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_5728_Библиотеки_им_академика_М_И_Перельмана
.pdf
process often involves the synthesis and experimental validation of selected molecules
to validate their effectiveness [14–16].
One of the advantages of de novo drug design is its potential to address previously
intractable targets and diseases. By custom-designing molecules tailored to a specific
target, this approach expands the scope of drug discovery beyond the limitations of nat-
ural compounds and existing drugs. It also offers the opportunity to tackle emerging
diseases for which no treatments currently exist, including those caused by newly dis-
covered pathogens. Despite its tremendous potential, de novo drug design faces several
challenges. The computational power required for simulating complex molecular inter-
actions and generating vast chemical libraries can be resource-intensive. Moreover,
predicting a molecule’s behavior in the intricate biological milieu of the human body
remains a formidable task. Balancing the need for specificity and selectivity while mini-
mizing off-target effects and toxicity poses a constant challenge in the development of
any drug molecule [17–19].
Therefore, de novo drug design represents a paradigm shift in drug discovery,
leveraging computational approaches, structural biology, and artificial intelligence to
create novel therapeutic compounds. This innovative strategy holds great promise for
addressing unmet medical needs, accelerating drug development timelines, and re-
ducing the reliance on serendipity in the search for new treatments. As technology
continues to advance, de novo drug design is poised to play an increasingly significant
role in shaping the future of pharmaceutical research and healthcare [20].
In the previous chapters, we have studied different computer-aided approaches
in which the probable potency of the known ligands is assessed either through struc-
ture-based molecular docking simulations or ligand ligand-based QSAR approach.
Contrary to the above two approaches, the de novo (meaning “from the beginning”)
drug designing approach is the process of finding a totally novel drug molecule from
scratch. Just like CADD, if a novel drug molecule is designed from the structure of the
receptor, then it is categorized as structure-based de novo drug design, and similarly,
if the reference ligand/ pharmacophore is considered for developing the novel drug
molecule, then this approach is known as ligand-b ased de novo drug designing. The
concept of the de novo drug design can be best understood as the inverse of the mo-
lecular docking and QSAR process. In both of these processes, the potency of the
known ligands is estimated either by calculating their binding with the target receptor
protein (in molecular docking) or by estimating their biological activity from a mathe-
matical relationship, derived from a series of known ligands (in the QSAR study).
However, in the case of de novo drug designing, instead of the known ligands, a series
of totally novel drugs are designed either from the structure of the target receptor
protein or the active known ligands against a particular disease (Fig. 11.1).
11.1 Introduction 249
Fig. 11.1: Classification of the de novo drug design into structural-based and ligand-based de novo drug designing.
250 Chapter 11 De novo drug designing approaches
11.2 Classification of de novo drug designing
The de novo drug designing process can be classified based on the template required
for the generation of the new drug molecules from scratch. Any de novo drug design-
ing process can be classified as:
– Structure-based de novo drug designing
– Ligand-based de novo drug designing
11.2.1 Structure-based de novo drug designing
Structure-based de novo drug designing is a powerful strategy in the field of pharma-
ceutical research, offering a targeted and efficient approach for the creation of novel
therapeutic compounds. At its core, this methodology relies on a deep understanding
of the three-dimensional structure of a target biomolecule, often a protein, associated
with a specific disease. With advances in structural biology techniques like X-ray crys-
tallography and cryoelectron microscopy, scientists can elucidate the precise atomic-
level details of these target proteins.
The key to structure-based de novo drug design lies in the rational design of mol-
ecules that can interact with the target protein in a highly specific manner. Computa-
tional tools and algorithms, such as molecular docking and virtual screening, are
employed to predict how potential drug candidates will bind to the target. These sim-
ulations assess the binding affinity, geometry, and stability of the interactions, helping
researchers identify promising molecules for further development. Machine learning
and artificial intelligence also play a significant role, as they can analyze large data-
sets of known protein–ligand interactions to guide the creation of new molecules. By
leveraging these technologies, researchers can generate novel compounds tailored to
fit the target’s active site and potentially disrupt its function, offering a precise and
customizable approach to drug discovery. Ultimately, structure-based de novo drug
design holds immense promise in expediting the development of innovative therapeu-
tics, as it enables scientists to create compounds with a higher likelihood of efficacy
and lower toxicity. This approach contributes to the ongoing pursuit of more effective
and targeted treatments for a wide range of diseases.
Structure-based de novo drug designing process is the approach in which the 3D
structure of the compounds is taken into account for the generation of novel drug mole-
cules. The main objective of this process is to design novel drug molecules that interact
with the target receptor in the desired and effective manner [21–23]. A complete flow
diagram of the structure-based de-novo drug designing process is given in Fig. 11.2. The
complete methodology of the structure-based de novo drug designing process is dis-
cussed below in detail:
11.2 Classification of de novo drug designing 251
Fig. 11.2: Overview of the complete workflow of the steps involved in the structure-based de novo drug
designing approach.
252 Chapter 11 De novo drug designing approaches
11.2.1.1 Obtaining the structure of the target protein
This is the very first step of any structure-based de novo drug designing approach. In
this method, the 3D structures of the target protein against which new drugs are to be
designed are obtained from the protein data bank. These structures present in the
protein data bank are derived experimentally either through X-ray crystallography,
NMR spectroscopy, or electron microscopy. The accuracy and quality of the 3D struc-
ture highly affect the accuracy of the structure-based de novo drug designing ap-
proach. Figure 11.3 depicts the structure of the human acetylcholinesterase enzyme
(PDB ID: 4EY7) obtained from the protein data bank (www.rcsb.org). This structure is
derived using the X-ray diffraction technique with a resolution of 2.35 Å.
11.2.1.2 Predicting the cavity/active site of the target protein
The cavity or the active site of any target receptor protein is an area of interest for
any researcher as it is the region where potent drug molecules bind in a specific man-
ner. Defining or predicting the cavity inside the target protein is the next step in the
structure-based de novo drug-designing process [24]. There are various approaches
through which the active site or cavity of the target protein can be calculated.
11.2.1.2.1 Evolutionary algorithm for cavity/active site prediction
This method is based on the principle that proteins having similar sequences of amino
acids will fold similarly, and hence, will have a similar 3D structure. Likewise, the pro-
teins that have similar structures will have similar active site pockets and therefore, if
the active site of a protein is already known, then the cavity of all the proteins that
have similar amino acid sequences can be determined using this information.
The similarity between the sequences of the amino acids among the proteins can
be accessed using many tools. One of the most commonly employed tools for this pur-
pose is BLAST, which is available open source. The most common in silico programs
Fig. 11.3: The crystal structure of enzyme acetylcholinesterase (AChE) enzyme obtained from the X-ray
diffraction technique obtained from the RCSB protein data bank (PDB ID: 4EY7).
11.2 Classification of de novo drug designing 253
available for the detection of the cavity of the protei ns based on evolutionary algo-
rithms include ConSurf, Rate4site, and Garlig. The quality of this type of cavity detec-
tion relies highly on the quality of alignment of the amino acid sequence of the
unknown protein and on the sequence of the protein of the known structure.
11.2.1.2.2 Energy-based prediction of the cavity/active site of the protein
This is another approach for determining the active site of the target protein. In this
method, a probe (a simple ligand) is placed inside the different regions of the protein
and the interaction energy of the amino acids of the protein with the probe is calcu-
lated. The regions that show minimum energy of the amino acid-probe interactions
are considered favorable regions. Different probes are utilized for measuring differ-
ent types of interactions, such as for assessing hydrophobic regions one type of probes
will be used and for evaluating hydrogen bond acceptor area, different type of probes
will be used. The final cavity of the protein molecule will be defined based on energet-
ically favored and forbidden regions, calculated using the different probes. The most
popular software for cavity detection using this algorithm include Qsitefinder, AutoLi-
gand, and Grid.
11.2.1.2.3 Geometry-based cavity/active site prediction of the protein
This is the most common approach in predicting the cavity of the protein. Whenever
a protein is crystallized to obtain its 3D structure, it is mostly crystallized with its in-
ternal ligand, which is the standard potent molecule against that receptor. Finding
out the region where this internal ligand is bound inside the protein is the region of
interest for any researcher and is defined as the active site or cavity of that protein.
This is the most accurate method for calculating the active site of the target protein.
In this approach, a cutoff value is put for the distance of the amino acids from the
internal ligand, and the region that falls within the cutoff value is defined as the cav-
ity or the active site of the protei n. Table 11.1 depicts different geometry-based pro-
grams for the detection of active sites/cavities within the target protein:
The ultimate goal of any of the abovementioned algorithms for cavity site detec-
tion is to accurately find the region inside the protein where the ligand can bind to
show the desirable pharmacological effect. The accuracy of this step is of utmost im-
portance for successful structure-based de novo drug designing. Figure 11.4 depicts
the active site of the human acetylcholinesterase enzyme predicted by the software,
Discovery studio.
11.2.1.3 Identifying the functional component of the binding site of the protein
For designing any novel drug molecule, it is indispensable to have complete knowledge
of the binding/active site of the target protein. For efficiently designing the novel drug
molecules, the active site of the target protein is studied for mainly three regions that
254 Chapter 11 De novo drug designing approaches
define the interaction of the ligand in most cases. These three regions include the hy-
drogen bond donor region, hydrogen bond acceptor region, and the hydrophobic area.
11.2.1.3.1 Hydrogen-bonding acceptor region
Hydrogen bonding is considered to be of the utmost importance whenever we define
a protein–ligand interaction. Hydrogen bonding is an electrostatic force of attraction
that comes into play when a hydrogen atom bound covalently to an electronegative
atom is placed in the vicinity of another atom that has a lone pair of electrons
(Fig. 11.5). The atom having a lone pair of electrons forms the dipole–dipole bond with
the proton, known as the hydrogen-bond acceptor. In the protein molecules, amino
Tab. 11.1: Commonly used geometry-based programs for the detection of active
site/cavity within the target protein.
S. no. Principle of the geometric
algorithm program
Examples of the software
Sphere-based algorithm – HOLE
– SURFNET
– PASS
GRID-based algorithm – POCKET
– LIGSITE
– DoGSite
Surface-based methods – NSA
– SCREEN
– MSPocket
Tessellation-based algorithms – CAST
– MOLE
– APROPOS
Fig. 11.4: The active site of the enzyme acetylcholinesterase (AChE) where the proposed ligand should
bind effectively.
11.2 Classification of de novo drug designing 255
acids aspartic acid (ASP) and glutamic acid (GLU) possess only the hydrogen-bond ac-
ceptor groups within them.
11.2.1.3.2 Hydrogen-bonding donor region
As discussed above, the hydrogen bond donor group is the electronegative atom that
is covalently bound to the hydrogen atom. It is the electronegativity of the covalently
bound atom that pulls the electron toward itself in the covalent bond and imparts a
partial positive charge to the proton atom. The amino acids that only have the hydro-
gen bond donor groups inside the target protein are arginine (ARG), lysine (LYS), and
tryptophan (TRP).
There are other amino acids inside the protein molecule that contain both hydrogen-
bond acceptor and donor groups. These amino acids include asparagine (ASN), glutamine
(GLN), histidine (HIS), serine (SER), threonine (THR), and tyrosine (TYR). Figure 11.6 de-
picts the hydrogen bonding acceptor and donor region inside the active cavity of the
acetylcholinesterase enzyme. The green region on the surface of the cavity of the enzyme
represents the hydrogen bond acceptor area whereas the purple color represents the hy-
drogen bond donor regions.
11.2.1.3.3 The hydrophobic region inside the cavity of the protein
Hydrophobic regions are also of utmost importance whenever we define or study the
interaction of protein–ligand inside the cavity of the target protein. The hydrogen-
bond regions are the polar regions within the protein and are considered the hydro-
philic regions. Likewise, some regions contain nonpolar groups inside the cavity of
the protein and are considered hydrophobic regions. Hydrophobic groups are mainly
responsible for the Vander-Waals type of interaction between the ligand and the tar-
get protein. The amino acids that form the hydrophobic regions of the protein binding
sites are phenylalanine (PHE), glycine (GLY), leucine (LEU), isoleucine (ILE), proline
(PRO), methionine (MET), tryptophan (TRP), alanine (ALA), and valine (VAL). The very
first method for defining the hydrophobic region inside the binding site of the target
protein is the molecular lipophilic potential (MLP). Some of the most popular software
for the determination of the hydrophobic regions of the binding site of the target pro-
tein are QGrid, PocketFinder, and PLIP.
Fig. 11.5: The concept of hydrogen bonding inside the cavity of the receptor with the ligand.
256 Chapter 11 De novo drug designing approaches
11.2.1.4 Designing of the molecule based on binding site analysis
De novo molecule designing based on binding site analysis is a specialized approach
in drug discovery that focuses on creating entirely new molecular structures tailored
to interact with specific target binding sites, typically on proteins involved in diseases.
This method begins with a deep understanding of the 3D structure of the target pro-
tein and its active binding site, often determined through techniques like X-ray crys-
tallography or cryoelectron microscopy. Computational tools and algorithms are then
employed to design molecules that fit snugly into the binding site, forming strong and
selective interactions. This process involves generating and evaluating various chemi-
cal structures to optimize binding affinity and other pharmacological properties. Ma-
chine learning and artificial intelligence techniques may assist in predicting and
refining the designs.
Designing the novel molecule is the core of any de novo drug designing approach
and is the next step of the structure-based de novo drug designing approach [25, 26].
Based on the analysis of the active site of the target protein, de novo drug designing is
done. The novel molecule can be designed by using any one of the following approaches:
a. Atom-based methods: In this method, the starting point for the generation of the
novel drug molecule is an atom. An atom is placed inside the cavity of the protein at a
suitable point where it interacts favorably with the atoms of the amino acids of the
receptor. This is a sequential approach in which each atom is added sequentially to
the previous atom and then all the parameters such as optimization of the geometry,
calculation of binding affinity, etc. are calculated at each step. A deep knowledge of
chemistry is the prerequisite for any atom-based de novo designing of the drug mole-
Fig. 11.6: The region of the active site of the enzyme acetylcholinesterase (AChE) where the green region
on the surface represents the hydrogen bond acceptor area, whereas the purple color represents the
hydrogen bond donor regions.
11.2 Classification of de novo drug designing 257
Fig. 11.7: Fragment-based approach for de novo designing of the ligand where different fragments are joined together as per the requirement of
the active site of the receptor.
258 Chapter 11 De novo drug designing approaches
Соседние файлы в папке Библиотека им академика М.И. Перельмана