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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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methods to decipher the functions and mechanisms of biomolecules at the atomic
level. By understanding their 3D structures, researchers can gain insights into their in-
teractions, functions, and potential as therapeutic targets [5]. This article explores the
chronological development of structural genomics, from its inception to present-day
approaches, and discusses its significance in various areas of biological research [6].
The early history of structural genomics can be traced back to the 1950s when X-ray
crystallography and nuclear magnetic resonance (NMR) spectroscopy became essential
tools for studying molecular structures. Pioneering scientists like Linus Pauling and
Max Perutz laid the groundwork for understanding protein structures, winning Nobel
Prizes for their significant contributions. Over the years, these techniques were re-
fined, and computational approaches for structure prediction began to emerge [7, 8].
The completion of the Human Genome Project in 2003 marked a turning point for
structural genomics. Researchers recognized that determining the structures of all the
proteins encoded by the human genome was necessary as soon as the human genome
sequence was known [9]. This striving objective directed to the creation of large-scale
structur al genomics initiatives worldwide. During the early 2000s, several interna-
tional consortia were formed to tackle the challenge of high-throughput protein struc-
ture determination [10]. Projects like the Protein Structure Initiative in the United
States, Structural Genomics Consortium, and European Structural Biology Initiative
were launched. These initiatives aimed to systematically determine the 3D structures
of thousands of proteins and make their data openly available to the scientific commu-
nity. One of the critical challenges of structural genomics was target selection [11, 12].
Deciding which proteins to study required careful consideration of their potential bio-
logical relevance and drag ability. High-throughput methodologies, including robotics,
automated crystallization systems, and streamlined NMR data collection, were devel-
oped to expedite the process of structure determination. Structural genomics has sig-
nificantly impacted drug discovery efforts [13]. By detecting probable drug targets and
designing more specific and effective drugs, researchers have been able to use struc-
ture-based drug design. The field has also enabled the study of protein–ligand interac-
tions and aided in the optimization of drug candidates [14]. As structural genomics
projects progressed, researchers recognized the importance of integrating various
structural biology techniques. Integrative approaches combining X-ray crystallography,
NMR spectroscopy, elec tron microscopy, and computational modeling have become
more prevalent in recent years [15, 16]. These methods provide a more comprehensive
understanding of complex biomolecular systems. Structural genomics faces several
challenges including the characterization of membrane proteins, large multi-protein
complexes, and intrinsically disordered regions [17]. Additionally, the integration of
structural data with other “omics” technologies is an ongoing endeavor. The future of
structur al genomics lies in the continued development of innovative experimental
techniques, computa tional methods, and collaborative efforts among researchers
worldwide. The knowledge gained from structural genomics has far-reaching implica-
tions in systems biology, understanding disease mechanisms, and advancing personal-
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ized medicine [18, 19]. Integrating structural information with other biolog ical data
provides a more holistic view of cellular processes and biological networks. Structural
genomics has come a long way from its early roots in X-ray crystallography and NMR
spectroscopy to the establishment of large-scale initiatives and integrative approaches.
The pursuit of understanding the 3D structures of biological macromolecules has been
influential in evolving the insights of biology and drug discovery [20, 21]. With contin-
ued advancements in experimental techniques and computational tools, structural ge-
nomics will undoubtedly play an even more critical role in shaping the future of
biology and medicine [22].
11.2 Structural genomics techniques
and approaches
X-ray crystallography: X-ray crystallography is a foundational technique in structural
genomics, enabling researchers to determine the atomic coordinates of crystallized
biomolecules. This section delves into the principles of X-ray crystallography from
crystal preparation to data collection and refinement. The advantages and limitations
of the method are discussed, along with notable achievements in determining protein
structures using X-ray crystallography [23, 24].
Nuclear magnetic resonance spectroscopy: It is a powerful technique for studying the
structures of biomolecules in solution. This section provides an in-depth overview of
NMR principles including chemical shifts, coupling constants, and relaxation. We also
discuss the application of NMR to determine the structures of proteins and nucleic
acids and its complementary role in structural genomics alongside cryo-electron mi-
croscopy (cryo-EM) and X-ray crystallography [25, 26].
Cryo-EM: This has revolutionized structural biology by allowing the determination of
high-resolution structures of large biomolecules and complexes without the need for
crystallization. In this section, we explore the technical aspects of cryo-EM including
sample preparation, data acquisition, and image processing. We highlight recent break-
throughs in cryo-EM that have significantly expanded its applications in structural ge-
nomics research [27, 28].
Homology modeling (comparative modeling): Known homologous structures are used
to predict molecules’ three-dimensional structures by comparative modeling. This sec-
tion explains the principles of homology modeling and its applications in cases where
experimental methods are not feasible. We discuss the challenges and considerations
in homology modeling and its integration with experimental data for enhanced accu-
racy [29, 30].
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Integrative structural biology: Integrative structural biology is an emerging approach
that combines data from multiple experimental techniques to gain a comprehensive
understanding of biomolecular structures and their interactions. In this section, we
explore the various integrative approaches such as hybrid methods and data-driven
modeling, which have been instrumental in studying large complexes and dynamic
systems [31, 32].
Protein–protein interactions: It shows a vital part in cellular processes and signal trans-
duction. In this section, we discuss the techniques used to study protein–protein interac-
tions including co-immunoprecipitation, yeast two-hybrid assays, and surface plasmon
resonance spectroscopy. The integration of structural data with these interaction studies
provides crucial insights into the mechanisms and specificity of protein–protein [33, 34].
Protein–ligand interactions: Considerate interactions of protein and ligand are essen-
tial for drug discovery and design. This section explores the techniques used to study
protein–ligand interactions such as isothermal titration calorimetr y, NMR spectros-
copy, and virtual screening. There is chief influence of structural information in ratio-
nal drug desi gn and the impact of structural genomics on the development of novel
therapeutics [35, 36].
Structural basis of disease: Structural genomics has shed light on the molecular basis
of various diseases including cancer, neurodegenerative disorders, and infectious dis-
eases. In this section, we examine case studies that illustrate how structural informa-
tion has advanced our understanding of disease mechanisms and facilitated the
development of targeted therapies [37, 38].
Impact on drug discovery, biotechnology, and personalized medicine: The integration of
structural genomics with drug discovery, biotechnology, and personalized medicine has
yielded transformative results. In this final section, we discuss the significant impact of
structural genomics on these fields including the rational design of drugs, enzyme engi-
neering for biotechnological applications, and personalized medicine approaches based
on individual genetic variations [39, 40].
11.3 Target identification using structural biology
HIV protease – target for antiretroviral therapy: Human immunodeficiency virus
replication requires HIV protease. In the late 1980s and early 1990s, structural biolo-
gists successfully discover the 3D structure of HIV protease using X-ray crystallogra-
phy [41, 42]. The crystal structure revealed the active site of the protease, which is
critical for its enzymatic activity in cleaving viral polyproteins during viral matura-
tion. Armed with this structural knowledge, researchers were able to design specific
inhibitors that can bind to active site, blocking its function and preventing viral repli-
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cation [43, 44]. The development of protease inhibitors, such as saquinavir and ritona-
vir, revolutionized HIV treatment and formed the backbone of highly active antiretro-
viral therapy, significantly prolonging the lives of HIV-infected individuals and
turning HIV from a deadly disease into a manageable chronic condition [45, 46].
Human epidermal growth factor receptor 2 (HER2): HER2 (breast carcinoma treat-
ment target) is a tyrosine kinase receptor that plays a crucial role in cell proliferation
and growth. Overexpression of HER2 is found in about 20–30% of breast cancer cases
and is associated with a more aggressive form of the disease. In the late 1990s, struc-
tural biologists determined an antibody known as trastuzumab (Herceptin
®
)com-
plexed with HER2 crystal structure. The structural examination discovered by what
method trastuzumab binds to a specific domain of HER2, inhibiting its signaling [47]
and promoting immune-mediated destruction of cancer cells [48]. Armed with this
structural knowledge, researchers developed targeted therapies like trastuzumab and
pertuzumab (Perjeta
®
), which have significantly improved the outcomes for patients
with HER2-positive breast cancer. These therapies have become a cornerstone of
treatment for this subtype of breast cancer and have dramatically increased survival
rates [49].
Protein kinases – targets for kinase inhibitors in cancer therapy: Protein kinases
play vital roles in cellular signaling pathways and are often dysregulated in cancer,
making them attractive drug targets. Structural biologists have contributed significantly
to the understanding of protein kinase structures, especially their ATP-binding sites and
active conformations. Armed with this knowledge, researchers have developed a class
of drugs known as kinase inhibitors [50]. For example, imatinib (Gleevec
®
) was de-
signed to specifically target the fusion protein (BCR-ABL), an active kinase found in
chronic myeloid leukemia (CML). The three-dimensional structure of BCR-ABL in com-
plex with imatinib provided insights into its mechanism of action, leading to its ap-
proval as a highly effective therapy for CML patients. Other kinase inhibitors, such as
erlotinib (Tarceva
®
), and gefitinib (Iressa
®
), target epidermal growth factor receptor
mutations usually found in patients of lung cancer. These inhibitors have demonstrated
significant clinical benefit and have become standard therapies in various cancer treat-
ments [51].
SARS-CoV-2 main protease (Mpro) – target for COVID-19 drug development: Dur-
ing the COVID-19 pandemic, structural biologists rapidly analyzed the crystal structure
of Mpro, which is vital for replication of virus. This structural information enabled the
virtual screening and design of impending inhibitors directing the active site of Mpro
[52]. One such example is remdesivir (Veklury
®
), which was initially developed as an
antiviral drug for Ebola but was later repurposed as a prospective management for
COVID-19. The crystal structure of Mpro complexed with remdesivir revealed how the
drug interacts with the viral protease, inhibiting its function and disrupting viral repli-
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cation. Remdesivir received emergency use authorization in many countries and be-
came one of the first treatments available for COVID-19 patients [53].
11.4 Utilizing structural information for rational
drug design
Structural genomics has emerged as powerful contrivance in rational drug design,
leveraging three-dimensional structural information to guide the advancement of
new therapeutics. By determining the atomic-level structures of drug targets using
techniques such as X-ray crystallography, NMR spectroscopy, and cryo-EM, structural
genomics provides crucial insights into the binding sites, active conformations, and
interactions of biomolecules with ligands [54]. This detailed knowledge enables re-
searchers to design molecules or biologics that can specifically relate with the target,
modulating its activity and inhibiting disease progression. Through structure-based
virtual screening, computational methods are u sed to identify potential drug candi-
dates from large chemical libraries, which are then evaluated for their predicted in-
teractions with the target of interest [55]. The integration of structural genomics data
with computational approaches expedites the drug discovery process by rationalizing
compound selection and reducing the time and resources required for experimental
testing [56]. Rational drug design with structural genomics has revolutionized the
pharmaceutical industry, allowing for the development of more potent and selective
drugs, while minimizing off-target effects and optimizing therapeutic outcomes [57].
11.5 Structure-based virtual screening approaches
It is a powerful computational approach that utilizes structural genomics data to ac-
celerate the meth od of identifying poten tial drug candidates from vast chemical li-
braries. This techniq ue relies on 3D structures of drug targets, typically obtained
through methods like X-ray crystallography, NMR spectroscopy, or cryo-EM as well as
the known structures of ligands or small molecules with known binding affinity to
the target [58]. In structure-based virtual screening, these structures are explored to
craft target’s binding site’s 3D model, and molecular docking algorithms are employed
to predict how potential drug candidates might interact with the target. By scoring
the binding energy and interactions between each candidate and the target, virtual
screening can prioritize the most promising compounds for further experimental vali-
dation [59]. Virtual screening based on structure significantly expedites the drug de-
velopment advancement by efficiently exploring a vast chemical space and guiding
the selection of compounds with a higher likelihood of binding and modulating the
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target’s activity. This approach has revolutionized the pharmaceutical industry, en-
abling researchers to identify potential lead compounds and optimize drug candidates
with improved potency and specificity, ultimately leading to the progression of effec-
tive and safer therapeutics [60].
11.6 Case studies of drugs designed through
structural genomics insights
11.6.1 Case study 1: imatinib (Gleevec
®
) – chronic myeloid
leukemia (BCR-ABL)
Imatinib, a breakthrough drug in the treatment of CML, was designed using structural
genomics insights. CML is caused by the Philadelphia chromosome, which results in
the formation of the fusion protein BCR-ABL, active tyrosine kinase. In the late 1990s,
researchers determined the crystal structure of the ABL kinase domain in complex
with a small molecule inhibitor, staurosporine, using X-ray crystallography. This struc-
tural information provided key insights into the ATP-binding site of kinase ABL and its
conformational changes upon inhibitor binding.
Armed with this structural knowledge, researchers at Novartis embarked on a ra-
tional drug design approach. They screened a vast chemical library to identify com-
pounds that could mimic staurosporine’sinteractionswiththeATP-bindingsiteof
ABL kinase. Through iterative medicinal chemistry efforts and molecular docking
studies, imatinib was designed as a selective and potent inhibitor of BCR-ABL. Imati-
nib’s unique structural features enabled it to fit snugly into the active site of the BCR-
ABL kinase domain, effectively inhibiting its activity. Imatinib’s successful clinical tri-
als, demonstrating significant therapeutic efficacy and minimal side effects, led to its
approval through the US FDA in 2001. Imatinib has since become a standard-of-care
treatment for CML, significantly prolonging patients’ survival and transforming the
prognosis of this once-fatal disease
11.6.2 Case study 2: oseltamivir (Tamiflu
®
) – targeting
influenza virus neuraminidase
Oseltamivir, marketed as Tamiflu
®
, is an antiviral drug designed through structural
genomics insights to combat influenza virus infections. Influenza viruses employ
neuraminidase, an enzyme, to cleave sialic acid residues from host cell surfaces, facil-
itating viral release and propagation. In the early 1990s, researchers resolute the 3D
structure of influenza neuraminidase using X-ray crystallography.
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The structural data revealed a deep pocket within the active site of neuraminidase,
which was crucial for its enzymatic activity. Researchers at Gilead Sciences recognized
this pocket as an attractive drug target. By utilizing the structural information and em-
ploying computational methods, they designed oseltamivir as a sialic acid mimetic that
could specifically bind to the active site pocket of neuraminidase [60].
The structure-based design of oseltamivir led to its development as a potent and
selective neuraminidase inhibitor. Clinical trials demonstrated its efficacy in reducing
the extent and brutality of influenza indications. In 1999, oseltamivir was sanctioned
by the FDA for treatment of influenza, and its prophylactic use during influenza out-
breaks has been instrumental in reducing the spread of the virus and mitigating the
impact of seasonal flu epidemics
11.6.3 Case study 3: raltegravir (Isentress
®
) – targeting
HIV integrase
HIV integrase is an essential enzyme prerequisite for the integration of genome of
virus into the host cell’s DNA during the HIV replication cycle. In the early 2000s, re-
searchers determined the crystal structure of HIV integrase using X-ray crystallogra-
phy. The structural insights revealed critical regions of the integrase active site,
creating it a crucial target for antiretroviral drug progression.
Based on the structural genomics data, researchers at Merck & Co. designed ralte-
gravir as an integrase strand transfer inhibitor (INSTI). Raltegravir specifically binds
to the active site of HIV integrase, blocking its ability to integrate viral DNA into the
host genome.
In clinical trials, raltegravir demonstrated potent antiretroviral activity with a favor-
able safety profile. It received FDA approval in 2007, becoming the initial FDA-approved
INSTI for the management of HIV infection. Raltegravir’s introduction revolutionized
HIV therapy, offering a highly effective and well-tolerated option for patients living with
HIV [63].
11.7 Structure-guided modifications for improved
drug candidates
Structure-guided modifications play a pivotal role in lead optimization phase of drug
development, where identified compound leads are systematically modified to en-
hance their potency, selectivity, pharmacokinetic properties, and safety profiles. This
approach utilizes structural statistics, attained through methods such as NMR spec-
troscopy, X-ray crystallography, and cryo-EM to guide the design of targeted structural
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changes in the lead compound. Below, we explore in detail how structure-guided
modifications lead to improved drug candidates:
Binding interactions: Structural data reveal s the specific interactions betwee n the
lead compound and the target’s binding site. By a nalyzing these interactions, re-
searchers can identify key residues involved in ligand binding and design modifica-
tions that strengthen the interactions. For example, introducing additional hydrogen
bonds or hydrophobic interactions can enhance the lead compound’s binding affinity
to the target [61].
Active site conformation: Structural data also provides insights into the conforma-
tional changes that occur in the target’s active site upon ligand binding. Researchers
can utilize this information to optimize the lead compound’s conformation to better
fit the target’s active site, maximizing its potency and selectivity [62].
Solubility and lipophilicity: Structural data aids in understanding the lead com-
pound’s solubility and lipophilicity. By identifying regions that contribute to poor sol-
ubility or excessive lipophilicity, researchers can introduce structural modifications
to improve the compound’s pharmacokinetic properties and enhance its bioavailabil-
ity [63, 64].
Off-target effects: Structural data allows researchers to assess potential off-target in-
teractions of the lead compound. By visualizing binding sites on nontarget proteins,
modifications can be made to minimize interactions with unintended targets, reduc-
ing the risk of off-target effects and enhancing the drug’s safety profile [65, 66].
Scaffold optimization: Structural data facilitates the optimization of the lead com-
pound’s scaffold, which forms the core structure of the drug candidate. By modifying
the scaffold while retaining key pharmacophores, researchers can explore a diverse
chemical space and identify compounds with improved activity and selectivity [67, 68].
Metabolic stability: Structural data aids in understanding the susceptibility of the
lead compound to metabolic degradation. By identifying vulnerable regions, research-
ers can introduce modifications that enhance the compound’s metabolic stability,
leading to longer half-life and increased efficacy [69].
Side chain optimization: Lead compounds often contain side chains that can be
modified to improve their properties. Structural data helps in identifying side chains
that can be replaced or altered to optimize the compound’s interactions with the tar-
get and improve its drug-like properties [70].
Prodrug design: Structural data can guide the design of prodrugs, which are inactive
compounds that undergo chemical modifications in the body to become active drugs.
By understanding how the prodrug interacts with the target and the modifications re-
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quired for activation, researchers can design prodrugs with improv ed pharmacoki-
netic properties and targeted delivery [71].
Selectivity and polypharmacology: Structural data allow researchers to assess the
lead compound’s selectivity against closely related targets or potential poly pharmaco-
logical effects. By modifying the compound’s structure, researchers can enhance its
selectivity for the intended target and reduce the risk of unwanted interactions with
off targets [72].
Resistance mechanisms: In the case of antimicrobial or antiviral agents, structural
data can be used to understand resistance mechanisms. This knowledge helps in de-
signing modifi cations that overcome resistance and improve the compound’s effec-
tiveness against drug-resistant strains [73].
11.8 Accelerating the drug development process
through structural insights
Structural genomics has revolutionized the drug development process by providing
essential structural insights into drug targets and facilitating the rational design of
novel therapeutics. One prominent drug example developed with the concept of struc-
tural genomics is the HIV protease inhibitor, darunavir (Prezista
®
). HIV protease is a
key enzyme required for the processing of viral polyproteins during viral maturation,
making it a crucial drug target. In the late 1990s, structural biologists determined the
three-dimensional structure of HIV protease using X-ray crystallography, revealing its
active site and critical residues involved in substrate cleavage. This structural infor-
mation was pivotal in the rational design of darunavir, a potent and highly selective
HIV protease inhibitor. By analyzing the site for binding and interactions of the en-
zyme with potential drug candidates, researchers at Tibotec (now part of Janssen
Pharmaceuticals) designed darunavir to specifically target the active site of HIV prote-
ase. The structural insights guided the optimization of the compound’s interactions,
leading to enhanced potency against drug-resistant HIV strains [74].
Darunavir was the first HIV protease inhibitor developed using structure-guided
design principles. Its success can be attributed to the precise understanding of the tar-
get’s three-dimensional structure, allowing researchers to optimize the lead compound,
and overcome drug resistance. Darunavir received FDA approval in 2006 and has since
become a critical component of antiretroviral therapy, significantly improving the treat-
ment outcomes of HIV-infected patients. The accelerated development of darunavir
exemplifies how structural genomics insights have transformed the drug discovery pro-
cess. By providing detailed information on the target’s three-dimensional structure,
structural genomics allows for rational drug design and structure-based optimization of
lead compounds. This approach expedites the lead identification and optimization
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phases, ultimately leading to the development of effective and safer medications for
complex diseases like HIV/AIDS. The success of darunavir highlights the significant im-
pact of structural genomics in accelerating drug development and exemplifies the po-
tential of this approach in discover ing breakthrough th erapies for various medical
conditions [75].
Bortezomib, marketed as Velcade
®
, is a proteasome inhibitor used in the treat-
ment of multiple myeloma and other types of cancer. It was developed with the in-
valuable contributions of structural genomics. The proteasome is a cellular complex
responsible for the degradation of proteins, and its dysregulation is implicated in can-
cer progression. In the late 1990s, structural genomics researchers unwavering 3D
structure of the 20S proteasome using X-ray crystallography. This structural insight
revealed the active site and binding pocket of the proteasome, providing a crucial
foundation for drug design. Armed with this structural information, resear chers at
Millennium Pharmaceuticals (now Takeda Oncology) embarked on a rational drug de-
sign approach. They screened a library of chemical compounds to identify molecules
that could fit into the proteasome’s active site and inhibit its function. Through itera-
tive medicinal chemistry efforts, they designed bortezomib, a reversible and specific
proteasome inhibitor. Bortezomib binds selectively to the proteasome’s active site, in-
hibiting its activity and preventing the degradation of key regulatory proteins in-
volved in cancer cell growth and survival. Bortezomib’s structural genomics-driven
development was groundbreaking, as it represented the first successful proteasome
inhibitor in clinical use. It received FDA approval in 2003 for the treatment of multiple
myeloma and later for other types of cancer. Bortezomib has since become a stan-
dard-of-care treatment, significantly extending the survival of patients with multiple
myeloma and other malignancies [76].
Pembrolizumab, marketed as Keytruda
®
, is an immune checkpoint inhibitor
used in cancer immunotherapy. Its development was influenced by structural geno-
mics, which played a cruci al role in understanding the molecular basis of immune
checkpoint interactions. Immune checkpoints are regulatory pathways that prevent
the immune system from attacking healthy cells excessively. However, cancer cells ex-
ploit these checkpoints to evade immune surveillance. In the early 2000s, structural
genomics researchers determined the three-dimensional structure of the PD-1 recep-
tor using X-ray crystallography. This structural insight was groundbreaking as it re-
vealed the binding site of PD-1 and its interactions with its ligands, PD-L1 and PD-L2,
which are articulated on cancer cells surface. Armed with this knowledge, researchers
at Merck & Co. (MSD) pursued the development of an antibody-based immune check-
point inhibitor targeting PD-1. By designing an antibody that fixes to PD-1, pembrolizu-
mab efficiently inhibits the PD-L1/PD-1 and PD-L2/PD-1 interactions, unleashing ability
of immune system to recognize and attack cancer cells. Pembrolizumab’s structural
genomics-influenced development marked a significant milestone in cancer immuno-
therapy. It received FDA approval in 2014 and has since been accepted for numerous
kinds of cancer comprising non-small cell lung cancer, melanoma, and others. Pem-
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