexes

63. Acknowledgement Mr. R. Parthasarathi Mr. B. Madhan Mr. J. Padmanabhan
Mr. M. Elango
Mr. S. Sundar Raman
Mr. R. Vijayraj
CSIR & DST, GOI

64. Big Thank You
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.