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11 Role ofGenomics andProteomics inDrug Discovery
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have been able to pinpoint the genes that are altered in cancer cells. These genes are
currently being researched as potential cancer therapeutic targets. For example, the
medication Herceptin targets the overexpressed HER2 protein found in many breast
cancer cells (Dean-Colomb and Esteva 2008).
Proteomics has also been utilized by researchers to pinpoint protein-protein
interactions that are crucial for the replication of HIV.New HIV/AIDS medications
are currently focusing on these interactions. For instance, HIV needs the protease
protein to replicate; hence, the medication Prezista targets it (Clark etal. 2017).
Genomics can be utilized to identify biomarkers. Biomarkers are molecules that
can be used to identify disease, forecast how the disease will proceed, or track how
well a treatment is working (Vasan 2006). Genes linked to disease can be found
using genomics, and biomarkers based on these genes can be created.
Proteomics can be used to identify drug resistance mechanisms as well. A signicant problem in the treatment of cancer is drug resistance. Proteins associated
with drug resistance can be found via proteomics, and this knowledge can then be
applied to the development of countermeasures (Li etal. 2011).
On top of this, knowledge in genomics and proteomics can also be used in creating a personalized medicine approach. Personalized medicine is a medical strategy
that considers a patient’s unique genetic and molecular make-up. Based on each
patient’s unique genetic and molecular prole, individualized treatment strategies
can be created using genomics and proteomics.
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11.2 Applications ofGenomics inDrug Discovery
11.2.1 Genomic Technologies andTechniques
Our understanding of genetics, gene expression, and the function of genes in health
and disease has been completely transformed by genomic technologies and methodologies. With their invaluable insights into target identication, biomarker discovery, and customized medicine, these cutting-edge technologies and techniques have
become indispensable in the drug discovery process.
Sanger sequencing was the rst technique for DNA sequencing that was widely
used. It still has a place in some applications and involves chain termination. DNA
can be sequenced in massively parallel using next-generation sequencing (NGS)
technologies such as Illumina and Ion Torrent, which accelerate and lower the cost
of the process. Research in genomics has tremendously advanced thanks to NGS
(Liu etal. 2012).
RNA sequencing (RNA-Seq) enables analysis of the dynamics of gene expression under varied situations by proling gene expression at the transcriptome level.
Technology using CRISPR-Cas9 method is used due to its precise genome-editing
capabilities. By using CRISPR-Cas9, it is now possible to selectively alter genes
and investigate their functions (Corsi etal. 2022).
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Single-nucleotide polymorphism (SNP) analysis is another method used in
genomic variation analysis. Understanding genetic differences linked to illnesses
and medication responses requires SNP genotyping. Copy number variation (CNV)
analysis can also be used to analyse genomic variation. The effects of differences in
the number of copies of particular genes or genomic regions on health and disease
are revealed by CNV analysis (Wheeler etal. 2013).
In order to nd conserved areas and evolutionary changes, comparative genomics compares the genomes of several species. It aids in comprehending the importance of genes in terms of function (Alföldi and Lindblad-Toh 2013). The genetic
make-up of entire microbial populations is studied using metagenomics. It can be
used in the drug discovery process, especially in determining how the human microbiome affects health (Chiu and Miller 2019). DNA methylation analysis is a method
used in the eld of epigenomics. Gene regulation is greatly inuenced by DNA
methylation patterns. To analyse DNA methylation, methods such as bisulte
sequencing are employed. Another technique in epigenomics is chromatin immunoprecipitation sequencing (ChIP-Seq). Mapping of protein-DNA interactions and
locating epigenetic changes are both facilitated by ChIP-Seq (Lhoumaud etal. 2019).
Functional annotation and pathway analysis can be carried out in drug discovery
by using methods such as gene ontology and pathway analysis. Gene Ontology
(GO) analysis classies genes according to their functions, supporting the interpretation of massive genomic data. In pathway analysis, drug target development
depends on the identication of biological networks and pathways connected to
disease processes (Zhou etal. 2017).
Single-cell RNA sequencing (scRNA-Seq) and long-read sequencing can be
used in the single-cell genomic drug discovery process. Researchers may examine
gene expression at the single-cell level using scRNA-Seq, which sheds light on cellular heterogeneity and development (Kharchenko 2021). Long-read sequencing is
another technology made available by PacBio and Oxford Nanopore that increases
the precision of genome assembly and corrects structural variations (Amarasinghe
etal. 2020). In genomic research, efcient data integration and analysis are essential. The interpretation of genomic data requires the use of bioinformatics tools and
databases.
Personalized medicine methods and the discovery of prospective drug targets
have both been made possible by the advancement of genomic technology and
methodologies, which have increased our understanding of the genetic basis of disease. These technologies have a lot of potential to advance medication discovery
and enhance patient outcomes as they develop further.
J. Sundarasekar and G. Sahgal
11.2.2 Human Genome Project andIts Impact
One of the most important scientic endeavours of the twentieth century is the
Human Genome Project (HGP). It was a cooperative worldwide project comprising
scientists from all around the world that began in 1990 and ended in 2003. The main
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objective of the HGP was to fully decode the human genome, revealing the DNA
sequence that forms the genetic code of Homo sapiens. Science, medicine, and society have all been profoundly impacted by this enormous project (Wilson and
Nicholls 2015).
In order to determine the precise sequence of the 3 billion base pairs of DNA that
make up our genetic code, the HGP sought to identify and map every gene in the
human genome. In order to provide a complete picture of our genetic make-up, this
effort required the sequencing of both the coding (exonic) and non-coding (intronic
and intergenic) sections of the genome (Wilson and Nicholls 2015). Advanced
genomic technologies and procedures, such as high-throughput DNA sequencing
techniques such as next-generation sequencing (NGS), were made possible by the
HGP.These technologies, which enable quicker, more economical sequencing and
a variety of genomic studies, have emerged as crucial instruments in genomic
research.
The project shed insights into genetic variation by demonstrating the extent of
copy number variations (CNVs) and single-nucleotide polymorphisms (SNPs) in
the human population. Understanding the genetic basis of many diseases, pharmacological reactions, and human variety has beneted greatly from this information
(Vemula etal. 2023). In order to better understand how genes are expressed and how
proteins function in health and disease, functional genomic and proteomic studies
were made possible thanks to the HGP.It has been possible to nd prospective
therapeutic targets and biomarkers due to this knowledge.
Personalized medicine has greatly beneted from the availability of a reference
human genome. Healthcare professionals can customize therapies to a patient’s
particular genetic prole by comparing an individual’s genome to the reference
genome, optimizing therapeutic outcomes, and reducing side effects. Research
into the genetic causes of illnesses such as cancer, cardiovascular ailments, and
unusual genetic conditions has also advanced because of the HGP.It has aided in
the discovery of fresh medication targets and the advancement of specialized
treatments.
In addition to worries about genetic privacy, discrimination, and the appropriate use of genetic information, the HGP presented signicant ethical and
legal issues. To shield people from genetic prejudice, laws such as the Genetic
Information Nondiscrimination Act (GINA) were passed in the USA (Johnson
and Shaw 2023). The HGP has thus signicantly inuenced public understanding of genomics and science education. Future generations of scientists have
been motivated by it, and the public is now more interested in genomics and
genetics.
Overall, the Human Genome Project has had a profound impact on society, science, and medicine. It has improved our knowledge of human genetics, sped up
advancements in medical science, and ushered in the era of customized medicine.
The HGP’s legacy persists as proof of the value of interdisciplinary scientic investigation and discovery as genomic research develops.
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J. Sundarasekar and G. Sahgal
11.2.3 Functional Genomics inDrug Discovery
In the context of a complete organism, functional genomics is a subeld of genomics that focuses on understanding how genes, their products (proteins and RNA),
and their interactions operate. By shedding light on how genes and gene products
affect health, disease, and treatment responses, it plays a crucial role in drug discovery. Through the identication of prospective therapeutic targets, the elucidation of
disease mechanisms, and the development of more efcient treatments, functional
genomic approaches have completely changed the way that drugs are developed.
Identication and verication of putative therapeutic targets are made possible
by functional genomics. Researchers are able to identify the genes that are directly
responsible for an illness by methodically examining the way those genes act. In
order to evaluate the effect of particular genes on processes relevant to disease,
methods such as RNA interference (RNAi) and CRISPR-Cas9 gene editing allow
for the selective silencing or change in those genes (Knudsen etal. 2023).
Drug development requires a thorough understanding of the mechanisms by
which medications work. The particular pathways and biological processes impacted
by potential therapeutic candidates are claried by functional genomics (Pearson
etal. 2016). Mechanism of action (MoA) studies help to rene medication candidates and lower the risk of side effects by revealing unwanted off-target effects.
Finding genes, proteins, or genetic variants that act as markers of disease, the
course of disease, or the response to treatment is crucial to the development of biomarkers. Functional genomics performs this role to perfection (Choy etal. 2019).
By directing treatment choices in accordance with a patient’s particular genetic prole, biomarkers enable customized medicine. High-throughput screening (HTS)
tests incorporate functional genomic approaches to quickly screen hundreds of
chemicals for their impact on cellular functioning. Lead compounds are identied,
and their potential as therapeutic candidates is evaluated utilizing HTS employing
functional genomic data (Wildey etal. 2017).
By identifying novel indications for current medications, functional genomics
aids efforts to repurpose existing medications. Researchers can nd potential candidates for drug repurposing in various disorders by examining how approved medications affect diverse biological pathways.
Pharmacogenomics, which studies how genetic variants affect a person’s
response to medications, relies heavily on functional genomics. It aids in identifying patients who might have negative side effects or ineffective treatments, and it
directs the choice of the most suitable treatments. Systems biology, a comprehensive method that models the intricate relationships between genes, proteins, and
other molecules, incorporates functional genomics. The thorough study of disease
networks and medication responses is made possible by systems biology.
For analysis and interpretation, functional genomic experiments create a tremendous amount of data that calls for sophisticated bioinformatics tools. Our understanding of pharmacological effects is improved by the integration of genomic data
with other “omics” data (such as proteomics and metabolomics). Lastly, functional
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genomics improves the selection of substances that move forward to clinical trials
by validating the efcacy and safety of therapeutic candidates in preclinical studies.
Functional genomics has developed into a crucial tool in the drug discovery process, making it easier to nd new drug targets, create focused medicines, and
improve treatment plans. Functional genomics will become more and more important as technology develops, helping to bring safer and more effective medications
to market and ultimately enhancing patient outcomes and customized treatment.
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11.2.4 Pharmacogenomics andPersonalized Medicine
Pharmacogenomics is a subeld of genomics that examines how a person’s genetic
make-up affects how they react to medications. It aims to comprehend how genetic
variants impact the effectiveness, safety, and propensity for unfavourable medication reactions. In order to optimize therapeutic advantages while limiting potential
negative effects, medical therapies can be customized using pharmacogenomics,
ushering in the era of personalized medicine.
Every person has a different genetic make-up, and this genetic variability greatly
affects how medications are digested, absorbed, and used by the body. Drug pharmacokinetics (how the body processes the drug) and pharmacodynamics (how the
drug interacts with its target) can be affected by genetic differences such as singlenucleotide polymorphisms (SNPs) and copy number variations (CNVs) (Raj 2019).
Therefore, pharmacogenomics enables medical professionals to customize medicine recommendations based on a person’s genetic prole. Genetic testing can be
used to determine the best medication, dosage, and potential hazards for a certain
patient, resulting in more targeted and efcient therapies.
In the eld of medicine, adverse drug reactions (ADRs) are a major concern.
Pharmacogenomics can aid in identifying patients who are more susceptible to
ADRs (Daly 2013). Healthcare professionals can improve patient safety by steering
clear of medications that potentially have serious adverse responses in some patients.
Pharmacogenomics has also completely changed the way cancer is treated in oncology by detecting specic genetic alterations in tumours (Zaimy etal. 2017). In order
to better effectively and safely treat cancer, targeted medicines and immunotherapies are created to specically target the genetic defects that fuel cancer growth.
Cardiovascular medicine can benet from pharmacogenomics as well, which
uses genetic information to direct the choice of anticoagulants, antiplatelet medications, and lipid-lowering medications. Reduced risk of cardiovascular events is the
goal of personalized cardiovascular therapy (Weeke and Roden 2014). Psychiatric
drugs can have unpredictable results and negative effects. The best choice of antidepressants, antipsychotics, and mood stabilizers can also be made with the aid of
pharmacogenomics (Alchakee etal. 2022). It increases the likelihood that people
with mental health disorders may nd a medicine that works well and has fewer
negative effects.
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It is necessary to address issues including cost-effectiveness, accessibility to
genetic testing, and the interpretation of intricate genetic data before pharmacogenomics may be used in clinical practice. Education and standards for medical practitioners are necessary for widespread adoption. Besides, pharmacogenomic
information is delicate and gives rise to privacy issues. To safeguard patients’ rights
and maintain condence in customized therapy, genetic information must be
kept secure.
In the near future, pharmacogenomic developments will enhance our knowledge
of drug–gene interactions and improve therapeutic strategies. Pharmacogenomics
combined with articial intelligence and data from other “omics” systems may
improve personalized treatment even more. A paradigm revolution in health care is
being ushered in by pharmacogenomics, which replaces the generalized model of
medicine with one that is more customized and precise. Pharmacogenomics promises to optimize treatment approaches, improve patient outcomes, and lessen the
burden of adverse drug reactions, heralding a paradigm-shifting chapter in medical
practice as our understanding of genetics and pharmacology advances.
J. Sundarasekar and G. Sahgal
11.3 Proteomics inDrug Discovery
11.3.1 Proteomic Technologies andTechniques
Understanding the functions, relationships, and dynamics of proteins in health and
disease depends heavily on the eld of proteomics, which is the study of all the
proteins present in a biological system. Because of the substantial advancements in
proteomic technology and methods, scientists are now able to investigate the enormous complexity of the proteome. These techniques are crucial in a variety of disciplines, including systems biology, biomarker identication, and drug discovery.
Liquid chromatography-mass spectrometry (LC-MS) is an effective method for
identifying and measuring proteins. It assesses the mass-to-charge ratios of proteins
and differentiates them according to their chemical characteristics. Proteins and
peptides in their intact state are analysed using matrix-assisted laser desorption/
ionization (MALDI). It includes using a laser to ionize samples and weigh the
results (McEwen etal. 2014). 2D-PAGE, however, produces a 2D map of the proteome by separating proteins according to their isoelectric point and molecular
weight. This method is helpful for comparing the levels of protein expression in
various samples (Issaq and Veenstra 2008). Protein-protein interactions, protein
binding, and antibody-antigen interactions can all be thoroughly examined using
protein microarrays. They help researchers understand signalling pathways and nd
possible therapeutic targets (Stoevesandt etal. 2009).
Several mass spectrometry-based proteomic workow techniques can be used in
proteomic drug discovery such as shotgun proteomics, label-free quantication, and
isobaric tagging. In shotgun proteomics, proteins are broken down into peptides and
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then analysed by MS to nd and count the proteins in a complicated mixture. Using
their MS signal intensity in label-free quantication, label-free approaches compare
the quantity of peptides in various samples. In isobaric tagging (e.g. TMT and
iTRAQ), labelling peptides with isobaric tags that can be differentiated during MS
analysis enables the relative quantication of proteins (Li etal. 2012; Rauniyar and
Yates III 2014).
Protein sequence databases are also used for protein identication. The NCBI’s
RefSeq database and UniProt are well-known databases. Experimental MS data are
matched to sequences in these databases by search techniques such as Mascot,
SEQUEST, and MaxQuant (Gevaert and Vandekerckhove 2000). Techniques such
as yeast two-hybrid (Y2H) assays and co-immunoprecipitation (co-IP) are used to
identify and validate protein-protein interactions in protein-protein interaction (PPI)
studies. PPI studies help elucidate cellular pathways and networks (Struk etal. 2019).
Proteins between various circumstances or samples can also be accurately quantied using stable isotope labelling techniques such as SILAC and iTRAQ.They are
crucial for examining dynamic proteome alterations (Chahrour et al. 2015).
Quantifying particular targeted proteins or peptides of interest is the main goal of
targeted proteomics. Common techniques include multiple reaction monitoring
(MRM) and selected reaction monitoring (SRM). It is helpful for clinical tests and
biomarker validation (Shi etal. 2016).
Three-dimensional structures of protein can be ascertained using methods such
as nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography.
Understanding protein function and drug design requires this structural information
(Harris etal. 2009). Proteomic data also must be processed, analysed, and interpreted using cutting-edge bioinformatics methods. They support the investigation of
pathways, functional annotations, and important protein alterations.
Our understanding of biology and illness has been completely transformed by
proteomic technology and methodologies. They are crucial to the development of
new drugs, the identication of biomarkers, and customized treatment. Proteomics
will become more and more important as technology develops because it can help
us understand the complexity of the proteome, which will lead to better treatments
and outcomes for patients.
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11.3.2 Protein Expression Proling
A crucial method in proteomics is protein expression proling, which includes systematically examining the kinds and amounts of proteins expressed in a given biological sample, cell, tissue, or organism at a certain time or under particular
circumstances. This method aids researchers in understanding how changes in protein expression relate to numerous physiological processes, illnesses, and therapeutic responses by offering insightful information about the functional state of a
biological system.
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J. Sundarasekar and G. Sahgal
Techniques forProtein Expression Proling
In 2D gel electrophoresis method, a 2D map of the proteome can be created by
dividing proteins according to their isoelectric point and molecular weight. It
enables researchers to contrast the patterns of protein expression in various samples.
In mass spectrometry (MS) method, protein identication and quantication are
performed using LC-MS and other MS-based techniques. Comprehensive data
regarding the proteome, such as the relative abundance of proteins in a sample, can
be obtained using MS.Using protein microarrays methods, immobilized proteins
are included in protein microarrays, which enable researchers to probe them with
labelled samples to analyse protein-protein interactions or quantify protein levels.
In western blotting technique, antibodies are used to locate particular proteins in
a sample. It can be applied to analyse protein expression in both qualitative and
quantitative ways. Label-free quantication method focuses on assessing the ion
intensities of proteins or peptides in MS spectra to enable relative protein quantication across samples without the use of chemical labelling. Stable isotope labelling
methods by isotope labelling amino acids in cell culture (SILAC) and isobaric tags
for relative and absolute quantication (iTRAQ) are used to assess protein expression levels quantitatively between various samples (Sydor and Nock 2003).
Applications ofProtein Expression Proling
In disease research, the identication of biomarkers linked to conditions including
cancer, Alzheimer’s disease, and diabetes depends on protein expression proling.
Prognostic and diagnostic information can be obtained from changes in protein
expression. For the purpose of drug discovery, knowing how proteins react to medication aids in identifying drug targets and creating novel therapeutic approaches is
important. Additionally, it may show possible medication resistance pathways.
In stem cell research, researchers can describe stem cells and their differentiated
offspring by using protein expression proling to track changes in protein levels
during cellular differentiation. For functional genomics, the addition of protein
expression data to genomic and transcriptome information offers a more complete
picture of cellular processes and regulatory systems. Lastly, in clinical proteomics,
protein expression proling can be utilized in the clinical setting to stratify patients,
choose treatments, and keep track of therapy outcomes (Sydor and Nock 2003).
Challenges andFuture Directions
Understanding the functional importance of changes in protein expression requires
integrating protein expression data with other “omics” data, such as genomes and
transcriptomics, which is a difcult task. Advanced bioinformatics tools and algorithms are needed for data processing, statistical analysis, and interpretation for
analysing large-scale protein expression data.
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Single-cell proteomic techniques have improved, allowing for the investigation
of individual cells and the discovery of cell-specic protein expression and cellular
heterogeneity (Theilgaard-Moench etal. 2011). In biological research and clinical
applications, protein expression proling is a potent method that offers a deeper
comprehension of the molecular pathways underlying health and disease. Protein
expression proling will become increasingly important in enhancing our understanding of proteomics and its implications for personalized medicine and drug
development as technologies grow and become more sophisticated.
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11.3.3 Structural Proteomics
The three-dimensional (3D) structures of proteins and their complexes are the main
focus of the branch of proteomics known as structural proteomics. It entails guring
out how atoms are arranged spatially within proteins and comprehending how their
structures connect to their functions. The study of biological pathways, the creation
of targeted medicines, and many other scientic elds depend heavily on structural
proteomics.
A well-known method that has proved essential in guring out the atomic structures of proteins is X-ray crystallography. The 3D structure is reconstructed using
diffraction patterns created by generating protein crystals and exposing them to
X-ray photons. Protein structures in solution are determined using nuclear magnetic
resonance (NMR) spectroscopy. NMR is a powerful tool for determining the structural details of proteins by observing the interactions between their atomic nuclei.
In recent years, cryo-electron microscopy (cryo-EM) has become a popular
structural biology approach. In order to get high-resolution structures of proteins
and complexes, even those that cannot crystallize, it entails imaging protein samples
at cryogenic temperatures. Homology modelling (comparative modelling) is used to
estimate a protein’s three-dimensional structure based on the structures of related
proteins when experimental methods are not practical (Manjasetty etal. 2012).
Designing medications that specically target particular binding sites requires an
understanding of the 3D structure of proteins, especially enzymes and receptors.
Rational drug design and virtual screening are made easier by using this structural
proteomics. Knowing the relevant proteins’ 3D structures is essential for locating
illness biomarkers. This may result in the creation of focused medicines and diagnostic tests. Furthermore, understanding the structure of a protein can reveal information about its catalytic mechanisms, ligand binding, and interactions with other
molecules. Models of biological processes and protein networks are created using
protein structures. Clarifying biological processes is made easier by comprehending
the architecture of important signalling proteins (Manjasetty etal. 2012).
There are many challenges ahead before proteomics can fully be exploited. The
future direction of proteomics lies in certain niche areas. Research in structural
proteomics is currently focused on guring out the structures of membrane proteins,
which are frequently difcult to crystallize. For full systems biology and knowledge
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of the molecular causes of diseases, structural proteomic data must be integrated
with genomes, transcriptomics, and metabolomics data.
Researchers can now examine protein structures at the single-cell level thanks to
the development of single-cell proteomics, which sheds light on cellular heterogeneity and diversity. As technologies such as cryo-EM and NMR spectroscopy continue to advance, we are better able to understand complicated protein structures
and protein-protein interactions (Sali et al. 2003). Between genomes, transcriptomics, and functional proteomics, structural proteomics lls a critical gap. It aids
in the discovery of biomarkers, therapeutic development, and our comprehension of
biological systems by providing precise information about protein structures. As
technology develops, structural proteomics will become increasingly important in
deciphering the proteome’s complexity and enabling customized therapy.
J. Sundarasekar and G. Sahgal
11.3.4 Proteomics inTarget Identication andValidation
Target identication and validation, which are essential phases in the drug discovery
process, are made possible by proteomics. These processes entail locating prospective molecular targets for therapeutic development and conrming their applicability to a particular illness or ailment. Here, we examine the application of proteomic
methods to this key stage of drug discovery.
Protein proles in biological materials can be thoroughly analysed using proteomic techniques. Researchers can nd proteins that are differentially expressed in
disease states by comparing the proteomes of healthy and diseased tissues or cells,
providing prospective therapeutic targets (Kumar et al. 2016). This technique is
known as proteome proling. Biomarkers are proteins that can signal the presence,
progression, or severity of a disease. Proteomics aids in the discovery of these markers. These biomarkers could potentially be used as drug development targets
(Cominetti etal. 2016). Proteomics can also be used to identify important nodes
(proteins) in protein-protein interaction networks that are linked to particular diseases by analysing these networks. If these proteins are targeted, disease-related
processes may be disrupted (Yugandhar etal. 2019).
Investigating the functionalities of possible pharmacological targets involves the
use of proteomic methods. This entails evaluating a protein’s function in disease
mechanisms and determining whether it is a suitable therapeutic target. However, to
determine how a particular protein ts into a larger biological system, proteomic
data can be combined with data from other omics disciplines (such as genomics and
transcriptomics). This supports why it is a worthwhile aim. Proteomic analysis of
phosphorylation events can reveal signalling networks and protein regulatory mechanisms. Identication of essential proteins for drug development can be aided by
phosphoproteomics (Xu etal. 2019).
High-throughput screening (HTS) can be used after prospective targets have
been found and validated. In order to test hundreds of chemicals for their capacity
to inuence the activity of the target proteins, HTS assays can be designed using
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