Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
.pdf
relationship (SAR) study, Maestro, ArgusLab, GRAMM, SYBYL-X Suite, and Sanjeevini
are used. Discovery Studio Visualizer is used for analysis of images. QSARPro program
is utilized for data analysis. For behavioral study, both Ethowatcher and MARS (multi-
modal animal rotation system) are used (Figure 6.3) [9, 10].
SBDD is a specific, efficient, and rapid process for the identification and optimization of
lead compounds as it focuses on the 3D structure of a target protein and knowledge
about the disease conditions at the molecular level [11]. There are several methods used
in SBDD including structure-based virtual screening (SBVS), de novo drug design, molec-
ular docking, and MD simulations. MD simulation technique is used for studying the
dynamic behavior of the macromolecules or proteins [12]. In MD simulations, chemical
bonds and atomic angles are modeled using simple virtual springs, and dihedral angles
are modeled using a sinusoidal function. These methods are applied to analyze binding
energy, ligand–protein interactions, and evaluation of the conformational changes that
occur during the docking studies [13]. The commonly used MD simulation software in-
clude AMBER, CHARMM, Desmond, DL_POLY, GROMACS, LAMMPS, NAMD, and Tinker.
AMBER is used as a biomolecular simulation program. It is a collection of codes that are
designed to work together and mainly divided into three major steps: preparation, sim-
ulation, and trajectory analysis. CHARMM is a commonly used molecular simulation
program. It is primarily designed to study biological molecules such as proteins, pepti-
des, lipids, nucleic acids, carbohydrates, and small molecule ligands (Figure 6.4) [14].
The calculations are based on different energy functions (quantum mechanical-
molecular mechanical force fields, all-atom classical potential energy functions) and
models such as explicit solvent, implicit solvent, and membrane models. Desmond is a
potential MD simulation program [15]. It is used to model explicit membrane systems.
GROMACS represents Groningen machine for chemical simulation. It is an efficient and
versatile MD simulation program with source code. It is suitable for the simulation of
macromolecules in aqueous and membrane environments. NAMD is a high-performance
biomolecular simulation program. LAMMPS refers to large-scale atomic/molecular mas-
sively parallel simulators. It is a classical MD code for materials (biomolecules, polymers)
modeling. Tinker supports a wide variety of classical molecular simulations, mainly bio-
molecular calculations (Table 6.1). It provides several force fields including the modern
polarizable atomic multipole-based AMOEBA model [16–18].
Computational
Based Approaches
DDDPlus
GastroPlus
AutoDock
GeneSpring
MapCheck
REST 2009
GLIDE
Maestro
ArgusLab
GOLD
BioSuite
Sanjeevini
Discovery Studio
PASS
Figure 6.3: Computation-based approaches.
94 Biswa Mohan Sahoo et al.
https://t.me/med1917
Structure-based drug design
Homology modeling: use
known similar structure and
modify sequence for desired
target
Pick next lead
in list.
Analyze and
optimize
Can lead be
modified and
optimized?
Choose drug target
Obtain pure preparation of target in solution
Determine structure by crystallography or NMR
Analyze structure to determine possible inhibitor
binding sites
Dock and score compounds from database
against target’s selected sites
Analyze ranked list of scored compounds and
optimize top pick for binding and selectivity
Purchase or synthesize lead and test for binding
in biochemical assays
Determine structure of target and lead using
NMR or XRC
Analyze structure of target and lead for
interactions
Is lead a nM inhibitor?
Make lead bioavailable and test for potency
Clinical trials
Commercial drug
Yes
Is lead a micromolar
inhibitor in solution?
Yes
No
No
Yes
No
Modify and
optimize lead
in silico
Figure 6.4: Steps involved in structure-based drug design.
6 Role of integrated bioinformatics in structure-based drug design 95
https://t.me/med1917
6.1.1.1.1 Docking study
Docking is a technique of virtual simulation of molecular interactions. Molecular
docking predicts the conforma tion and binding of ligands within a target active site
with high accuracy [19]. So, it is considered as the most useful technique in structure-
based drug design. During docking studies, various factors are considered such as
electrostatic interactions, van der Waals interactions, Coulombic interactions, and hy-
drogen bond formation . Computations of these factors or interactions play a major
role in generating docking scores [20]. The docking results represent the binding affin-
ity of the ligand towards the receptor site. Suitable ligands are identified with the
help of virtual screening [21]. Then, the selected compounds or ligands from the
screening database are ranked and tested experimentally to determine their pharma-
cological activities. Several steps involved in docking include: (a) get the complex
from PDB; (b) clean the complex; c) add the missing hydrogens/side chain atoms and
minimize the complex; (d) clean and minimize the complex; (e) separate the mini-
mized complex in macromolecule (lock) and ligand (key); (f) prepare the docking suit-
able files for lock and key; (g) prepare all the needing files for docking; (h) run the
docking; and (i) analyze the docking results (Figure 6.5) [22].
Several molecular docking tools available for protein–ligand interaction studies in-
clude AutoDock, AutoDock Vina, GOLD, CDOCKER, FlexX, GLIDE, DOCK6, and Swiss-
Dock. AutoDock is used to predict the binding free energies of small molecules to
protein targets [23]. AutoDock Vina calculations focus on a sophisticated gradient opti-
mization method and achieve approximately two orders of magnitude improvement
in speed and better accuracy in predicting binding interactions as compared to Auto-
Dock [24]. GOLD refers to genetic optimization for ligand docking. It is an automated
ligand docking program that allows full ligand conformational flexibility with partial
flexibility of the protein. It explores the binding conformations using a genetic algo-
rithm. CDOCKER represents CHARMM-based DOCKER [25]. It is an automated MD dock-
ing program that uses the CHARMm19 family of force fields. This tool provides full
flexibility of ligand and CHARMM engine with reduced computation time. FLEXX is a
fully automated docking tool for flexible ligands. It generates reliable results with good
Table 6.1: List of molecular dynamic simulation software.
Name of software Simulation system
AMBER Proteins, nucleic acids, and carbohydrates
CHARMM Proteins, lipids, carbohydrates, and nucleic acids
Desmond Proteins and lipids
DL_POLY Membranes and proteins
GROMACS Proteins, lipids, carbohydrate, and nucleic acids
LAMMPS Proteins, lipids, carbohydrates, and nucleic acids
NAMD Proteins, lipids, carbohydrates, and nucleic acids
Tinker Proteins and nucleic acids
96 Biswa Mohan Sahoo et al.
https://t.me/med1917
accuracy. This technique depends on the selection and placement of base fragments of
ligands [26]. It is observed that the best base fragments interacting with the active site
produce a good score. GLIDE refers to grid-based ligand docking with energetics [27]. It
performs an exhaustive search of the positional, orientational, and conformational
space of a ligand binding to a receptor with reasonable computational speed. The scor-
ing of the binding conformations is based on the Chem Score function. DOCK 6 is a
docking program that evaluates the conformational sampling of small molecules based
on the anchor-and-grow search algorithm. SwissDock is a web server that allows the
docking of small molecules into the target proteins [28, 29]. The major steps involved
Target Selection
Search of Target
Structure in PDB
Structure
available
Structure
unavailable
Selection of
Structure
Homology
modeling
Download
Structure with
PDB code
Validation
of Structure
Target Preparation
Drawing of
Ligand Structure
using Chem Draw
Ligand
Preparation
Ligand
Selection
Evaluation of
docking results
Docking
Studies
Figure 6.5: Various steps involved in molecular docking.
6 Role of integrated bioinformatics in structure-based drug design 97
https://t.me/med1917
in SBDD include preparation of target structure, identification of the ligand binding
site, compound library preparation, molecular docking and scoring functions, MD sim-
ulation, and binding free energy calculation (Figure 6.6).
There are two types of molecular docking studies: flexible docking (induced fit) and rigid
docking (lock and key). In the case of flexible docking, the internal geometry of the inter-
acting partners may change when the complex is formed, while the internal geometry of
both the receptor and ligand are treated as rigid in rigid docking (Figure 6.7) [30].
During docking studies, several algorithms are applied such as fast shape matching,
Monte Carlo simulation, incremental construct ion, distance geometry, genetic algo-
rithms, and simulated annealing [31]. Similarly, the scoring functions are the mathe-
matical methods used for calculating the binding affinity of ligands. Scoring consists
of two different expressions, which include the ranking of the generated configura-
tions based on the affinity of ligands towards the target protein and virtual screening
Identification of target binding siteProtein Isolation and Purification
Elucidation of protein structure
(X-ray crystallography, NMR
spectroscopy, Homology modelling)
Molecular Docking and
Structure Based Virtual
Screening (SBVS)
Lead Molecules
Molecular Dynamics (MD)
simulation and Binding
free energy calculation
Biological Evaluation
Chemical synthesis of
top hit molecules
Figure 6.6: Basic steps involved in the structure-based drug design approach.
Docking
Receptor
Receptor
Complex
Ligands
Figure 6.7: Molecular docking process.
98 Biswa Mohan Sahoo et al.
https://t.me/med1917
[32]. There are various scoring functions used in computational approaches such as
LigScore, F-Score, G-Score, D-Score, Chem Score, Drug Score, X-Score, and Gold Score
(Table 6.2) [33]. In flexible docking, three types of algorithms (stochastic, systematic,
and simulation methods) are designed to determine the ligand flexibility (Figure 6.8).
The systematic algorithms are intended to analyze degrees of freedom [34]. The algo-
rithms used for energy minimization include the Newton–Raphson method, steepest
descent, least squares method, and conjugate gradient [35].
Figure 6.8: Algorithms involved in molecular docking.
Table 6.2: Algorithms for molecular docking studies.
Type of
SBDD
Program Whether
flexible
protein?
Whether
flexible
ligand?
Description
Virtual
screening
DOCK No Yes Docks either small molecules or fragments; includes
solvent effects
Flex X No Yes Incremental construction
Flex E Yes Yes Incremental construction
SLIDE Yes Yes Anchor fragments placed
ADAM No Yes Fragments aligned based on hydrogen bonding
AUTODOCK Yes Yes Uses averaged interaction energy grid to account for
receptor conformations and simulated annealing for
ligand conformations
MCDOCK No Yes Monte Carlo to sample ligand placement
Pro DOCK Yes Yes Monte Carlo minimization for flexible ligand, flexible site
Dock Vision No No Monte Carlo minimization
6 Role of integrated bioinformatics in structure-based drug design 99
https://t.me/med1917
6.1.1.1.2 Pharmacophore modeling
Pharmacophore modeling is applied to evaluate the probability of binding of drug mol-
ecules to the target protein binding sites [36]. A pharmacophore constitutes both steric
and electronic features that are essential to confirm the optimal supramolecular inter-
actions with a specific biological target. Both docking and pharmacophore modeling are
widely used in virtual screening studies to identify novel lead compounds against sev-
eral targets [37]. A pharmacophore describes the framework of molecular features that
are vital for the biological activity of a compound. Pharmacophore models are built by
using the structural information about the active ligands or targets. Pharmacophore
modeling is employed at the various stages of the drug discovery process. Several appli-
cations of this technique include virtual screening, drug target fishing, ligand profiling,
docking, prediction of ADMET (absorption, distribution, metabolism, excretion, toxicity)
property, etc. [38] (Figure 6.9).
Buy this technique, Human Pim-1 kinase is evaluated as a valuable anticancer drug
target. Similarly, Amgen discovered beta-site amyloid precursor protein cleaving en-
zyme 1 (BACE1) towards inhibitors against Alzheimer’s disease. The discovery of PI3K
inhibitors, which block the phosphatidylinositol-3 kinases are involved in respiratory,
cardiovascular, rheumatoid arthritis, and cancer diseases [39, 40]. A pharmacophore
model elucidates the spatial arrangement of chemical features in ligands that are re-
quired for interaction with the target receptor. Some of the chemical features used in
Table 6.2 (continued)
Type of
SBDD
Program Whether
flexible
protein?
Whether
flexible
ligand?
Description
De novo
design
LUDI No Yes Docks and scores fragments
GRID No Yes Calculates binding energies for functional groups
SMoG No Yes Knowledge-based scoring function; molecules built by
joining rigid fragments
Grow Mol No Yes Builds ligands from a library of atom types
Group Build No Yes Builds ligand from a predefined library of fragments
HOOK No Yes Searches database of molecular skeletons for fit to
binding site; hooks two MCSS functional groups to
skeleton
SPROUT No Yes Generates skeletons that fit site, substitutes atoms into
skeleton to give molecule with correct properties
CAVEAT No Yes Searches database of small molecules to connect
fragments
100 Biswa Mohan Sahoo et al.
https://t.me/med1917
pharmacophore modeling include hydrogen bond donors, hydrogen bond acceptors, ar-
omatic ring systems, hydrophobic areas, positively charged ionizable groups, and nega-
tively charged ionizable groups. Ligands having different scaffolds but a similar spatial
arrangement of key interacting functional moieties can be identified using pharmaco-
phore-based virtual screening [41]. The bioactive conformation of the molecules within
the target binding site can be incorporated into the pharmacophore model. The pharma-
cophore model is also often used in QSAR studies in the molecular alignment stage.
Some frequently used programs that allow automatic construction of the pharmaco-
phore model include Catalyst, PHASE, LigandScout, GALAHAD, and PharmMapper. The
pharmacophore model generated should have optimum sensitivity and specificity to
minimizethechancesoffalsenegativeandfalsepositiveresultsandmustbevalidated
using an independent external test set (Table 6.3) [42].
Figure 6.9: Pharmacophore modeling in drug discovery.
Table 6.3: List of pharmacophore modeling tools.
Tools Description
Catalyst Catalyst program is based on an algorithm that identifies three-dimensional
configurations of chemical features common to a set of ligands.
DISCO It is an automated pharmacophore method. It examines the data to find all
pharmacophore hypotheses that fit and serve as a complement to D QSAR.
6 Role of integrated bioinformatics in structure-based drug design 101
https://t.me/med1917
The homology modeling of possible drug targets was performed by using MODELLER
v9.20. It is a commonly used computer program for predicting the 3D structure of the
target protein. For three-dimensional structure building, template sequences are re-
trieved from the NCBI-Blast search against the protein data bank (PDB). The most ap-
propriate model is selected from the several models that are generated by MODELLER.
Table 6.3 (continued)
Tools Description
e-Pharmacophore The e-pharmacophore method generates energetically optimized, structure-based
pharmacophore models, which can be used for screening of millions of compounds.
The method uses the glide XP scoring function to score protein–ligand interactions.
GASP It uses a genetic algorithm (GA) for the superimposition of a set of flexible ligands
where the ligands possessing the lowest number of chemical features are chosen as a
template, onto which other molecules are fitted.
GALAHAD It is a pharmacophore program developed to perform flexible alignment of small
molecules that bind to a target protein.
LigandScout It is a fully automated tool for generating pharmacophore models. It detects and
classifies protein–ligand interactions (hydrogen bond interactions, charge transfers,
and lipophilic regions).
PharmaGist It is a freely available web server used for generating ligand-based pharmacophore
models, wherein the input is a set of drug-like molecules that have a binding affinity
to the target protein.
PharmMapper It is a freely available web server. It is commonly used for the identification of
potential target receptors for a given small molecule using the pharmacophore
mapping approach.
Pharmer It is a pharmacophore search program that organizes molecular data using the
Pharmer KDB-tree and bloom fingerprints that allow rapid screening of millions of
molecules.
PHASE It is an advanced pharmacophore-based tool that comprehensively maps the common
spatial arrangement of functional groups in a set of bioactive ligands using a novel
tree-based partitioning algorithm.
Pocketv. It is an automated program to generate a pharmacophore model from a given
protein−ligand complex structure.
Shape It is a structure-based pharmacophore program. It increases the efficiency of
database searching by considering the topographical constraints of the target binding
site.
Snooker It is a structure-based pharmacophore tool that generates pharmacophore
hypotheses from homology models.
ZINCPharmer It is an online web server for the screening of small molecules from the ZINC database
using the Pharmer pharmacophore search program.
102 Biswa Mohan Sahoo et al.
https://t.me/med1917
The model is visualized by using RasMol software. Several steps involved in homology
modeling include target sequence, template recognition, alignment, backbone genera-
tion, loop modeling, side chain modeling, model optimization, and model validation
(Figure 6.10) [43–45].
Quantitative structure–activity relationships (QSARs) studies are based on the princi-
ple that variations in the bioactivity of the compounds can be correlated with changes
in the molecular structures [46]. They are widely used in the drug discovery process
in the hit-to-lead identification or lead optimization. A statistical model is constructed
using these correlation studies, and the final model can be used to predict the biologi-
cal activity of new molecules [47]. The key requirements for the generation of a reli-
able QSAR model are: (a) a s ufficient number of data sets with biological activities
obtained from common experimental protocols, (b) appropriate selection of training
and test set compounds, (c) no autocorrelation among the physiochemical properties
of the ligands that may cause overfitting of the data, and (d) the applicability and pre-
diction of the final model must be checked using internal and external validation
methods. 3D-QSAR programs include the HypoGen module of Catalyst, PHASE, com-
parative molecular field analysis (CoMFA), and comparative similarity indices analy-
sis (CoMSIA). QSAR technique can be classified into two types: linear and nonlinear,
based on chemometric methods. The linear method includes linear regression (LR),
Figure 6.10: Homology modeling.
6 Role of integrated bioinformatics in structure-based drug design 103
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана