ChapterPDF Available

Abstract and Figures

Target identification is the most critical step in chemical genetics and drug discovery. A variety of novel bioactive chemical compounds have been discovered by phenotypic-based screening methods. However, identifying molecular targets for these bioactive compounds is a very laborious process. With the development and advancements in new methodologies and techniques for identification and biological analysis, various target identification methods have been established such as affinity-based methods, genetic-based techniques, computational approaches, and chemical proteomics. In this chapter, we will classify the target identification approaches, various molecular target identification methods, their work schemes, and the applications of target identification techniques in discovery of targets of small molecules, while keeping in view that each and every method is associated with some advantages as well as disadvantages. The efficient execution of every scientific process involves significant infrastructure set up and expertise in multiple disciplines (chemistry, proteomics, genomics, and bioinformatics). The collaborative work scheme comprising integrated knowledge of all of these disciplines will ensure the maximum potential is utilized in academic and industrial grounds.KeywordsTarget identificationProtein targetsAffinity-based methodsPhenotypic-based methods
Content may be subject to copyright.
Target Identification in Drug Discovery
Azhar Rasul1,*, Ammara Riaz1, Iqra Sarfraz1, Samreen Gul Khan2, Ghulam Hussain3, Rabia
Zara1, Ayesha Sadiqa1, Gul Bushra4, Saba Riaz1, Muhammad Javid Iqbal1, Mudassir Hassan1,
Khatereh Khorsandi5
1Department of Zoology, Faculty of Life Sciences, Government College University, Faisalabad,
2Department of Chemistry, Faculty of Physical Sciences, Government College University,
Faisalabad, 38000, Pakistan
3Neurochemical Biology and Genetics Laboratory, Department of Physiology, Faculty of Life
Sciences, Government College University, Faisalabad, Pakistan
4Department of Bioinformatics and Biotechnology, Faculty of Life Sciences, Government
College University, Faisalabad, 38000, Pakistan
5Department of Photodynamic, Medical Laser Research Center, Yara Institute, ACECR, Tehran,
*Corresponding author:
Dr. Azhar Rasul, Assistant Professor, Department of Zoology, Government College University,
Faisalabad 38000, Pakistan.
Tel: +92-3218409546; Email:;
Target identification is the most critical step in chemical genetics and drug discovery. A variety
of novel bioactive chemical compounds have been discovered by phenotypic based screening
methods. However, identifying molecular targets for these bioactive compounds is a very
laborious process. With the development and advancements in new methodologies and
techniques for identification and biological analysis, various target identification methods have
been established such as affinity based methods, genetic based techniques, computational
approaches and chemical proteomics. In this chapter, we will classify the target identification
approaches, various molecular target identification methods, their work schemes and the
applications of target identification techniques in discovery of targets of small molecules.
Keywords: Target identification, protein targets, affinity based methods, phenotypic based
Drug discovery confers to a critical step in medicinal sciences which is based upon a cascade of
integrated events carried out to search the “cure” for “incurables”. Drug discovery comprises
different complicated procedures that are focused on the chemical and pharmaceutical
optimization and advancement of the novel compounds for the cure of human diseases [1, 2].The
first step in the discovery of drug is the identification of the biological origin of the disease and
potential targets for the intrusion. Paul Ehrlich is thought to be founder of modern drug discovery
and chemotherapy. He identified the selective drugs which can fight against the infectious
diseases. He also paved the way towards the discovery of synthetic compounds or drugs for the
cure of bacterial diseases [3].
Target identification is a first and foremost step in drug discovery. It is the process of identifying
the direct molecular targets like small molecules, nucleic acid and proteins. Target identification
can be approached by computational methods, direct biochemical methods and genetic
interactions. A good target meets all needs like efficacy and clinical safety because good target
facilitate the better relationship between the drug and disease. It has been challenging to attain
predominant paradigm from “one target, one drug” to “one drug, multiple target [4]. It includes
the isolation of the individual target, validation of the target on the basis of regulation of
biological functions and their binding interactions and last one is hit identification which leads
towards the discovery of synthetic compounds who have potency to cure the intended disease [2,
The 259 agents that were approved by FDA, 75 agents were first-in-class drugs with new
molecular mechanism of action. From these drugs (67%) were small molecules and (33%) were
biologics. Contribution of phenotypic screening in the development of the first in class small
molecules agents surpassed that of target based approaches. So the yield of phenotypic screening
is 28 drugs and yield of target based screening is 17 drugs, respectively in an time the major
focus was on target-based approaches [6].
The current therapies which is based upon less than the 500 molecular targets such as (45%) are
G-protein coupled receptors, (28%) are enzymes, (11%) are hormones and factors, (5%) are ion
channels and (2%) are nuclear receptors [7]. For the cancer treatment 154 cancer drugs approved
by the FDA and these drugs are classified into three main categories like 5 drugs which act
through non-protein or unknown targets, 64 drugs are anticancer and 85 drugs can be assigned
protein targets [8].
There is the great need of obtaining information about drug efficacy and also their targets which
should be based on disease related parameters. The disease associated target identification
contributes to find out the molecule and their underlying mechanism whose valid target can be
therapeutically modulated. The platform is designed for the association of drug targets with
disease by using genome wide information from broad area of data sources [9, 10]. Small
molecule identification is the foremost parts of the drug discovery and target identification and
identification of their bonding with proteins is the key of understanding their cellular mechanism.
Small molecules simultaneously target several proteins [11].
Approaches for drug target identification
There are several approaches which have been proposed for drug target identification of cellular
targets of bioactive molecules. The four groups of these approaches are;
1. Affinity based approaches which rely in direct interaction of proteins with small
molecules [12].
2. Phenotype based approaches refer to the methods and techniques which are meant to
compare the biological profiles of small molecules with already known reference drugs
3. The genetic based approaches are used to identify the genes which are responsible behind
the resistance produced by small molecules and small molecule-sensitive clones [13].
4. Computational approaches predict the drug targets on the basis of chemical similarities
between the proteins and molecules of interest [14, 15].
Out of all of these techniques, affinity based approaches directly identify the small molecule
targets while other approaches identify the small molecules indirectly.
Figure 1. Approaches for target identification
Direct or affinity based approaches
Identification of targets for small molecules is a crucial step in discovery of drugs. Direct or
affinity based approaches remains the most commonly used approaches for molecular target
identification. During recent years, various techniques based on affinity purification, protein
stability and ligand based protein labeling have been developed to increase the sensitivity of
target identification. Here, we will discuss three approaches of target identification by direct or
affinity based approaches including photo affinity labelling (PAL), drug affinity responsive
target stability (DARTS) and thermal shift assays.
Target identification by photo affinity labelling
PAL is a recent technique in drug discovery and medicinal chemistry used to study the protein-
ligand interactions for identification of targets of ligands. It is a most frequently used probing
tool in drug target identification. PAL is employed to identify the targets of hit compounds/drugs
obtained from phenotypic screening in early drug discovery [16].
The concept of PAL was introduced by Westheimer in 1960s while incorporating an aliphatic
diazo group in chymotrypsin (enzyme) using the process of acylation. These molecules were
crosslinked via photolysis. In this technique, a chemical probe is used which can make covalent
bond to its target when activated in the presence of light. This can be done by incorporating a
photoreactive moiety with probe compound. When irradiated with a light of specific wavelength,
the photgroup makes a reactive intermediate which reacts with nearby molecule (target protein)
(Figure 2A). The main photoactive group which can be used for PAL include phenyldiazirines,
benzophenones and phenylazides and can make the intermediates like carbine, diradical and
nitrene upon irradiation by specific wavelengths. PAL can be used to identify a wide range of
small molecules target proteins in many diseases [16].
There are several successful examples of PAL in the identification of targets of drugs. Here we
present some of the examples of PAL that lead to the identification of drug targets during recent
One successful application of effective PAL was the identification of the molecular target of
pyrrolidinone and its structural analogues. Pyrrolidinone was discovered to possess highly
selective nutrient-dependent cytotoxicity against number of cancer cell lines in a phenotypic
screening of 6000 small molecules, which sparked the interest for the identification of its target
in cancer cells. SAR studies were conducted for enhancing compound potency, prior to target
identification and validation. The photoaffinity labeling was performed to chemical probe and
cancer cells were given treatment with photoaffinity probe. The resulting proteins were separated
on SDS-PAGE and visualized. A 50 kDa protein was identified as possible target protein of hit
compound. Excision of the identified 50 kDa protein band from gel, followed by digestion and
analysis, established the target protein as fumarate hydratase (FH). Thus, PAL identified FH as
the pharmacological target of hit compound (Figure 2B). Further biochemical studies and in
vitro enzymatic assay based screening confirmed dose-dependent, competitive inhibition of FH
by hit compounds which in turn validated the results of target identification by PAL.
Figure 2. Schematic representation of target identification by PAL A) Workflow for the
identification of molecular targets of drugs by PAL B) Identification of FH as molecular target of
PAL has been utilized successfully for the design of photo affinity probes and identification of
various molecular targets of ligands/drugs (Table 1).
Table 1. Target identification of small molecules by PAL
Validation technique
(ES10), liver fatty
acid binding
protein (L-FABP)
uorescence-tagged and
(PB)-tagged probes
Pyrrolidinone 1
Fumarate hydratase
In vitro fumarate
hydratase activity
protein 47(TIP47)
Azido- and tritium-
labeled photo affinity
RNA interference
assays with siRNA
Ribosomal Protein
b. Target identification by DARTS
DARTS is a widely applicable technique used to identify small molecule protein interactions
without the need of prior modification in the drug [22]. This particular technique is performed by
treating the cell lysate with drug, after which the digestion with proteases is performed. This
process is followed by the separation of samples with the help of SDS PAGE, staining and MS
analysis in order to identify the protein of interest [23].
DARTS presents a great efficacy in identifying newer proteins targeted by compounds. Like
affinity chromatography, it is also an affinity based model starting form complex protein samples
and selectively enriching the target proteins while performing the depletion of non-target
proteins which offer resistant against proteases. This technique has an advantage over affinity
chromatography in terms of analyzing lower affinity interactions and does not require washing
Figure 3. Schematic presentation of target identification by DARTS
Voacangine is a natural compound isolated from Voacanga Africana, which has been known to
possess antiangiogenic effect. Voacangine have potential to effectively reduce VEGF-induced
chemoinvasion activity on HUVECs. Kim et al. investigated the mechanism of action of
voacangine by using label-free DARTS which led to the successful identification of VEGFR2 as
target protein. HUVECs cells were lysed, and cell lysates was incubated at room temperature
with voacangine at the appropriate doses, DARTS was performed by proteolytic digestion of cell
lysates and immunoblotting with primary antibodies was used to validate the obtained results
There are many other successful examples of DARTS for identification of protein targets of
bioactive such as molecular target of resveratrol, a compound in red grapes, has been identified
as eIF4A [26], identification of Cathepsin D and Thioredoxin-like protein 1 as target proteins of
Dichloroacetate [27], identification of actin as main target for 5-epi-sinuleptolide [28].
Target Identification by Cellular Thermal shift assay (CETSA)
Thermal shift assay refer to biochemical methods used to investigate the stabilization of target
proteins upon binding of ligand and increased probability of crystal formations in biological
sample [29]. This technique is also meant to measure the alterations in denaturation temperature
of proteins under variable drug concentrations, ionic strength, buffer pH, sequence mutation and
redox potential. Commonly employed methods of assessing thermal shifts include thermfluor or
differential scanning fluorimetry (DSF) [30]. Thermal shift assay performed in cellular format is
known as CETSA [31].
Figure 4. Brief illustration of protocol for target identification by CETSA
CETSA has been successfully used to identify protein targets of bioactive drugs/compounds.
Nagasawa et al. discovered NPD10084 as anti-proliferative compound during screening of
compounds against colorectal cancer cells. 2DE-CETSA technique was used for profiling of the
thermally shifted proteins after treatment with NPD10084. HCT116 cell lysates were either left
unheated or heated with variable temperatures after being treated with DMSO or NPD10084.
After being tagged, each protein sample was subjected to 2D DIGE analysis. The thermally
stabilized spots matched the PKM protein, and MS analysis confirmed the obtained results [32].
1. Phenotype based methods
The limited knowledge about complex interactions among genomic and chemical space has
narrowed down the process of identification of novel drugs and their targets [33]. Precise
identification of drug target interactions followed by drug target validation is considered as the
initial step in the channel of drug discovery. Up till now, many potential drug target interactions
have been reported, but still it is considered that many of them are still in the process of
discovery [34]. Phenotype based assays are gaining importance day by day among other drug
discovery processes. The action mechanism of drugs can be determined by making the
comparison between the treated and untreated cells. The modes of comparison may include small
and focused alterations in the phenotype of cells while in other cases the potential drug targets
may lead to prominent alterations in phenotypes e.g., cell death and apoptosis. Physical
alterations in the cells including the changes in the morphology of cells during cell cycle are very
quick and need careful check and balance at molecular level. In the recent years, advancements
in “omics” fields such as proteomics and genomics have initiated the production of economical
and high throughput methodologies to analyze and compare the molecular frameworks inside the
living organisms at large scale [12].
2.1 Proteomics
Proteins play prime roles in cellular functions. Total expressed proteins in a cell as well as an
organism at any specified time can be studied using proteomics technology. Proteome analysis
can provide complete information about the cellular processes and hence play important role in
the diagnosis as well as treatment of a wide spectrum of pathological conformations. The term
“Proteome” was introduced in 1990s, since then Proteomics has always been a principal tool in
health sciences and clinics. Proteomics refer to the analysis of cell protein profiles. In the process
of drug discovery, proteomics has played valuable role because proteins are the main drug
targets in disease conditions. Proteomics along with system biology plays significant part in the
selection of protein biomarkers and comprehensively understanding the disease associated
pathways to design a compound which can mitigate or modulate a specific chemical pathway or
cycle [35]. For example, in case of high grade serous ovarian cancer patients, cancer/testis
antigen 45 (CT45) was found to serve as a non-mutant tumor antigen as well as cell intrinsic
enhancer of chemo sensitivity with the help of functional assays as well as quantitative
proteomicss. Moreover, CT45 was also linked to DNA damage signaling by acting as a regulator
of protein phosphatase 4 (PP4) [36].
In the recent times, both direct and indirect approaches have gained importance in drug target
identification for small molecules. Direct approaches were introduced by Schreiber and
coworkers, who worked on affinity matrix conjugated with FK506. Since then, remarkable
progress has been made in the field of proteomics. Mass spectrometry (MS) has enabled us to
identify voluminous data regarding the proteins present in a little volume of given sample. Other
techniques used for identification of target proteins include two dimensional electrophoresis and
sodium docecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE) [37].
Advanced techniques like ABPPs [38], SILAC [39], ICAT [40], iTRAQ [41], protein
microarrays [42] and ChemProteoBase [37] can be also used in drug target identification.
a. Affinity Based Protein Profiling (ABPPs)
Affinity based protein profiling is a famous approach in chemical proteomics. In this technique,
small molecular probes (soluble in a particular medium) are employed to capture the specific
class of proteins which make covalent bonds with these probes through a reactive group. The
reactive probe is usually incorporated with an affinity tag by means of spacer [43].
As an initial step, the biological sample is incorporated with the small molecule probe in order to
allow it to bind with the protein, for which it has the affinity for. After that, the probe-protein
adducts are caught on the solid support having the affinity tag on it e.g., in case of P450
(cytochrome proteins), the reactive group is modulated so as to bind with the proteins and their
active conformations. Hence, this particular strategy can be employed to functionally
characterize the cytochrome P450 superfamily [44].
SILAC is the abbreviation of stable isotope labeling by amino acids in cell culture. Along with
LC-MS/MS, this technique is the most widely employed method to quantify protein abundance.
In this technique, two populations of cells are grown; one with light or natural amino acids while
the second one is grown under exposure to heavy amino acids like 13C6-arginine and 15N2-
lysine. After that, the two populations are incorporated followed by a number of cell divisions
ensuring the full labeling of whole genome. The cells are subjected to mass spectrometry. The
relative abundance of proteins can be assessed by comparison of ratio of ion densities between
SILAC peptide pairs [45].
This technique was employed by Voigt et al., 2013 in order to identify the mitosis inhibitors. In
their experiment, tetrahydropyran derivatives were synthesized by using the process of Prins
cyclization between polymer-bound aldehyde and homoallylic alcohol. The resulting compounds
were treated with the HeLa and KB-V1 cells. It was noticed that tetrahydropyran derivatives
target the CSE1L and tubulin proteins in a synergistic manner [46].
c. Isotope coded affinity tag (ICAT)
Isotope coded affinity tag (ICAT) offers the identification of a variety of peptides in two
samples. The ICAT reagents comprising of thiol-specific reactive group, an isotopically light or
heavy linker and an affinity tag (like biotin) are introduced in the samples [47]. The presence of
linkers (light or heavy) provides the probes which contain variable molecular masses but have
identical chemical structure. The two protein samples can be mixed, digested and purified by
using the techniques like affinity chromatography, multidimensional liquid chromatography and
mass spectrometry. Afterwards, the presence of light and heavy signals quantifies the relative
protein abundance. This technique is an excellent non-gel based method and is very useful for
the evaluation of proteins in the presence of proteins in diseased conditions and the effect of
drugs on relative protein abundances. The major drawback associated with this technique is that
it can be used only in case of peptides having cysteine as their essential constituent; hence it can
generate only limited data for protein quantification [48].
d. ChemProteoBase
ChemProteoBase is a proteomic profiling system which is used for target analysis of compounds
by integrating the proteome analysis acquired with the help of two-dimensional gel
electrophoresis system [37].
This particular database system was used by Futamura et al., 2013 to identify the molecular
target of pyrrolizilactone (fungal metabolite). The study proposed that pyrrolizilactone is a
proteasome inhibitor, which is able to inhibit the trypsin-like activity of proteasome. It was
analyzed with the help of two phenotypic profiling systems namely; ChemProteoBase and
MorphoBase used alongwith two dimensional gel electrophoresis system [49].
Figure 5. Target Identification by phenotypic based approach (A) Work scheme for
Identification of molecular targets of small molecule by ChemProteoBase Profiling B)
Identification of proteasome as molecular target of Pyrrolizilactone by ChemProteoBase
2.2 Morphology based cell Assays
Visual analysis is an efficient way to screen bioactive molecules leading to the discovery of
potential druggable targets followed by chemicobiological validation. MorphoBase, an
encyclopedic cell morphology database, was established by Futamura et al. in 2012 to discover
small drug-like molecules. The roots of this method date back to 2004, when Perlman et al.
examined 100 compounds at 13 3-fold dilutions based on cellular components in HeLa cells.
They classified the compounds with similar targets into same groups [50].
MorphoBase comprises of developing a high content image method followed by phenotype
profiling system with the help of software which can determine the similarities between
compounds on the basis of multiparametric phenotypes responses based upon statistical analysis.
This database system focuses to classify the test compounds on the basis of their modes of action
and predict the side effects of these compounds. Using this method, the molecular targets of
compounds of interest can be determined in an unbiased manner. This method is a rather simpler
one consisting of a microscope, 96-well plate and fluorescent nuclear staining. MorphoBase
profiling is a rather new database system which can pinpoint the exact mechanism of action of a
drug or compound of interest on the basis of morphological changes taking place in the treated
cells [51].
Futamura et al., 2012 used MorphoBase examined the effects of 30 well reference drugs on the
basis of cell morphology of two mammalian cell lines; HeLa and rat kidney cells infected with
ts25 (a T-class mutant of Rous sarcoma virus Prague stain), srcts- NRK cells in a dose and time
dependent manner. After that, these cells were treated with aforementioned drugs followed by
progressive alterations in cell morphology. The cell morphology was classified into three
categories namely; flattened, polygonal and rounded. These categories were further sub grouped
based upon their variable size and presence of spikes, vacuoles and granular structures. Contrary
to that, HeLa cells showed rather simpler morphological alterations which were classified into
the categories like flattened, round up and toxic or growth inhibition like different phases of cell
cycle (G1/S, G2/M and flattened with embossed nucleus). This preliminary data from these
observations was insufficient to classify the drugs into therapeutic groups. It was however,
possible to discriminate the phenotypic responses of different cell lines against certain drugs on
the basis of their mechanism of action. To minimize the human errors in recording phenotypic
responses, cells were analyzed by automatic system i.e., IN Cell Analyzer which performed high
content image analysis and generated the quantitative morphological data by recognizing the
cytoskeletal and subcellular components systematically. This analyzer collected 1500 cells from
each well of treated cells and analyzed the cells by using the image segmentation procedures.
The next procedure was to sort this multimetric data by using multivariate statistical tools in
order to analyze, visualize and rank the multiparametric high content phenotype results. Principal
component analysis (PCA) was performed to visualize the phenotypic responses. The results of
statistical analysis concluded that the drugs having similar mode of actions form a cluster. Using
this system, the researchers (Futamura et al., 2012) identified NPD6689, NPD8617 and NPD
8969 as tubulin inhibitors [50].
Figure 6. Steps in MorphoBase profiling
3. Genetic approaches for target identification
Human genetics progressively revealing its power to increase the chances of successful drug
discovery. Early history of classic and successful history of clinical practice of translation from
genetics begin in 2003 with the discovery of the gene PCSK9 causing hypercholesterolemia due
to rate gain of function mutations [52] Few years later, results of targeted sequencing of PCSK9
exposed that considerable decrease in plasma level of low density lipoprotein that ultimately
result in reduce incidence of coronary cardiac diseases. [53].
The use of genomic method for drug discovery significantly improves current models in two
aspects, first ideal drug mutation model which states that single gene knockdown in a cell or an
organism result in dysfunction of only one protein as though it may be a ideal target for a drug
and secondly the powerful aspect of performing a genetic screen to search out any mutation in
the genome that is responsible for phenotypic effects that allow the organisms to reveal the
important functions [54].
As exploration of human genome for drug discovery is not possible, so model organisms
utilization such as, Caenorhabditis elegans (nematode), Saccharomyces cerevisae and
Schi~osaccharomyces pombe (Yeast) and Drosophila melanogaster (fruit fly) is facile and easy
to approach. If there is need of potential drug targets for DNA repair and cell division then yeast
is choice of organism. In case of multicellular genome conservation studies, fruit fly are
potentially valuable models. [3].
4.1 CRISPR Cas system
Clustered regularly interspaced short palindromic repeats (CRISPRs) are repeated sequences of
DNA obtained from prokaryotes, bacteria and archea. In 1987 it was discovered accidently by a
team of Japanese scientists Ishino et al., when during an experiment they inserted an unusual
series of repeated sequence interspaced with a specific sequence; spacer sequence in E.coli,
while analyzing responsible gene for alkaline phosphatase. [55]. Furthermore, they elaborated
that during an attack of virus on prokaryotes, CRISPR arrays are transcribed into short sequences
in order to make small CRISPR RNA (crRNA). These generated arrays perform function to
instruct CRISPR associated sequence protein to cut complementary DNA or viral RNA
sequences on the bases of CRISPR Cas system type. Functional role of Cas proteins as nucleases
was discovered by Makarvoa et al. They conducted a relative genome investigation of CRISPR
as well as Cas genes and projected the CRISPR Cas system function in resemblance to
interference RNA in which protein complexes lead to silencing the genes through mRNA
cleavage. Some of Cas proteins are responsible for DNA cleavage and other cleaves RNA. For
instance, Cas9 performs cleavage of DNA whereas Cas13 cleaves RNA [56].
Different CRISPR-Cas systems and their application
CRISPR-Cas9 is general tool for genome engineering of eukaryotic cellular systems. it was first
discovered from Streptococcus pyogenes. In this technique segments guide of RNA (gRNA) is
used to target nuclease proteins in genomic site, which ultimately result in generation of double
strand break (DSB) in DNA tracked by the process of DNA repair. This process of DNA repair
adapts two processes, one is homology directed repair (HDR) and other is non-homologous end
join (NHEJ) path in occurrence of a donor pattern of nucleotide sequence. [57].
Some researches have reported that insertions and deletions may cause frame shift mutation and
functional dysfunctioning of target genes while following NHEJ path. In comparison, HDR is
error free process and perform repairing of DSB in accordance with homologous template DNA
donor sequences. CRISPR has been labeled as an efficient approach due to its adaptability,
scalability, multiplex gene mutation and ease of application.[58]
One study indicated CRISPR-Cas9 screening led towards identification of novel targets in
therapy of acute myeloid leukemia by the use of AML cell lines. According to previous reports
mRNA decapping enzyme scavenger (DCPS) is essential for survival if AML cells. To check out
the interaction among DCPS with the pre-mRNA mechanisms of enzymes of metabolic
enzymes, spliceosomes, the technique of mass spectroscopy is used. CRIPSER-Cas9 technology
assist to find out pre-mRNA metabolic pathway by categorizing DCPS as a target for AML
therapy to treat muscular atrophy exhibited anti-leukemic activity [59].
The field of drug discovery growing efficiently on the basis of new genetic discoveries which
integrates large scale genomics in to target identification. Intrant drug resistance of an organism
is generally categorized as operative method in identification particular cellular targets.
For example, a research study indicates leucyl-tRNA synthetase as a target of antifungal
tavaborole obtained from culture of Saccharomyces cerevisiae in occurrence of drug to create
impulsive resistance mutants. DNA of the target was fragmented into size of 4 to 10 kb and then
inserted into yeast vector to screen the aptitude to shield tavaborole-susceptible cells by
tavaborole selection [60]. Vectors clone sequence of small persisting colonies exhibit those
mutations in leucyl-tRNA synthetase cause prevention of binding of drug to target. [61].
Another study reported the competent gene editing ability of CRISPER in primary T-cell based
immunotherapy likewise next generation chimeric antigen receptor (CAR) T cells. Adjusted
approach of Cas9/RNP transfection of primary human T cells and mouse with TCR stimulation
consequence in complete elimination of expression of target gene at population level justifying
its selection [62]. Cas13 is a RNA targeting single component enzyme which have nucleotide
binding (HEPN) domains from eukaryotes and prokaryotes that directly target single stranded
RNA. It also hold a nuclease that differ in function and responsible maturation of catalyzing
crRNA that result in creation of Cas13: crRNA complex that is a competent for target RNA
binding CRISPR-Cas9 technology [63]
Figure 7. Various steps in Cas9 Technology
Drug target identification acts as a key stone in drug discovery. With the advancement of
knowledge and technology, phenotype based models have replaced the affinity based models.
Identification of natural compounds to be used as drugs involves a great ordeal of
transcriptomics, metabolomics, genomics as well as proteomics intricated carefully in a centrally
aligned framework of computational approaches. Every technique has some advantages as well
as some disadvantages. We need to carefully analyze the pros and cones of all these techniques
before establishment planning the methodology required to carry out an experiment.
1. Ator, M.A., J.P. Mallamo, and M. Williams, Overview of drug discovery and development. Curr
Protoc Pharmacol, 2006. Chapter 9: p. Unit9 9.
2. Hughes, J.P., et al., Principles of early drug discovery. Br J Pharmacol, 2011. 162(6): p. 1239-49.
3. Bosch, F. and L. Rosich, The contributions of Paul Ehrlich to pharmacology: a tribute on the
occasion of the centenary of his Nobel Prize. Pharmacology, 2008. 82(3): p. 171-9.
4. Schenone, M., et al., Target identification and mechanism of action in chemical biology and drug
discovery. Nat Chem Biol, 2013. 9(4): p. 232-40.
5. Macalino, S.J., et al., Role of computer-aided drug design in modern drug discovery. Arch Pharm
Res, 2015. 38(9): p. 1686-701.
6. Swinney, D.C. and J. Anthony, How were new medicines discovered? Nat Rev Drug Discov, 2011.
10(7): p. 507-19.
7. Drews, J., Drug discovery: a historical perspective. Science, 2000. 287(5460): p. 1960-4.
8. Santos, R., et al., A comprehensive map of molecular drug targets. Nat Rev Drug Discov, 2017.
16(1): p. 19-34.
9. Koscielny, G., et al., Open Targets: a platform for therapeutic target identification and validation.
Nucleic Acids Res, 2017. 45(D1): p. D985-D994.
10. Floris, M., et al., Genetic-Driven Druggable Target Identification and Validation. Trends Genet,
2018. 34(7): p. 558-570.
11. Jung, H.J. and H.J. Kwon, Target deconvolution of bioactive small molecules: the heart of
chemical biology and drug discovery. Arch Pharm Res, 2015. 38(9): p. 1627-41.
12. Tulloch, L.B., et al., Direct and indirect approaches to identify drug modes of action. IUBMB Life,
2018. 70(1): p. 9-22.
13. Zheng, X.S., T.F. Chan, and H.H. Zhou, Genetic and genomic approaches to identify and study the
targets of bioactive small molecules. Chem Biol, 2004. 11(5): p. 609-18.
14. Katsila, T., et al., Computational approaches in target identification and drug discovery. Comput
Struct Biotechnol J, 2016. 14: p. 177-84.
15. Sliwoski, G., et al., Computational methods in drug discovery. Pharmacol Rev, 2014. 66(1): p.
16. Smith, E. and I. Collins, Photoaffinity labeling in target- and binding-site identification. Future
Med Chem, 2015. 7(2): p. 159-83.
17. Mesange, F., et al., Identification of two tamoxifen target proteins by photolabeling with 4-(2-
morpholinoethoxy)benzophenone. Bioconjug Chem, 2002. 13(4): p. 766-72.
18. Kotake, Y., et al., Splicing factor SF3b as a target of the antitumor natural product pladienolide.
Nat Chem Biol, 2007. 3(9): p. 570-5.
19. Takeuchi, T., P.T. Schumacker, and S.A. Kozmin, Identification of fumarate hydratase inhibitors
with nutrient-dependent cytotoxicity. J Am Chem Soc, 2015. 137(2): p. 564-7.
20. Jessen, K.A., et al., The discovery and mechanism of action of novel tumor-selective and
apoptosis-inducing 3,5-diaryl-1,2,4-oxadiazole series using a chemical genetics approach. Mol
Cancer Ther, 2005. 4(5): p. 761-71.
21. Webb, Y., et al., Photoaffinity labeling and mass spectrometry identify ribosomal protein S3 as a
potential target for hybrid polar cytodifferentiation agents. J Biol Chem, 1999. 274(20): p. 14280-
22. Pai, M.Y., et al., Drug affinity responsive target stability (DARTS) for small-molecule target
identification. Methods Mol Biol, 2015. 1263: p. 287-98.
23. Moller, M., et al., Concordance between morphological and biochemical estimates of fluid
spaces in rat brain cortex slices. Exp Brain Res, 1974. 21(3): p. 299-314.
24. Lomenick, B., R.W. Olsen, and J. Huang, Identification of direct protein targets of small
molecules. ACS Chem Biol, 2011. 6(1): p. 34-46.
25. Kim, Y., et al., Identification and Validation of VEGFR2 Kinase as a Target of Voacangine by a
Systematic Combination of DARTS and MSI. Biomolecules, 2020. 10(4).
26. Lomenick, B., et al., Target identification using drug affinity responsive target stability (DARTS).
Proc Natl Acad Sci U S A, 2009. 106(51): p. 21984-9.
27. Gong, F., et al., Dichloroacetate induces protective autophagy in LoVo cells: involvement of
cathepsin D/thioredoxin-like protein 1 and Akt-mTOR-mediated signaling. Cell Death Dis, 2013.
4: p. e913.
28. Morretta, E., et al., Discovering the Biological Target of 5-epi-Sinuleptolide Using a Combination
of Proteomic Approaches. Mar Drugs, 2017. 15(10).
29. Elgert, C., et al., Thermal shift assay: Strengths and weaknesses of the method to investigate the
ligand-induced thermostabilization of soluble guanylyl cyclase. J Pharm Biomed Anal, 2020. 181:
p. 113065.
30. Dart, M.L., et al., Homogeneous Assay for Target Engagement Utilizing Bioluminescent Thermal
Shift. ACS Med Chem Lett, 2018. 9(6): p. 546-551.
31. Jafari, R., et al., The cellular thermal shift assay for evaluating drug target interactions in cells.
Nat Protoc, 2014. 9(9): p. 2100-22.
32. Nagasawa, I., et al., Identification of a Small Compound Targeting PKM2-Regulated Signaling
Using 2D Gel Electrophoresis-Based Proteome-wide CETSA. Cell Chem Biol, 2020. 27(2): p. 186-
196 e4.
33. Dobson, C.M., Chemical space and biology. Nature, 2004. 432(7019): p. 824-8.
34. Yu, H., et al., A systematic prediction of multiple drug-target interactions from chemical,
genomic, and pharmacological data. PLoS One, 2012. 7(5): p. e37608.
35. Amiri-Dashatan, N., et al., Proteomics Applications in Health: Biomarker and Drug Discovery and
Food Industry. Iran J Pharm Res, 2018. 17(4): p. 1523-1536.
36. Huang, F., et al., Chemical proteomics: terra incognita for novel drug target profiling. Chin J
Cancer, 2012. 31(11): p. 507-18.
37. Hiroyuki Osada, et al., A Target Identification System Based on MorphoBase, ChemProteoBase,
and Photo-Cross-Linking Beads, in Concepts and Case Studies in Chemical Biology, Herbert
Waldmann and P. Janning, Editors. 2014, John Wiley & Sons, Inc.
38. Wiedl, T., et al., Activity-based proteomics: identification of ABHD11 and ESD activities as
potential biomarkers for human lung adenocarcinoma. J Proteomics, 2011. 74(10): p. 1884-94.
39. Mann, M., Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol, 2006.
7(12): p. 952-8.
40. Tunon, J., et al., Proteomic strategies in the search of new biomarkers in atherothrombosis. J Am
Coll Cardiol, 2010. 55(19): p. 2009-16.
41. Ross, P.L., et al., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-
reactive isobaric tagging reagents. Mol Cell Proteomics, 2004. 3(12): p. 1154-69.
42. Hall, D.A., J. Ptacek, and M. Snyder, Protein microarray technology. Mech Ageing Dev, 2007.
128(1): p. 161-7.
43. Bantscheff, M., A. Scholten, and A.J. Heck, Revealing promiscuous drug-target interactions by
chemical proteomics. Drug Discov Today, 2009. 14(21-22): p. 1021-9.
44. Wright, A.T. and B.F. Cravatt, Chemical proteomic probes for profiling cytochrome p450 activities
and drug interactions in vivo. Chem Biol, 2007. 14(9): p. 1043-51.
45. Wang, J., et al., Target identification of natural and traditional medicines with quantitative
chemical proteomics approaches. Pharmacol Ther, 2016. 162: p. 10-22.
46. Voigt, T., et al., A natural product inspired tetrahydropyran collection yields mitosis modulators
that synergistically target CSE1L and tubulin. Angew Chem Int Ed Engl, 2013. 52(1): p. 410-4.
47. Adam, G.C., E.J. Sorensen, and B.F. Cravatt, Chemical strategies for functional proteomics. Mol
Cell Proteomics, 2002. 1(10): p. 781-90.
48. Moseley, F.L., et al., The use of proteomics to identify novel therapeutic targets for the treatment
of disease. J Pharm Pharmacol, 2007. 59(5): p. 609-28.
49. Futamura, Y., et al., Identification of a molecular target of a novel fungal metabolite,
pyrrolizilactone, by phenotypic profiling systems. Chembiochem, 2013. 14(18): p. 2456-63.
50. Futamura, Y., et al., Morphobase, an encyclopedic cell morphology database, and its use for drug
target identification. Chem Biol, 2012. 19(12): p. 1620-30.
51. Muroi, M., Y. Futamura, and H. Osada, Integrated profiling methods for identifying the targets of
bioactive compounds: MorphoBase and ChemProteoBase. Nat Prod Rep, 2016. 33(5): p. 621-5.
52. Abifadel, M., et al., Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat
Genet, 2003. 34(2): p. 154-6.
53. Cohen, J., et al., Low LDL cholesterol in individuals of African descent resulting from frequent
nonsense mutations in PCSK9. Nat Genet, 2005. 37(2): p. 161-5.
54. Cohen, J.C., et al., Sequence variations in PCSK9, low LDL, and protection against coronary heart
disease. N Engl J Med, 2006. 354(12): p. 1264-72.
55. Ishino, Y., M. Krupovic, and P. Forterre, History of CRISPR-Cas from Encounter with a Mysterious
Repeated Sequence to Genome Editing Technology. J Bacteriol, 2018. 200(7).
56. Makarova, K.S., et al., A putative RNA-interference-based immune system in prokaryotes:
computational analysis of the predicted enzymatic machinery, functional analogies with
eukaryotic RNAi, and hypothetical mechanisms of action. Biol Direct, 2006. 1: p. 7.
57. Hsu, P.D., E.S. Lander, and F. Zhang, Development and applications of CRISPR-Cas9 for genome
engineering. Cell, 2014. 157(6): p. 1262-1278.
58. Jinek, M., et al., A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial
immunity. Science, 2012. 337(6096): p. 816-21.
59. Yamauchi, T., et al., Genome-wide CRISPR-Cas9 Screen Identifies Leukemia-Specific Dependence
on a Pre-mRNA Metabolic Pathway Regulated by DCPS. Cancer Cell, 2018. 33(3): p. 386-400 e5.
60. Rock, F.L., et al., An antifungal agent inhibits an aminoacyl-tRNA synthetase by trapping tRNA in
the editing site. Science, 2007. 316(5832): p. 1759-61.
61. Ioerger, T.R., et al., Identification of new drug targets and resistance mechanisms in
Mycobacterium tuberculosis. PLoS One, 2013. 8(9): p. e75245.
62. Selvam, C., et al., Therapeutic potential of chemically modified siRNA: Recent trends. Chem Biol
Drug Des, 2017. 90(5): p. 665-678.
63. Abudayyeh, O.O., et al., C2c2 is a single-component programmable RNA-guided RNA-targeting
CRISPR effector. Science, 2016. 353(6299): p. aaf5573.
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.
Full-text available
Advancing in genome sequencing has greatly propelled the understanding of the living world, however, it is insufficient for full description of a biological system. Focusing on, proteomics has emerged as another large-scale platform for improving the understanding of biology. Proteomic experiments can be used for different aspects of clinical and health sciences such as food technology, biomarker discovery and drug target identification. Since proteins are main constituents of foods, proteomic technology can monitor and characterize protein content of foods and their change during production process. The proteomic biomarker discovery is advanced in various diseases such as cancer, cardiovascular diseases, AIDS and renal diseases which provide non-invasive methods by the use of body fluids such as urine and serum. Proteomics is also used in drug target identification using different approaches such as chemical proteomics and protein interaction networks. The development and application of proteomics has increased tremendously over the last decade. Advances in proteomics methods offer many promising new directions of studying in clinical fields. In this regard, we want to discuss proteomics technology application in food investigations, drug and biomarker discovery will be discussed.
Full-text available
Protein thermal shift assays (TSAs) provide a means for characterizing target engagement through ligand-induced thermal stabilization. Although these assays are widely utilized for screening libraries and validating hits in drug discovery programs, they can impose encumbering operational requirements, such as the availability of purified proteins or selective antibodies. Appending the target protein with a small luciferase (NanoLuc) allows coupling of thermal denaturation with luminescent output, providing a rapid and sensitive means for assessing target engagement in compositionally complex environments such as permeabilized cells. The intrinsic thermal stability of NanoLuc is greater than mammalian proteins, and our results indicate that the appended luciferase does not alter thermal denaturation of the target protein. We have successfully applied the NanoLuc luciferase thermal shift assay (NaLTSA) to several clinically relevant protein families, including kinases, bro-modomains, and histone deacetylases. We have also demonstrated the suitability of this assay method for library screening and compound profiling.
Full-text available
CRISPR-Cas systems are well known acquired immunity systems that are widespread in Archaea and Bacteria. The RNA-guided nucleases from CRISPR-Cas systems are currently regarded as the most reliable tools for genome editing and engineering. The first hint of their existence came in 1987, when an unusual repetitive DNA sequence, which subsequently defined as a cluster of regularly interspersed short palindromic repeats (CRISPR), was discovered in the Escherichia coli genome during the analysis of genes involved in phosphate metabolism. Similar sequence patterns were then reported in a range of other bacteria as well as in halophilic archaea, suggesting an important role for such evolutionarily conserved clusters of repeated sequences. A critical step towards functional characterization of the CRISPR-Cas systems was the recognition of a link between CRISPRs and the associated Cas proteins, which were initially hypothesized to be involved in DNA repair in hyperthermophilic archaea. Comparative genomics, structural biology and advanced biochemistry could then work hand in hand, culminating not only in the explosion of genome editing tools based on CRISPR-Cas9 and other class II CRISPR-Cas systems, but also providing insights into the origin and evolution of this system from mobile genetic elements denoted casposons. To celebrate the 30 th anniversary of the discovery of CRISPR, this minireview briefly discusses the fascinating history of CRISPR-Cas systems, from the original observation of an enigmatic sequence in E. coli to genome editing in humans.
Full-text available
Phenotypic assays are becoming increasingly more common among drug discovery practices, expanding drug target diversity as lead compounds identified through such screens are not limited to known targets. While increasing diversity is beneficial to the drug discovery process and the fight against disease, the unknown modes of action of new lead compounds can hamper drug discovery as, in most cases, the process of lead compound optimization is made difficult due to the unknown nature of the target; blindly changing substituents can prove fruitless due to the inexhaustible number of potential combinations, and it is therefore desirable to rapidly identify the targets of lead compounds developed through phenotypic screening. In addition, leads identified through target-based screening often have off-target effects that contribute towards drug toxicity, and by identifying those secondary targets, the drugs can be improved. However, the identification of a leads mode of action is far from trivial and now represents a major bottleneck in the drug discovery pipeline. This review looks at some of the recent developments in the identification of drug modes of action, focusing on phenotype-based methods using metabolomics, proteomics, transcriptomics, and genomics to detect changes in phenotype in response to the presence of the drug, and affinity-based methods using modified/unmodified drug as bait to capture and identify targets. © 2017 IUBMB Life, 2017.
Full-text available
Sinuleptolide and its congeners are diterpenes with a norcembranoid skeleton isolated from the soft coral genus Sinularia. These marine metabolites are endowed with relevant biological activities, mainly associated with cancer development. 5-epi-sinuleptolide has been selected as a candidate for target discovery studies through the application of complementary proteomic approaches. Specifically, a combination of conventional chemical proteomics based on affinity chromatography, coupled with high-resolution mass spectrometry and bioinformatics, as well as drug affinity responsive target stability (DARTS), led to a clear identification of actins as main targets for 5-epi-sinuleptolide. Subsequent in-cell assays, performed with cytochalasin D as reference compound, gave information on the ability of 5-epi-sinuleptolide to disrupt the actin cytoskeleton by loss of actin fibers and formation of F-actin amorphous aggregates. These results suggest the potential application of 5-epi-sinuleptolide as a useful tool in the study of the molecular processes impaired in several disorders in which actin is thought to play an essential role.
Thermal shift assay is a fluorescence dye based biochemical method to determine the melting point of a protein. It can be used to investigate the ligand-induced stabilization of proteins and helps to increase the likelihood of crystallization in biological samples. Dimeric proteins like soluble guanylyl cyclase (sGC) have specific structural and functional properties which may pose a challenge in thermal shift measurements. In this paper, thermal shift assay was used to examine ligand-induced thermostabilization of the dimeric heme-containing protein soluble guanylyl cyclase. Adjustment of the parameters buffer solution, pH, protein / dye ratio and protein amount per well yielded a one-phase melting curve of sGC with a sharp transition and high reproducibility. We found that thermal shift measurement is not affected by heme state or heme content of the enzyme preparation. We used the method to investigate the thermostabilization of sGC induced by the heme-mimetic activator drugs cinaciguat, BAY 60-2770 and BR 11257 in combination with non-hydrolyzable nucleotides. Measurements with the dicarboxylic drugs cinaciguat and BAY 60-2770 yielded steep melting curves with high amplitudes. In contrast, in the presence of the monocarboxylic sGC activator BR 11257, melting curves appear flattened in the dye-based measurements. In the present paper, we show that activity-based thermostability measurements are superior to dye-based measurements in detecting the thermostabilizing influence of sGC activator drugs.
The cellular thermal shift assay (CETSA) has recently been devised as a label-free method for target validation of small compounds and monitoring the thermal stabilization or destabilization of proteins due to binding with the compound. Herein, we developed a modified method by combining the CETSA and proteomics analysis based on 2D gel electrophoresis, namely 2DE-CETSA, to identify the thermal stability-shifted proteins by binding with a new compound. We applied the 2DE-CETSA for analysis of a target-unknown compound, NPD10084, which exerts anti-proliferative activity against colorectal cancer cells in vitro and in vivo, and identified pyruvate kinase muscle isoform 2 (PKM2) as a candidate target protein. Interestingly, NPD10084 interrupted protein-protein interactions between PKM2 and β-catenin or STAT3, with subsequent suppression of downstream signaling. We thus demonstrate that our 2DE-CETSA method is applicable for identification of target compounds discovered by phenotypic screening.
Choosing the right biological target is the critical primary decision for the development of new drugs. Systematic genetic association testing of both human diseases and quantitative traits, along with resultant findings of coincident associations between them, is becoming a powerful approach to infer drug targetable candidates and generate in vitro tests to identify compounds that can modulate them therapeutically. Here, we discuss opportunities and challenges, and infer criteria for the optimal use of genetic findings in the drug discovery pipeline.
To identify novel targets for acute myeloid leukemia (AML) therapy, we performed genome-wide CRISPR-Cas9 screening using AML cell lines, followed by a second screen in vivo. Here, we show that the mRNA decapping enzyme scavenger (DCPS) gene is essential for AML cell survival. The DCPS enzyme interacted with components of pre-mRNA metabolic pathways, including spliceosomes, as revealed by mass spectrometry. RG3039, a DCPS inhibitor originally developed to treat spinal muscular atrophy, exhibited anti-leukemic activity via inducing pre-mRNA mis-splicing. Humans harboring germline biallelic DCPS loss-of-function mutations do not exhibit aberrant hematologic phenotypes, indicating that DCPS is dispensable for human hematopoiesis. Our findings shed light on a pre-mRNA metabolic pathway and identify DCPS as a target for AML therapy.