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brolizumab has shown remarkable clinical benefits, leading to durable responses and
improved survival rates in a subset of patients [77].
11.9 Structural genomics and drug resistance
Structural genomics has emerged as a valuable tool in understanding drug resistance
mechanisms, shedding light on the atomic-level interactions between drugs and their
targets. Drug resistance is a significant challenge in the field of drug development, as
many pathogens and cancer cells can adapt and become resistant to therapeutic inter-
ventions over time. Structural genomics plays a vital role in elucidating the molecular
basis of drug resistance by providing detailed three-dimensional structures of drug
targets and their complexes with drugs.
By comparing the structures of drug-resistant and drug-sensitive targets, re-
searchers can identify key mutations or structural changes that confer resistance.
This information helps in predicting how specific mutations might affect drug bind-
ing, leading to decreased affinity or altered drug–target interactions. Structural geno-
mics also aids in understanding how resistance mutations impact the stability and
conformation of the target, influencing drug recognition and efficacy.
The insights gained from structural genomics can guide the rational design of next-
generation drugs to overcome resistance. By analyzing the structural features of both
the wild-type and resistant targets, researchers can develop compounds that can effec-
tively bind to the target despite the presence of resistance mutations. This approach al-
lows for the optimization of drug candidates with enhanced potency and selectivity
against drug-resistant strains. Moreover, structural genomics has also facilitated the
learning of drug resistance in milieu of immune checkpoint inhibitors used in cancer
immunotherapy. By revealing the interactions between immune checkpoints and their
ligands, structural genomics has contributed to understanding how cancer cells can
evade immune surveillance and develop resistance to immune checkpoint blockade.
This knowledge is instrumental in designing new immunotherapeutic strategies to over-
come resistance and enhance the efficacy of cancer immunotherapy [78].
11.10 Mechanisms of drug resistance at
the molecular level
Genomics involves studying the inheritance of traits and conditions from one genera-
tion to another, while genomics focuses on examining an individual’s entire genome
including interactions between genes and environmental factors [79]. Genomics is par-
ticularly relevant in understanding complex diseases like cardiovascular disease,
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asthma, diabetes, and cancer, as they are influenced by combination of environmental
and genetic factors. This field offers new opportunities for developing therapies and
treatments for such diseases [80].
The impact of antibiotic resistance is a pressing concern in healthcare. According
to the CDC’s 2019 antibacterial immunity terror study, over 2.8 million illnesses and
about 35,000 deaths in the United States are instigated by antibacterial resistance
each year, resulting in significant additional healthcare costs [81]. To address this
issue, substantial funding and resources are dedicated to discovering, responding to,
and containing antibioti c-resistant infections associated with healthcare, food, and
the general population [82].
Antimicrobial resistance, particularly antibiotic resistance, is a result of microor-
ganisms developing immunity to the antimicrobial agents once effective against them.
Antibiotic resistance mainly pertains to pathogenic bacteria and can be divided into
microbiological and objective resistance. The growth and spread of drug resistance
are influenced by drug selection pressure, a significant factor in evolutionary biol-
ogy [83].
It is worth noting that while developed countries can establish a clear link be-
tween antimicrobial usage and drug resistance, obtaining primary evidence in devel-
oping countries may be challenging due to the lack of data on antibiotic usage.
Nonetheless, efforts to combat antimicrobial resistance are crucial worldwide, partic-
ularly in poorer developing nations where antibiotic usage is lower but not immune
to resistance due to economic conditions [84].
The initiation of technical improvements and rise of the scientific age brought
about advent of infectious diseases, leading to a significant escalation in global morbid-
ity and mortality rates. To combat these pathogens, the research community delved pro-
found into their molecular mechanisms, epidemiology, and host-pathogen interactions,
aiming to discover current antimicrobial methods for survival of host and safe [85]. The
discovery of antibiotics marked a significant milestone in medical intervention, but in-
discriminate usage has directed to the rise of drug-resistant microbial populations. This
article examines the historical development of antibiotic resistance and the factors sub-
sidizing to the occurrence of drug resistance in various pathogens [86].
Microbial tolerance and resistance have emerged as essential survival mecha-
nisms against toxic agents and environmental challenges. Pathogens have developed
multiple drug resistance to combat various therapeutic treatments [87]. The first in-
stancesofdrugresistancewereobservedagainst penicillin and sulfonamides in
Staphylococcus aureus. As antibiotics became widely used, the prevalence of antibi-
otic-resistant strains increased in hospitals, affecting vulnerable individuals with
weakened immune systems. Various pathogens, including Shigella, Salmonella, Vibrio
cholerae, Escherichia coli, and Pseudomonas aeruginosa, developed resistance to mul-
tiple antimicrobials over time. This section highlights the historical development of
antibiotic resistance and its impact on public health [88, 89].
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Since the introduction of antimicrobial drugs, antibiotic resistance has been a
major global concern. The finding of penicillin in 1928 by Sir Alexander Fleming revolu-
tionized medical treatment, but soon, drug-resistant microorganisms started to emerge.
Penicillinase-producing S. aureus appeared in the 1950s, leading to spread of multidrug-
resistant S. aureus. The introduction of broad-spectrum antibiotics such as ampicillin
and piperacillin also faced challenges with advent of resistant strains [90, 91]. The usage
of third-generation cephems and carbapenem in the twenty-first century contributed to
the rise of resistant strains in various pathogens including drug-resistant tuberculosis
and multidrug-resistant P. aeruginosa. Resistance in cancer cells to chemotherapy also
became a chief fear in cancer research [92].
Antibiotic resistance emerges as a result of a variety of factors that affect the im-
mune profiles of hosts, the fitness cost of pathogens, and the co-selection of resistant
species through genetics. Factors such as natural resistance, genet ic mutations, and
the transfer of genes horizontally all contribute to the development of resistance. The
level of exposure to drugs, drug pharmacokinetics, the host’ s microbiota, and the
pressure exerted by antibiotic use are all pivotal in shaping resistance dynamics [93,
94]. The widespread and indiscriminate application of antibiotics, both in human
medicine and agriculture, in conjunction with the use of harsh chemicals and chemo-
therapy, encourages the emergence of drug resistance. Furthermore, microbial genet-
ics and horizontal gene transfer contribute to the concurrent selection of multiple
forms of resistance [95].
Ongoing research into drug development remains a primary focus on a global
scale, as the effectiveness of powerful drugs has been insufficient in countering mi-
crobial threats. The term “drug” encompasses a broad spectrum of foreign substances
including antibiotics, herbicides, and therapeutic agents designed to combat viruses,
parasites, and cancer. However, the persistent battle between hosts and microbes has
compromised medical interventions, leading to the emergence of multidrug-resistant
organisms like Streptomyces [96]. Some of these organisms have acquired resistance
to seven or more antibiotics, resulting in a phenotype resistant to approximately 20
drugs. Consequently, the environment has become a reservoir for pathogens that ex-
hibit heightened tolerance [97].
The rise of novel infectious agents, such as those responsible for AIDS and SARS,
has elevated the criteria for resistance and introduced fresh complexities for health-
care professionals. Rapidly evolving mechanisms of drug resistance have now become
hallmarks of formidable microorganisms within the environment, giving rise to new
illnesses and heightened levels of tolerance, ultimately contributing to higher mortal-
ity and morbidity rates. To address these challenges, it is imperative to gain a more
profound comprehension of microbial genetics and the manipulation of genes [98].
The molecular mechanisms of resistance can be broadly categorized into two
main types: intrinsic and acquired resistance. D etailed discussions regarding these
molecular strategies related to resistance mechanisms will be presented in the subse-
quent sections.
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11.10.1 Intrinsic drug resistance
It is the natural and inherent capability of a microorganism to resist the ef fects of
certain antimicrobial drugs even in the absence of prior exposure to those drugs. This
resistance arises as a result of specific characteristics and features that the microor-
ganism possesses as part of its natural genetic makeup. These features act as protec-
tive mechanisms, making the microorganism less susceptible to the drug’saction.
Intrinsic resistance can be found in various species of microorganisms, and it may be
related to the structure of their cell wall, membrane permeability, presence of efflux
pumps that can remove drug from cell, or the lack of drug targets. For example, some
Gram-negative bacteria have an outer membrane that acts as a barrier and reduces
the penetration of certain antibiotics, making them intrinsically resistant to those
drugs [99].
11.10.2 Acquired drug resistance
Acquired drug resistance refers to the progression of resistance in a microorganism
as a result of exposure to antimicrobial drugs over time. It occurs due to genetic
changes within the microorganism’s DNA either through mutations or the acquisition
of new genetic material from other resist ant organisms. Once a microorganism en-
counters an antimicrobial medication, it creates a selective pressure that supports the
survival and proliferation of those microorganisms that have developed resistance
mechanisms. The nonresistant microorganisms are eliminated, leaving behind a pop-
ulation of resistant ones.
Acquired resistance can occur through several mechanisms, such as:
a) Mutation: Random mutations in the microbial DNA can result in changes in the
drug target or metabolic pathways, making the drug less effective.
b) Horizontal gene transfer: Microorganisms can acquire resistance genes from
other resistant organisms through processes like conjugation, transformation, or
transduction. These new genes can confer resistance to specific drugs.
c) Gene amplification: Some microorganisms can amplify specific genes that encode
for drug t argets, leading to an increased production of the target and reduced
drug effectiveness.
Acquired drug resistance is a major concern in healthcare settings, as it can lead to
the development of multidrug-resistant strains of bacteria, making infections difficult
to treat and posing a significant threat to public health [100].
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11.11 Designing drugs to overcome resistance using
structural information
Designing drugs to overcome resistance using structural information is a powerful ap-
proach that involves utilizing 3D structures of drug targets and resistance mecha-
nisms. This enables researchers to develop targeted and effective drugs to combat
drug-resistant microorganisms. Below a re the different categories of drug design
strategies, along with a running example in each category:
1. Understanding drug targets: Understanding the structural details of drug targets
is crucial for rational drug design. For example, in the treatment of HIV, the re-
verse transcriptase enzyme is a vital drug target. Analyzing its three-dimensional
structure allows researchers to design antiretroviral drugs that specifically bind
to the enzyme’s active site, inhibiting its function and blocking viral replication.
One such drug is Tenofovir, which is effective against drug-resistant HIV strains
due to its precise binding to reverse transcriptase [101].
2. Exploiting structural differences: Structural information helps in identifying key
differences between drug-resistant and susceptible forms of targets. In the case of
Staphylococcus aureus methicillin-resistant resistance is due to mutated penicil-
lin-binding proteins (PBPs). By studying the structural alterations in the mutated
PBPs, researchers can develop new antibiotics like Linezolid, which can bind to
an alternative site on the ribosome, bypassing resistance and effectively inhibit-
ing bacterial protein synthesis [102].
3. Rational drug design: Rational drug design relies on structural insights to create
drugs with optimal interactions at the target site. For instance, in cancer treat-
ment, researchers analyze the structure of specific oncogeni c proteins such as
Bcr-Abl in CML. This knowledge led to the development of Imatinib, a drug that
binds specifically to the active site of Bcr-Abl, inhibiting its tyrosine kinase activ-
ity and effectively treating the disease [103].
4. Multitarget approach: Combating resistance often requires targeting multiple
pathways simultaneously. In the case of malaria, the parasite Plasmodium falcipa-
rum has developed resistance to traditional antimalarial drugs. Researchers have
used structural information to identify multiple drug targets such as dihydrofo-
late reductase and heme detoxification enzymes. Combining drugs that act on
these different targets, like Artemether-Lumefantrine, provides a multitarget ap-
proach to tackle drug-resistant malaria [104].
5. Combination therapy: Combination therapy refers to the use of several drugs that
operate through different mechanisms of action in order to counter resistance.
For tuberculosis treatment, structural information guides the selection of drugs
targeting different stages of the Mycobacterium tuberculosis lifecycle. Combining
drugs like Rifampicin and Isoniazid, which inhibit RNA and cell wall synthesis,
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respectively, provides a synergistic effect, reducing the likelihood of resistan ce
development [105].
6. Repurposing existing drugs: Structural insights allow for repurposing existing
drugs to combat resistance. For example, in competition, in contradiction of bac-
teria which is drug-resis tant, researchers have modified existing antibiotics like
Aztreonam to improve their binding to bacterial cell wall targets, effectively over-
coming resistance mechanisms and expanding their therapeutic utility [106].
7. Virtual screening: Virtual screening involves computational methods to identify
potential drug candidates. In the search for new drugs against drug-resistant in-
fluenza viruses, researchers use structural information to screen vast chemical
databases. This process led to the identification of Oseltamivir, which targets the
neuraminidase enzyme, inhibiting viral replicationandcounteringdrugresis-
tance [107].
8. Structural-based optimization: Structural information guides the optimization of
drug properties to enhance efficacy against resistance. In cancer chemotherapy,
researchers modify existing drugs like Doxorubicin to improve their interactions
with DNA and overcome drug efflux pumps, thus increasing their effectiveness
against resistant cancer cells [108].
Structural genomics is area of research that focuses on the 3D structures of proteins,
biological macromolecules, and nucleic acids at a large scale. It aims to detect the struc-
tures of all possible protein folds and their functions, thereby enabling a better under-
standing of the relationship between the structure and function of biomolecules. This
knowledge has significant implications for personalized medicine, where treatments
and preventive measures are tailored to an individual’s genetic makeup [109].
The field of structural genomics has its roots in the early 2000s when the Human
Genome Project was completed, providing a complete sequence of the human ge-
nome. This monumental achievement created a vast amount of genomic data that re-
quired extensive functional annotation to comprehend the part of individual proteins
and genes in health and disease. To accomplish this, scientists began using structural
biology techniques, such as NMR spectroscopy and X-ray crystallography to analyze
3D structures of proteins in a high-throughput manner. This gave rise to the concept
of structural genomics, aiming to map the all proteins structures are encoded in a ge-
nome [110].
Examples related to the use of structural genomics in personalized medicine are
1. Targeted drug design: By knowing 3D structure of a specific protein associated
with a disease, researchers can design drugs that specifically target that protein’s
active site or interacting regions. For example, the drug Gleevec (Imatinib) was
developed to treat CML by targeting fusion protein BCR-ABL, which is characteris-
tic of this cancer. Gleevec works by inhibiting the tyrosine kinase activity of the
BCR-ABL protein. This activity is essential for the uncontrolled proliferation of
leukemic cells in CML. By specifically targeting the abnormal tyrosine kinase ac-
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tivity of BCR-ABL, Gleevec inter feres with the signaling pathways that promote
cell growth and survival in CML cells. This ultimately leads to the inhibition of
leukemia cell proliferation and induction of apoptosis (cell death), thereby halting
the progression of the disease. Understanding the structure of BCR-ABL is critical
for the development of drugs like Gleevec because it allows scientists to design
molecules that can specifically target and inhibit the aberrant activity of this fu-
sion protein while minimizing side effects on normal cells. Gleevec’s success in
targeting BCR-ABL has revolutionized the treatment of CML and serves as a para-
digm for targeted therapy in cancer treatment [111, 112].
2. Pharmacogenomics: Structural genomics can help in understanding how genetic
variations affect drug metabolism and response in individuals. Certain genetic
variations can impact the structure of drug-metabolizing enzymes or drug targets,
leading to variability in drug efficacy and adverse reactions among patients. By
identifying these genetic variants and linking them to structural changes, person-
alized medicine can optimize drug selection and dosage for each patient, improv-
ing treatment outcomes and minimizing side effects [113, 114].
3. Biomarker identification: Identifying biomarkers that are associated with specific
diseases or drug responses is a crucial aspect of personalized medicine. Structural
genomics can aid in considerate structural basis of biomarker-protein interac-
tions, which can help in the development of diagnostic tests for early disease de-
tection or monitoring treatment efficacy.
4. Vaccines: In the context of infectious diseases, structural genomics can be used to
identify and characterize surface proteins or antigens of pathogens. Understand-
ing the three-dimensional structure of these antigens helps in designing more ef-
fective vaccines by focusing on regions that induce a strong immune response.
Personalized vaccines can be tailored based on an individual’s unique immune
profile to enhance the immune system’s recognition of the pathogen [115].
11.12 Combating drug resistance through
structure-based approaches
Personalized nutrition is a type of dietary intervention that is tailored to the individu-
al’s genetic makeup. This type of intervention can be used to improve health, prevent
disease, and optimize athletic performance. The authors highlight the fact that there
is an evidence of growing body that suggests that genetics shows role in determining
how people respond to different foods [116]. For example, some people may be more
sensitive to gluten than others, while others may be more likely to develop obesity if
they eat a high-fat diet.
The challenges of personalized nutrition: One challenge is that the field is still in
its early stages, and there is not yet a clear understanding of how to incorporate ge-
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netic information into dietary interventions [117]. Another challenge is that genetic
testing can be expensive, and not everyone has access to it. The future of the field,
and there is a belief that personalized nutrition has the impending to transform the
approach we think about diet and health. Personalized nutrition is a type of dietary
intervention that is tailored to the individual’s genetic makeup.
There is a growing body of evidence that suggests that genetics shows a part in
determining how people respond to different foods. Personalized nutrition has the po-
tential to improve health, prevent disease, and optimize athletic performance. The
field of personalized nutrition is still in its early stages, and there are challenges to
overcome. The authors are optimistic about the future of personalized nutrition, and
they believe that it has the prospective to modernize the manner we think about diet
and health. In addit ion to the points mentioned in the work, there are a number of
other challenges that need to be addressed before personalized nutrition can become
widely adopted [118]. These include the need for more research to identify the genetic
variants that are associated with different dietary responses, the need to develop
more affordable and accessible genetic testing, and the need to develop more user-
friendly software and tools for interpreting genetic data. Despite these challenges, the
potential benefits of personalized nutrition are significant. Personalized nutrition has
the potential to improve the health of millions of people, and it could help to prevent
or delay the commencement of enduring diseases such as heart disease, obesity, and
cancer [119, 120].
11.13 Challenges and limitations
of structural genomics
Structural genomics is a critical component of drug discovery, offering insights into
the three-dimensional structures of target proteins and facilitating the design of inno-
vative therapeutics. Nevertheless, this field encounters various challenges and limita-
tions that influence its application within drug discovery processes [121]. One primary
challenge is the selection and validation of suitable drug targets. While structural ge-
nomics can provide a wealth of structural data, not all identified targets are biologi-
cally relevant or easily druggable. The intricate task of pinpointing and confirming
targets that have a direct impact on disease pathways is essential. Additionally, mem-
brane proteins and protein complexes, which are often vital drug targets, pose a sig-
nificant challenge due to their hydrophobic nature and transient interactions, making
their structural determination complex [122].
Moreover, structural genomics may not capture the complete structural diversity
of the proteome. Certain proteins, due to factors such as instability or difficulty in crys-
tallization, may remain underrepresented in structural databases [123]. The dynamic
nature of proteins and their ability to adopt multiple conformations further complicate
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matters as traditional structural techniques capture static snapshots that may not en-
compass the full range of conformations relevant for drug binding and functionality.
Furthermore, while structural insights aid in understanding protein–ligand interac-
tions, accurately predicting the binding of small molecules remains intricate, and not
all proteins possess suitable binding pockets for effective interactions [124, 125].
A crucial consideration in drug discovery is the contextual understanding of a
protein’s role in cellular p athways and disease mechanisms. Although structural in-
formation is invaluable, comprehensive knowledge of the biological context is essen-
tial for successful drug development. Integrating structural data with other biological
and chemical information, such as genomics, proteomics, and compound libraries, is
imperative for guiding rational drug design [126].
Additionally, producing sufficient quantities of target proteins for structural stud-
ies can be a resource-intensive process. Ensuring the accuracy and reliability of the
generated structural data is paramount. Moreover, interpreting the extensive amount
of structural information necessitates sophisticated computational tools and exper-
tise. The challenges extend to lead optimization, where the identification of potential
drug candidates is merely the beginning; refining these lead compounds to meet de-
sired pharmacokinetic and toxicological properties is a complex endeavor [127].
Ethical and legal considerations also arise in sharing and utilizing structural data,
especially when it pertains to potential drug targets. Issues related to data sharing, intel-
lectual property rights, and commercial interests need to be carefully navigated [128].
Despite these challenges, structural genomics continues to significantly im pact
drug discovery by providing valuable insights into target proteins and facilitating in-
formed drug design. Advances in te chnology, methodology, and computational tools
are continually being developed to overcome these limitations, augmenting the role
of structural genomics in the development of novel therapeutic interventions [129].
11.14 Data quality and validation issues
in structural biology
Data quality and validation are critical aspects of structural biology, ensuring that the
structural information obtained through various techniques accurately represents the
biological macromolecules being studied. Rigorous data quality control and validation
processes are essential to ensure that the structural models generated are reliable
and meaningful [130]. Here, I will explain data quality and validation issues in struc-
tural biology in detail, along with relevant examples:
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11.14.1 Data quality issues
1. Resolution and precision: The resolution of a structural model refers to the level
of detail at which the structure is determined. Higher resolution structures pro-
vide more accurate atomic positions. Low-resolution structures may lack finer de-
tails, which can impact the interpretation of functional sites or interactions.
Example structure of ribosome, a complex molecular machine, required advance-
ments in cryo-EM technology to achieve higher resolutions, enabling better un-
derstanding of its intricate functions [131].
2. Artifacts and noise: Experimental techniques, as cryo-EM and X-ray crystallogra-
phy, can introduce artifacts or noise into the data. These can arise from factors
like sample preparation, radiation damage, or detector imperfections. Distin-
guishing true biological signals from noise is crucial. Researchers often use statis-
tical methods and validation tools to identify and mitigate artifacts. For instance,
the cryo-EM structure of the ATP synthase complex had to account for potential
blurring effects caused by sample movement during imaging [132].
3. Model bias: Structural biologists often build models based on existing knowledge
or homologous structures. This can introduce bias into the model, affecting its ac-
curacy. It’s essential to validate the models using independent experimental data.
An example is the prediction of protein structures in the critical assessment of
structure prediction (CASP) competition, where models are assessed without
prior knowledge of the experimental structures [133].
11.14.2 Data validation issues
1. R-factor and validation statistics: X-ray crystallography contains R-factor as a key
indicator of data quality, representing covenant between the collected data and
the model. Lower R-factors indicate better fit. However, solely relying on R-factors
can be misleading. Validation statistics like MolProbity assess factors such as steric
clashes, bond lengths, and angles, providing a comprehensive picture of the model
quality [134].
2. Cryo-EM map validation: Cryo-EM generates density maps, and validating these
maps is crucial. Researchers use Fourier shell correlation (FSC) curves to assess
resolution and the Gold Standard FSC to compare independently refined half-
maps. Maps should also agree with known biochemical or structural information.
For example, the validation of the SARS-CoV-2 spike protein structure ensured
that the density map matched the expected locations of key features [135].
3. Deposition and peer review: Structural data are placed in public databases as Pro-
tein Data Bank (PDB). However, not all deposited structures undergo the same
level of peer review. While many structures are validated by reviewers, some
may have limited validation due to various reasons. The scientific community’s
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