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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5728_Библиотеки_им_академика_М_И_Перельмана
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it provides insight into the most stable state of a molecule, which is often the biologi-
cally active form. In the context of ligand-receptor interactions, identifying the global
minimum of a ligand’s conformation can help predict its binding affinity to a target
protein. Molecular mechanics and energy minimization methods are commonly used
to search for global minima by iteratively adjusting ato mic coordinates to reach the
lowest possible energy state. However, finding the true global minimum can be com-
putationally challenging , especially for large and flexible molecules, and it may re-
quire the use of advanced search algorithms.
8.5.2 Bioactive conformers
In the context of drug discovery, bioactive conformers refer to specific molecular con-
formations of a drug or ligand that exhibit a high affinity for a target protein, result-
ing in a pharmacologically relevant biological response. These conformations are
crucial because they represent the geometric arrangements of atoms that facilitate op-
timal interactions with the target, such as binding to an enzyme’s active site or recep-
torsiteonacellsurface.Theconceptofbioactive conformers underscores the
importance of molecular flexibility. Molecules are not rigid structures; they can adopt
a range of conformations due to the rotation of bonds and other degrees of freedom.
To identify bioactive conformers, researchers use various computational techniques,
including molecular docking and molecular dynamics simulations.
8.5.3 Molecular docking
Molecular docking involves the prediction of how a ligand interacts with a target pro-
tein. The goal is to identify the most favorable binding pose and orientation of the
ligand within the protein’s binding site. Docking programs sample different ligand
conformations and orientations to find those that optimize binding interactions. The
bioactive conformers are typically the ones that exhibit the strongest binding affinity.
8.5.4 Molecular dynamics simulations
Molecular dynamics simulations track the dynamic behavior of molecules over time.
They are used to explore the flexibility and motion of both ligands and target pro-
teins. Through these simulations, researchers can identify bioactive confo rmers by
observing which ligand conformations maintain stable interactions with the protein
throughout the simulation period. These conformations represent states with high bi-
ological activity.
8.5 Concept of global minima and bioactive conformer 179
In summary, the concepts of global minima and bioactive conformers are integral
to CADD in drug discovery. Global minima represent the most stable molecular con-
formations, while bioactive conformers are specific arrangements of atoms within a
molecule that exhibit high binding affini ty and biolo gical activity. Identifying these
conformations helps researchers design and optimize drug candidates with enhanced
pharmacological properties, ultimately contributing to the development of safer and
more effective drugs [30].
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References 181
Chapter 9
Molecular docking and drug–receptor interactions
Molecular docking is like a key fitting into a lock, unlocking the secrets of drug-receptor interactions.
– Dr.ArthurJ.Olson
9.1 Introduction
Molecular docking in computer-aided drug designing (CADD) is a pivotal computa-
tional technique employed to predict the interactions between small drug-like mole-
cules and biological macromolecules, primarily proteins [1, 2]. This approach plays a
crucial role in the early stages of drug discovery and development by assisting re-
searchers in identifying potential drug candidates with high binding affinities and
specificities for target proteins [3]. By simulating the binding process, molecular dock-
ing aids in the rational design of new drugs, optimizati on of existing ones, and the
exploration of possible binding modes and conformations [4]. Molecular docking in
CADD is a crucial computational technique that aids in the identification and optimi-
zation of potential drug candidates by simulating and predicting their interactions
with target proteins. It enhances the efficiency and success rates of drug discovery
programs by facilitating the rational design of novel therapeutic agents [5, 6].
Molecular docking operates on the principles of molecular physics and chemis-
try, utilizing algorithms and scoring functions (SFs) to evaluate the binding energy
and affinity between ligands (drug molecules ) and receptors (target proteins) [7, 8] .
The main objectives of molecular docking in CADD include identifying lead c om-
pounds, understanding the binding mechanism, and estimating binding affinities to
prioritize molecules for further experimental validation. This computational ap-
proach requires detailed knowledge of the three-dimensional structures of the tar-
get proteins and the ligands of interest [9]. Often, these structures are obtained
through experimental techniqu es like X-ray crystallography or NMR spectroscopy.
Alternatively, homology modeling can be employed to predict protein structures
when experimental data is unavailable [10]. The success of molecular docking relies
heavily on the accuracy of SFs, which estimate the free binding energy between the
ligand and the receptor. Various SFs and algorithms, such as Autodock, Vina, and
Glide, are empl oyed to evaluate binding affinities and rank potential drug can di-
dates [11]. Molecular docking in CADD is a versatile tool applicable to a wide range
of therapeutic areas, including oncology, infectious diseases, neurology, and cardio-
vascular diseases. It accelerates the drug discovery process by reducing the time
and cost required for experimental screening and by providing valuable insights
into the interactions between molecules and their targets [12].
https://doi.org/10.1515/9783111434858-009
From its emergence in the early 1980s till date, there has been lots of improvement
in the molec ular docking algorithm and it has given us many successful marketed
drugs against various diseases. Molecular docking has gained so much popularity in the
research fraternity that whenever a researcher performs any CADD approach, it is the
molecular docking that he performs first in most cases. As discussed already in the pre-
vious chapters, the CADD approach is classified into two types, structure-based drug de-
signing (SBDD) and ligand-based drug designing (LBDD). Molecular docking is an SBDD
approach in which we try to predict how efficiently the proposed drug molecules could
bind to the desired receptor whose 3D structure is known to us [13, 14]. The basic princi-
ple behind any molecular docking approach is that it works on the hypothesis of lock
and key, where the receptor acts as the lock and the ligand is the key. Like how every
lock is opened with a specific key, every receptor shows its biological activity only
when it binds with its specific ligand (Fig. 9.1)
9.2 Theory of molecular docking
At present, there are numerous molecular docking softwares available; some of them
are commercial while some are available for open access. Whichever software we use
for the study, all have a similar algorithm that performs two functions [15]:
Fig. 9.1: The concept of molecular docking resembles the lock (receptor) and key (ligand) where every
ligand (lock) has a specific key (receptor).
9.2 Theory of molecular docking 183
– 2.1 Conformational search (search algorithm)
– 2.2 Scoring of the ligand–receptor binding
Both of these functions are of utmost importance for performing successful molecular
docking and assessing whether the proposed ligand will bind with the receptor or not
[16]. Molecular docking software differ from each other in the approach they use for
performing this conformational search and scoring of the ligand–receptor binding. To
understand the concept of molecular docking, it becomes indispensable to understand
the different algorithms used for performing these two basic functions of any molecu-
lar docking program [17, 18].
In the previous chapters, the concepts of bioactive conformer and global minima of
a drug molecule have been explained in detail. A bioactive conformer is the conforma-
tion of a drug molecule that has the highest probability of binding with the receptor
molecule to generate a biological response. Likewise, global minimum is considered as
that conformation of the drug molecule that has the lowest energy among all the possi-
ble conformations of that molecule. Bioactive conformation can be different from the
global minima or it could be the same. The only objective of any molecular docking
program is to find the bioactive conformer of the drug molecule and then assess how
this conformation binds with the target receptor protein. Generally, a bioactive confor-
mation lies near the global minima of that drug molecule. Any molecular docking pro-
gram, through its search algorithm, finds a bunch of conformations (commonly known
as poses) of the drug molecule that lie close to the global minima and then checks the
binding of each of these conformations with the receptor protein. Scoring of bindings
with the receptor protein is given to each conformation and the conformation that
shows the best binding with the receptor, in terms of the score, is considered the bioac-
tive conformation.
9.2.1 Conformational search (search algorithms of the molecular docking)
The bioactive conformer of any drug is of primary interest to any computational sci-
entist. It is quite possible that the energy of the bioactive conformer can vary slightly
from the global minima. The very first purpose of any molecular docking software is
to search for ligand conformations that can produce the desired pharmacological re-
sponse. Search algorithms are defined as the set of certain rules and parameters that
are used for finding those conformations of a ligand that can act as the probable bio-
active conformation, which mostly falls near the global minima. Hence, through any
search algorithm, at first, the global minimum is located, and then certain conforma-
tions are selected near the global minima, out of which the bioactive conformation is
to be selected through the SF [19]. There are various search algorithms applied by mo-
lecular docking programs in search of the conformations of the ligand that can act as
potential bioactive conformers (Fig. 9.2).
184 Chapter 9 Molecular docking and drug–receptor interactions
9.2.1.1 Systematic search algorithms
Systematic search algorithms are fundamental in conformational search during mo-
lecular docking. They explore the vast conformational space of ligands to identify en-
ergetically favorable binding poses [20]. These algorithms systematically sample
different conformations by varying dihedral angles, torsion angles, and bond rota-
tions. Methods like Monte Carlo and genetic algorithms perform exhaustive searches
while minimizing energy and maximizing ligand–receptor complementarity. By rigor-
ously examining multiple conformations, systematic search algorithms enhance the
accuracy of molecular docking predictions, enabling the identification of optimal li-
gand poses for drug–target interactions. Their systematic approach significantly con-
tributes to the success of computational drug discovery and the design of effect ive
therapeutic agents [21].
These are the most exhaustive search algorithms in which all the possible confor-
mations of the ligand are explored in the search for the possible conformations for a
bioactive conformer. As the degree of freedom is increased in any drug molecule, the
possible conformations due to the various combinations also increase exponentially.
This problem can be overcome by employi ng an incremental construction approach
for the search for possible bioactive conformations. One another major drawback of
the systematic search algorithm is that it can result in finding out the local minima
instead of the global minima. This drawback can be overcome by initiating the sys-
Fig. 9.2: Different search algorithms used by various molecular docking programs in search of the
bioactive conformer.
9.2 Theory of molecular docking 185
tematic search algorithm at different points of the ligand simultaneously. Systematic
search algorithms can be further classified as follows.
9.2.1.1.1 Exhaustive search algorithm
The exhaustive search algorithm is a notable member of systematic search algorithms
within the field of conformational search in molecular docking. This algorithm operates
by systematically exploring the entire conformational space of a molecule, evaluating
potential binding orientations exhaustively. It does not rely on heuristics or shortcuts,
ensuring a thorough examination of all possible configurations. While it guarantees the
discovery of the global energy minimum, it can be computationally intensive and time-
consuming for large molecules. Nevertheless, the exhaustive search algorithm remains
valuable in precise molecular docking studies, where a comprehensive assessment of
all potential binding poses is essential for accurate predictions [20].
This is the true systematic search algorithm in which a small increment is made
in the structural parameter every time to compute a new conformation. The possible
number of conformations that can be generated depends on the number of the rotat-
able bonds present in the molecule. The larger the number of rotatable bonds, more
will be the conformations that can be generated. In other words, the complexity of
calculating all the possible conformations will be increased in the molecules in which
the number of rotatable bonds is higher. Hence, the exhaustive search algorithm is
only applicable to the less flexible molecules, and for more flexible molecules, other
algorithms of systematic search are preferred.
9.2.1.1.2 Fragment-based search algorithms
Fragment-based search algorithms are an integral part of conformational search techni-
ques in molecular docking. These algorithms systematically explore the vast conforma-
tional space of molecules by dividing them into smaller, more manageable fragments.
They then search for energetically favorable combinations of these fragments within
the binding site of a target protein. Fragment-based algorithms can be classified into
systematic search methods, which thoroughly explore conformational space by exhaus-
tively evaluating various fragment combinations. This classification aids in the precise
prediction of molecular interactions and contributes significantly to drug discovery by
identifying potential ligand–protein binding modes efficiently. These systematic search
algorithms can cater to the problem of high flexibility in calculating the different con-
formations. There are various fragment-based search algorithms which are described
below.
Incremental construction approach
As the n ame suggests, this algorithm initially breaks the drug molecule into small
fragments. These small fragments consist of a rigid portion, also known as the anchor,
186 Chapter 9 Molecular docking and drug–receptor interactions
and flexible fragments. Initially, the anchor of the molecule is docked inside the bind-
ing site of the receptor and then one by one, flexible portions of the molecule are
docked by taking into consideration their flexibility [22]. The one major advantage of
this method is that the flexibility or number of rotatable bonds is considered sepa-
rately for each fragment and their most active conformation is calculated separately.
All the fragments are then combined to obtain the probable most active conforma-
tions of the drug molecule [23]. The most common molecular docking software that
employs this algorithm includes DOCK, FLEXX, FLOG, and Surflex.
Fast shape matching approach
The basic principle behind this approach is that the shape of the receptor’s cavity re-
ceptor is considered first and the conformations of the ligand that match best inside
the shape of that cavity are then considered for finding the bioactive conformation
(Fig. 9.3). There are various approaches to matching the shape of the cavity and the
ligand’s conformations. The shape of the cavity can be mapped either through shape
complimentary, desolvation, or electrostatic parameters. ZDOCK, DOCK, EUDOK, SYS-
DOCK, e-HiTS, and MS-DOCK are the common molecular docking software that em-
ploys this approach for the search algorithm [24].
Fig. 9.3: Fast shape matching approach for finding the bioactive conformer of the ligand that fits best
inside the cavity of the receptor.
9.2 Theory of molecular docking 187
Distance geometry approach
The distance geometry approach is the application of mathematical geometric algebra
to computational chemistry. The basic principle of this approach is that at first, a
GRID is created based on the cavity of the receptor and then different conformations
of the ligand are placed inside this GRID. Based on the distance-based calculations,
conformations are accepted or rejected for a probable bioactive conformer. FLOG is
the molecular docking software that employs this search algorithm [25].
9.2.1.2 Stochastic or random search algorithms
Stochastic or random search algorithms are essential tools in the field of conforma-
tional search within molecular docking. These methods explor e the vast conforma-
tional space of molecules by generating random conformations or pe rturbations,
making them invaluable for finding low-energy configurations.Examplesinclude
Monte Carlo and genetic algorithms. Unlike deterministic methods, stochastic ap-
proaches do not rely on predefined search paths, allowing for a more comprehensive
exploration of potential binding poses. While inherently probabilistic, these algo-
rithms often incorporate energy evaluations to guide the search toward energetically
favorable conformations, enhancing their effectiveness in predicting ligand–protein
binding modes in the context of molecular docking. In this type of algorithm, the use
of probability concept is employed for the search of the probable bioactive conform-
ers. A random change is made in the conformation of the ligand and the conforma-
tions that have a high probability of being the bioactive conformers are accepted and
the rest are rejected. Basically, there are four types of stochastic search algorithms
employed in different molecular docking programs [26].
9.2.1.2.1 Monte Carlo search algorithm
The Monte Carlo search algorithm, as part of stochastic or random search approaches,
serves as a valuable tool in molecular docking studies by enabling the exploration of
conformational space in a probabilistic manner, ultimately enhancing the accuracy of
predicting ligand–protein interactions and aiding drug discovery efforts. The Monte
Carlo search algorithm is a powerful technique employed in the realm of stochastic or
random search algorithms for conformational search tasks within molecular docking
studies. Unlike deterministic methods, Monte Carlo methods rely on probabilistic sam-
pling to explore the vast conformational space of molecules effectively. In the context
of molecular docking, this algorithm is utilized to explore various spatial arrangements
and orientations of a ligand (drug-like molecule) within the binding site of a target pro-
tein. It achieves this by randomly generating molecular conformations and evaluating
their energies using SFs. The key idea is to accept or reject these conformations, based
on their energy differences and temperature-dependent probabilities, allowing the algo-
rithm to explore both high-energy and low-energy regions of the conformational land-
scape. Monte Carlo search excels in its ability to escape local energy minima, which can
188 Chapter 9 Molecular docking and drug–receptor interactions
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