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210
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Molecular docking method.
Various Docking Approaches.
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Docking
Met Applications
programs
Glide
•Belong to an exhaustive search
algorithm
•Accurate docking module
•The module precomputes grid
representation of protein and ligand
•Ligand conformations are generated in
ligand torsion-angle space, that have
low potential energies
•Glide scoring methods are based upon
geometry and shape of various poses
•Feasible poses are shortlisted by using
a scoring method and approximate
positioning
•Then high-resolution search is done
using molecular mechanics energy
function
•This is followed by the Monte-Carlo
process that examines torsional minima
18,23
GOLD
(Genetic
Optimization
for Ligand
Docking)
•It is a genetic algorithm
•GOLD Suite Software includes
GOLD, Goldmine, and Hermes.
Hermes is required to input data, while
Goldmine is required for analysis and
post-processing of results, during the
application of GOLD docking module
•It is used for docking of flexible ligand
and protein or target-bearing hydroxyl
groups or hydrogen bonds
•The scoring function of GOLD is based
upon Cambridge Structural database and
on results (empirical) due to chemical
interactions (weak)
•The GOLD results are evaluated on the
basis of hydrogen bonds, steric energy
(due to the interaction between protein
and ligand), and ligand’s internal energy
(Lennard-Jones potential)
211 Drug Discovery for Aging and Neurological Disorders
Used to design inhibitors of
cytochrome P450, Falcipain,
and Aurora kinases
GOLD module was used
to design Casein Kinase
inhibitors and SKLB1002, a
VEGFR2 inhibitor.
Insights into phase I
metabolism reaction by
cytochrome P450 enzymes
212
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Docking
programs
SEED
DOCK
Surflex
FLEXX
Auto
18,23
DOCK
Met Applications
•Belong to fragmentation algorithm.
•Based upon “anchor and grow method”
Module has led to the
development of FK506
immunophilin and inhibitor
of BCL6, a B-cell lymphoma
oncogene
•Belong to fragmentation algorithm
•In 1986, Desjarlais et al. did
modifications in DOCK algorithm
•In the binding sites, the fragments of
Inhibitors of anthrax toxin.
Optimization is going on
for topoisomerase- I as an
anticancer drug
ligand were separately docked and then
they were joined further—this was done
to permit the ligand flexibility.
•Belong to fragmentation algorithm.
•Based upon “anchor and grow method.”
•Accurate docking algorithm.
anthrax, and bacterial
development of Plasmepsin
II and IV inhibitors,
Anthrax edema factor, and
respectively
•Belong to fragmentation algorithm
•Based upon “anchor and grow method”
•In this, a fragment of the ligand is
docked in the protein binding site. Then
Inhibitors of
α-glucosidase (screening
INTERBIOSCREEN
chemical database) and
RNA Editing Ligase-1
the fragment is extended in all directions
and all possible conformations. The
extended fragments with no/minimal
(causal organism of African
trypanosomiasis)
steric hindrances are selected
•It is also a genetic algorithm and uses a
Lamarckian genetic algorithm
•Autodock functions in the following
Insights into phase I
metabolism reaction by
cytochrome P450 enzymes
way: (1) with the use of AutoDock
tools coordinate files are prepared,
(2) atomic affinities are precalculated
using AutoGrid, (3) ligands are docked
via AutoDock module, (4) results
are analyzed and interpreted using
AutoDock tools
213 Drug Discovery for Aging and Neurological Disorders
Force
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• Molecular dynamics (MD) simulation: MD, based on Newtonian
physics, was first developed in the 1970s. After molecular docking
or comparative/homology modeling or computerized models of
molecular system-generated after NMR/X-ray crystallography, the
protein–ligand pair (or any other pair in question) is subjected to MD
simulations. MD is performed in the presence of a solvent to mimic
the physiological condition and leverage the pair to interact in every
possible conformation in the allowed virtual conformational space.
Here, the molecular forces acting on each atom of ligand and proteins
are calculated. The force exerted by atoms is quantified following
Newton’s second law of motion. Newton’s second law of motion
states that the acceleration of an object is dependent upon two variables, the net force acting upon the object and the mass of the object.
The acceleration is always in the same direction as the net force.
18,20
acceleration =
= m * a
or F
net
net
mass
In this system, Newton’s laws of motion also govern the atom’s position
change. This process is repeated several times, and every time, the simulation
time is increased by only 1 or 2 quadrillionths of a second. The system runs
through time, and as time lapses, the potential energy of the atom changes with
reference to time. The differentiation of potential energy of every atom with a
change in position is calculated using a dened set of equations and parameters,
18,24
which collectively generate a force eld.
In other words, the force elds
are determinants of different forces acting between different atoms during the
molecular dynamics simulations. The force elds are generated via association
between bonded and nonbonded atoms. The chemical bonds, atomic angles,
and dihedral angles are responsible for bonded force. The simple virtual
springs and sinusoidal function are used to model chemical bonds and atomic
angles, and dihedral angles, respectively.
25
On the other hand, Vander Waals,
electrostatic interactions contribute to nonbonded forces. Lennard-Jones 6-12
potential and Coulomb’s law are used to model Vander Waals and electrostatic
interactions, respectively. To compute the results of MD, many mathematical
calculations are required, hence, to perform this study, clusters of computers
or supercomputers (having several parallel processors) are used. In MD,
different types of force elds, such as AMBER, NAMD (Nanoscale Molecular
Dynamics), CHARMM, and GROMOS are frequently used. The Message
Passing Interface (MPI) facilitates easy and simultaneous implementation of
214
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
these force elds on multiple processors. There are different types of MD viz.,
metadynamics, umbrella sampling, and replica-exchange MD. They give better
results and help in clear visualization of docking results. Further, an amalgamation of molecular docking with molecular dynamic simulation and binding
free energies, for example, BEAR (Binding Estimation After Renement)
is a promising approach in the molecular docking technique. BEAR, a post-
docking tool, provides clear and rened MD results and predicts the stability
of ligand–protein complex via binding free energy methods—MM-PBSA
and MM-GBSA. Other advanced free-energy predictors are thermodynamic
integration (TI), funnel metadynamics, and free energy perturbation (FEP).
They can be applied to interpret the post-docking result, but they are too
costly.19 Docking results are also analyzed by root-mean-square deviation
(RMSD), B-factor analysis, and template modeling score (TMS).
26–28
Maestro
is a Schrodinger software visualization tool. The results obtained after docking
and simulation are analyzed, interpreted, and visualized using a powerful and
versatile Maestro computational tool.
23
In Alzheimer’s disease (AD) β-amyloid aggregates are responsible for
neurodegeneration in brain. Docking and simulation studies were applied
to identify β-amyloid inhibitors. Several compounds were identied among
them Aβ42, and beta-site amyloid precursor protein (APP) cleaving enzyme
(BACE) inhibitors (signicant role in AD pathogenesis) were obtained.
20–22
In 1990, Johnson et al. proposed a ligand-based approach, based on the “Similarity Property Principle.” According to this principle, structurally similar
molecules are expected to have similar properties. In this method, the structure
of the target remains unknown. With the help of a set of reference ligands, it
is determined whether test compounds can interact with the target or not. The
test compounds that interact with the target are screened, and their 2D and 3D
structures are analyzed. LB-CADD functions in the following way: (1) similarity measures—the chemical composition of the active site is known, so the
compounds that are chemically similar w.r.t target’s active site are chosen, (2)
quantitative structure–activity relationship (QSAR) model—These models are
constructed by correlating the chemical structures with their experimentally
determined biological activity, so that the resultant QSAR model can predict
the biological activity of unsynthesized chemical structures. In the QSAR
approach, biological activity predominates the entire hit to lead, lead optimization, and finally DMPK/ADMET optimization process, while in the similarity
measure approach, chemical structures are given preference.
18
215 Drug Discovery for Aging and Neurological Disorders
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Software and computer-associated drug discovery and development tools and
techniques.
23
216
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
There are various determinants of LB-CADD, such as molecular descriptors (functional groups, electronegativity and partial charges, polarizability,
and octanol/water partition coefficient), binary molecular fingerprints, 2D
descriptors of molecular constitution, and 3D descriptors of molecular
configuration and conformation that guides the co-ordinated functioning of
this drug discovery tool. The knowledge-based, graph-theoretical methods
, molecular mechanical, and quantum-mechanical tools determine the
following chemical determinants: functional group, geometry, ring content,
volume, molecular weight, surface areas, interatomic and bond distances,
atom types, molecular walk counts, electronegativities, symmetry, atom
distribution, topological charge indices, and aromaticity indices, etc.
18
In similarity searches, fingerprint methods, similarity networks, off-target
predictions (polypharmacology approach), and fingerprint extensions
(MTree) are used. In 2006, Bologa et al. identified new agonists of GPR30
(estradiol receptor family) by applying 2D fingerprint and 3D shapesimilarity searches. In 2009, Keiser et al. improvised the chemical similarity
approach by introducing statistical models into this method. This approach
is known as the similarity ensemble approach (SEA). In this method,
statistical models are used to foresee whether the ligand will bind to the
target or not. The comparison between the sets of interacting ligands is done
based on Tanimoto coefficients, which are based on standard 2D Daylight
fingerprints (Daylight Chemical Information Systems, 2013). The evaluation of competent ligands is done via Raw Similarity Scores, calculated by
summing up all Tanimoto coefficients (for the sets having a value greater
than 0.57). Another method MTree, a topological model, is an amalgamation
of polypharmacology and similarity measures, which predicts and identifies
new chemotypes and molecular scaffolds.
18
Mueller et al. applied qQuantitative structure–activity relationship
(QSAR) to decipher modulators of mGlu5/mGluR5 signaling. In the
central nervous system (CNS) and peripheral nervous system (PNS),
glutamate is the principal neurotransmitter. The glutamate transmits
neuronal signals via ionotropic (N-methyl-D-aspartate (NMDA), kainate,
217 Drug Discovery for Aging and Neurological Disorders
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and amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)
receptors), and metabotropic receptors (mGluRs).29 Metabotropic receptors are G-protein-coupled receptors (GPCRs), which are responsible for
neuronal cell migration, proliferation, and inflammation in microglia. In
CNS, mGluRs critically regulate the presynaptic transmission (release of
neurotransmitters) depending upon the activity of human beings. Based on
the signaling pathways and mode of action of drugs, mGluRs are divided
into three groups: group I (mGluR1, mGluR5), group II (mGluR2, mGluR3),
and group III (mGluR4, mGluR6, mGluR7, and mGluR8). Among them,
mGluR5 is prominently expressed in the astrocytes, helps in the modulation
of neuronal cell proliferation and is also associated with schizophrenia. The
group I mGluRs evoke neuroinflammatory responses and neurodegeneration
(by secreting excess glutamate) in Alzheimer’s, Parkinson’s, Huntington’s
motor neuron disease (MND), and amyotrophic lateral sclerosis (ALS).
Therefore, the treatment of neurological problems needs the use of either
positive allosteric modulators (PAM) or negative allosteric modulators
(NAM). The treatment modality is to decrease the expression of group I
receptors and increase the expression of group II and III receptors, except in
schizophrenia where PAM is used. Figures 5 and 6 depict the efficiency of
ANN QSAR models in finding the agonists (PAM) and antagonists (NAM)
of mGluR5 signaling in comparison to traditional HTS. Autotaxin (autocrine motility factor) is linked with AD, cancer, diabetes, and other chronic
diseases. QSAR has been utilized to synthesize autotaxin inhibitors. CoMFA
and CoMSIA 3D-QSAR methods have been applied to develop anticancer,
anti-hepatitis B-virus (HBV) drugs.
24–28
The traditional and QSAR method to deduce mGluR5 agonists.
28
218
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
The traditional and QSAR method to deduce mGluR5 antagonists.
18
CANDO (http://protinfo.org/cando) is an open system analysis software tool
that generates leads with evolutionary connection and has multiple modes
of action—potent enough to be categorized as a “drug.” This tool efficiently
characterizes the “on” and “off” target effects, which pave the way for drug
repurposing. CANDO is based on Moore’s Law of pharmacotherapeutics.
30
The Computational Analysis of Novel Drug Opportunities determines the
lead compounds in the following way31:
• Evolutionary and bioinformatic analysis—A rough prediction is
drawn about whether the ligand will interact with the protein (target)
or not, based on the evolutionary connections and databases.
219 Drug Discovery for Aging and Neurological Disorders
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• Molecular docking and simulations—The rough poses are refined
and finalized using fragment-based docking and molecular dynamic
simulation (all-atom knowledge-based force fields) studies. During
docking and simulation studies, the binding is minutely assessed via
digitized interface interaction with each biomolecule. Interaction with
different biomolecules improves the hits of query compounds.
• Calculation of binding free energy—The stable interaction between
the query and target structure is analyzed by calculating the binding
free energy.
CANDO is a way ahead in the drug discovery approach as compared with
virtual screening or traditional high-throughput screening (HTS).
30
Features of CANDO:
• Drug discovery based on evolutionary lineage—CANDO platform
rests its belief in the fact that human beings, plants, other organisms,
and microorganisms living in the same environment exert beneficiary
as well adverse effects on each other. This affirms that the natural
products (query compounds) and potent drug candidates function
simultaneously via interaction with several biomolecules and acti
vation of multiple mechanisms of action in the human system. The
evaluation of “signatures of interaction” is done numerically or digi
tally. This indicates the bonding between compound–target protein
(protein library) and envisages the multiscale relationship a compound
can have with other biomolecules. Further “on-target” (positive) and
“off-” or “anti-target” (adverse/negative) effects are analyzed through
similarity and dissimilarity of query compound-–proteome interac
tion signatures, respectively. This sets the ground for lead discovery
and drug repurposing.
30
• Predictive bioanalytics—The evolutionary basis of lead discovery
directs the predictive bioanalytics, which converts a query compound
into a drug (targeting a particular disease or showing a definite indi
cation) by combining a homology-driven method (functional at the
atomic scale) and heterogeneous biodatabases (recognizes signature
interactions at various levels). It utilizes data mining methods to
predict the behavioral pattern of biomolecules and search for NCEs
(new chemical entities). CANDO platforms generate a number of
leads/computing cycles via statistical multiplier effects. Its efficiency
is on par with whole-genome shotgun sequencing. This has led to the
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