simplified receptor based pharmacophore approach to retrieve potent ptp lar inhibitors using...

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: [email protected]

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 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,

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.


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


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).


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.


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

HN

O

OH

OH

OO

OO

O

HO

O

3 (IC50= 1.53 μM)

O

OCH3

HO

O

O

4 (IC50= 6.7 μM)

N

HNNH

O

OH

OH

O

OO


(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.


(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,


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


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

BrNH

HN

O

O

OH

IBS-143450

O

NH O

HN O

NHS

OO

O

NH2

PTP-13811

HN

O

O

O

HN

OHO

OH

NCI-333755

HN

O

O

O

NH

HN

O

HO

NCI-343732

O

O

N

OH

OH

O

ONH

O

NCI-361664

OHHN

S OO

NH

O

OO

O

NCI-267689

NO

HN

HN

OHO O

O

NCI-334187

S

NH

O

OO

NHHO

OHO NCI-333765

O

O

NH

N

HN

O

OHHN

O

O

NCI-118517


(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.

REFERENCES

[1] Hunter, T. Signaling—2000 and beyond. Cell, 2000, 100, 113-127. [2] Ventura, J.J.; Nebreda, Á. Protein kinases and phosphatases as

therapeutic targets in cancer. Clin. Transl. Oncol., 2006, 8, 153-160.

[3] Stoker, A.W. Protein tyrosine phosphatases and signalling. J.

Endocrinol., 2005, 185, 19-33. [4] Stone, R.L.; Dixon, J.E. Protein-tyrosine phosphatases. J. Biol.

Chem., 1994, 269, 31323-31326. [5] Kappert, K.; Peters, K.G.; Bohmer, F.D.; Ostman, A. Tyrosine

phosphatases in vessel wall signaling. Cardiovasc. Res., 2005, 65, 587-598.

[6] Stoker, A.W. Receptor tyrosine phosphatases in axon growth and guidance. Curr. Opin. Neurobiol., 2001, 11, 95-102.

[7] Zhang, Z.Y. Functional studies of protein tyrosine phosphatases with chemical approaches. Biochim. Biophys. Acta, Proteins

Proteomics, 2005, 1754, 100-107. [8] Zhang, Z.Y.; Wang, Y.; Wu, L.; Fauman, E.B.; Stuckey, J.A.;

Schubert, H.L.; Saper, M.A.; Dixon, J.E. The Cys (X) 5Arg catalytic motif in phosphoester hydrolysis. Biochemistry, 1994, 33, 15266-15270.

[9] Tonks, N.K. Protein tyrosine phosphatases: from genes, to function, to disease. Nat. Rev. Mol. Cell Biol., 2006, 7, 833-846.

[10] Andersen, J.N.; Mortensen, O.H.; Peters, G.H.; Drake, P.G.; Iversen, L.F.; Olsen, O.H.; Jansen, P.G.; Andersen, H.S.; Tonks, N.K.; Moller, N.P.H. Structural and evolutionary relationships among protein tyrosine phosphatase domains. Mol. Cell Biol., 2001, 21, 7117-7136.

[11] Tabernero, L.; Aricescu, A.R.; Jones, E.Y.; Szedlacsek, S.E. Protein tyrosine phosphatases: structure-function relationships. FEBS J., 2008, 275, 867-882.

[12] Alonso, A.; Sasin, J.; Bottini, N.; Friedberg, I.; Osterman, A.; Godzik, A.; Hunter, T.; Dixon, J.; Mustelin, T. Protein tyrosine phosphatases in the human genome. Cell, 2004, 117, 699-711.

[13] Tiganis, T.; Bennett, A.M. Protein tyrosine phosphatase function: the substrate perspective. Biochem. J., 2007, 402, 1-15.

[14] Burke, T.R.; Zhang, Z.Y. Protein-tyrosine phosphatases: structure, mechanism, and inhibitor discovery. Biopolymers, 1998, 47, 225-241.

[15] Zhang, Z.Y. Chemical and mechanistic approaches to the study of protein tyrosine phosphatases. Acc. Chem. Res., 2003, 36, 385-392.


[16] Paul, S.; Lombroso, P.J. Receptor and non-receptor protein tyrosine phosphatases in the nervous system. Cell. Mol. Life Sci., 2003, 60, 2465-2482.

[17] Bourdeau, A.; Dubé, N.; Tremblay, M.L. Cytoplasmic protein tyrosine phosphatases, regulation and function: the roles of PTP1B and TC-PTP. Curr. Opin. Cell. Biol., 2005, 17, 203-209.

[18] Nam, H.J.; Poy, F.; Krueger, N.X.; Saito, H.; Frederick, C.A. Crystal structure of the tandem phosphatase domains of RPTP LAR. Cell, 1999, 97, 449-458.

[19] Beltran, P.J.; Bixby, J.L. Receptor protein tyrosine phosphatases as mediators of cellular adhesion. Front. Biosci., 2003, 8, 87-99.

[20] Elchebly, M.; Cheng, A.; Tremblay, M.L. Modulation of insulin signaling by protein tyrosine phosphatases. J. Mol. Med. 2000, 78, 473-482.

[21] Johnson, K.G.; Van Vactor, D. Receptor protein tyrosine phosphatases in nervous system development. Physiol. Rev., 2003, 83, 1-24.

[22] Kulas, D.T.; Goldstein, B.J.; Mooney, R.A. The transmembrane protein-tyrosine phosphatase LAR modulates signaling by multiple receptor tyrosine kinases. J. Biol. Chem., 1996, 271, 748-754.

[23] Asante-Appiah, E.; Kennedy, B.P. Protein tyrosine phosphatases: the quest for negative regulators of insulin action. Am. J. Physiol. Endocrinol. Metab., 2003, 284, 663-670.

[24] Goldstein, B.J. Protein-tyrosine phosphatases: emerging targets for therapeutic intervention in type 2 diabetes and related states of insulin resistance. J. Clin. Endocrinol Metab., 2002, 87, 2474-2480.

[25] Aicher, B.; Lerch, M.M.; Muller, T.; Schilling, J.; Ullrich, A. Cellular redistribution of protein tyrosine phosphatases LAR and PTPsigma by inducible proteolytic processing. J. Cell Biol., 1997, 138, 681-696.

[26] Pulido, R.; Serra-Pages, C.; Tang, M.; Streuli, M. The LAR/PTP delta/PTP sigma subfamily of transmembrane protein-tyrosine-phosphatases: multiple human LAR, PTP delta, and PTP sigma isoforms are expressed in a tissue-specific manner and associate with the LAR-interacting protein LIP. 1. Proc. Natl. Acad. Sci. USA, 1995, 92, 11686-11690.

[27] Ahmad, F.; Goldstein, B.J. Functional association between the insulin receptor and the transmembrane protein-tyrosine phosphatase LAR in intact cells. J. Biol. Chem., 1997, 272, 448-457.

[28] Hashimoto, N.; Feener, E.P.; Zhang, W.R.; Goldstein, B.J. Insulin receptor protein-tyrosine phosphatases. J. Biol. Chem., 1992, 267, 13811-13814.

[29] Ahmad, F.; Considine, R.V.; Goldstein, B.J. Increased abundance of the receptor-type protein-tyrosine phosphatase LAR accounts for the elevated insulin receptor dephosphorylating activity in adipose tissue of obese human subjects. J. Clin. Invest., 1995, 95, 2806-2812.

[30] Zhang, W.R.; Li, P.M.; Oswald, M.A.; Goldstein, B.J. Modulation of insulin signal transduction by eutopic overexpression of the receptor-type protein-tyrosine phosphatase LAR. Mol. Endocrinol., 1996, 10, 575-584.

[31] Miscio, G.; Tassi, V.; Coco, A.; Soccio, T.; Di Paola, R.; Prudente, S.; Baratta, R.; Frittitta, L.; Ludovico, O.; Padovano, L. The allelic variant of LAR gene promoter-127 bp T? A is associated with reduced risk of obesity and other features related to insulin resistance. J. Mol. Med., 2004, 82, 459-466.

[32] Zabolotny, J.M.; Kim, Y.B.; Peroni, O.D.; Kim, J.K.; Pani, M.A.; Boss, O.; Klaman, L.D.; Kamatkar, S.; Shulman, G.I.; Kahn, B.B. Overexpression of the LAR (leukocyte antigen-related) protein-tyrosine phosphatase in muscle causes insulin resistance. Proc. Natl. Acad. Sci. USA, 2001, 98, 5187-5192.

[33] Chagnon, M.J.; Uetani, N.; Tremblay, M.L. Functional significance of the LAR receptor protein tyrosine phosphatase family in development and diseases. Biochem. Cell Biol., 2004, 82, 664-675.

[34] Li, L.; Dixon, J.E. Form, function, and regulation of protein tyrosine phosphatases and their involvement in human diseases. Semin. Immunol., 2000, 12, 75-84.

[35] Phung, T.L.; Mooney, R.A.; Kulas, D.T.; Sparks, C.E.; Sparks, J.D. Suppression of the protein tyrosine phosphatase LAR reduces apolipoprotein B secretion by McA-RH7777 rat hepatoma cells. Biochem. Biophys. Res. Commun., 1997, 237, 367-371.

[36] Ren, J.M.; Li, P.M.; Zhang, W.R.; Sweet, L.J.; Cline, G.; Shulman, G.I.; Livingston, J.N.; Goldstein, B.J. Transgenic mice deficient in

the LAR protein-tyrosine phosphatase exhibit profound defects in glucose homeostasis. Diabetes, 1998, 47, 493-497.

[37] Tautz, L.; Pellecchia, M.; Mustelin, T. Targeting the PTPome in human disease. Expert Opin. Ther. Targets, 2006, 10, 157-177.

[38] Zhang, Z.Y. Protein tyrosine phosphatases: prospects for therapeutics. Curr. Opin. Chem. Biol., 2001, 5, 416-423.

[39] Good, A.C.; Cheney, D.L.; Sitkoff, D.F.; Tokarski, J.S.; Stouch, T.R.; Bassolino, D.A.; Krystek, S.R.; Li, Y.; Mason, J.S.; Perkins, T.D.J. Analysis and optimization of structure-based virtual screening protocols 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success. J.

Mol. Graph Model., 2003, 22, 31-40. [40] Kapetanovic, I.M. Computer-aided drug discovery and

development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171, 165-176.

[41] McInnes, C. Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol., 2007, 11, 494-502.

[42] Oranit, D.; Alexandra, S.P.; Ruth, N. Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr. Med. Chem., 2004, 11, 71-90.

[43] Doddareddy, M.R.; Jung, H.K.; Lee, J.Y.; Lee, Y.S.; Cho, Y.S.; Koh, H.Y.; Pae, A.N. First pharmacophoric hypothesis for T-type calcium channel blockers. Bioorg. Med. Chem., 2004, 12, 1605-1611.

[44] Li, H.F.; Lu, T.; Zhu, T.; Jiang, Y.J.; Rao, S.S.; Hu, L.Y.; Xin, B.T.; Chen, Y.D. Virtual screening for Raf-1 kinase inhibitors based on pharmacophore model of substituted ureas. Eur. J. Med. Chem., 2009, 44, 1240-1249.

[45] Sakkiah, S.; Krishnamoorthy, N.; Gajendrarao, P.; Thangapandian, S.; Lee, Y.; Kim, S.; Suh, J.K.; Kim, H.H.; Lee, K.W. Pharmacophore mapping and virtual screening for SIRT1 activators. Bull. Korean Chem. Soc., 2009, 30, 1153-1156.

[46] Jansen, J.M.; Martin, E.J. Target-biased scoring approaches and expert systems in structure-based virtual screening. Curr. Opin.

Chem. Biol., 2004, 8, 359-364. [47] Herman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.;

Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28, 235-242.

[48] Studio, D. Version 2.0; Accelrys. Inc.: San Diego, CA. 2008. [49] Böhm, H.J. LUDI: rule-based automatic design of new substituents

for enzyme inhibitor leads. J. Comput. Aided Mol. Des., 1992, 6, 593-606.

[50] Böhm, H.J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des., 1992, 6, 61-78.

[51] Takeuchi, Y.; Shands, E.F.B.; Beusen, D.D.; Marshall, G.R. Derivation of a three-dimensional pharmacophore model of substance P antagonists bound to the neurokinin-1 receptor. J. Med.

Chem., 1998, 41, 3609-3623. [52] Deng, J.; Lee, K.W.; Sanchez, T.; Cui, M.; Neamati, N.; Briggs,

J.M. Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors. J.

Med. Chem., 2005, 48, 1496-1505. [53] Boppana, K.; Dubey, P.K.; Jagarlapudi, S.; Vadivelan, S.;

Rambabu, G. Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models. Eur. J. Med. Chem., 2009, 44, 3584-3590.

[54] Lee, J.; Baek, S.; Kim, Y. Receptor-oriented Pharmacophore-based in silico screening of human catechol o-methyltransferase for the design of antiparkinsonian drug. Bull. Korean Chem. Soc., 2007, 28, 379-385.

[55] Barillari, C.; Marcou, G.; Rognan, D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J. Chem. Inf. Model., 2008, 48, 1396-1410.

[56] Muthas, D.; Sabnis, Y.A.; Lundborg, M.; Karlén, A. Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering. J. Mol. Graph. Model., 2008, 26, 1237-1251.

[57] Ghose, A.K.; Herbertz, T.; Salvino, J.M.; Mallamo, J.P. Knowledge-based chemoinformatic approaches to drug discovery. Drug Discov. Today, 2006, 11, 1107-1114.


[58] Deng, Z.; Chuaqui, C.; Singh, J. Knowledge-based design of target-focused libraries using protein- ligand interaction constraints. J. Med. Chem., 2006, 49, 490-500.

[59] López-Rodríguez, M.L.; Benhamú, B.; de la Fuente, T.; Sanz, A.; Pardo, L.; Campillo, M. A three-dimensional pharmacophore model for 5-hydroxytryptamine6 (5-HT6) receptor antagonists. J. Med. Chem., 2005, 48, 4216-4219.

[60] Greene, J.; Kahn, S.; Savoj, H.; Sprague, P.; Teig, S. Chemical function queries for 3D database search. J. Chem. Inf. Comp. Sci., 1994, 34, 1297-1308.

[61] Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today, 2006, 11, 580-594.

[62] Vadivelan, S.; Sinha, B.N.; Rambabu, G.; Boppana, K.; Jagarlapudi, S. Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J. Mol. Graphics Modell., 2008, 26, 935-946.

[63] Voigt, J.H.; Bienfait, B.; Wang, S.; Nicklaus, M.C. Comparison of the NCI open database with seven large chemical structural databases. J. Chem. Inf. Comput. Sci., 2001, 41, 702-712.

[64] Martin, Y.C. 3D database searching in drug design. J. Med. Chem., 1992, 35, 2145-2154.

[65] Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual screening—an overview. Drug Discov. Today, 1998, 3, 160-178.

[66] Lipinski, C.A. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol., 2004, 1, 337-341.

[67] Lipinski, C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods, 2000, 44, 235-249.

[68] van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2, 192-204.

[69] Wang, H.Y.; Cao, Z.X.; Li, L.L.; Jiang, P.D.; Zhao, Y.L.; Luo, S.D.; Yang, L.; Wei, Y.Q.; Yang, S.Y. Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors. Bioorg. Med. Chem. Lett., 2008, 18, 4972-4977.

[70] Schneidman-Duhovny, D.; Nussinov, R.; Wolfson, H.J. Predicting molecular interactions in silico: II. Protein-protein and protein-drug docking. Front. Med. Chem., 2006, 3, 585-613.

[71] Taylor, R.D.; Jewsbury, P.J.; Essex, J.W. A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des., 2002, 16, 151-166.

[72] Zavodszky, M.I.; Sanschagrin, P.C.; Kuhn, L.A.; Korde, R.S. Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening. J. Comput. Aided Mol. Des., 2002, 16, 883-902.

[73] Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49, 6177-6196.

[74] Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins:

Struct. Funct.Genet., 2003, 52, 609-623. [75] Sybyl, T.I. 1699 South Hanley Rd. St. Louis, Missouri. 63144. [76] Combes, R.D.; Rodford, R.A. The Use of Expert Systems

forToxicity Prediction: Illustrated with Reference to the DEREK Program 2004.

[77] Long, A.; Combes, R.D. Using DEREK to predict the activity of some carcinogens/mutagens found in foods. Toxicol. In Vitro, 1995, 9, 563-569.

[78] Combes, R.D.; Judson, P. The use of artificial intelligence systems for predicting toxicity. Pestic. Sci. 2006, 45, 179-194.

[79] Marchant, C.A.; Briggs, K.A. In silico tools for sharing data and knowledge on toxicity and metabolism: Derek for windows, meteor, and vitic. Toxicol. Mech. Methods, 2008, 18, 177-187.

[80] Tsujikawa, K.; Kawakami, N.; Uchino, Y.; Ichijo, T.; Furukawa, T.; Saito, H.; Yamamoto, H. Distinct functions of the two protein tyrosine phosphatase domains of LAR (leukocyte common antigen-related) on tyrosine dephosphorylation of insulin receptor. Mol. Endocrinol., 2001, 15, 271-280.

[81] Yang, T.; Bernabeu, R.; Xie, Y.; Zhang, J.S.; Massa, S.M.; Rempel, H.C.; Longo, F.M. Leukocyte antigen-related protein tyrosine phosphatase receptor: a small ectodomain isoform functions as a homophilic ligand and promotes neurite outgrowth. J. Neurosci., 2003, 23, 3353-3363.

[82] Bialy, L.; Waldmann, H. Inhibitors of protein tyrosine phosphatases: Next generation drugs. Angew. Chem. Int. Ed., 2005, 44, 3814-3839.

[83] Dewang, P.M.; Hsu, N.M.; Peng, S.Z.; Li, W.R. Protein tyrosine phosphatases and their inhibitors. Curr. Med. Chem., 2005, 12, 1-22.

[84] Ling, Q.; Huang, Y.; Zhou, Y.; Cai, Z.; Xiong, B.; Zhang, Y.; Ma, L.; Wang, X.; Li, X.; Li, J. Illudalic acid as a potential LAR inhibitor: synthesis, SAR, and preliminary studies on the mechanism of action. Bioorg. Med. Chem., 2008, 16, 7399-7409.

[85] Vintonyak, V.V.; Antonchick, A.P.; Rauh, D.; Waldmann, H. The therapeutic potential of phosphatase inhibitors. Curr. Opin. Chem. Biol., 2009, 13, 272-283.

[86] Hoffman, H.E.; Blair, E.R.; Johndrow, J.E.; Bishop, A.C. Allele-specific inhibitors of protein tyrosine phosphatases. J. Am. Chem.

Soc., 2005, 127, 2824-2825.

Received: March 4, 2011 Revised: May 12, 2011 Accepted: June 3, 2011

simplified receptor based pharmacophore approach to retrieve potent ptp lar inhibitors using...

Education

phosphorylated protein

classical phosphatases

cellcell adhesion

protein tyrosine kinases

cell survival

different cell

potent ptplar inhibitors

hydrolysis of tyrosine