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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
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market has been declining. In order to achieve this goal, the use of computer-aided
drug design (CADD) techniques by leading pharmaceutical industries and other re-
search organizations for the initial stage of drug discovery became crucial for acceler-
ating the drug development process in a more cost-effective manner and minimizing
failures in the final stage [1, 4, 5].
A promising strategy that employs computational methods and technologies to
speed up the discovery and development of novel therapeutic compounds is known
as computer-aided drug design. CADD gives researchers the ability to anticipate and
analyze how drugs will interact with their target biomolecules by using a variety of
computational tools, which helps in the rational design of effective and trustwo rthy
drugs [6–9]. Molecular modeling, virtual screening, molecular dynamic (MD) simula-
tions, and quantitative structure–activity relationship (QSAR) analysis are a few of
the approaches included in CADD. These approaches make use of computational
power to analyze massive databases of chemical compounds, forecast their character-
istics and behaviors, and identify the most promising therapeutic candidates for fur-
ther investigation [10–13]. There are several advantages to CADD, and they have been
well investigated and documented. The several advantages of CADD are given below:
(i) Faster and less expensive: In contrast to conventional trial-and-error methods,
CADD enables the quick screening and optimization of a large number of com-
pounds in silico, which may save time and money [14–16].
(ii) Enhanced effectiveness: In ord er to create drugs more effectively, researchers
might use CADD to concentrate on substances that are expected to have a better
chance of success [17, 18].
(iii) Improved target recognition: Through the application of CADD, it is possible to
find potential therapeutic targets and develop compounds that particularly target
them, resulting in more efficient medications with minimal adverse effects
[19–21].
(iv) Improved accuracy: For the purpose of developing more accurate drugs, CADD
may aid in optimizing drug candidates for particular characteristics, including po-
tency, selectivity, and bioavailability [22, 23].
(v) Fewer experiments on animals: CADD may lessen the requirement for animal
testing by minimizing the number of drugs that must be evaluated [24–26].
(vi) Enhanced pharmacokinetics: Using CADD, pharmacokinetics may be improved
by predicting a compound’s characteristics of absorption, distribution, metabo-
lism, and excretion (ADME) [27–29].
(vii)Forecasting toxicity: With the use of CADD, possible safety concerns may be dis-
covered early in the drug development process. CADD can anticipate the toxicity
of substances [30, 31]. A general workflow diagram of CADD is given in Figure 7.1.
Structure-based drug design (SBDD) and ligand-based drug design (LBDD) techniques
are the two basic categories into which CADD methodologies may be divided. The
available target structural data determines which CADD approach is used. Under-
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standing target structures is necessary in order to employ SBDD technologies. X-ray
crystallography or nuclear magnetic resonance are two common methods for experi-
mentally obtaining target information [32–34]. To forecast the three-dimensional
structures of objects when neither is accessible, computational techniques like homol-
ogy modeling may be utilized. The use of structure-based technologies, such as virtual
high-throughput screening and direct docking techniques on targets and potential
therapeutic compounds, is made feasible by knowledge of the structure. By estimating
the free binding energies, the affinity of molecules to targets may be assessed. Follow-
ing that, potential medication compounds go through further screening and optimiza-
tion. In vitro tests are done to determine the activity of the final lead compounds
chosen. As an alternative, ligand-based techniques are often utilized when the target
structure cannot be predicted computationally or cannot be established empirically.
However, these techniques are dependent on the knowledge of the target’s known ac-
tive binders [35–42]. With the help of CADD, many drugs that are FDA approved are
commercially available in the market [43–45]. Molecular docking strategies and de
Figure 7.1: General workflow diagram of computer-aided drug design.
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novo ligand (antagonists, agonists, inhibitors, etc. of a target) design are two techni-
ques that are often utilized in SBDD. In SBDD, MD simulations are widely utilized to
provide knowledge about not just how ligands connect with target proteins but also
about the paths of interaction. When the targets of drugs are membrane proteins,
where membrane permeability is thought to be crucial for treatment efficacy, this is
particularly significant [46–49].
LBDD is the primary substitution for SBDD. LBDD is an alternate strategy to apply
when the possible drug target structure is unidentified, and predicting it using techni-
ques like homology modeling or ab initio structure prediction is difficult or unaccept-
able. However, this technique critically depends on understanding the small molecules
that attach to the desired target. Some well-liked LBDD strategies include pharmaco-
phore modeling, molecular similarity methods, and QSAR modeling. The chemical fin-
gerprints of known ligands that bind to a target are utilized in molecular similarity
approaches to scan molecular libraries for compounds with similar fingerprints. In li-
gand-based pharmacophore modeling, screening is carried out using the shared struc-
tural characteristics of ligands that bind to a target. The association between the
structural characteristics of ligands that bind to a target and the accompanying biologi-
cal activity impact is modeled using a computer technique called QSAR [50–54].
7.2 Role of X-ray crystallography in CADD
For determining the structures of proteins and other macromolecules found in living
things, X-ray crystallography is now the method of choice. People who are interested
in all areas of biology are increasingly expressing the need for structural data to ad-
dress open-ended queries. A three-dimensional molecular structure may be obtained
from a crystal through X-ray crystallography. Crystals formed from a purified sample
are subjected to a high concentration of X-rays. The processed diffraction patterns
may then be used to d etermine the size of the unit of repetition that makes up the
crystal and the packing symmetry of the crystal. A pattern of the diffraction spots is
used to determine this. Spot intensities may be utilized to identify “structure factors”
that may be utilized to create an electron density map. This map’s quality may be im-
proved using a variety of techniques until it is sufficiently clear to allow the molecular
structure to be built using the protein sequence. The final structure is then adjusted
to match the map and adopt a conformation that is more favorable from a thermody-
namic standpoint [55–58]. Figure 7.2 depicts the workflow of X-ray crystallography
and the steps involved in the determination of structure by X-ray crystallography. A
crystal is placed on a goniometer for a single-crystal X-ray diffraction (XRD) evalua-
tion, which is used to position the crystal at certain orientations. In order to create a
diffraction pattern of reflections, a monochromatic X-ray beam that has been pre-
cisely focused illuminates the crystal. When used in X-ray crystallography, elastic scat-
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tering is used to change the direction of the entering X-rays after diffraction. The in-
coming and exiting X-rays have the same energy and wavelength. By evaluating the
angles and intensities of these diffracted beams with the use of Fourier transforms, a
crystallographer may then create a three-dimensional image of the density of electrons
inside the crystal. The average atomic locations, chemical bonding, crystallographic dis-
order, and other details about the crystal may all be inferred from this electron density.
If the crystals are too tiny or do not have an internal structure that is homogeneous
enough, poor resolution or even errors could take place [55–57, 59–62]. Three funda-
mental processes make up the single-crystal X-ray crystallography method. The creation
of a suitable crystal of the investigated substance is the first and often most challenging
stage. All of the crystal’s dimensions should be more than 0.1 mm in size. It should also
be suitably big, pure in composition, have a regular structure, and not have any severe
internal flaws like fractures or twinning. The crystal is then exposed to a strong X-ray
beam, typically of a single wavelength, to create a predictable pattern of reflection.
Each compound exhibits a distinct diffraction pattern, which is used to quantify the an-
gles and intensities of diffracted X-rays. The intensity of every spot is measured at
every orientation of the crystal, and as the crystal slowly rotates, old reflections fade
away, and new ones form. Since each set generally comprises tens of thousands of re-
flections and somewhat more than half a complete rotation of the crystal, many data
sets may need to be gathered. In the end, a model based on the arrangement of atoms
inside the crystal is obtained and refined computationally with complimentary chemi-
cal information. A crystal structure is the ultimate, polished representation of the
atomic arrangement, and it is often kept in a public database [57, 63–68].
When it comes to target molecules and their interactions with possible drug can-
didates, X-ray crystallography is a crucial tool in CADD. X-ray crystallography assists
in rational drug des ign and optimization by revealing the three-dimensional struc-
tures of proteins and other biomolecules [69–72]. Atomic-level information of the tar-
get molecule and it s interactions with lig ands or therapeutic candidates is provided
by X-ray crystallography. This knowledge is essential for comprehending the binding
method, recognizing important interactions, and directing the development of power-
ful and targeted medications. The exact understanding of the binding site and the in-
teractions that take place there enables rational drug design. By creating compounds
that can successfully interact with the target, X-ray crystallography helps in the opti-
mization of ligands. By using fragment-based drug discovery techniques, it makes it
possible to find tiny molecule fragments that may be utilized to create therapeutic
candidates. The computer models employed in CADD are experimentally validated
using X-ray crystallography. It aids in improving scoring functions and computational
methods by helping to confirm the precision of anticipated binding modes [73–78].
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7.3 Molecular recognizable tools in X-ray
crystallography
The determination of the three-dimensional structures of molecules using X-ray crys-
tallography often makes use of molecular recognizable techniques. These tools assist
researchers in correctly assigning atomic locations and interpreting the electron den-
sity maps produced from XRD data [79–83]. In X-ray crystallography, several parame-
ters are used to analyze and interpret crystal structures and they are given below:
(i) Bond lengths and angles: From the XRD data, the bond lengths and angles be-
tween the atoms in the crystal structure can be determined. Bond length and
angle variations may provide information about interactions or distortions in
molecules [84, 85].
Figure 7.2: The workflow of X-ray crystallography and the steps involved in the determination of structure
by X-ray crystallography.
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(ii) Stere ochemical parameters: Understanding how the atoms are arranged in
space in a crystal structure depends on the stereochemical properties, including
chirality, planarity, and conformational analysis. The three-dimensional form
and geometry of molecules may be determined with the use of these factors [84].
(iii) Hydrogen bonding: When analyzing the stability and characteristics of molecu-
lar crystals, hydrogen bonding is a key factor. For a better understanding of inter-
molecular interactions, X-ray crystallo graphy offers data on the existence and
strength of hydrogen bonds [65–67, 84–86].
(iv) Analysis of packing: The process of examining the arrangement of molecules in-
side the crystal lattice is known as crystal packing analysis. Information on pack-
ing motifs, vacant spaces, and intermolecular interactions is also provided. The
packing study reveals the stability, polymorphism, and capability for molecular
recognition of the crystal [87, 88].
(v) Intermolecular interactions: Several intermolecular interactions, including van
der Waals interactions, π–π stacking, electrostatic interactions, and dipole–dipole
interactions, may be seen and analyzed using X-ray crystallography. Understand-
ing these interactions is essential for comprehending supramolecular assemblies,
binding sites, and molecular recognition [89, 90].
(vi) Solvent effects: The presence of solvent molecules in the crystal lattice may be
determined using X-ray crystallography. Solvent molecules may affect how mole-
cules are packed and interact inside crystal structures, and their presence can
provide information about the stability and behavior of the crystal [91, 92].
(vii)Electron density maps: A thorough illustration of the distribution of electrons in
the crystal structure is provided by electron density maps, which are produced
from the XRD data. These maps enable the identification of specific atoms as well
as the connectivity and electron density of interactions that are both bound and
unbounded [93–95].
Following the determination of the structure, a variety of tools are available for visu-
alizing and analyzing the gathered structural data. Some common techniques for visu-
alizing crystal structures discovered using X-ray crystallography are listed below:
(i) PyMOL: A well-liked and adaptable tool for visualizing crystal structures discovered
by X-ray crystallography is PyMOL. It provides an extensive variety of visualization and
analysis capabilities, which are especially helpful for analyzing macromolecular struc-
tures. By loading crystal structure files in PyMOL, users may see the atomic coordinates
in three dimensions and see them as files, commonly in PDB format. It provides several
rendering choices, such as diverse representations (such as sticks, spheres, and ribbons)
and customizable colors. To learn more about the structure’s general form and atom ar-
rangement, users may simply rotate, zoom in, and alter it. X-ray crystallography creates
electron density maps that show how the electrons are distributed throughout the crystal.
These electron density maps are displayable via PyMOL, enabling you to see the experi-
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mental results and how well the atomic model fits the electron density. This may help in
the development, improvement, and validation of models. For the analysis of crystal
structures, PyMOL offers a wide range of tools. It is possible to quantify the separations,
angles, and dihedral angles between atoms, compute surface areas, and volumes, and an-
alyze molecular interactions. Additionally, PyMOL provides sophisticated analytical tools,
including the discovery of protein–ligand binding sites and electrostatic potential map-
ping. PyMOL offers an intuitive user interface that makes it usable for both novice and
experienced users [96–99].
(ii) CCP4mg: The CCP4 software package, which is extensively used in the area of mac-
romolecular crystallography, has a graphical user interface called CCP4mg. It offers a
simple interface for visualizing and evaluating crystal structures discovered by X-ray
crystallography. It stands for “CCP4 Molecular Graphics” and enables interactive visu-
alization and study of electron density maps and crystal structures. It offers several
rendering choices, structural manipulation tools, and analysis capabilities [100, 101].
(iii) VMD: The University of Illinois at Urbana-Champaign’s Theoretical and Computa-
tional Biophysics Group created the molecular visualization program VMD (visual
MD). It provides a broad variety of characteristics, including crystal structures, for vi-
sualizing and analyzing biomolecular systems. PDB and CIF are only a few of the main
X-ray crystallographic file formats that are supported by VMD [102].
(iv) COOT: X-ray crystallography’s macromolecular model development, visualization,
and validation are all made possible by the widely used software program known as
COOT (crystallographic object-oriented toolkit). It offers a variety of tools and functions
to help with the interpretation and refinement of crystal structures. It is intended to
help crystallographers develop models and validate the structures of macromolecules.
With its user-friendly interface, atomic models and electron density maps may be inter-
actively viewed and changed. Based on experimental data, COOT offers tools for creat-
ing and changing protein, nucleic acid, and ligand structures. It allows for interactive
model development by letting users manually position atoms, alter bond geometry, and
fill in blank areas of the electron density map. It enables real-time model quality visual-
ization and evaluation, supporting iterative structural modification. It also includes
tools for using electron density maps to fit atomic models. To improve the fit between
the model and the experimental data, it offers a variety of algorithms and optimization
techniques. This allows precise positioning and atomic coordinate refinement, depend-
ing on the electron density. Additionally, COOT has facilities for model validation and
quality assessment. It enables users to examine geometrical factors, spot any mistakes
or collisions, and rate the overall strength of the construction. To give thorough analysis
and direction for structural refinement, COOT interfa ces with additional validat ion
tools like MolProbity [103, 104].
(v) Jmol: For the purpose of visualizing crystal structures derived from X-ray crystallog-
raphy, Jmol is a flexible, free, and open-source Java-based molecular viewer. Although
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Jmol was not intended to be used in crystallography, it does handle a variety of file for-
mats and has a number of visualization and analysis tools that are helpful in this setting.
To examine the structure’s general design and atom configurations, it offers a variety of
visualization methods, such as ball-and-stick, space-filling, and cartoon representations.
Additionally, X-ray crystallography experiment-derived electron density maps may be
seen in Jmol. The contour level and transparency of the electron density depiction may
be changed using controls provided by Jmol. Additionally, it offers a number of tools for
studying crystal structures. As well as calculating surface areas and volumes, users can
measure the separations, angles, and dihedral angles between atoms. The scripting fea-
tures of Jmol enable the automation of analytical operations and the creation of unique
analytical processes. Investigating and displaying molecular interactions inside the crys-
tal structure is made possible by Jmol. It is possible to recognize noncovalent connec-
tionssuchashydrogenbondsandprotein–ligand interactions. Understanding both the
general stability of the structure and the functional importance of particular interactions
may be aided by this. Jmol further offers an application programming interface (API)
that enables users to expand its features or create unique programs for crystallographic
visualization [105–107].
(vi) ChimeraX: A sophisticated set of tools for visualizing and analyzing crystal struc-
tures derived from X-ray crystallography are provided by ChimeraX, a program for
next-generation molecular visualization. It offers an easy-to-use user interface, cut-
ting-edge visuals, and a number of functions developed especially for structural biology.
X-ray crystallography-derived electron density maps may be seen using ChimeraX. In
order to evaluate the fit and visualize the experimental data, the electron density may
be overlaid over the atomic model. ChimeraX provides sophisticated tools, including
contouring, slicing, and isosurface rendering, for manipulating and viewing electron
density maps. ChimeraX offers resources for the interactive refining of crystal struc-
tures and model construction. The fit to electron density may be improved, bond
lengths and angles can be changed, and the atomic coordinates can be manually edited.
ChimeraX further provides real-time feedback on the validity and quality of the models,
assisting users in iteratively improving the structure. For crystal structures, ChimeraX
offers a large selection of analysis and annotation tools. Within the structure, you may
measure separations, angles, and torsional angles; compute surface areas; spot hydro-
gen bonds; and examine molecular interactions. For the automation and modification
of analytical procedures, ChimeraX also provides scripting capabilities. For presenta-
tions and publications, ChimeraX enables users to produce crystal structure illustra-
tions, animations, and videos of the highest quality. In order to create representations
of the building that are aesthetically pleasing, it provides powerful rendering capabili-
ties, lighting settings, and programmable visual effects. Virtual reality (VR) visualiza-
tions and interactive 3D presentations are also supported by ChimeraX [108, 109].
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(vii) VESTA: For the visualization and study of crystal structures in X-ray crystallogra-
phy, VESTA (visualization for electronic and structural study) is a potent software ap-
plication. It has a variety of features and capabilities that make it easier to evaluate
and comprehend crystallographic data. Crystal structure visualization is made simple
andeasybyVESTA’s user-friendly interface. It enables interactive visualiz ation of
atoms, unit cells, crystallographic symmetry, and associated structural characteristics.
Different atom representation methods, bond kinds, and color palettes are only a few
of the choices for visualization. By offering tools to look at and work with the struc-
ture, VESTA makes it possible to analyze crystallographic data. Coordination numbers
and polyhedral representations may be calculated as well as bond lengths, bond an-
gles, and torsion angles can be measured. Maps of electron density and difference are
also supported for visualization by VESTA. In cryst allography, it is essential to com-
prehend crystallographic symmetry. Powerful capabilities are available in VESTA for
visualizing and examining crystal symmetries. It can show symmetry-related atoms,
planes, and axes, making it easier to spot symmetry components and understand how
they interact with one another in crystal structures. The creation of supercells, which
are bigger unit cells made by duplicating the initial unit cell in various directions, is
made possible by VESTA. Studying crystal structures with certain periodicities or look-
ing into the impact of doping or substitutions on the crystal lattice may benefit from
this feature. Additionally, VESTA offers editing features that lets crystal structures to
be changed. It allows for the changing of lattice properties as well as the addition,
removal, or relocation of atoms. Building models of intricate crystal formations or in-
vestigating potential structures may both benefit from this capability [110–112].
(viii) Mercury: Mercury is a popular piece of software that helps with the investigation
and visualization of crystal structures in X-ray crystallography. In order to help re-
searchers read and comprehend the results of their experiments, it offers a variety of
features and functions. Mercury provides a full suite of tools for visualizing crystal
structures. As a result, atoms, bonds, unit cells, and symmetry components may all be
interactively shown. Users may customize the display to meet their unique require-
ments by choosing from a variety of visualization and representation methods, such as
ball-and-stick, space-filling, and thermal ellipsoids. Mercury comes with a number of in-
struments for confirming crystal structures. It enables users to carry out tests for geo-
metric quality, such as bond lengths and angles, and to evaluate the model’s fit to the
experimental data using R-factor analysis. Mercury also interfaces with the Cambridge
Structural Database (CSD) to verify structural consistency and compare the crystal struc-
ture with related chemicals in the database [113].
(ix) XtalDraw: X-ray crystallography uses the freeware program XtalDraw to display
and visualize crystal structures. It is mainly made for molecular structures and crys-
tallographic symmetry drawings. Users of XtalDraw are able to see crystal structures
revealed by XRD studies. The viewing and manipulation of atomic coordinates, unit
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cells, and crystal symmetry components are all made possible via a graphical user in-
terface [114, 115].
(x) PHENIX: A complete software package called PHENIX (Python-based Hierarchical
ENvironment for Integrated Xtallography) was created for the determination, im-
provement, and validation of macromolecular structures utilizing X-ray crystallogra-
phy data. It provides a variety of tools and techniques for different phases of
structure determination. PHENIX comes with modeling tools that let users develop
atomic models interactively using data from experiments. It allows for the modifica-
tion of amino acid sequences, fitting of side chains, and placement of ligands and sol-
vent molecules into the electron density maps [116–118].
(xi) SHELX: A popular set of X-ray crystallographic tools called SHELX focuses on de-
termining and fine-tuning the crystal structures of small molecules. It is renowned for
its reliable algorithms as well as for having a wide range of structural solutions and
refinement capabilities. Options are available in SHELX for producing molecular
graphics and viewing crystal structures. It can produce pictures fit for publishing and
export files in common formats for further analysis or external program visualization
[119–121].
(xii) Diamond: For the investigation, visualization, and improvement of crystal struc-
tures in X-ray crystallography, the software program Diamond is often used. It offers
a selection of tools and algorithms to deal with different facets of crystallographic
data. Among other crystallographic data, Diamond provides visualization tools for
electron density maps, crystal structures, and other data. It enables interactive modi-
fication, examination, and visualization of the atomic model, as well as the display of
symmetry components, packing configurations, and bonding interactions. It is fre-
quently utilized for the determination and study of crystal structures in academia, re-
search facilities, and industry [122, 123].
(xiii) XtalView: Macromolecular crystal structures may be seen, examined, and fine-
tuned using the software program XtalView in X-ray crystallography. It offers a num-
ber of tools and functions that help with the interpretation and improvement of crys-
tallographic data. With the help of XtalView, users may see three-dimensional crystal
formations. It offers resources for visualizing electron density maps, atomic coordi-
nates, and crystallographic symmetry components. Zoom in/out, rotate, and display
choices may all be changed by users as they interactively explore the structure [124].
(xiv) JANA2006: For the analysis, solution, and refining of X-ray crystallographic data,
one of the most used software programs is JANA2006. The processing and analyzing
crystallographic data offer a complete collection of tools and methods. When dealing
with complicated crystal structures and difficult experimental settings, JANA2006 is
renowned for its adaptability and resilience. Electron density maps may be analyzed
and interpreted using JANA2006. It makes it easier to observe and outline the electron
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