MOLECULAR DOCKING

MOLECULAR DOCKING
Saramita De Chakravarti
Computational Biology Laboratory
S V Chembiotech, Bangalore
saramita16@chembiotech.com
1Molecular Docking by Saramita Chakravarti

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
2
Molecular Docking by Saramita
Chakravarti

Drug discovery
Chemical + biological system  desired response?
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Chakravarti

TRADITIONAL DRUG DESIGN
Lead generation:
Natural ligand / Screening
Biological Testing
Synthesis of New Compounds
Drug Design CycleDrug Design Cycle
If promising
Pre-Clinical Studies
4Molecular Docking by Saramita Chakravarti

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
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Chakravarti

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
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Chakravarti

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
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Chakravarti

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

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

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

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

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
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Chakravarti

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
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Chakravarti

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

Influenza virus b/beijing/1/87 neuraminidase
complexed with zanamivir
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Chakravarti

Influenza virus b/beijing/1/87 neuraminidase
complexed with zanamivir
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Chakravarti

Plasma alpha antithrombin-iii and pentasaccharide
protein with heparin ligand
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Chakravarti

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

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
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Chakravarti

Questions
• Search
–What is it?
–When/why and which search?
• Scoring
–What is it?
• Dimensionality
–Why is this important?
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Chakravarti

Spectrum of search
• Local
– Molecular Mechanics
• Short - Medium
– Monte Carlo Simulated Annealing
– Brownian Dynamics
– Molecular Dynamics
• Global
– Docking
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Chakravarti

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

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

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

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

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
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Chakravarti

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

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
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Chakravarti

Search parameters
• Population size
• Crossover rate
• Mutation rate
• Local search
–energy evals
• Termination criteria
–energy evals
–generations
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Chakravarti

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 30
Chakravarti

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
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Chakravarti

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 =∑1/2Kθ (θijk-θ0)2
• Where Kθ is the bending force
constant
• VTorsion =∑V/2 (1+ Cos n(ϕ+ϕ0))
• Where V is the barrier to rotation,
ϕ is torsional angle
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Chakravarti

Molecular mechanics: Theory
• Lennard-Jones type of 6-12 potential is used to
describe non-bonded and weak interaction
• Vvdw= ∑(Aij/rij
12
-Bij/rij
6
)
• Simple Columbic potential is used to describe
electrostatic interaction
• Velec=∑(qiqj/εrij)
• Out of plane bending/deformation is described
by the following expression
• Vop= 0.5 Kop δ2
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Chakravarti

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Chakravarti

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
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Chakravarti

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
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Chakravarti

The energy expression
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Chakravarti

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
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Chakravarti

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
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Chakravarti

Molecular dynamics (MD)
simulations
• A deterministic method based
on the solution of Newton’s
equation of motion
Fi = miai
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) 40
Chakravarti

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 41
Chakravarti

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}
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Chakravarti

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
10-14
s (10fs). Therefore integration step should be 0.5-1 fs
43
Chakravarti

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
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Chakravarti

Durations of some real molecular
events
Event 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
45
Chakravarti

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
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Chakravarti

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
47
Chakravarti

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
48
Chakravarti

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 49
Chakravarti

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 50
Chakravarti

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)
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Chakravarti

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
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Chakravarti

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
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Chakravarti

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
54
Chakravarti

P1
’
P1
H-bond donor or acceptor
3.5-6.5Å 3.5-6.5Å
8.5-12Å
Symmetric diol docked into
HIV active site
3D pharmacophore
3D hit
Initial
idea for
inhibitor
Expand ring to give diol
and incorporate urea
Stereochemistry required
for optimal binding
Final Molecule selected
for clinical Trials
55
Chakravarti

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

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

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

Binding Sites in
triple helix
Binding Energy (Kcal/mol)
Gallic acid
(Gal)
Catechin (Cat)
Epigallocatechi
ngallate
(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
Binding energies different complexesBinding energies different complexes
between polyphenols and triple helixbetween polyphenols and triple helix
59
Chakravarti

Interfacial interacting volume Vs BindingInterfacial interacting volume Vs Binding
energy of the collagen-poly phenol complexenergy of the collagen-poly phenol complex
Interacting Interfacial Volume (Å3
)
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Chakravarti

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

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 the
complexescomplexes
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Chakravarti

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.
63
Chakravarti

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

MOLECULAR DOCKING

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