Fast Identification of Novel Lymphoid Tyrosine Phosphatase Inhibitors

Article pubs.acs.org/jmc

Fast Identification of Novel Lymphoid Tyrosine Phosphatase Inhibitors Using Target−Ligand Interaction-Based Virtual Screening Xuben Hou,†,# Rong Li,‡,# Kangshuai Li,§ Xiao Yu,§ Jin-Peng Sun,*,‡ and Hao Fang*,† †

Department of Medicinal Chemistry, Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmacy, Shandong University, Jinan, Shandong 250012, China ‡ Key Laboratory Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine, Shandong University, Jinan, Shandong 250012, China § Department of Physiology, School of Medicine, Shandong University, Jinan, Shandong 250012, China S Supporting Information *

ABSTRACT: Lymphoid-specific tyrosine phosphatase (Lyp), a critical signaling regulator of immune cells, is associated with various autoimmune diseases, including type 1 diabetes, rheumatoid arthritis, and systemic lupus erythematosus. Recent research suggests that Lyp is a potential drug target for autoimmune diseases. Herein, we applied a target−ligand interaction-based virtual screening method to identify novel Lyp inhibitors. Nine Lyp inhibitors with novel scaffolds were identified with eight reversible inhibitors (Ki values ranged from 2.87 to 28.03 μM) and one covalent inhibitor (Ki = 40.98 ± 13.19 μM). The top four compounds (A2, A15, A19, and A26) displayed selectivity over other phosphatases in preliminary experiments, and kinetic analysis indicated that these compounds are competitive inhibitors of Lyp. Compounds A15 and A19 up-regulated TCR (T cell receptor) mediated signaling and transcriptional activation through inhibition of Lyp activity in T cells. The new chemotypes of Lyp selective inhibitors identified through the target−ligand interaction-based virtual screening may provide new leads for Lyp targeted therapeutic development.

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INTRODUCTION Protein tyrosine phosphatases (PTPs) play important roles in many human diseases, including inflammation, cancer, and metabolic and immunological disorders.1−3 Among numerous PTPs, the lymphoid-specific tyrosine phosphatase (Lyp, encoded by the PTPN22 gene) is predominantly expressed in immune cells and negatively regulates T cell receptor (TCR) signaling pathways.4,5 Genetic studies have shown that a classical missense C1858T polymorphism in the PTNP22 gene is a common risk factor for multiple autoimmune diseases. The encoded gene product has a Trp at position 620 instead of an Arg (R620W), which is in the C-terminal regulatory domain of Lyp.6,7 The Lyp-R620W variant has a diminished affinity with the SH3 domain of Csk, which potentiates the inhibitory effect of Lyp in T cell signaling and makes this variant an important pathogenic factor in autoimmune disorders.6−8 Conversely, several Lyp variants, such as R263Q or R266W, exhibit reduced activity and have impaired inhibitory activity in T cell signaling, which results in a reduced risk of several autoimmune diseases.9,10 Although the mechanism, through which the Lyp-R620W variant plays a role in autoimmunity, remains unclear, functional studies have demonstrated that this variant is a gain-of-function mutant and reduced TCR signaling could be found in carriers of Lyp-R620W.7,9,11 Therefore, a specific Lyp inhibitor has great therapeutic potential for treating auto© XXXX American Chemical Society

immune diseases, especially in patients carrying the W620 polymorphism.8,12,13 In the past few years, several types of Lyp inhibitors have been discovered, including the benzofuran salicylic acid-based inhibitors (I-C11,14 I-C11 derivatives,15 and 8b12), the thiobarbituric acid-based compound LTV-1,8 and the quinolin-2(1H)-one-based noncompetitive inhibitor 4e.16 Because of the limited structural diversity in the scaffolds of available agents, there is a need for developing novel Lyp inhibiting chemotypes to evaluate their therapeutic potential. Our previous study reported the crystal structure of the Lyp catalytic domain, either alone or in complex with I-C11 (PDB code 2QCJ or 2QCT).14 More recently, the cocrystal structure of the Lyp catalytic domain in complex with 8b, which is a more potent and selective Lyp inhibitor than I-C11, was also determined (PDB code 4J51). Therefore, virtual screening (also referred to as in silico screening) studies with these X-ray crystallographic structures were initiated to discover Lyp inhibitors with novel scaffolds. In the past few decades, virtual screening has been used for the identification of bioactive compounds with diverse chemical structures.17−22 Typically, virtual screening allows the evaluation of very large compound databases in search of active hits, using information generated from either a set of active Received: March 27, 2014

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dx.doi.org/10.1021/jm500692u | J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

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compounds or the binding site of the target protein.23 In general, virtual screening methods are classified as ligand-based virtual screening (LBVS), which is based mainly on the concepts of ligand similarity,18,24 including druglike filters,25 2D similarity,26 field-based screening,27 3D-shape similarity,28 pharmacophore,29 and 3D-QSAR,30 or structure-based virtual screening (SBVS), which is based predominantly on molecular docking.31 Alternatively, the LBVS method can serve as the first stage in the workflow of virtual screening because it is much faster than the SBVS method.32 For example, a large database can be screened initially using a given pharmacophore model to generate a smaller subset for the subsequent docking-based screen. Thus far, virtual screening methods have already been successfully used in the discovery of inhibitors against several phosphatases, including SHP-2, 33−35 lmw-PTP, 36 and PTP1B.37−39 Currently, there is only one docking-based virtual screening study that has been performed toward Lyp,40 leading to the identification of inhibitors possessing the scaffold of 2benzamidobenzoic acid, which had already been reported previously in other PTP inhibitors.41−44 The selectivity of these inhibitors over other phosphatases was not assessed.40 In the present study, an interaction-based pharmacophore (IBP) model was developed from a cocrystal structure of the Lyp−inhibitor complex and subsequently applied to filter the commercial Specs database. The resulting IBP hits were submitted to an in silico docking procedure. To improve the outcome of the docking-based virtual screening, two validation files were constructed for the evaluation of four docking programs: LigandFit,45,46 Surflex,47 Gold,48 and Glide.49,50 A total of nine Lyp inhibitors were identified from 29 selected compounds. The best inhibitors A15 and A19 had selectivity over other protein phosphatases and efficacy at promoting TCR-mediated cellular signaling and transcriptional activation. Overall, the strategy led to novel lead compounds, which provide important structural clues for further development of new selective Lyp inhibitors.

Figure 1. (A) Comparison of the cocrystal structures of Lyp−8b (orange, 4J51) and Lyp−I-C11 (blue, 2QCT) with ligands colored based on atom type. (B) Interaction-based pharmacophore model mapping of 8b with the hydrogen bond acceptor (green spheres) and the hydrophobic features (turquoise spheres).

generate the IBP model for the following reasons. First, 8b is a more potent and selective Lyp inhibitor (0.26 μM) than I-C11 (4.6 μM).56 Second, the resolution of the Lyp−8b cocrystal structure (2.3 Å) is higher than the crystal structure of Lyp−IC11 (2.6 Å).57 Thus, the interactions between 8b and surrounding residues were generated automatically and converted into pharmacophore features. These interactionbased features were clustered, and the features that best represented the binding mode of Lyp and 8b were given priority. Finally, the IBP model was constructed with two hydrogen bond acceptor features and two hydrophobic features, which facilitate ligand binding in the Lyp active site (Figure 1B). Protocol Development for Docking-Based Virtual Screening. In the past 2 decades, important progress has been achieved in the development of docking algorithms for predicting ligand−receptor binding modes and for virtual screening. Examples include Dock,58 Gold,48 Glide,49,50 Surflex,47,59 LigandFit,45,46 Autodock,60 and Autodock vina.61 Typically, molecular docking programs consist of two components: (i) searching for a preferential ligand conformation and (ii) scoring of the resulting geometries.31 The docking score is the key factor for measuring the binding affinities of docked compounds and plays an important role in the selection of virtual screening hits for biological activity assays.62,63 However, current score functions were designed based on special testing sets,64−66 which lead to the large differences in accuracy for various target proteins.64,67,68 Therefore, it was necessary to identify the scoring function that is most suitable for the screening of Lyp inhibitors. Two validation files were constructed using a combination of several reported Lyp inhibitors8,14 and the NCI Diverset (1596 compounds with diverse chemotypes)69,70 or the DUD database (2950 active compounds for 40 targets).71,72 Both enrichment factor (EF)73 and success rate values were calculated to evaluate 12 docking scores, including seven scores (LigScore1, LigScore2, PLP1, PLP2, PMF, Jain, and DockScore) in LigandFit, TotalScore in Surflex, GlideScore (G-score) in Glide, and two scores (GoldScore and ChemScore) in Gold. Among the various docking protocols evaluated, the GoldScore (from the Gold program) exhibited better performance than other scores in both the NCI Diverset and the DUD database. At 1%, 2.5%, 5%, and 10% of the total database screened, the EF values achieved for GoldScore were 3.85, 5.38, 5.0, and 4.42 in the NCI database and 3.85, 4.62, 2.69, and 3.08 in the DUD database, respectively. Conversely, DockScore obtained quite different results from two validation files.

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RESULTS AND DISCUSSION Interaction-Based Pharmacophore Modeling. Interaction-based pharmacophore (IBP) modeling has been employed with some success in the identification of novel structures with significant activity toward various biological targets.51−55 This strategy is especially useful when there is a lack of experimentally validated ligands and when searching for new structural scaffolds. It can also be used to extract detailed information from the target structure (usually a crystal structure) for the design of new series of compounds. Currently, two Lyp crystal structures in complex with benzofuran salicylic acid-based inhibitors (8b and I-C11) are available, and these two inhibitors assume similar binding modes (Figure 1A). The carboxyl group and the phenolic hydroxyl group of the salicylic acid moiety form both hydrogen bonds and charge−charge interactions with residues in the Lyp catalytic site (P-loop). Two substituted side chains of benzofuran in these molecules form hydrophobic patches, which allowed extensive and specific van der Waals interactions with Lyp, including Gln274 and Thr275 from the Q-loop, Tyr60 and Ile63 from the phospho-tyrosine binding loop (pTyr-loop), and Phe28 and Lys32 from the “Lyp-specific insert” (Figure 1A). On the basis of these binding interactions, an interaction-based pharmacophore (IBP) model was built using the Catalyst Module in Discovery Studio. The Lyp−8b crystal structural complex (PDB code 4J51) was chosen to B



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Figure 2. Validation of different docking programs with diverse scoring functions using (A) the NCI Diverset and (B) the DUD database. Footnote “a” in the figure indicates that success rate is defined as follows: (number of Lyp inhibitors recognized)/(number of total active Lyp inhibitors).

Although DockScore outputted the highest EF value (EF5% = 8.85) in the NCI Diverset, it failed in the screening of the DUD database (EF = 0 at 1%, 2.5%, 5%, and 10% sampling). In addition, LigScore1, LigScore2, and PLP exhibited moderate discriminatory powers, whereas other scoring functions such as PLP2, PMF, and Jain returned lower EF values. Surprisingly, the G-score (Glide program), which has been successfully used in virtual screening for other targets,74,75 achieved the lowest EF values (EF = 0 at 1% and 2.5% of the NCI Diverset and 1%, 2.5%, and 5% of the DUD database) using either the HTVs mode or the SP mode. Regarding the calculation of the success rate, different scoring functions in a certain docking program produced similar results. As shown in Figure 2, Surflex, Gold, and Glide all obtained a high value (98.1%), whereas LigandFit failed to reorganize several active ligands (success rate = 76.9%). In addition, redock studies were performed using the crystal structure of the Lyp−8b complex (PDB code 4J51) to evaluate the abilities of these docking protocols to reproduce the native structure of 8b. The root-mean-square deviation (rmsd) was calculated by comparing the redocked conformer of 8b with its crystal structure. Commonly, an rmsd value of less than 2 Å is widely accepted as accurate in molecular docking. It is apparent that the rmsd values for Gold (with GoldScore) and Glide are better than the rmsd values obtained from the other docking protocols (Supporting Information Table S4). Taken together, Gold with GoldScore is the most suitable virtual screening tool for the Lyp inhibitors in our study and was further selected for the docking-based virtual screening approach. Virtual Screening Strategy. We performed target−ligand interaction-based virtual screening according to the workflow summarized in Figure 3. Initially, the interaction based pharmacophore model was used for the initial screening of the commercial Specs database (containing 204 380 compounds). All ligand conformations were matched to the IBP model and ranked according to the FitValues. Compounds that could map at least three pharmacophore features were

Figure 3. Protocol for target−ligand interaction-based virtual screening.

considered as final hits for the IBP-based screening. Thus, a total of 7103 unique compounds (3.5% of the database) were selected as IBP hits and these compounds entered the next step. According to the evaluation of the 12 scoring functions, the Gold program with GoldScore performed best in the tests of two screening databases. Therefore, the initial IBP hits selected from the pharmacophore-based screening were docked into the active site of Lyp using Gold. All these compounds were docked successfully and sorted based on their GoldScore values. The top-ranked 248 compounds (3.5% of the IBP hits) were selected for cluster analysis and visual selection, in which priority was given to structures that possessed novel scaffolds. Finally, 29 hits were ultimately chosen for further biological evaluation (Figure 4 and Supporting Information Figure S1). Enzyme Inhibition and Kinetic Analysis. All 29 compounds were purchased from the Specs database, and the C



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Figure 4. Chemical structures of the nine novel molecules that show inhibitory activity against Lyp.

potential irreversible inhibitors of Lyp. Therefore, we performed time-dependent inhibition studies76 of these five inhibitors as well as A2 and A19 (Supporting Information Figure S11). The results revealed that most of these virtual screening hits bind Lyp reversibly except for A25, which showed covalent inhibition against Lyp. Further studies confirmed A25 as an irreversible inhibitor with Ki and kinact of 40.98 ± 13.19 μM and 0.1263 ± 0.0117 min−1, respectively (Supporting Information Figure S12). Structural Analysis and Predicted Binding Patterns of Potent Inhibitors. To evaluate the structural novelty of these newly indentified inhibitors with respect to known Lyp inhibitors, the Tanimoto similarity values (T) were calculated based on the FCFP_4 fingerprints.77 Typically, structures with T > 0.85 are considered similar.78 According to the results in the Supporting Information Table S3, these inhibitors possessed T values of 100 67.8 ± 4.3 >100

>100 60.6 ± 66.1 ± 38.8 ± 50.7 ± >100 21.0 ± 74.8 ± H

5.9 7.7 8.0 15.4 3.4 10.9

>100 96.04 ± 18.2 37.5 ± 7.0 >100 50.1 ± 4.7 >100 31.8 ± 5.6 >100

29.7 ± >100 78.5 ± 63.8 ± >100 >100 42.5 ± >100

7.3 8.3 5.7

5.1



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Figure 7. Effects of A15, A2, A26, and A19 on TCR signaling. (A) Proposed mechanism of how the Lyp inhibitor effects TCR-induced signaling. (B) Effects of A2, A15, A19, and A26 on the anti-CD3 (OKT) induced phosphorylation of LCK pY394 and ERK pT202 and pY204 as detected by the phosphorylation-specific antibody. The GAPDH level was used as a control. (C) Statistical analysis of the phosphorylation of LCK pY394 in T cells treated with the anti-CD3 antibody and preincubated with different inhibitors. All experiments were repeated at least in triplicate: (∗∗) p < 0.01 compared with control cells. (D) Statistical analysis of the anti-CD3 antibody induced phosphorylation of ERK pT202 and pY204 in T cells preincubated with different inhibitors. All experiments were repeated in triplicate: (∗) p < 0.05 compared with control cells.

Figure 8. Effects of A15 and A19 on TCR-mediated transcriptional activation. (A) The cells were transfected with the indicated plasmids (see Experimental Section) and preincubated with 20 μM A15 or A19 for 1 h. After stimulation for 6 h with 0.5 μg of the anti-CD3 (OKT) antibody, the activity of NFAT/AP-1 transcription was measured by the dual-luciferase assay. The activity of co-transfected Renilla luciferase was used for normalization: (∗) p < 0.05 compared with control cells. (B) Dose−response curve of A15 on anti-CD3 (OKT) induced NFAT/AP-1 transcriptional activation. Four concentrations of A15 were applied to the T cells, and the fold increase in NFAT/AP-1 transcription is shown.

diseases. These new Lyp inhibitors identified through target− ligand interaction-based virtual screening provide good starting points for the development of more potent and selective Lyp inhibitors.

identify Lyp inhibitors. The IBP model was generated based on the experimentally determined cocrystal structure of a Lyp− inhibitor, and the docking method was evaluated carefully using two validation files. Nine identified compounds bearing different chemotypes showed inhibition against Lyp at the enzymatic level. Kinetic analysis confirmed that these compounds inhibit Lyp competitively. Interestingly, compound A25 showed covalent inhibition against Lyp, whereas the other active hits bind Lyp reversibly. The most potent four compounds (A15, A2, A19, and A26) also showed certain degrees of selectivity over other protein phosphatases. We further confirmed that compounds A15 (Ki = 2.87 μM) and A19 (Ki = 10.34 μM) were capable of specifically targeting Lyp at the cellular level using T-cell-based assays and NFAT/AP-1 reporter assays. Finally, we believe that the discovery of novel Lyp inhibitors is important not only because such compounds could be new drug candidates for the treatment of autoimmune disorders such as type 1 diabetes but also because they constitute chemical probes for the elucidation of the biological functions of Lyp and their potential roles in the treatment of other

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EXPERIMENTAL SECTION

Materials. p-Nitrophenyl phosphate (pNPP, 4264-83-9) was purchased from Sangon Biotech Co., Ltd. Ni-NTA agarose was obtained from Amersham Pharmacia Biotech. The anti-Src/pY416 and ERKpT202/pY204 antibodies were obtained from Cell Signaling Technology. The mouse anti-GAPDH monoclonal antibody was obtained from ZSGB-BIO Co. The anti-CD3 (OKT3) was obtained from eBioscience. The luciferase reporter kit was purchased from Promega Corporation (catalog number E1960). Lyp siRNAs were synthesized by China RiboBio Co., Ltd. (Guangzhou, China). Lypspecific antibodies (mouse) were obtained from R&D (catalog number MAB3428). Lipofectamine2000 was purchased from Invitrogen. The selected compounds were purchased from Specs with purity of more than 95% (confirmed by the supplier, using LC−MS or 1H NMR; details are summarized in Supporting Information Table S1 and Table S2). All other chemicals and reagents were purchased from Sigma. Pharmacophore Generation. The crystal structure of the Lyp− inhibitor (8b) complex (PDB code 4J51) was chosen to generate the I



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interaction-based pharmacophore (IBP) model, which could represent key target−ligand interaction features that are responsible for the biological activity of active compounds. All interactions generated from Lyp−8b complexes were converted into pharmacophore features automatically using Discovery Studio 3.5 (Accelrys, Inc.). Only hydrogen bonds within a distance of 3.5 Å were considered as hydrogen bonding features. In terms of hydrophobic features, both aromatic and aliphatic interactions were regarded as hydrophobic interactions in the analysis. Considering the fact that there were many hydrophobic interactions between 8b and Lyp, we prioritized the pharmacophore features that overlapped with hydrophobic groups of 8b. Finally, all these selected interaction-based pharmacophore features were clustered and the most essential features for target− ligand interactions were chosen to build the IBP model. Preparation of the Validation Databases and Evaluation Methods. To select the most suitable docking protocols for Lyp inhibitors, two validation databases were constructed using 52 reported Lyp inhibitors and the NCI Diverset56 and the DUD database.57 The main purpose of mixing active compounds with decoys was to evaluate the discriminating abilities of different virtual screening methods. The NCI Diverset contains 1895 compounds with diverse structures and has been used previously in the evaluation of docking. Alternatively, the Discovery of Decoys (DUD) database is one of the most widely used validation databases. We chose 2950 bioactive compounds with activity toward 40 targets to build the DUD database. Generally, the DUD database is stricter than the NCI Diverset because it is more difficult to discriminate Lyp inhibitors from known bioactive compounds for other targets. Herein, the enrichment factor (EF) calculation was employed to quantitatively evaluate the performance of 12 different docking scores.

Figure 9. Effects of A15 or A19 on T cell functions in T cells overexpressing various phosphatases. Jurakat T cells were overexpressed with different protein phosphatases and preincubated with the control vehicle, 20 μM A15 or A19 for 1 h. After stimulation for 6 h with 0.5 μg of the anti-CD3 (OKT) antibody, the activity of NFAT/ AP-1 transcription was measured by the dual-luciferase assay. The activity of co-transfected Renilla luciferase was used for normalization: (∗) cells simulated with the anti-CD3 antibody as compared with that without simulation; (#) cells overexpressing phosphatases as compared with the control.

Figure 10. A15 and A19 altered the TCR signaling by specific inhibition of Lyp. (A) Comparison of the effects of A15 and A19 on the anti-CD3 (OKT) induced phosphorylation of LCK pY394 and ERK pT202 and pY204 in control siRNA treated T cells or Lyp-siRNA treated T cells. Lyp expression was specifically knocked down by transfecting Jurkat T cells with Lyp siRNA. The GAPDH level was used as a control. The phosphorylation levels of LCK pY394, ERK pT202, and pY204 were monitored by specific antibodies. (B) Statistical analysis of the phosphorylation of LCK pY394 (A) in T cells treated with the anti-CD3 antibody, preincubated with different inhibitors and with control siRNA or Lyp siRNA. (C) Statistical analysis of the anti-CD3 antibody induced phosphorylation of ERK pT202 and pY204 (A) in T cells preincubated with different inhibitors and with or without Lyp knockdown. All experiments were repeated in triplicate: (∗) cells incubated with inhibitors as compared with the control; (#) siRNA interference cells as compared with the control; NS, not significant. J



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compounds were clustered based on the FCFP_6 fingerprints calculation and then selected manually. Other parameters that are not mentioned were set at default values. Protein Expression and Purification. The catalytic domain of Lyp (residues 1−294) with an N-terminal His tag was prepared and used for in vitro studies as described previously.10,14 BL21 (DE3) cells were transformed with the expression plasmids and cultured in LB medium with shaking at 37 °C. The culture temperature was adjusted to 18 °C when the cultures reached an OD600 of 0.6, and expression was induced for 12 h with 0.3 mM IPTG at an OD600 of 0.8. The cells were harvested by centrifugation and resuspended in lysis buffer (20 mM Tris [pH 8.0], 300 mM NaCl, and 1 mM PMSF). After centrifugation, the supernatant was incubated with Ni-NTA resin with end-to-end mixing for 1 h at 4 °C. The beads were collected and washed with 20 mL of wash buffer (20 mM Tris [pH 8.0], 300 mM NaCl, and 5 mM imidazole) and eluted with an imidazole gradient (20 mM Tris [pH 8.0], 300 mM NaCl, and 20−200 mM imidazole). The protein was further purified through CM Sefinose85 with elution by a salt gradient. The low-salt solution contained 20 mM MES (pH 6.0), 100 mM NaCl, 1 mM EDTA, and 2 mM DTT. The high-salt solution contained 20 mM MES (pH 6.0), 1 M NaCl, 1 mM EDTA, and 2 mM DTT. After purification using CM Sefinose, the protein was further concentrated and stored at −80 °C. The expression and purification of the other His-tagged proteins were performed as described previously.84,86−88 IC50 Measurements. The kinetics assay for the phosphatasecatalyzed pNPP hydrolysis in the presence of small molecule inhibitors was conducted as described.84 For the different phosphatases, the pNPP concentration was used at their respective Km values.89 The effect of each inhibitor on the PTP-catalyzed pNPP hydrolysis was determined at 25 °C in 50 mM 3,3-dimethyl glutarate buffer, and the ionic strength was adjusted to 0.15 M with NaCl (buffer A). After quenching of the reaction at certain time points by using 1 M NaOH, product generation was detected by monitoring the absorbance of pNPP at 405 nm. The IC50 value was obtained using GraphPad Prism by fitting the data to eq 2 as follows:

Moreover, we have also provided the ROC plots for evaluation, because visual images have obvious advantages. The EF after screening x% of the library was calculated according to eq 1: EF =

x% Nexperimental x% Nexpected

=

x% Nexperimental x% Nactive ·x %

(1)

where Nexperimental is the number of experimentally found active structures in the top x% of the sorted database, Nexpected is the number of expected active structures, and Nactive is the total number of active hits in database. Ligand Preparation. All compounds in the Specs database and two validation databases were prepared with Ligprep (Schrodinger, Inc.). During this process, the OPLS_2005 force field was employed and the possible ionization states of each compound were generated at the pH range of 7.0 ± 2.0 using Ionizer. In Silico Docking. The crystal structure of Lyp−8b (PDB code 4J51) was used for docking-based virtual screening. Four available docking programs (LigandFit, Surflex, Gold, and Glide) with 12 different scoring functions were employed. The details of each program are shown below with default settings using for the critical parameters. LigandFit. LigandFit is a docking method based on shape. Invaginations in target protein were detected as active site regions employing an automated cavity detection algorithm. Conformational search was performed based on the sophisticated Monte Carlo method to generate a ligand shape that matches with the shape of the active site. Then energy minimization of ligand poses was further performed using a grid-based method, and the target−ligand interaction energy was calculated using seven score functions: LigScore1, LigScore2, PLP1, PLP2, Jain, PMF, and DockScore. Surflex. This method combines a molecular similarity algorithm with an empirical scoring function. Protein structures were prepared using SYBYL-X 1.1 (Tripos, Inc.), adding hydrogen atoms, protonating the ionizable residues at neutral pH, and calculating AMBER FF99 charges for the protein. The active site (also called protomol) was generated based on the ligand in the crystal structure, and probe atoms were used to determine favorable target−ligand interactions. Ligand fragments were aligned using the best interaction points, which provide direction for fragment growth. TotalScore was used to sort the docked conformations of all compounds in the database. Gold. Gold is a flexible docking method based on a genetic algorithm. Herms visualization tool was employed to prepare protein structures. Hydrogens were added automated with all waters and ligand molecules deleted. The 14 Å radius regions surrounding the active ligand of the crystal structure were defined as the active site. A genetic algorithm (GA) method was employed to generate new conformations for each molecule. Both GoldScore and ChemScore were used to evaluate binding affinities of each ligand conformation. A maximum of 10 GA runs were performed for each ligand. Glide. Glide is also a flexible docking method, and all dockings were carried out using Maestro, version 7.5 (Schrodinger, Inc.). The hydrogen atoms and the charges were added using the Protein Preparation module of Maestro. The grid-enclosing box was defined as a region that encloses residues within 14 Å of the cocrystal ligand, which represents the shape and properties of the active site. In the docking process, both high-throughput virtual screening (HTVS) and standard-precision (SP) modes were used to generate the minimized conformation for each compound in a database, and the Glide scoring function (G-Score) was adopted to calculate the binding affinities. Virtual Screening. The carefully constructed IBP model was adopted to screen the entire Specs database, using the flexible searching method in Discovery Studio 3.5 (Accelrys, Inc.). Before the IBP screening, a maximum of 255 conformers were generated for each ligand using the CAESAR method. The IBP hits were selected according to the FitValue scores, which measures the matching degree between ligands and pharmacophore. All selected IBP hits were then subjected to the following docking-based screening using Gold and ranked according to the GoldScore. Finally, the top ranking

AI =

(A 0)(IC50) IC50 + [I]

(2)

Ki Measurements. The Lyp-catalyzed hydrolysis of pNPP in the presence of inhibitors was assayed at 25 °C in assay buffer A. The reaction was initiated by the addition of pNPP (ranging from 0.2 to 5 times the Km) to a reaction mixture containing Lyp and various fixed concentrations of the inhibitors and then quenched by the addition of 1 M NaOH. The inhibition constant, Ki, and the inhibition pattern were evaluated by fitting the data to the Michaelis−Menten equation for competitive inhibition (eq 3) using a Lineweaver−Burk plot. K mobs 1 1 = + ν Vmax[S] Vmax

⎛ [I] ⎞ K mobs = K m⎜1 + ⎟ Ki ⎠ ⎝

(3)

Cell Culture, RNA Interference, and Immunoblotting Assay. Jurkat T cells were purchased from ATCC and were grown at 37 °C in RPMI 1640 medium supplemented with 10% FBS as described previously.10,14 For knockdown of Lyp, Jurkat T cells were transfected with RNAi using Lipofectamine2000 (Invitrogen) for 48 h. The sequence of the siRNA against Lyp was 5′-AA GGCAGACAAAACCTATCCT-3′,90 and the sequence of control RNAi was 5′AA GAACGGCATCAAGGTGAAC-3′. Then the Jurkat T cells were preincubated with a small molecule inhibitor (20 μM) or DMSO for 1 h. The cells were then stimulated with 5 μg/mL anti-CD3 antibody (OKT3) or medium for 5 min and then lysed in lysis buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 10 mM NaF, 2 mM EDTA, 10% glycerol, 1% NP-40, 0.25% sodium deoxycholate, 1 mM NaVO4, and a protease cocktail). The protein concentrations of the lysates were measured using the BCA protein quantitation kit (Beyotime). Equal amounts of K



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each cell lysate were denatured in 2× SDS loading buffer and boiled for 10 min. The protein samples were subjected to Western blotting. Dual Luciferase Assay. The Jurkat T cells were electroporated to co-transfect the following: an NFAT/AP-1-TK-luciferase plasmid, a Renilla-TK plasmid, and the Lyp catalytic domain, STEP, PTPN18, MEG2, VHR, PPM1A, or PTP1B. Forty-eight hours after transfection, the Jurkat cells were incubated with either inhibitor or DMSO for 1 h. The cells were then stimulated with or without 5 μg/mL anti-CD3 antibody for 6 h. The dual luciferase activity was measured according to the manufacturer’s instructions, and the NFAT/AP-1 transcriptional activity was normalized to the Renilla luciferase activity.

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ASSOCIATED CONTENT

S Supporting Information *

Chemical structures, inhibition data, characterization data, kinetics parameters, proposed binding modes, pharmacophore mapping, and NMR spectra. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*J.-P.S: phone, +86-531-88381910; fax, +86-531-883825025; email, [email protected]. *H.F.: phone, +86-531-88382731; fax, +86-531-88382548; email, [email protected]. Author Contributions #

X.H. and R.L. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are very grateful to Professor Renxiao Wang at Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, for providing molecular docking programs and other necessary software. This work was supported by grants from the National Natural Science Foundation of China (Grants 81373281, 31000362, 31270857, and 31100580), National Key Basic Research Program of China (Grant 2013CB967700), Shandong Natural Science Fund for Distinguished Young Scholars (Grnat JQ201319), the Program for New Century Excellent Talents in University (Grant NCET-12-0337), Independent Innovation Foundation of Shandong University, IIFSDU (Grant 2012JC003), and Program for Changjiang Scholars and Innovative Research Team in University, PCSIRT (Grant IRT13028).

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