Simplified Receptor Based Pharmacophore Approach to Retrieve Potent PTP-LAR Inhibitors Using Apoenzyme M. Elizabeth Sobhia* Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India Abstract: The design of biological active compounds from the apoenzyme is still a challenging task. Herein a simple yet efficient technique is reported to generate a receptor based pharmacophore solely using a ligand-free protein crystal structure. Human leukocyte antigen-related phosphatase (PTP-LAR) is an apoenzyme and a receptor like transmembrane phosphatase that has emerged as a drug target for diabetes, obesity and cancer. The prior knowledge of the active residues responsible for the mechanism of action of the protein was used to generate the LUDI interaction map. Then, the complement negative image of the binding site was used to generate the pharmacophore features. A unique strategy was followed to design a pharmacophore query maintaining crucial interactions with all the active residues, essential for the enzyme inhibition. The same query was used to screen several databases consisting of the Specs, IBS, iniMaybridge, NCI and an in-house PTP inhibitor databases. In order to overcome the common bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski’s Rule of Five, SADMET properties and validated by docking studies in Glide and GOLD. These docking studies not only suggest the essential ligand binding interactions but also the binding patterns necessary for the LAR inhibition. The ligand pharmacophore mapping studies further validated the screened protocol and supported that the final screened molecules, presumably, showed potent inhibitory activity. Subsequently, these molecules were subjected to Derek toxicity predictions and nine new molecules with different scaffold were obtained as non-toxic PTP-LAR inhibitors. The present prospective strategy is a powerful technique to identify potent inhibitors using the protein 3D structure alone and is a valid alternative to other structure-based and random docking approaches.

1. Current Computer-Aided Drug Design, 2011, 7, 159-172 1
1573-4099/11 $58.00+.00 © 2011 Bentham Science Publishers Ltd.
Simplified Receptor Based Pharmacophore Approach to Retrieve Potent
PTP-LAR Inhibitors Using Apoenzyme
Dara Ajay and M. Elizabeth Sobhia*
Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S.A.S.
Nagar, Punjab 160062, India
Abstract: The design of biological active compounds from the apoenzyme is still a challenging task. Herein a simple yet
efficient technique is reported to generate a receptor based pharmacophore solely using a ligand-free protein crystal
structure. Human leukocyte antigen-related phosphatase (PTP-LAR) is an apoenzyme and a receptor like transmembrane
phosphatase that has emerged as a drug target for diabetes, obesity and cancer. The prior knowledge of the active residues
responsible for the mechanism of action of the protein was used to generate the LUDI interaction map. Then, the
complement negative image of the binding site was used to generate the pharmacophore features. A unique strategy was
followed to design a pharmacophore query maintaining crucial interactions with all the active residues, essential for the
enzyme inhibition. The same query was used to screen several databases consisting of the Specs, IBS, MiniMaybridge,
NCI and an in-house PTP inhibitor databases. In order to overcome the common bioavailability problem associated with
phosphatases, the hits obtained were filtered by Lipinski’s Rule of Five, SADMET properties and validated by docking
studies in Glide and GOLD. These docking studies not only suggest the essential ligand binding interactions but also the
binding patterns necessary for the LAR inhibition. The ligand pharmacophore mapping studies further validated the
screened protocol and supported that the final screened molecules, presumably, showed potent inhibitory activity.
Subsequently, these molecules were subjected to Derek toxicity predictions and nine new molecules with different
scaffold were obtained as non-toxic PTP-LAR inhibitors. The present prospective strategy is a powerful technique to
identify potent inhibitors using the protein 3D structure alone and is a valid alternative to other structure-based and
random docking approaches.
Keywords: Human leukocyte antigen-related phosphatase (PTP-LAR), receptor-based pharmacophore model, SADMET based
virtual screening, inhibitors, docking.
1. INTRODUCTION
Protein tyrosine phosphorylation is an important step in
biological process which regulates key cellular mechanisms
such as cell survival and proliferation to apoptotic cell death
in many eukaryotes [1, 2]. The phosphorylation and
dephosphorylation are two major post-translational
modifications in physiological processes, which regulate
functions like positive or negative signaling pathways [3].
The two groups of enzymes that greatly control the level of
protein tyrosine phosphorylation are protein tyrosine kinases
(PTKs) and protein tyrosine phosphatases (PTPs) [4]. The
kinases catalyze the transfer of a phosphate group from ATP
to the substrate proteins; whereas phosphatases catalyse the
hydrolysis of tyrosine-phosphorylated protein and restore the
substrate to its dephosphorylated state [5]. The balanced and
dynamic interplay between these PTKs and PTPs is crucial
and controls different cell signaling pathways such as gene
transcription, ion-channel activity, metabolism, the immune
response and cell survival. In brief the three important
functions of PTPs are cell-cell adhesion, insulin signaling
and cell-substrate adhesion [6, 7].
The phosphatases super family is defined by the PTP
fingerprint-sequence ([I/V]HCXAGXXR[S/T]G) absolutely
*Address correspondence to this author at the Department of
Pharmacoinformatics, National Institute of Pharmaceutical Education and
Research (NIPER), S.A.S. Nagar, Punjab 160062, India; Tel: +91-0172-
2214682-2025; E-mail: mesophia@niper.ac.in
conserved in all the PTPs [8]. This sequence also serves as
the catalytic site containing the active residues like cysteine,
glutamine, aspartic acid and serine which are essential for
dephosphorylation of the phosphotyrosin proteins [9]. The
mechanism of catalysis involves two steps: in the first step,
Cys acts as nucleophile while Arg is involved in the
phosphate binding to produce cysteinyl-phosphate reaction
intermediate. In the second step, Asp acts as both general
acid and base during the hydrolysis reaction and converts the
phospho-Cys enzyme to its resting Cys-SH state, thus
regenerating the free enzyme [10, 11]. Based on the
conserved signature motif, PTPs are divided into three major
subfamilies viz. classical, dual-specific, and low molecular
weight phosphatases [12, 13]. The classical and low
molecular weight phosphatases strictly target pTyr
(phosphorylated tyrosine) proteins; whereas the dual-specific
phosphatases target all the three phosphorylated residues viz.
pTyr, pSer, and pThr proteins [7, 14, 15]. The classical
phosphatases are further subdivided into two groups:
transmembrane (receptor-like) [16] and non-transmembrane
(intracellular) PTPs [17].
Human leukocyte antigen-related phosphatase (LAR) is a
receptor-like classical transmembrane phosphatase and a
negative regulator of multiple receptor tyrosine kinases [18].
The PTP-LAR consists of two structures, an extracellular
and intracellular structure, embedded in between the
phospholipids cell membrane. The extracellular structure
includes three immunoglobulin-like domains and eight
fibronectin type III-like domains. The Intracellular structure

2. 2 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
consists of two tandem phosphatase catalytic domains, that is
a membrane proximal domain (D1) and a membrane distal
domain (D2) as shown in Fig. (1). Recently, LAR has
acquired much interest because various biochemical and
pharmacological studies evidenced it as a potential target for
diabetes, obesity and cancer [19-22]. The LAR is widely
detected on various insulin-sensitive tissues like the muscle,
the liver, and the adipocytes indicating its importance in the
insulin receptor (IR) signal transduction [23, 24]. The in
vitro studies revealed that LAR has a physical association
with the IR as well as the kinases and decreases
autophosphorylation by 47% [25, 26]. In addition, LAR was
shown to deactivate the IR by 3.1 times, and the kinase by
2.1 times more rapidly than the PTPlB through preferentially
dephosphorylating Tyr1150, a critical active residue [27, 28].
The in vitro studies in Chinese hamster ovary cells and rat
hepatoma cell line showed dephosphorylation of IR with
LAR binding [29, 30]. The genetic studies such as the single-
strand conformation polymorphism (SSCP) analysis
indicated that LAR reduced the risk of obesity and insulin
resistance [31, 32], thus suggesting PTP-LAR as a potential
target for treating diabetes and cancer [33-38].
Fig. (1). Schematic representation of PTP-LAR.
The major approaches to retrieve biologically active
compounds from the ligand free proteins are structure based
rational drug design and random docking [39-41]. In random
docking, screening of large number of databases is time-
consuming and target-dependent; whereas the structure-
based drug design is an alternately inexpensive in silico
approach [42]. The two different ways to generate
pharmacophore hypotheses are direct and indirect methods.
The direct method uses both the ligand and the receptor
information to generate a pharmacophore query; whereas the
indirect method uses only the experimentally observed data
of the ligand set [43-45]. However, the former method
became more prominent because of the availability of the
protein crystal complexes. The receptor-based
pharmacophore approach (binding active site pharmacophore
hypotheses) is one of the in silico direct method [46]. In
most cases, a pharmacophore is generated using a set of
ligands of known activity or using protein-ligand complexes.
However, it is difficult to generate a pharmacophore query
from a homology model or the apoenzymes alone, without
having any prior information on the inhibitors or its
complexes. In many cases either the use of statistical data
derived from in vitro experiments or identifying the most
reliable binding atomic probe would limit the generation of
receptor based pharmacophore. So there is still a need for a
simple, yet efficient, computational approach which can
directly use the 3-D information of the protein structure
alone to generate a workable pharmacophore query. The
objective of the current study is to fill this gap by using the
knowledge based approach to generate pharmacophore
model from the active residues responsible for the
mechanism of action of the protein. To design and optimize
potent LAR inhibitors we followed combinatorial
approaches which include receptor based pharmacophore
generation, 3D database search, SADMET screening, and
molecular docking followed by Derek toxicity prediction
(Fig. 2).
2. MATERIALS AND METHODS
2.1. Protein Preparation and Active Site Identification
The X-ray crystal structure of human PTP-LAR from
Protein Data Bank (RCSB-PDB) with PDB ID: 1LAR was
used for in silico studies [47]. The structure used for the
pharmacophore generation was prepared by the Prepare
Protein Protocol implemented in Discovery Studio2.5 and
was minimized using CHARMm forcefield at a pH of 7.4
[48]. Then molecular surface analysis was performed for
easy identification of the binding cavities. The Site Finder
tool from Discovery Studio was used to search and identify
the different binding sites of the protein. The final corrected
protein was taken as input for further pharmacophore
studies.
2.2. Interaction Generation
The protocol Interaction Generation from Discovery
Studio2.5 was used to generate interactions from the protein
active binding site using the LUDI method [49]. This
generates a complement negative image of the active binding
site in the form of the interaction map, which is then used to
construct the pharmacophore features. The various functional
features specifically hydrogen bond donors (HBD),
hydrogen bond acceptors (HBA) and the lipophilic groups
(H) are distinguished in the active site. The density of the
polar sites parameter was set as 25 which specify the density
of the vectors in the interaction site for the hydrogen bonds.
The density of lipophilic sites parameter was also set as 25,

3. Potent PTP-LAR Inhibitors Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 3
which specify the density points in the interaction site for the
lipophilic atoms.
2.3. Pharmacophore Refinement
The generation of a simple and sensible pharmacophore
model directly from the interaction map is quite complicated,
as the LUDI interaction map generated consists of hundreds
of features which can produce thousands of possible
pharmacophore hypotheses [50]. Therefore, it is necessary to
determine which of these features are actually important in
the pharmacophore. Here in, two different strategies were
used to generate a rational and valid pharmacophore model.
In strategy- , a pharmacophore model was generated by
clustering the neighboring same features to respective
cluster. This clustering is based on the root-mean-squared
displacement between the matching features and the distance
matrix function of the number of common features. The
distance matrix is presented in the form of the dendrogram,
which is used to cluster the desired number of quantitative
pharmacophore features close to the geometric center [51-
54].
In strategy- , a pharmacophore model was generated
targeting only the active residues responsible for the
mechanism of action of the protein. The characteristic
features that produce interactions with the exposed solvent
accessible atoms of the active residues like Tyr1355,
Cys1522, Ser1523, Asp1490, and Gln1566 were manually
selected to generate the 3D query. Then, the pharmacophore
model was further refined by adding the excluded volume.
This exclusion constraint represents the volume unoccupied
by the atoms of the ligand [55-59].
2.4. Database Searching
The pharmacophore-based 3D database search approach
was used to identify ligands that map the pharmacophore
hypothesis [60-62]. The best selected pharmacophore query
was used for virtual screening of 3D databases such as Specs
(1,99,501 molecules), NCI (2,60,071 molecules), MiniMay
Fig. (2). A representation showing of the sequential in silico steps followed to generate receptor based pharmacophore.

4. 4 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
Bridge (2000 molecules), and IBS (4,29,190 molecules). In
addition, an in-house chemical database of 561 molecules
was also prepared using all the ligands bound with the
protein phosphatases (PTPs) obtained from the protein data
bank (PDB) [47, 63]. The best and the flexible search
parameters were applied for the database search to retrieve
putative hits compounds [64, 65].
2.5. SADMET Screening
The SADMET descriptors calculation based virtual
screening was performed using Discovery Studio2.5. The
obtained hits from the 3D database search were further
subjected to Lipinski’s Rule of Five (Molecular Weight
<500, HBD <5, HBA <10 and LogP<5) as the first filter [66,
67]. Then the SADMET properties were calculated using the
ADMET Descriptors protocol. This protocol uses QSAR
models to estimate a range of ADMET related properties for
small molecules [68, 69]. The different descriptors selected
for the calculation are ADMET solubility, ADMET
absorption level, ADMET AlogP98, ADMET CYP2D6
probability and ADMET hepatotox probability.
2.6. Docking
The retrieved molecules from the SADMET screening
were prepared and docked into the active site of the protein
1LAR [70-72]. The molecular docking was performed using
GLIDE5.0 module in Schrödinger package (2009) [73]. The
protein crystal structure was corrected using the Prepare
Protein Wizard and was minimized using OPLS_2005 force
field with a RMSD of 0.30Å at a pH of 7.4. The extra
precision (XP) flexible GLIDE docking simulation was
carried out by performing 1000 poses per docking run with
an output of 5 poses per ligand and a RMSD deviation less
than 0.5 Å. The Glide uses a stochastic search docking
algorithm to search ligand positions and orientations via a
Monte Carlo sampling of pose conformation to obtain an
accurate docked pose. The Maestro9.0 interface from
Schrödinger package was used for the visualization and
analysis of the active site and the docking interactions. Then,
the best hits retrieved form GLIDE were docked again using
GOLD program for validation (the Cambridge Data Center,
Cambridge, U.K.) [74]. For this the protein was prepared
using Sybyl7.1 (Tripos International, St Louis, USA) and
minimized by applying Amber99 force field [75]. The
parameter Gasteiger Huckel charge was set with maximum
iterations of 500 and a gradient of 0.05 Kcal/Mol Å. The
GOLD uses an evolutionary genetic algorithm to explore the
full range of ligand conformational flexibility and allows
partial flexibility of the target protein. The docking
simulation was carried out by performing 100000 genetic
algorithm operations and it was terminated if the top three
poses were within 1.5Å RMSD.
2.7. Toxicity Studies (Derek)
Deductive Estimation of Risk from Existing Knowledge
(Derek) is a knowledge-based system focused on molecular
substructures associated with qualitative and semi-
quantitative known toxicological endpoints predictions [76,
77]. The screened compounds from the docking studies were
further subjected to toxicity prediction using Derek. The
different toxicological end points selected for the prediction
are carcinogenicity, chromosome damage, genotoxicity,
hepatotoxicity, hERG channel inhibition, mutagenicity,
ocular toxicity, reproductive toxicity, respiratory
sensitization, skin sensitization, and thyroid toxicity. The
Derek compares different molecular structure alerts which
are responsible for the toxicity endpoints. Many of these
rules are generic in nature and are derived from mechanistic
organic chemistry based on a sets of related chemicals rather
than on specific chemicals [78, 79].
3. RESULTS AND DISCUSSION
3.1. Protein Preparation and Active Site Identification
The crystal structure protein 1LAR was corrected to
avoid unnecessary errors, and to obtain correct and accurate
predictions. PTP-LAR is made up of 1907 amino acids and
the D1 domain is made up of sequence starting from the
amino acid 1352 to 1607; whereas the D2 domain sequence
starts from 1639 to 1898 amino acids [18]. The missing loop
segment of four residues viz. Lys1624, Ala1625, His1626,
and Thr1627 was added between Ser1623 and Ser1628. The
protein atoms were standardized and the atoms from
Phe1876 were reordered removing its alternate
conformations and the water molecules. The Site Finder tool
identified 12 binding sites; out of which two sites viz. site 5
and site 9 were common with identical residues from the
PTP signature motif. The literature survey of several in vitro
studies on LAR suggests that both the D1 and D2 domains
share very similar structural characteristics. The studies also
revealed that the conserved cysteine residue present in the
signature motif of D1 domain is essential for the catalytic
activity; whereas, the D2 domain shows regulatory function
[80, 81]. The active residues responsible for the mechanism
of action are located in the four main loops of D1 domain i.e.
Ser1523 of PTP signature loop, Asp1490 of WPD loop,
Tyr1355 of pTyr-recognition loop, and Gln1566 of Q loop
(Fig. 3a). The residues Asn1428, His1521, Arg1528, and
Thr1529 are also involved in the catalysis mechanism. The
catalytic site is centered at Cys1522, and surrounded by four
different loops along with an extra loop starting from
Thr1424 to Lys1433.
3.2. Selection of Pharmacophore
The Interaction Protocol has generated 821 interactions
and complement features considering 2132 receptor binding
site atoms. These various interactions include 417 hydrogen
bond acceptors, 294 hydrogen bond donors, and 110
lipophilic fragment sites (Fig. 2). In general, these
interactions are refined by clustering the same neighboring
features to their respective cluster. Likewise, in strategy- , a
pharmacophore model was generated using dendrogram
clustering method considering two clusters for each feature
i.e. the hydrogen bond acceptor (HBA), the hydrogen bond
donor (HBD) and the hydrophobic (H) (Fig. 3b). However,
this pharmacophore model showed no interactions with any
of the active residues. For a compound to be a potent
inhibitor it should make strong interaction with the residues
responsible for the mechanism of action and should stabilize

5. Potent PTP-LAR Inhibitors Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 5
the ligand at the binding site. In order to achieve these
criteria a new pharmacophore was generated in strategy- ,
targeting only those features that generated interaction with
the active residues. From different interactions generated by
each atom of the active residues, the characteristic features
that showed dominant interactions with the solvent
accessible atoms of the binding cavity were manually
selected to generate the 3D queries. The two manually
selected hydrogen-bond donor (D) features were directed
towards the solvent accessible oxygen atoms of the active
residues Asp1490 and Asn1566. The two selected hydrogen-
bond acceptor (A) features of the model pointed interaction
with the nitrogen and sulfur of the active residues Cys1522
and Ser1523. The dendrogram clustering approach was used
to cluster all the hydrophobic features to six respective
clusters. Then, the hydrophobic feature which was close to
Tyr-1355 was selected, assuming that it would show
favorable - interaction with the phenyl ring of tyrosine.
The second feature was close to active residue Asn1566, so
that it can make better -cation interactions. Therefore, two
hydrophobic features which produce direct interaction with
the active residues were selected and the remaining four
features were omitted. Further refinement of the
pharmacophore was performed by adding exclusion spheres.
This exclusion spheres were merged with the strategy-
pharmacophore model to generate a final pharmacophore
feature query (Fig. 3c). This query has two hydrogen-bond
acceptors (A), two hydrogen-bond donors (D), and two
hydrophobic features (H) as the pharmacophore features
required for the inhibition of PTP-LAR.
3.3. Pharmacophore and SADMET Based Virtual
Screening
The final pharmacophore query from the strategy- was
selected to screen putative hit compounds from various 3D
databases. The first screen yielded 2612, 1, 1687, 343 and 25
molecules from the NCI, MiniMaybridge, interbioscreen
(IBS), Specs, and the in-house PTP databases respectively.
The search method has retrieved a total of 4,668 molecules
from the five databases containing 8,91,323 compounds. The
Lipinski’s Rule of Five was used as a filter to retrieve 1,736
compounds with more drug-like properties. Then based on
the cutoff value of different SADMET descriptors calcu-
lated, the molecules were categorized into four prediction
levels i.e. good, moderate, poor and very poor (Table 1). The
compounds with a pharmacophore Maxfitvalue of 3,
showing good and moderate prediction level were
considered; eventually, 360 molecules were screened. From
the SADMET analysis, compounds with presumable good
bioavailability were screened and selected for further
validation by docking studies.
3.4. Docking Analysis
Since crystal structure of PTP-LAR is available the
screened molecules were further validated by docking
studies. These docking studies not only suggested the
essential ligand binding interactions but also the ligand
binding patterns necessary for the LAR inhibition. The 360
molecules from the SADMET screenings were docked in
Fig. (3). Figures showing the active site residues responsible for the
mechanism of action a) active residues in ribbon view b) the
hypothesis generated using strategy- c) the hypothesis generated
using strategy- (Features are color coded, green: hydrogen bond
acceptor, magenta: hydrogen bond donor, cyan : hydrophobic, and
grey: exculsion spheres).

6. 6 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
Table 1. SADMET Properties with Optimum Values of
Descriptors Used for the Prediction Level
Properties
SADMET Descriptors
(Good Prediction Levels)
SADMET Descriptors
(Moderate Prediction
Levels)
Solubility ADMET Solubility -4 to -2
ADMET Solubility
-2 to 0
Absorption
ADMET Absorption
Level 0 (inside 95%)
ADMET Absorption
Level 1 (inside 99%)
Distribution
ADMET AlogP98
4.0 to 5.0 (>95%)
ADMET AlogP98
<4.0 (<90%)
Metabolism
ADMET CYP2D6 probability
< 0.5 (non-inhibitors)
Toxicity
ADMET hepatotoxicity
probability < 0.5 (non-toxic)
GLIDE and were screened based on their number of
hydrogen bond count, Glide gscore and their Glide emodel.
Top 100 poses from the docking gscore (cutoff value -4.75)
and those exhibiting the maximum hydrogen contact (cutoff
value 5) were screened and 38 molecules were selected (Fig.
4). These 38 molecules, in addition to four currently known
inhibitors reported from the literature with IC50 < 10μM
(Table 2), were docked in GOLD in order to validate the
screened compounds [82-86]. The Hermes module of
Cambridge Crystallographic Data Centre (CCDC) was used
to calculate different descriptors such as the hydrogen bond
count, the Gold score fitness, exposed hydrophobic count
and close contacts count (Table 3). These descriptors were
used to predict the best ligand conformation and its mode of
ligand binding pattern. The docking analysis showed that the
molecules with bicyclic rings were top ranked showing high
GOLD fitness score when compared to the currently known
standard inhibitor (illudalic acid IC50=1.3μM) (Table 2) [84].
This indicates that LAR inhibitors with bicyclic ring scaffold
Fig. (4). Flow chart representation of the sequential in silico strategies used for virtual screening.

7. Potent PTP-LAR Inhibitors Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 7
may possibly increase the potency. All the screened
molecules demonstrated the same interactions with the active
residues Tyr1355, Asp1490, Cys1522, Ser1523 and
Gln1566, as designed by the pharmacophore query from
strategy- . These interactions not only include strong
hydrogen bond but also - interaction, and -cation
interactions that stabilize the ligand in the active binding site.
In addition, the ligands also showed van der Waals,
hydrophobic and apolar interactions with other active
binding site residues such as Asn1428, His1521, Arg1528,
and Thr1529. The analysis also revealed that the molecules
with sulfonamide group showed improvement in docking
scores. Further analysis showed that inhibitors having
carboxylic acids or amides as linkers are likely to form more
hydrogen bond interaction with the protein. The 3D
information provided by these docking studies is meaningful,
as essential interactions required for the inhibition are
retained by the screened molecules. Detailed discussion of
the best molecule screened from each database is described
below (Fig. 5).
3.4.1. PTP-14252
The docking analysis of PTP-14252 (Fig. 5a) show the
highest number of hydrogen descriptor contact count (Table
3) with the receptor active site. The thiophene ring docked in
the center of the cavity formed a -cation interaction with
Lys1433 and also a hydrophobic interaction. The carboxylic
acid attached at the 2nd position of the thiophene ring makes
two hydrogen bonds (1.78Å and 1.92Å) with the active
residue Ser1523, and Ala1524. The carboxymethoxy group
at the 3rd position of the thiophene ring interacts with
another active residue Tyr1355, through a hydrogen bond
(2.33Å). The phenyl ring forms a -cation interaction with
Arg1528, which is another active residue. The hydroxyl
group attached at the meta-position of the ring plays a major
role by forming three hydrogen bonds with Arg1528
(2.21Å), Trp1488 (2.40Å), and Pro1489 (1.82Å). The
oxygen groups attached to the 2nd and 3rd position of the
thiophene ring interact with an active residue Gln1566,
through two hydrogen bonds (2.28Å, 1.78Å). Thus, out of
six pharmacophore features selected from strategy- , four
interactions with the active residues are maintained.
3.4.2. IBS-142587
The docking of the screened IBS compound 142587 (Fig.
5b) show four important interactions with the active site
residues indicating the probability of potent inhibition. The
4th hydroxyl group of tetrahydrothiophene forms a strong
hydrogen bond (2.36Å) with an active residue Gln1566. The
oxygen of the acetamide interacts with the important active
residue Ser1523 and Ala1524, through two hydrogen bonds
(2.42Å and 1.61Å). The oxy group attached to the acetamide
moiety forms a hydrogen bond (2.07Å) with another crucial
active residue Cys1522. The oxochroman ring produced two
-cation interactions with the residue Arg1528 and Lys1533.
The 5-hydroxyl group attached to the same ring formed a
strong hydrogen bond (1.99Å) with Thr1424.
3.4.3. NCI-361664
The compound NCI-361664 (Fig. 5c) is non-toxic and it
is the top scored molecule obtained from the database. The
hydroxyl group at the 4th position of the tetrahydrofuran ring
forms strong hydrogen bonds (2.30Å and 1.59Å) with two
active residues Ser1523 and Gln1566. The dihydro-
pyrimidine ring stabilizes the docking pose with four
hydrogen bonds and one -cation interaction with Arg1528
Table 2. The Currently Known Inhibitors Reported in Literature with their Experimental IC50 Values
1 (IC50= 1.3 μM)
O
OH
HO
O
O
2 (IC50= 1.3 μM)
N
HN
H
N
O
OH
OH
O
O
O
O
O
HO
O
3 (IC50= 1.53 μM)
O
OCH3
HO
O
O
4 (IC50= 6.7 μM)
N
HN
N
H
O
OH
OH
O
O
O

8. 8 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
(2.25Å). The oxygen at the 2nd position of dihydropyrimi-
dine ring makes two hydrogen bonds (2.25Å and 2.24Å)
with the active residue Arg1528 and Typ1488. The 3H and
the oxygen at the 4th
position of the same ring make two
hydrogen bonds (1.13Å, 1.36Å) with Gly1492. The 2-
methoxy attached to the phenyl ring forms a hydrogen bond
(2.31Å) with His1491. The screened molecules from the NCI
database show seven strong hydrogen bonds and stabilized
the ligand in the binding cavity by interacting with the active
residues.
Table 3. Docking Result of Screened Inhibitors with its Best Dock Pose Number as Obtained by GOLD
Sl. No Compound Name Hydrogen Bond Descriptor Count Gold Score Fitness Hydrophobic Descriptor Count
Close Contact
Descriptor Counta
1 PTP-14252|dock8 13 33.41 9 38
2 NCI-667532|dock2 13 42.88 17 36
3 NCI-361664|dock4 11 40.94 18 46
4 PTP-14250|dock8 10 32.48 6 38
5 PTP-24538|dock2 10 47.23 11 48
6 IBS-4764|dock9 10 41.84 17 30
7 PTP-14250|dock6 10 28.86 8 32
8 IBS-142587|dock1 10 51.82 9 44
9 Molecule-4|dock5 10 22.64 26 46
10 PTP-13811|dock2 9 59.03 16 48
11 IBS-18889|dock2 9 68.98 26 43
12 IBS-14904|dock4 9 35.22 12 25
13 NCI-334187|dock8 9 52.94 20 49
14 IBS-39671|dock3 9 44.22 20 34
15 NCI-9200|dock5 9 46.47 11 33
16 NCI-333765|dock3 9 55.03 17 39
17 PTP-24538|dock4 9 41.99 12 40
18 PTP-14261|dock6 9 40.09 14 32
19 NCI-118517|dock1 9 61.81 13 44
20 Molecule-2|dock5 9 32.54 37 43
21 PTP-13441|dock1 8 48.19 14 28
22 PTP-24538|dock1 8 37.46 6 30
23 IBS-91778|dock8 8 63.75 21 50
24 IBS-39981|dock2 8 53.21 20 38
25 NCI-343732|dock1 8 68.68 17 45
26 NCI-174268|dock3 8 46.67 21 38
27 IBS-14384|dock2 8 34.57 9 30
28 IBS-172863|dock2 8 52.23 16 39
29 IBS-60431|dock10 8 58.56 26 42
30 NCI-319078|dock7 8 26.76 31 34
31 IBS-191451|dock2 8 55.86 22 48
32 IBS-72185|dock10 8 51.44 24 35
33 Molecule-1|dock7 8 28.79 12 26
34 Molecule-3|dock5 8 34.37 12 37
35 NCI-262673|dock7 7 50.71 19 46
36 IBS-143450|dock4 7 43.81 16 23
37 NCI-333755|dock7 7 51.85 16 42
38 NCI-267689|dock7 7 60.384 23 49
Descriptor contact count is the calculated maximum number of possible contacts a molecule can make with the receptor viz. hydrogen bond, -interactions, van der Waals and
hydrophobic interactions.

9. Potent PTP-LAR Inhibitors Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 9
(a)
(b)
(c)
(Fig. 5) contd…..
(d)
Fig. (5). Three dimensional Gold docking models of three screened
lead compounds from the database along with current reported
inhibitor molecule-1.
3.4.4. Illudalic Acid (Molecule-1)
The docking studies of currently known inhibitor
molecule-1 (illudalic acid) with PTP-LAR (Fig. 5d) show
that hydroxyl group at the 3rd position interacts with the
active residue Gln1566 by a strong hydrogen bond (2.26Å).
The oxygen of 5-carbaldehyde group forms strong hydrogen
bond interaction (2.96Å) with Arg1528 as reported by Qing
Ling et al. [84]. Apart from the above interaction, the
hydroxyl group makes hydrogen bond interaction (2.59Å)
with Glu1428, and the phenyl ring shows -cation
interaction with Arg1528 and Lys1433. The ligand
orientation analysis performed in this study is very similar to
the one performed by Qing Ling et al.
3.5. Toxicity Studies (Derek)
The Derek toxicity prediction has increased the chance of
selecting the most promising safe molecules (Table 4). The
general compounds such as PTP-14250, IBS-4764 and IBS-
18889 showed skin sensitization because of the present of
phenol resorcinol precursor. Many compounds from the IBS
database such as IBS-160431, IBS-172863, and IBS-191451
were predicted majorly for ocular toxicity and photo toxicity
because of the present of aryl sulphonamide moiety. The
compounds PTP13441, IBS-42587 and NCI-9200 etc.
showed peroxisomes proliferators because of the present of
beta-O/S-Substituted carboxylic acid or its precursor.
Compounds with alkyl aldehyde precursor and substituted
vinyl ketone groups were predicted to produce genotoxicity
and chromosome damage. The compounds from the NCI
database like NCI-174268, NCI 667532, and NCI-9200 were
predicted to cause carcinogenicity as they have the
substituted pyrimidine/purine and furan groups. The
compound IBS-191451 showed thyroid toxicity because of
4-Aminoaryl sulphonamide or its precursor. The currently
known inhibitors molecules 1 and 3 from the dataset were
predicted with the highest toxic endpoints of six. Finally,

10. 10 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
toxicity prediction by Derek yielded nine molecules out of
38 ligands that are free from toxicological endpoints. These
nine compounds come from three of the databases used for
the screening studies: one from the in-house PTP database
(PTP-13811), one from the IBS database (IBS-143450), and
seven from the NCI (NCI-333755, NCI-343732, NCI-
361664, NCI- 334187, NCI-267687, NCI-333765 and NCI-
118517) (Table 5).
4. CONCLUSIONS
The pharmacophore concept has proven to be extremely
successful, not only in rationalizing the structure-activity
relationships, but also by its large impact in developing
appropriate 3D-tools for efficient virtual screening. In the
present study, knowledge-based approach was used to design
a simple and efficient technique to generate a receptor-based
pharmacophore as a part of structure-based drug design
strategy. Our method can thus be used independently to
identify potent hits molecules without using any prior
information from the ligand or its complexes. The LUDI
interaction map and the complementary pharmacophore
features of the active site were used to preferentially target
the features of the active residues alone and generate 3D
queries. Based on this concept a pharmacophore model was
designed with two hydrogen-bond acceptor (A), two
hydrogen-bond donor (D), and two hydrophobic (H)
features. The docking analysis also showed a minimum of
four interactions being maintained out of the six
pharmacophore features selected, highlighting cogent
evidence to confirm the mentioned interactions to be indeed
Table 4. Derek Toxicity Predictions Showing the Toxic Endpoint of Compounds with its Structural Alerts
Compound Name Endpoints Alert Name
IBS-191451 Bladder urothelial hyperplasia Aryl sulphonamide
NCI- 667532 Carcinogenicity Furan
IBS-14384,14904,91778,NCI-9200 Carcinogenicity Polyhalogenated aromatic
NCI- 174268 Carcinogenicity Substituted pyrimidine or purine
Molecule1 and 3 Chromosome damage, Mutagenicity, Genotoxicity
Nephrotoxicity
Hepatotoxicity
Skin sensitisation
Alkyl aldehyde or precursor
Indan or derivative
Aromatic aldehyde
Aldehyde precursor
IBS-39671 Chromosome damage Psoralen
IBS-191451 Hepatotoxicity Aminophenylsulphonamide
IBS-39671,39981,NCI-667532 Hepatotoxicity Furan
IBS-172185 Hepatotoxicity Thiazole or derivative
NCI- 319078 HERG channel inhibition HERG Pharmacophore II
NCI- 319078 Irritation (of the respiratory tract) Ethanolamine
PTP-13811, IBS-143450,
NCI- 118517, 267689, 333755, 333765,
334187, 343732, 361664, Molecule-2 and 4
Nothing to Report Nothing to Report
IBS-160431,172863,191451, Ocular toxicity Aryl sulphonamide
NCI- 174268 Ocular toxicity Purine base or analogue
PTP13441,14250,14252,14261,
IBS42587,18889,91778,NCI9200
Peroxisome proliferation beta-O/S-Substituted carboxylic acid or
precursor
IBS-18889,39671 Photoallergenicity Coumarin, Furocoumarin
IBS-160431,172863,191451, Phototoxicity Aryl sulphonamide
NCI- 667532 Hepatotoxicity 3-Cyanopyridine
IBS-191451 Hepatotoxicity Aromatic sulphonamide
NCI- 262673 Hepatotoxicity O-Tertiary butyl ester or carbamate
NCI- 667532 Nephrotoxicity Aromatic nitrile
IBS-191451 Nephrotoxicity Aromatic sulphonamide
IBS-91778,NCI-9200 Nephrotoxicity Halogenated benzene
PTP-14250,14252,IBS-4764 Skin sensitisation Phenol or precursor
IBS-18889,39671,39981 Skin sensitisation Phenyl ester
IBS-18889 Skin sensitisation Resorcinol or precursor
IBS-191451 Thyroid toxicity 4-Aminoaryl sulphonamide or precursor

11. Potent PTP-LAR Inhibitors Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 11
crucial for ligand-protein interactions. The final nine
screened molecules were superimposed onto the
pharmacophore model of strategy- . The results indicate that
out of the 9 screened molecules, 8 molecules were mapped
unto the pharmacophore fitting method (Fig. 6). This showed
that the screened molecules have the characteristic
pharmacophore features as designed by strategy- . The
ligand pharmacophore mapping studies further validated the
screened protocol and supported that the final screened
molecules presumably showed potent inhibitory activity.
Although we could not achieve large-scale benchmarks, the
interactions obtained from these docking studies ameliorate
the accuracy of the model. However, the general protocol
presented in this study is sensitive and specific enough to
prioritize virtual hits of interest using an apoenzyme
exclusively.
Finally, we have successfully identified new potent
inhibitors using a unique sequential in silico strategies
comprising of the receptor based pharmacophore, SADMET
based virtual screening, and docking study followed by
a) NCI-334187
Table 5. 2D Chemical Structures of the Final Nine Selected Hits from the Virtual Screening Procedure
N
O
Br
NH
HN
O
O
OH
IBS-143450
O
NH O
HN O
NH
S
O
O
O
NH2
PTP-13811
HN
O
O
O
HN
OHO
OH
NCI-333755
H
N
O
O
O
NH
HN
O
HO
NCI-343732
O
O
N
OH
OH
O
ON
H
O
NCI-361664
OH
HN
S OO
N
H
O
O
O
O
NCI-267689
N
O
H
N
HN
O
HO O
O
NCI-334187
S
N
H
O
O
O
NH
HO
OHO
NCI-333765
O
O
N
H
N
HN
O
OHHN
O
O
NCI-118517

12. 12 Current Computer-Aided Drug Design, 2011, Vol. 7, No. 3 Ajay and Sobhia
(Fig. 6) contd…..
b) NCI-333765
c) NCI-343732
d) NCI-118517
Fig. (6). Some of the final screened compounds mapped onto the
pharmacophore model of strategy-II. a) NCI-334187 b) NCI-
333765 c) NCI-343732 d) NCI-118517.
toxicity study using Derek. These methods allowed us to
screen nine compounds of different scaffolds as PTP-LAR
inhibitors from a databases containing 8,91,323 molecules.
The compounds obtained from the NCI and PTP databases
were found to be promising and free from toxic endpoints.
The compounds PTP-13811 and NCI-361664 could be
further subjected to in vitro analysis in the search for better
leads that could improve the treatment of obesity and
diabetic disease.
ACKNOWLEDGEMENTS
The authors thank Department of Pharmaceuticals
Minister of Chemicals and fertilizers, Department of Science
and Technology (DST) and Council of Scientific and
Industrial Research (CSIR) New Delhi for financial
assistance.
ABBREVIATIONS
PTP-LAR = Human leukocyte antigen-related phosphatase
PTPs = Protein tyrosine phosphatases
PTKs = Protein tyrosine kinases
IR = Insulin receptor.
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Received: March 4, 2011 Revised: May 12, 2011 Accepted: June 3, 2011