Transcript MOLECULAR DOCKING

MOLECULAR DOCKING
V. Subramanian
Chemical Laboratory
Central Leather Research Institute
Adyar, Chennai
[email protected]
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
Drug discovery
Chemical + biological system  desired response?
TRADITIONAL DRUG DESIGN
Lead generation:
Natural ligand / Screening
Biological Testing
Drug Design Cycle
If promising
Synthesis of New Compounds
Pre-Clinical Studies
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
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
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
chemoinformatics play a crucial role
and
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
Biological Testing
and Compound Design
If promising
Synthesis of New Compounds
Pre-Clinical
Studies
Structure based drug design
• SBDD:
• drug targets (usually proteins)
• binding of ligands to the target (docking)
↓
“rational” drug design
(benefits = saved time and $$)
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
Determine IC50
Model inhibitor
with
computational
tools
Drug
Structure Based Drug Design have the potential to shave off years and millions of dollars
Working at the intersection
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Structural Biology
Biochemistry
Medicinal Chemistry
Toxicology
Pharmacology
Biophysical Chemistry
Natural Products Chemistry
Chemical Ecology
Information Technology
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
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
Oxygen transport molecule (101M)
with surface and myoglobin ligand
Influenza virus b/beijing/1/87 neuraminidase
complexed with zanamivir
Influenza virus b/beijing/1/87 neuraminidase
complexed with zanamivir
Plasma alpha antithrombin-iii and
pentasaccharide protein with heparin ligand
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
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
Questions
• Search
– What is it?
– When/why and which search?
• Scoring
– What is it?
• Dimensionality
– Why is this important?
Spectrum of search
• Local
– Molecular Mechanics
• Short - Medium
– Monte Carlo Simulated Annealing
– Brownian Dynamics
– Molecular Dynamics
• Global
– Docking
Details of search
Level-of-Detail
• Atom types
• Terms of force field
– Bond stretching
– Bond-angle bending
– Torsional potentials
– Polarizability terms
– Implicit solvation
Kinds of search
Systematic
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Exhaustive
Deterministic
Dependent on granularity of sampling
Feasible
only
for
low-dimensional
problems
• DOF, 6D search
Kinds of search
Stochastic
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Random
Outcome varies
Repeat to improve chances of success
Feasible for higher-dimensional problems
• AutoDock, < ~40D search
Stochastic search methods
• Simulated Annealing (SA)
• Evolutionary Algorithms (EA)
– Genetic Algorithm (GA)
• Others
– Tabu Search (TS)
• Hybrid Global-Local Search
– Lamarckian GA (LGA)
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
Search parameters
Simulated Annealing
• Initial temperature (K)
• Temperature reduction factor (K-1cycle)
• Termination criteria:
– accepted moves
– rejected moves
– cycles
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
Search parameters
• Population size
• Crossover rate
• Mutation rate
• Local search
– energy evals
• Termination criteria
– energy evals
– generations
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, … tor n) = n
• Total DOF, or Dimensionality,
D=3+4+n
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
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
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
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
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
The energy expression
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
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
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)
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
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}
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
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
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
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
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
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
Application of structure based drug
design: HIV protease inhibitors
• The starting point is the series of Xray 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
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
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)
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
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
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
P1
3.5-6.5Å
8.5-12Å
P1’
3.5-6.5Å
H-bond donor or acceptor
3D hit
3D pharmacophore
Symmetric diol docked into
HIV active site
Final Molecule selected
for clinical Trials
Initial
idea for
inhibitor
Stereochemistry required
for optimal binding
Expand ring to give diol
and incorporate urea
Host-Guest Interactions with
Collagen: As molecules
Dominated by Geometrical factors
and Solvent Accessible Volumes
Energy minimized structure of 24mer collagen triple helix
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
Binding energies different complexes
between polyphenols and triple helix
Binding Energy (Kcal/mol)
Binding Sites in
triple helix
Catechin (Cat)
Epigallocatechi
ngallate
(EGCG)
Pentagalloyl
glucose (PGG)
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
Gallic acid
(Gal)
9th residue Ser
of C-chain (α2)
Interfacial interacting volume Vs Binding
energy of the collagen-poly phenol complex
Interacting Interfacial Volume (Å3)
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
Plot of inverse of interacting interfacial volume
(1/Int.Vol.) Vs inverse of binding energy(1/B.E) of the
complexes
Acknowledgement
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Mr. R. Parthasarathi
Mr. B. Madhan
Mr. J. Padmanabhan
Mr. M. Elango
Mr. S. Sundar Raman
Mr. R. Vijayraj
CSIR & DST, GOI
Big Thank You
Others have done the work. Some
have used the work. I have
spoken only on behalf of their
behalf.