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Virtual Screening of Novel and Selective Inhibitors of PTP1B over TCPTP Using a Bidentate Inhibition Strategy Xi Chen, Qiang Gan, Changgen Feng, Xia Liu, and Qian Zhang J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00040 • Publication Date (Web): 02 Apr 2018 Downloaded from http://pubs.acs.org on April 2, 2018

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Journal of Chemical Information and Modeling

Virtual Screening of Novel and Selective Inhibitors of PTP1B over TCPTP Using a Bidentate Inhibition Strategy Xi Chena, Qiang Gana*, Changgen Fenga*, Xia Liub, Qian Zhanga a

State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, No.5, Zhongguancun South Street, Haidian District, Beijing 100081, China; b College of Science, China Agricultural University, Beijing 100193, China

Abstract. Protein tyrosine phosphatase 1B (PTP1B), a promising target for type II diabetes, obesity and cancer therapeutics, plays an important negative role in insulin signaling pathways. However, the lack of selectivity over other PTPs, especially for TCPTP, is still a challenge for inhibitor development. Recent studies have suggested that the second pTyr binding site, close to the catalytic domain, may elevate binding affinity while bringing selectivity to inhibitors. Inspired by these studies, a virtual screening method based on a bidentate strategy was employed to identify novel selective inhibitors of PTP1B. Targeting both the active site and the second pTyr binding site of PTP1B, three compounds (CD00466, JFD02943, JFD02945) were found to be competitive inhibitors (Ki range from 1.79 µM to 10.49 µM). The most effective compound, CD00466, exhibited selectivity over TCPTP (31-fold). Using molecular dynamics simulation and the MM/GBSA binding free energy calculation, this study confirmed that the three inhibitors

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bound to PTP1B in a bidentate pattern. Our work indicates that bidentate virtual screening is a potential approach to the further investigation of selective PTP1B inhibitors.

INTRODUCTION Protein tyrosine phosphatases (PTPs) are a class of enzymes that modulate multiple signaling pathways and are involved in many human diseases, including cancer, diabetes, obesity, and autoimmune diseases.1,2 Among this superfamily, protein tyrosine phosphatase 1B (PTP1B) is responsible for the dephosphorylation of insulin and leptin receptors.3,4 PTP1B was first isolated from human placenta by Tonks and co-workers5 in 1988; it was not until 1994 that Barford et al.7 disclosed its crystal structure. It is a highly conserved enzyme similar to other PTPs, which all possess the same (I/V)HCXAGXXR(S/T)G signature motif, a WPD loop, and a YRD motif.8,9 Two individual studies of PTP1B knockout mice by Elchebly et al.10,11 and Klaman et al.10,11 revealed that PTP1B deficiency improved insulin sensitivity, decreased blood glucose and insulin levels, and prevented insulin resistance from developing in response to a high-fat diet. These works shed light on the important negative role of PTP1B in insulin and leptin downregulation. Moreover, these studies aroused interests in investigation of possible inhibitors for type II diabetes and of obesity therapeutics.12 Overexpression of PTP1B has also been shown to be associated with breast tumorigenesis, and thus this line of study may provide new treatments for breast cancer.13 Efforts have been devoted to developing PTP1B inhibitors over the years. Currently reported inhibitors include phosphotyrosine (pTyr) mimetics, small molecule compounds, and natural products.14-16 However, determining how to improve the selectivity of inhibitors over other PTPs, especially for TCPTP, remains one of the significant challenges in inhibitor research.


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TCPTP, abundant in hematopoietic cells, is the closest cousin of PTP1B, which presents 74% identity in its sequence and 86% in its catalytic domain (residues 43 to 288) of PTP1B.17 TCPTP-deficient mice died within 3–5 weeks of birth as a result of impaired B-cell and T-cell, demonstrating that TCPTP is functional in both hematopoiesis and the immune system18. The muscle-specific TCPTP-deficient mice showed no changes in insulin signaling and there was no effect on insulin resistance induced by a high-fat diet.19 These results demonstrate that the physiological function of TCPTP is utterly different from PTP1B, thereby posing a strict requirement for PTP1B inhibitors to have sufficient selectivity over TCPTP. Fortunately, recent studies have succeeded in achieving selective inhibitors over TCPTP, which have been discussed in several reviews.14,20,21 These studies suggest that bidentate binding to the active site and the second pTyr binding site may improve inhibitor selectivity. Puius et al.22 first proposed bidentate inhibitors in a study of bis-(para-phosphopheny) methane. They analyzed the crystal structure of the complex and found that the shallow binding pocket adjacent to the active site generated interactions between inhibitors and the residues Arg24 and Arg254. However, sequence alignment between PTP1B and TCPTP have shown that Arg24 and Arg254 are conserved for both enzymes, while the distinct residues Phe52/Tyr54, Ala27/Ser29 and Cys32/His34 (PTP1B/TCPTP) at the second pTyr binding site could be used to identify differences between PTP1B and TCPTP.23 Further studies found that Gln262 and Met258 on the Q-loop were also essential anchors between the two binding sites, which contributed to improvements of the binding affinity.24,25 Thus, bidentate inhibition targeting these residues in PTP1B may be a useful strategy to develop selective inhibitors. In this work, a virtual screening method based on the bidentate inhibition strategy was developed to identify potent and selective inhibitors of PTP1B. To ensure that bidentate


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inhibitors can be identified, docking pose validations and evaluations of three scoring functions of DOCK 6 were performed in advance. After filtering the database of small compounds, three inhibitors were selected from among 16 compounds. The best inhibitor, CD00466, exhibited an inhibition of 0.73 µM and a selectivity of 31-fold over TCPTP. The ligand-protein complexes of the three inhibitors were then submitted to the molecular dynamics simulation. We applied the MM-GBSA method to calculate the binding free energy from the MD trajectories and investigated the major residues by energy decomposition. MATERIALS AND METHODS Binding Mode Validation. A binding mode validation was performed to ensure that the active site and the second pTyr binding site could both be identified. DOCK 6.5 software26 was used for preliminary screening, and the identification of the two binding sites was achieved by generating spheres by the sphgen program associated with manual selection. Eleven complexes in four types of binding modes were investigated. The receptor structures were prepared by the tLEaP module in Amber1427, and the abnormal residues were corrected. The protonation states of amino acids were adjusted to pH 7.4 by PROPKA 3.0.28 Each docking position was superimposed on its crystal structures, and the root-mean-square deviation (RMSD) value between the ligands was calculated. Generally, the results are considered acceptable when the RMSD value is less than 1. Receiver Operating Characteristic (ROC) Analysis. A collection of 200 reported inhibitors and 1000 inactive compounds were created for the ROC analysis and filtered by following Lipinski’s rule of 5.29 The active compounds were generated from the top 200 PTP1B inhibitors ranking by Ki in the Binding Database.30 An inactive set of 1000 drug-like compounds was selected from the DUD database.26,31 In this study, an ROC curve was applied to evaluate the performance of different scoring functions in the bidentate docking strategy and to determine the


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cutoff value of each function. The area under the ROC curve (AUC) is an indicator of specificity. The cutoff to distinguish between the active and the inactive compounds is the score associated with the highest Youden's index32. Several indicators were implied according to the cutoff value, including accuracy, recall, precision, F1-measure, and enrichment factor (EF) as reported in a recent study.33 Accuracy indicates how well the function to identify the true positives and true negatives from the entire sample. Accuracy = (TP + TN) / (TP + TN + FP + FN)

(1)

Here, TP, TN, FP, and FN stand for the number of true positives, true negatives, false positives and false negatives, respectively. Precision is the fraction of true positives among the predicted active compounds. It is defined as Precision = TP / (TP + FP)

(2)

Recall is a measure of performance to identify true positives from inhibitors, defined by Recall = TP / (TP + FN)

(3)

The F1-measure is the harmonic mean of precision and recall. It is a comprehensive evaluation of these two indicators. F1= 2 (Precision × Recall) / (Precision + Recall)

(4)

The enrichment factor which often used in the evaluation of virtual screening protocol was calculated by EF = Precision × (TP + TN + FP + FN) / (FP + FN)

(5)

Taken of these indicators into account together, an overall view of the scoring functions was obtained.


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Virtual Screening. A collection of 54928 small molecules from the database of Maybridge was filtered. The stereoisomers of each molecule were generated by obconformer in Open Babel.34 The polar hydrogens and the charges of AM1-BCC were introduced by UCSF Chimera 1.10.1 module35. The entire database was first screened with the DOCK 6.5 package. The receptor was treated as entirely rigid in DOCK, while the ligand was treated as flexible. To detect the active and second pTyr binding site, we computed the molecular surface of the receptor and carefully generated sphere clusters surrounding the two binding pockets by the sphgen module36 and manual adjustment. The maximum number of orientations of ligands was set to 500. Each ligand pose was scored, and the top 10% molecules were filtered by AutoDock Vina.37 In this step, the flexibility of the side chains of the binding sites was considered, which consisted of Cys215 and Arg221 at the active site, Asp48 at the YRD motif, and Arg24 and Arg254 at the second pTyr binding site. The docking box was centered by the coordinates of the two sites, and was large enough for the combination of bidentate inhibitors (30 Å × 30 Å × 23 Å). Each compound was subjected to 10 GA runs, and the lowest binding affinity was selected for further biological testing. PTP Assays. The inhibition of PTP1B and TCPTP was determined by measuring the pnitrophenol released from the pNPP substrate. Recombinant human PTP1B and TCPTP proteins were purchased from Sino Biological Inc (Beijing, China). The buffer consisted of 25 mM HEPES (pH 7.2), 5 mM DTT, 2.5 mM EDTA, and 50 mM NaCl. The reaction was started with a mixture of 5 µg of the enzyme, 1 mM BSA (5 µl), and the compounds in solution (in 1% DMSO, 20 µl) at a different concentration, then filled to 80 µl with buffer. After incubation for 10 min at 35 °C, 1.5 mM pNPP reagent was added and incubated at 35 °C for another 20 min. The reaction was stopped by adding 120 µl of 2 mM NaOH. The absorbance at 405 nm was determined, and


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the IC50 value was calculated by the percentage inhibition versus the inhibitor concentration plot. Sodium orthovanadate (Na3VO4) was used as the positive control. For determination of the inhibition constant Ki, various concentrations from 0.05 to 1 mM of the substrate were added to the reaction. Data were analyzed by fitting the Lineweaver−Burk plot, and the inhibition pattern was identified. Inhibitors Redock and Binding Mode Analysis. The binding modes of active compounds associated with PTP1B and TCPTP were calculated by AutoDock.38 The flexible residues were set the same as in the AutoDock Vina calculation. The 65 Å × 55 Å × 60 Å grid box was generated with a grid spacing of 0.375 Å and 100 GA runs were performed per compound with 3,000,000 steps of energy evaluations and 150 individuals in the population. The docking conformations were clustered by RMSD, and the best conformation was selected from the largest cluster with the best binding affinity. MD Simulations. The crystal structure of PTP1B used in the virtual screening and MD simulations were retrieved from the RCSB Protein Data Bank (PDB ID: 1Q1M). All inhibitors were optimized by the semiempirical AM1 method, and the electrostatic potentials were calculated at the (HF)/6-31G* level in Gaussian 03.39 The RESP partial charges were derived from electrostatic potentials by using the Amber antechamber program.40 The force field parameters for the protein and ligands were generated with Amber ff99SB41 and general amber force field (GAFF)42, respectively. A rectangular water box was generated with TIP3P water molecules with an edge of 10 Å. Sodium ions were added to neutralize the complex. The particle mesh Ewald (PME) method was used for long-range electrostatic interactions with a cutoff distance of 8 Å. The MD simulations were performed by using Amber14.32 We minimized the system in two stages: First, restrain the protein and the ligand by 10.0 kcal/(mol·Å2), and relax


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the solvent environment by 1000 steps of steepest descent and the 1000 steps of conjugate gradient. Second, 5000 steps of steepest descent and subsequent 15000 steps of the conjugate gradient were employed in the whole system without restraint. Next, a 50 ps NVT simulation for heating the system to 300 K was used, followed by a 500 ps NPT simulation at 1 atm, with harmonic restraints of 2 kcal/(mol·Å2) on the complex. The temperature was maintained by the Langevin thermostat, and the pressure was kept by the Berendsen barostat with a relaxation time of 2 ps. Finally, a 10 ns MD simulation without restraint was performed for each ligand. During the MD simulation, all hydrogen atoms were constrained by the SHAKE algorithm43 and the time step was 2 fs. Analysis of the RMSD and hydrogen bonds was employed on the MD trajectories by using the Amber ptraj module.44 Binding Free Energy Calculations. The MM/GBSA approach was employed for evaluating the binding free energy of each ligand. For the ligand-protein system, the binding free energy change (∆Gbind) was calculated according to the following equations ∆Gbind = Gcom − (Grec + Glig)

(6)

∆Gbind =∆H−T∆S≈∆EMM +∆Gsol –T∆S

(7)

∆EMM = ∆Einternal + ∆Eele + ∆Evdw

(8)

∆Gsol =∆GGB +∆GSA

(9)

∆Gbind consists of the molecular mechanical energy change in the gas phase (∆EMM), the desolvation free energy (∆Gsol), and the entropy (−T∆S). ∆EMM is the sum of the internal energy (∆Einternal), vdW Waals energy (∆Einternal) and the electrostatic interaction energy (∆Eele). The solvation free energy (∆Gsolv) includes the electrostatic solvation energy (∆GGB) and the nonpolar contributions (∆GSA). ∆GGB was determined by using the GB model of Hawkins and coworkers (GBHCT).45,46 The solute dielectric constant was set to 1, and the exterior dielectric


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constant was set to 80. The non-polar contributions (∆GSA) were based on the solvent accessible surface area (SASA), which was estimated by the LCPO algorithm47: ∆GSA = 0.0072 × ∆SASA. The radius of the probe molecule for molecular surface determination was 1.4 Å. The entropy term was evaluated by using the normal-mode analysis in the nmode program of Amber. For each snapshot, 1000 steps of energy minimized were performed before the normal-mode analysis. An ensemble of 200 snapshots from the last 2 ns of trajectories was implemented in the binding free energy, while only 50 frames were used for normal-mode analysis, considering the expensive computational cost. RESULTS AND DISCUSSION Binding Mode Validation and ROC Analysis. The key to virtual screening of bidentate inhibitors is to find an appropriate docking strategy that enables the recognition of both the active site and the second pTyr binding site. We verified the binding pose of the docking results to investigate the accuracy of identification. In this process, four types of binding modes of inhibitors extracted from 11 complexes’ X-ray structures were tested (Table 1). The bidentate inhibitors showed the closest predictive orientation compared with their X-ray structures. The RMSD was found to be lower than 0.5 Å, which implied the docking model could distinguish between the active binding site and the second pTyr binding site as expected. The most representative result was obtained from 1Q1M with the lowest RMSD value of 0.213 Å. Inhibitors that only bound to the active site could also be well predicted, with the RMSD lower than 1 Å. In contrast, inhibitors binding to the YRD motif were poorly identified, probably because the WPD loop conformation of the receptor did not coincide with the dock spheres in the bidentate pattern. Additionally, their inhibitory activity did not precisely match the scoring value, and the RMSD values of the unmatched compounds were also large. The observations suggest


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that the receptor configuration had a significant effect on the docking results. Taken together, it was appropriate to use 1Q1M as a receptor. It also showed the highest selectivity (23.77-fold) over TCPTP in all crystal structures of the ligand-protein complex.48 The hydrogen bonds of the inhibitors were rearranged with Ser216 and Arg221 in the active sites, and with the Arg24, Arg254, and Gln262 in the second pTyr binding sites. As the score functions showed a considerable difference in diverse target proteins, it was necessary to make a general view of virtual screening to determine whether the score was suitable for the bidentate inhibitors. ROC curves were used to evaluate the sensitivity of the docking protocol, and to find the appropriate cutoff value for distinguishing between active and inactive compounds. ROC curve is an efficient classifier and has been successfully applied in virtual screening of drugs.49,50 Sensitivity and specificity are the two indicators of the ROC curve, reflecting the relationship between the true positives rate (TPR) and false positives rate (FPR). The area under the curve (AUC) was used to evaluate the classification model. The closer the AUC value is to 1, the higher the accuracy of the test. Grid-Based Score, DOCK3.5 Score, and Hawkins GB/SA Score implemented in DOCK 6 were examined. As shown in Figure 1, the ROC curves revealed that all three functions could separate the active compounds from the database in the bidentate docking model, where the AUC value was above 0.85 (Table 2). Among them, the Grid-Based Score exhibited a better distinction ability than the other two. The AUC value was 0.954, and the enrichment factor (0.75), precision (0.62), recall (0.87) and accuracy (0.94) were obtained based on the cutoff value, indicating that Grid-Based Score could not only identify the active compounds but also classify the inactive ones. The F1-measure, which weighed the harmonic mean of accuracy and recall, was a satisfactory 0.72 considering the diversity of inhibitors. The DOCK3.5 Score returned a lesser performance with the F1-


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measure of 0.68 lower than Grid-based score, showing a weaker distinguishing ability. The Hawkins GB/SA score was worst-performing with a reduced EF value and the lowest precision. Based on their general performance, we selected the Grid-Based Score and the DOCK3.5 Score for virtual screening.

Table 1. Detailed information about the inhibitors’ binding pattern, docking scores, and the RMSD values between the docking position and the crystal structure for the binding mode validation.

PDB code

Binding Site

1C83 1BZC

Activea

1KAK 1Q1M

Potency Potency Mean Measure [µM] Score

RMSDd [Å]

Ki

14

-54.634

0.381

Ki

12

-64.710

0.684

IC50

26

-58.839

0.947

Ki

6.9

-72.715

0.213

Ki

0.055

-70.254

0.48

Ki

0.27

-84.193

0.386

Ki

2.1

-78.274

0.293

a

2QBP 1QXK

Active

+ Secondb

1XBO 1G7G

Activea

Ki

0.25

-68.002

2.488

1NL9

+

Ki

0.022

-63.396

3.641

2CM7

YRDc

IC50

0.210

-55.795

6.824

+Secondb IC50

0.005

-57.075

1.903

Activea 1Q6T

+YRDc a

Active binding site. b Second pTyr binding site. docking pose and the X-ray structure.

c

YRD motif.


d

RMSD value between the

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Figure 1. ROC curves based on the docking model of the Grid-Based Score, Hawkins GB/SA Score, and DOCK3.5 Score. Table 2. Performance indicators of the Grid-Based Score, Hawkins GB/SA Score, and DOCK3.5 Score in ROC analysis. Score function

AUCa Cutoff

Accuracy Recall Precision EFb

Grid-Based

0.954

-44.90

0.945

0.870

0.617

7.496

DOCK3.5

0.932

-22.67

0.923

0.921

0.543

5.858

GB/SA

0.883

-26.80

0.886

0.930

0.474

4.573

a

Area under the ROC curve. bEnrichment factor.

Virtual Screening of Bidentate Inhibitors. After validation of the docking protocol, a virtual screening target to the active site and the second pTyr binding site of the receptor was performed using the workflow shown in Figure 2. A collection of 54928 small molecules contained in the commercial Maybridge database was screened in this study. The collection was prepared by generating all of the random stereoisomers, protonation states, and charges of each ligand. Then, by treating the receptor as immobile, ligands were docked to the two binding sites of PTP1B


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using DOCK 6 and scored by the Grid-Based Score and DOCK3.5 Score. The van der Waals and electrostatic interactions between the ligand-protein complexes were evaluated. The entire database was sorted from best to worst, and the top 500 molecules (top 1%) of the list were selected by removing the ligands with the wrong binding mode. Then the remaining molecules were further screened by Autodock Vina. We considered the flexibility of the amino acids around the two target sites. The selected flexible residues were the active site of Cys215 and Asp221, the recognition motif of Asp48, and the second binding site of Arg24 and Arg254. To ensure diversity of the screened compounds, clustering analysis, and visual judgments were performed for each docking conformation. Finally, 16 compounds were selected for the next biological activity evaluation. The docking scores are shown in Table S1.

Figure 2. Workflow for the bidentate target-based virtual screening.


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PTP Inhibition and Kinetic Analysis. The inhibitory activity of the 16 compounds (Figure S1) was determined by measuring the inhibition of enzymatic hydrolysis of the substrate pnitrophenyl phosphate (pNPP) at 35 °C and pH 7.2. All 16 compounds were purchased from Maybridge, with a purity of more than 90% according to Maybridge. Sodium vanadate was selected as the positive control. The screening was first proposed at a concentration of 100 µM, and the compounds with an inhibition of more than 50% were further tested for dose-dependent inhibitory (Table S1). Figure 3 shows the structures of the most effective compounds. Compounds CD00466 and JFD02943, as shown in Figure 4, exhibited dose-dependent inhibitory activity against PTP1B with IC values of 0.73 ± 0.05 µM and 4.96 ± 0.58 µM, respectively. The 50

inhibitory activity of TCPTP was assayed to investigate the selectivity of these two inhibitors. Surprisingly, both compounds exhibited selectivity over TCPTP, and the corresponding selectivity (TCPTP IC50/PTP1B IC50) was calculated as 31.32-fold and 5.48-fold (Table 3). This suggested that the method of screening the target on both the active site and the second pTyr binding site might directly lead to more potent and selective inhibitors. Based on these two inhibitors, a search for similar compounds in the database was conducted to determine the inhibitory activity. Four compounds in the top 1% of the database were found (Table S2), and all of them were analogs of JFD02943. Among them, JFD02945 showed a satisfactory inhibitory activity, with IC of 4.41 ± 0.48 µM and less than 2-fold selectivity over TCPTP. Moreover, all three inhibitors 50

were filtered for pan assay interference compounds (PAINS)51 and passed, indicating that they were unlikely to be false positive inhibitors. Inhibition kinetics study of compounds CD00466, JFD02943, and JFD02945 was performed. As presented in Figure 5, the Lineweaver-Burk plots which show the increase of the Michaelis-


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Menten constant (Km) and the small change of maximum velocity (Vmax), revealed that all of the compounds were reversible inhibitors. This proved that the inhibitory effect was mainly achieved by interaction with the active site of PTP1B as expected. The Km value of pNPP was 0.84 mM, similar to the literature results.52,53 The inactivation constants (Ki) values of CD00466, JFD02943, and JFD02945 were calculated as 0.75 µM, 4.69 µM and 4.80 µM, respectively.

Figure 3. Structures of three novel compounds that exhibited inhibition against PTP1B.


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Table 3. Inhibition potency (IC50) of CD00466, JFD02943, and JFD02945 against PTP1B and TCPTP. IC50 (µM)

Selectivitya

Compound PTP1B

TCPTP

CD00466

0.73 ± 0.05

22.87 ± 0.98

31.32

JFD02943

4.96 ± 0.58

27.19 ± 1.31

5.48

JFD02945

4.41 ± 0.48

7.864 ± 0.55

1.79

Na3VO4 b

10.46 ± 0.22

11.85 ± 1.23

1.13

a

Selectivity calculated by TCPTP IC50/PTP1B IC50. bNa3VO4 was used as the positive control.

Figure 4. The inhibition percentage of CD00466, JFD02943, and JFD02945 targeting PTP1B and TCPTP, Na3VO4 was used as the positive control.


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Figure 5. PTP1B kinetic assays of compounds CD00466, JFD02943, and JFD0295. The Lineweaver−Burk plots show that all compounds display competitive inhibition associated with the pNPP substrate.

Binding Mode Analysis of Potent Inhibitors. The binding poses of the three compounds were calculated by AutoDock4 and the conformations (Figure 6) were selected from the largest cluster with the lowest binding energy. All of the compounds could interact with the active site and secondary pTyr binding site of PTP1B. Since the secondary pTyr binding site only acts as an auxiliary binding agent and does not provide catalytic activity22, the inhibitory effect of the compound is mainly achieved by occupying the active site, which was also confirmed by the


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kinetic study. For the most potent inhibitor compound CD00466, the nitrobenzene group bound firmly to the active site via the salt bridges with the side chain Arg221, and a strong interaction of cation–π with Phe182 was formed. A hydrogen bond was formed between the isothiazole group and Tyr46, which located the linker to the gap between the two binding sites. The other nitrobenzene group was bound to the second site via hydrogen bonds with Arg254. This interaction may also increase the selectivity over TCPTP as well as enhancing the affinity of the inhibitor and stabilizing the binding position. For the other two compounds JFD02943 (FmocGlu(tBu)-Cys(tBu)-Thr(tBu)) and JFD02945 (Fmoc-Ser(tBu)-Phe-Gln-Glu(tBu)), both are peptide inhibitors, leading to a similar way of binding. The π-π interaction of the Fmoc-group with Try46 and Phe182 was observed, which ensured the binding of the compound to the active site. The amide linker might bind to Gln262 close to the active site with several hydrogen bonds. There were also differences between the two compounds. For JFD02943, the Thr-group formed a hydrogen bond with Arg254, thereby binding to the second pTyr binding site. However, for JFD02945, the steric hindrance of the Phe-group was more considerable, which hindered the tendency of the compound to bind to the second site. Instead of binding to the second pTyr binding site, it formed a cation-π interaction and a hydrogen bond with Arg24 and bound to the region below the second pTyr binding site. A hydrogen bond was also noticed with the nearby residue Gln21. The absence from the second pTyr binding site may be a reason for low selectivity over TCPTP. The interaction of compounds with TCPTP was also analyzed. Table S3 shows the binding free energy calculated by Autodock 4. For CD00466, the binding mode to TCPTP was similar to PTP1B, which was associated with two sites. However, its binding effect was significantly decreased since the predicted binding free energy was reduced to -5.91 kcal/mol. The interaction


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with the catalytic site was only through the salt bridge formed with Arg221 and the hydrogen bond with Ala217. At the second pTyr binding site, only the hydrogen bonds formed with Arg254 were observed. For the other two peptide compounds JFD02943 and JFD02945, the orientations of the compounds were reversed entirely when they bound to TCPTP. The Fmoc groups were more likely to bind to the vicinity of the second pTyr site. For JFD02943, the Fmocgroup inserted into the second pTyr binding site with Arg24 by the cation-π interaction, whereas Thr-group was bound to the active site by the salt bridge with Arg221. For JFD02945, the Fmocgroup was located around Gln262, while multiple hydrogen bonds were formed between the Glugroup and residues of the catalytic site, and π-π interactions were also observed with Tyr47. By analyzing the binding pattern, we speculate that the second pTyr binding site of TCPTP tends to be combined with a simpler fragment with smaller steric hindrance since the pocket is shallower than PTP1B. It was also found that both selective compounds CD00466 and JFD02943 interacted with Arg24 at the second site, which was also reported as one of the possible factors to improve inhibitor selectivity22,24,54,55. However, since the inhibitor-protein complex crystal structure of TCPTP has not been established yet, TCPTP crystals (1L8K) used for docking may not satisfy the configuration associated with the inhibitor, since the WPD loop was open.17 This may lead to deviation from the actual situation to a certain extent.


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Figure 6. Proposed binding modes of CD00466, JFD02943, and JFD02945 complexed with PTP1B (1Q1M) and TCPTP (1L8K), respectively. The yellow dotted line is the hydrogen bond, the blue dotted line is the aromatic H-bond, and the magenta dotted line is the salt bridge.

Molecular dynamics simulations and MM-GBSA. Molecular dynamics simulation of 10 ns for each ligand-protein complex was carried out using the Amber14 package. With the purpose


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of investigating the energetic contributions of the inhibitors binding process, the MM/PBSA method was applied to calculate binding free energy. The experimental binding free energy was calculated by using the values of Ki. RMSD of the three ligand-protein complexes against to the original structures indicated that the system achieved equilibrium and the bidentate binding modes of the inhibitors were stable. Hydrogen bonds analysis. H-bond networks between ligands and PTP1B during MD simulations were monitored by the Amber ptraj module. This investigation revealed that the dominant H-bonds of the CD00466 complex were H-bonds with Arg221, Phe182 near the active site, and Arg254 at the second pTyr binding site (Table S4). Figure S4 shows the average number of H-bonds during the MD period. The average total number of H-bonds in CD00466 complex ranged from 1~2, which was lower than our expectations. However, the H-bonds at both the active site and the second pTyr binding site held the ligand to the binding sites and provided stability. Different from CD00466, the H-bonds of the JFD02943 complex were more inclined to form with the second pTyr binding site rather than the active site. Two H-bonds formed with Arg24 and two with Arg254 were observed, respectively. Moreover, H-bonds with Gly259 and Gln262 stabilized the trend of the ligand between the two sites. The total number of its H-bonds was 3~4, more than the number for CD00466. A similar pattern was also observed for the JFD02945 complex. It showed that the H-bond with Asp48 played a significant role in the active site. Three H-bonds were formed with Arg24 and Gln21, which bound the ligand to the region slightly below the second pTyr binding site. The contribution of H-bonds in the MD simulation ensured complexes to maintain the bidentate binding, which implied that these compounds can effectively inhibit PTP1B.


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Binding Free Energy Calculation. The trajectories for the MM/GBSA method were extracted from the last 2 ns of the MD simulation and the results are summarized in Table 4. In particular, the binding energy for CD00466 was -9.92 kcal mol-1, which was more efficient than for JFD02943 (-8.85 kcal mol-1) and JFD02945 (-6.69 kcal mol-1). This result matched our expectations and the experimental binding energy values. The energetic decomposition results showed that van der Waals and electrostatic interactions were the main contributors to the binding affinity for all compounds. For solvation energy, nonpolar solvation contributed to the binding energy, while polar solvation showed negative effects. Energetic decomposition of inhibitor–residue pairs was achieved to explore the principal contributions of the individual residues. Figure 7 illustrates the comparison of the three compounds, and Table S5 lists the major contributions of the residues. Consistent with our binding mode analysis, the residues at the active site and the second pTyr binding site provide the majority of binding free energy for all three inhibitors, thus confirming that these compounds do interact efficiently with PTP1B by bidentate binding. The side chain residues of the YRD motif and WPD-loop were identified as major contributors at the active site and enhanced binding with the active site by favorable van der Waals contributions to the total binding free energy. For both JFD02943 and JFD02945, the Fmoc group interacted with Tyr46 and Phe182 by hydrophobic interactions. The Fmoc group, which is usually used as the amino-protecting group, was found to enhance binding affinity for peptide inhibitors.56,57 For compound CD00466, in addition to the energy contribution of Tyr46 and Phe182, it interacted more with contributors of Val49, Lys120, Ala217, and Arg221, which indicates why CD00466 is the most potent inhibitor among the three compounds. Residues at the Q-loop between the two binding sites, such as Met258 and Gln262, were detected as important contributors to all compounds


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stabilization by van der Waals energy. Interestingly, the energy contribution at the second pTyr binding site was quite complicated. Gln21 presented interactions with JFD02943 and JFD02945 by basically equal electrostatic and van der Waals contribution. Arg254 stabilized CD00466 and JFD02943 by electrostatic energy. Moreover, all compounds showed interactions with Arg24, but the energy decomposition for each of them was quite different. Overall, interactions of CD00466 and JFD02943 with the second pTyr binding site were more diverse than that of JFD02945, which might be one of the reasons why these two compounds showed a specific selectivity over TCPTP. Compared with the binding affinity to PTP1B, it is worth mentioning that the bidentate binding strategy could enhance the combination of the complex, while it may also affect the direction of the ligand position, thereby affecting its binding to the active site. This comprehension needs to be taken into consideration in selective drug design and molecular docking.

Table 4. Individual term contributions to the total binding free energies (kcal/mol) for ligandPTP1B complexes calculated with the MM-PBSA method Energetic terms

CD00466

JFD02943

JFD02945

∆Eele

-15.85 ± 0.77

-38.46 ± 0.26

-38.34 ± 0.06

∆EvdW

-44.81 ± 0.37

-44.07 ± 0.45

-38.17 ± 0.17

∆GGB

30.89 ± 0.51

51.24 ± 0.74

45.94 ± 0.15

∆GSA

-6.09 ± 0.03

-6.66 ± 0.06

-5.67 ± 0.28

∆H

-35.86 ± 0.38

-37.95 ± 0.67

-36.24 ± 0.91

-∆TS

25.94 ± 0.94

29.09 ± 1.47

29.551 ± 1.22

∆Gbinding

-9.92 ± 0.64

-8.86 ± 0.53

-6.69 ± 0.39

∆Gexpa

-8.70

-7.57

-7.55


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a

experimental binding free energy was calculated by ∆Gexp = RT ln Ki for competitive inhibitors.

Figure 7. Binding free energy decomposition for the main residues of compound CD00466, JFD02943, and JFD02945.

CONCLUSION In this study, we performed bidentate-binding virtual screening to identify selective inhibitors of PTP1B, since the second pTyr binding site may be a promising site for increasing the selectivity of inhibitors against TCPTP and some other PTPs. In contrast to the previous virtual screening method of exploring a single binding site, both the active site and the second binding site were set as binding targets simultaneously. By binding pose validation and ROC curve analysis, our screening method was confirmed to be able to recognize the two binding sites while identifying PTP1B inhibitors from other compounds. The Maybridge database was screened, and 16 compounds were tested for inhibitory activity. Using this methodology, we have identified compound CD00466, JFD02943, and JFD02945 as potential bidentate PTP1B inhibitors. The most potent compound CD00466 exhibited a satisfactory inhibition of 0.73 ± 0.05 µM and a


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satisfactory selectivity of 31-fold against TCPTP. Kinetic studies further demonstrated that all three compounds were competitive inhibitors. It is suggested that the hydrogen bonds with Arg24, Arg254, and Gln262 are crucial for compounds to bind to the second pTyr binding. The docking results along with PTP1B and TCPTP show that these interactions may affect inhibitor selectivity. By hydrogen bond analysis over 10 ns MD simulation, we confirmed that all three compounds could form hydrogen bonds with both sites. Finally, the total binding free energy of each compound was computed using the MM/GBSA method and was in agreement with our experimental results. It is not surprising to find that Arg24 and Arg254 at the second site, and Phe182 at the active site are as major contributors to the total binding energy. Met258 and Gln262 in the Q-loop also help to attract the compounds, thus stabilizing the binding position. The study provides evidence that the bidentate binding strategy is a promising screening method to identify selective PTP1B inhibitors.

ASSOCIATED CONTENT Supporting Information. The following files are available free of charge on the ACS Publications website. Chemical structures, docking scores and inhibitory activities of 16 compounds; 2D interaction diagram of predicted binding modes; Detailed information on MD; H-bond analysis during MD; Decomposition of the relative binding free energy using the MM-GBSA method. AUTHOR INFORMATION Corresponding Author


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* Email: [email protected] (Qiang Gan) *Email: [email protected] (Changgen Feng) Notes The authors declare no competing financial interest. ACKNOWLEDGMENT The authors are grateful to the Supercomputing Center of Chinese Academy of Sciences, for providing software and experiment facilities. Molecular graphics images in this work were produced by the UCSF Chimera package from the Computer Graphics Laboratory, University of California, San Francisco (supported by NIH P41 RR-01081).

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Virtual Screening of Novel and Selective Inhibitors ... - ACS Publications

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