A New Target for an Old Drug: Identifying Mitoxantrone as a

Article pubs.acs.org/jmc

A New Target for an Old Drug: Identifying Mitoxantrone as a Nanomolar Inhibitor of PIM1 Kinase via Kinome-Wide Selectivity Modeling Xiaobo Wan,†,‡ Wei Zhang,‡ Li Li,‡ Yuting Xie,‡ Wei Li,†,‡ and Niu Huang*,†,‡ †

Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China National Institute of Biological Sciences, Beijing, No. 7 Science Park Road, Zhongguancun Life Science Park, Changping District, Beijing 102206, China

‡

S Supporting Information *

ABSTRACT: The rational design of selective kinase inhibitors remains a great challenge. Here we describe a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. We retrospectively assessed this protocol by computing the binding profiles of 17 well-known kinase inhibitors against 143 kinases. Next, we predicted the binding profile of the chemotherapy drug mitoxantrone, and chose the predicted top five kinase targets for in vitro kinase assays. Remarkably, mitoxantrone was shown to possess low nanomolar inhibitory activity against PIM1 kinase and to inhibit the PIM1-mediated phosphorylation in cancer cells. We further determined the crystal complex structure of PIM1 bound with mitoxantrone, which reveals the structural and mechanistic basis for a novel mode of PIM1 inhibition. Although mitoxantrone’s mechanism of action had been originally thought to act through DNA intercalation and type II topoisomerase inhibition, we hypothesize that PIM1 kinase inhibition might also contribute to mitoxantrone’s therapeutic efficacy and specificity.

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“binding site signature” method identifies the hot spot bindingsite residues critical for the inhibitor binding based on the available crystal structures, and then relates the inhibitor binding specificity with the conservation of the hot spot residues from multiple sequence alignment analysis.5 This method was applied in a retrospective study using a data set containing 15 kinase inhibitors and about 280 kinase targets, and satisfactory results were obtained.6 The X-ReactKIN method was developed to estimate the potential for cross-activity by combining sequence, structure, and inhibitor binding similarities of the ATP-binding sites using a machine learning approach, which was trained using available inhibitor selectivity data.7,8 Another approach, implemented in the program SCR, models kinase−inhibitor interactions in full atomic detail and computes the relative binding energies using an empirical scoring function treating protein flexibility through side chain rotamer sampling.9 The KinDOCK server was set up to offer rapid and straightforward modeling of kinase−inhibitor complexes by comparative docking.10 Nevertheless, the fundamental process of receptor−ligand binding is governed by thermodynamics principles. Accurately calculating the absolute binding free energies of a given ligand against different biomolecular targets is critical for the ligand

INTRODUCTION Protein kinases are one of the most important drug target families; however, the rational design of selective kinase inhibitors remains challenging due to the sequence and structural conservation across the protein kinase family, especially at the conserved ATP-binding sites where most kinase inhibitors bind.1 To date, 15 small molecule kinase inhibitors targeting ATP sites have been approved for marketing and many more are in different stages of clinical trials. All of these kinase drugs are multikinase inhibitors to a certain extent.2 For example, a well-known kinase inhibitor, sorafenib, inhibits several kinases with IC50 < 100 nM, including Braf, VEGFR2, and PDGFRβ kinases.3 Generally, the kinase inhibitor selectivity studies have been limited to within the kinase family members. One exception is our recent investigation that showed that sorafenib binds to a 5hydroxytryptamine receptor (5-HT2B) at low nanomolar affinity, which revealed the unexpected ligand cross-reactivity between GPCR orthosteric ligands and kinase inhibitors.4 The target binding profile is essential for understanding the mode of action of a drug molecule. Experimentally profiling all possible drug leads against all possible kinase targets would be cost prohibitive; therefore, computational prediction of promiscuous kinase binding propensities would be a great advance. Several computational approaches have been reported to predict the kinase inhibitor selectivity profile.5−10 The © XXXX American Chemical Society

Received: January 9, 2013

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dx.doi.org/10.1021/jm400045y | J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

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Scheme 1. The Flowchart of Our Computational Modeling Procedure

structural kinome. Although the total number of kinase targets is limited by the availability of the crystal structures (covering about 30% of the kinome), the gap could be bridged by protein structure prediction. To reduce the complexity of kinase conformational changes, we focused only on the crystal structures in an active conformation in the present study (detailed in the Experimental Section). This led to a high-quality data set containing 17 wellknown kinase inhibitors (Figure 1) and 143 kinase targets.18 Thus, a total of 2,431 binding interactions were modeled (http://www.huanglab.org.cn/kinome/kinome-ligand.tgz), and the key comparison results are summarized in Table 1 and Figure 2. The enrichment factor (EF20) reflects the ability of the calculations to correctly retrieve the true targets among the top 20% ranking targets, while the predictive index (PI) is a more semiquantitative measurement of the power of the target ranking order.19 Note that PI value (ranging from 1 to −1) of 1 indicates the perfect prediction, and 0 indicates prediction to be completely random. Overall, the averaged EF20 value of 2.81 and the averaged PI value of 0.35 indicate a reasonable prediction performance of our computational approach in ranking order of the kinase targets for a given inhibitor. Among them, the p38 mitogenactivated protein kinase (MAPK) inhibitor VX745 (EF20 = 5, PI = 0.6) and epidermal growth factor receptor (EGFR) kinase inhibitor gefitinib (EF20 = 5, PI = 0.57) achieve the most significant enrichment and the highest correlation (Table 1). Interestingly, the inhibitor selectivity profile largely affects its prediction performance, where the more selective an inhibitor is, the better the prediction accuracy becomes (Table 1 and Figure 2). For example, p38α shares 66% sequence identity with p38γ, however, the highly selective p38 MAPK inhibitor VX-745 was reported to be more than 1000-fold selective for p38α (Kd = 2.8 nM) over p38γ (Kd > 10,000 nM). Based on the visual examination of modeled complex structures (Figure S3 in the Supporting Information), one hydrogen bond forms between carbonyl group on VX-745 and hinge backbone amide group of Met109 in the p38α-VX-745 complex model (3.1 Å)

binding specificity prediction. Fortunately, the ligand binding specificity prediction against similar targets may be less complicated due to the cancellation of certain free energy components.11,12 A molecular mechanics generalized Born/ surface area (MM-GB/SA) scoring method was developed to be an intermediary between high throughput docking and molecular dynamics-based free energy calculations, and has been demonstrated to be capable of capturing inhibitor selectivity for structurally related proteins.13,14 Therefore, we expect that this approach may be practically applicable to model the kinase inhibitor selectivity on a kinome-wide scale, and the derived structural and energetic information may be directly applied to “design in” (i.e., engineering desirable binding spectrum) and “design out” (i.e., eliminating the unwanted offtarget interaction) specific kinase binding activities.15 Here we combine comparative docking and a physics-based sampling and scoring method to model the kinase inhibitor selectivity. We assessed our computational protocol in reproducing the binding profiles of 17 kinase inhibitors against 143 protein kinases. These kinases, ligands, and binding affinity data may be a useful benchmarking set for kinase selectivity modeling (available online at http://www.huanglab.org.cn/ kinome/kinome.xls). We then asked whether we could discover novel kinase targets for an FDA-approved chemotherapy drug mitoxantrone (MX), originally known to act through DNA intercalation and type II topoisomerase (Topo II) inhibition.16,17

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RESULTS AND DISCUSSION

Assessment of Kinome-Wide Selectivity Modeling. Briefly, our computational modeling procedure (Scheme 1) consists of four steps: 1. Selecting the representative kinase crystal structure in an active conformation. 2. Predicting the ligand binding pose using comparative docking. 3. Sampling the binding site conformation with the presence of the docked ligand. 4. Scoring the resulting complex structure using the MM-GB/SA scoring function. This automatic workflow allows rapid evaluation of compounds of interest against the entire B



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Figure 1. Chemical structures of 17 kinase inhibitors investigated in the present work.

However, exceptional cases do exist. For example, sunitinib, staurosporine, and SB-203580 are clearly showing significant structural deviations in different complex structures, with RMSDs as large as 3.47 Å, 4.24 Å, and 4.39 Å, individually. Although the majority of the available kinase cocrystal structures are in active conformational states,20−22 and the active conformational states for different kinases generally have higher structural conservation, it is not always true that the inhibitors shall only bind to the active conformational states; not to mention the single representative active conformation. The complicated interaction of a given ligand with a subset of protein conformations may account for several failure cases, including sunitinib23,24 and SB-203580.25 For example, sunitinib was cocrystallized with cKIT kinase in its inactive conformation23 while it binds to ITK kinase in the active

while the corresponding interaction is lost in the p38γ-VX-745 complex model (4.0 Å). The structural characteristics agree with the calculated binding energies of −51.2 and −48.5 kcal/ mol, respectively. Clearly, our approach is adept at distinguishing the high affinity binding interactions from the weak ones, but performs poorly in differentiating the different targets with binding affinities in the same range. Our key assumptions are that an inhibitor binds different kinases in the same binding mode, and the same kinase binds different inhibitors in the same conformational state, presumably an active conformation. These assumptions are partially supported by the crystal structure analysis, where the averaged root-mean-square deviation (RMSD) values of each inhibitor in different crystal complex structures are generally smaller than 2.5 Å (Table S1 in the Supporting Information). C



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Table 1. Retrospective Study on Computational Prediction of Kinase Inhibitor Selectivity inhibitor (HET ID)

template PDB ID (chain)

target (Xray)a

Kd X-ray (nM)

Kd lowest (nM)

primary target (lowest Kd)

no. of targetsb

no. of predicted targets in top 20%c

EF20c

PId

VX-745 (52P) gefitinib (IRE) BI-2536 (R78) LY-333531 (LY4) CP-690550 (MI1) erlotinib (AQ4) dasatinib (1N1) vandetanib (ZD6) A-674563 (SS3) flavopiridol (CPB) VX-680 (VX6) sunitinib (B49) TAE-684 (GUI) staurosporine (STU) crizotinib (VGH) SB-203580 (SB2) R406 (585)

3FC1 (X) 2ITY (A) 2RKU (A) 2J2I (B)

p38a EGFR PLK1 PIM1

2.8 1 0.19 270

2.8 1 0.19 2.5

p38a EGFR PLK1 PRKCQ

1 2 2 1

1 2 2 1

5.00 5.00 5.00 5.00

0.60 0.57 0.38 0.35

3LXK (A)

JAK3

0.16

0.16

JAK3

4

3

3.80

0.59

1M17 (A) 2GQG (B) 2IVU (A)

EGFR ABL1 RET

0.67 0.046 34

0.67 0.046 4.6

EGFR ABL1 RIPK2

5 24 11

3 13 5

3.04 2.75 2.32

0.44 0.64 0.25

2UZT (A) 3BLR (A)

PKACa CDK9

56 6.4

0.51 0.69

CLK2 ICK

9 3

4 1

2.27 1.70

0.45 0.07

2F4J (A) 2Y7J (A) 2XB7 (A) 3A62 (A)

ABL1 PHKg2 ALK S6K1

7.5 5.9 1.1 1.3

3.9 0.075 0.49 0.024

AurA PDGFRb ROS1 SLK

9 27 41 82

2 9 13 21

1.69 1.68 1.61 1.32

0.29 0.04 0.13 0.29

2YFX (A)

ALK

3.3

2.1

MET

8

2

1.28

0.31

3MPA (A)

p38a

12

12

p38a

4

1

1.25

0.15

3FQS (A)

SYK

19

0.71

FLT3

34

8

1.21

0.30

a

Target (X-ray) is the inhibitor bound kinase in the X-ray template structure. bNumber of targets is the number of the kinases with the experimental Kd values less than 100 nM. cNumber of predicted targets in top 20% denotes the number of true positives in top 20% of ranked kinase targets, which corresponds to the enrichment factors (EF20) compared to random selection. dPI is semiquantitative measurement of the correlation of predicted binding affinity with experimental values.

to be more accurate with systematic improvements.11 However, rigorous ligand binding free energy calculations are computationally expensive, are complicated to apply, and cannot satisfy the practical requirements of computational efficiency for large data sets. With the consideration of the approximations made in our approach, it is likely that the cancellation of certain free energy components is critical for our approach to succeed. Ultimately, we can also envision a strategy to integrate physicsbased methods and informatics (e.g., sequence and structural analysis) to improve the overall prediction performance. While it is beyond the scope of our current study, it is critical to develop a rigorous method to estimate the conformational free energy landscape as a response to the binding of different types of inhibitors, especially inhibitors bound to inactive conformational states like imatinib.27 Prospective Target Profiling of MX. We next investigated the predictive power of our approach. The chemotherapy drug MX was chosen based on three criteria: the availability of a cocrystal structure, lack of kinome-wide profiling data, and the pharmaceutical importance. MX is approved by the FDA for the treatment of certain types of cancers, mostly in acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and metastatic hormone-refractory prostate cancer.28−30 An important therapeutic feature of MX is its selective induction of apoptosis in myeloid leukemia cell31 and B-chronic lymphocytic leukemia cells.32 MX was also conferred to benefit in progression-free and overall survival in children with relapsed ALL.33 However, the exact mechanism of action is not clear, and its known targets of DNA and Topo II can hardly interpret its specificity and efficacy against a narrower range of tumors than other anthracycline drugs used in the clinic.34

Figure 2. The two-dimensional plot for the kinome-wide selectivity modeling of 17 kinase inhibitors. The higher EF20 value and the PI value represent the higher prediction accuracy for a given inhibitor.

conformation.24 A simple alternative is to choose several different crystal structures as the template for modeling. Therefore, we modeled the inhibitor selectivity using different cocrystal structures in failure cases (e.g., sunitinib, SB-203580), and selected the best score for final evaluation as suggested in a previous study.26 Unfortunately, no improvement was observed (Table S2 in the Supporting Information). Our further approach of applying molecular dynamics simulations did not improve the results either (Table S3 in the Supporting Information). This is consistent with a previous study that the rigid kinase backbone treatment had better performance than allowing more sampling.9 Physics-based approaches model the protein−ligand interactions in a physically realistic manner, and have the potential D



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included in Table S6 in Supporting Information. The overall structure of the PIM1 kinase resembles other published crystal structures.38 PIM1 kinase adopts a typical kinase fold consisting of N-terminal and C-terminal domains linked by a hinge region. However, the presence of residue Pro123 at the hinge region disrupts the canonical hydrogen bonding pattern with the adenine moiety of ATP or bound inhibitors due to the lack of the hydrogen bond donor. One MX molecule interacts with PIM1 in a non-ATP mimetic binding mode without forming the classic hydrogen bond with the hinge region (Figure 4A).

Based on the cocrystal structure of MX bound with protein kinase B (PknB, PDB ID: 2FUM, IC50 ∼ 0.8 μM),35 a Ser/Thr protein kinase in Mycobacterium tuberculosis, we predicted the potential mammalian kinase targets of MX. Five protein kinases thereby emerged as top scoring hits and were submitted to primary screening using the radiometric kinase assay (Reaction Biology Corp., Malvern, PA)36 at a compound concentration of 1 μM (Table 2). Encouragingly, PIM1 kinase was completely Table 2. The Predicted Top 5 Kinase Targets of MX and Experimental Validation Using Radiolabeled Kinase Assaya predicted rank

kinase target

energy score (kcal/mol)

kinase act. (%)

1 2 3 4 5

CK2a2 PIM1 CHK2 BTK AKT1

−97.8 −95.8 −94.7 −93.3 −92.0

81.8 0.86 59.7 45.9 108.7

All 5 kinase were tested at a MX concentration of 1 μM in the presence of 10 μM ATP in duplicate.

a

inhibited by 1 μM MX. In addition, we also tested five kinases randomly chosen; none of them were active (Table S4 in Supporting Information). We subsequently determined the dose response curve for PIM1 inhibition using the homogeneous time-resolved fluorescence (HTRF)-based technology, and the IC50 value of 50.9 ± 7.4 nM was determined from three independent tests (Figure 3). Note that we would not predict PIM1 to be a target

Figure 4. Cocrystal structures of the PIM1−MX and the Tau SRE RNA−MX. (A) The detailed view of the ATP-binding site of the PIM1 kinase domain with the MX (yellow in sticks) compared with the predicted binding pose (PDB code: 2FUM) of MX (orange in lines). The protein is colored in slate cartoon, and charged residues forming hydrogen bonds with MX are highlighted. (B) Focused view of the tau SRE RNA−MX interaction (PDB code: 2KGP). The MX is shown yellow in sticks, and RNA is shown in slate.

The RMSD value of MX between predicted and crystal structures is 2.27 Å while the critical interactions between MX and PIM1 ATP site are well conserved. This suggests our comparative docking and refinement strategy is reliable in structural prediction of kinase−MX complexes. The planar dihydroxyanthraquinone moiety occupies the adenine binding pocket, enclosed by hydrophobic residues Leu44, Phe49, Val52, Ala65, Ile104, Leu120, Val126, Leu174, and Ile185. Extensive charged and polar interactions are formed between its flexible hydroxyethylamino moieties and surrounding residues, one interacting with Asp186, Asn172, Asp167, and Lys169 and the other with Asp131 and Asp128 (Figure 4A). Although it is a completely different scenario, MX interacts with the splicing regulatory element (SRE) of microtubuleassociated protein tau RNA (Figure 4B)39 in similar binding characteristics to PIM1, where the hydrophobic stacking interactions between anthraquinone moiety and neighboring RNA bases and the extensive hydrogen bonding interactions between hydroxyethylamino groups and RNA backbone phosphates are the common features. To our knowledge, MX is the first small-molecule DNA intercalation drug identified to

Figure 3. The inhibition of MX on PIM1 measured by HTRF kinase assay. The half-maximum inhibitory concentration (IC50) was measured at 7 concentration points by repeating three times.

of MX based on the low sequence identity between PIM1 and PknB in ATP site (Table S5 in Supporting Information). We also validated the inhibitory activity of MX against Bruton’s tyrosine kinase (IC50 ∼ 1 μM). In addition, MX was reported to inhibit casein kinase 2 with IC50 value of 0.66 μM.37 Therefore, MX inhibits three out of the predicted top five kinases from low micromolar to nanomolar range. Considering that our computational approach performs well for treating selective kinase inhibitors, we hypothesize that MX may only inhibit very few kinases at low nanomolar activities. X-ray Crystal Complex Structure of PIM1−MX. To further validate the predicted binding pose, the crystal complex structure of PIM1−MX was determined at 2.7 Å resolution. Coordinates and detailed methods for the solved crystal structure have been deposited to the PDB database with the accession ID 4I41. The detailed crystallization information is E



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Figure 5. MX competitively binds to the PIM1 substrate binding site. (A) The atomistic interactions between the substrate binding site (slate in cartoon) and MX (yellow in sticks). (B) Kinetic study of the interaction of substrate with PIM1. Kinetic studies (n = 2) were performed in the presence of MX at concentrations of 0, 50, and 100 nM, respectively. (C) The overlay of cocrystal structure PIM1−pimtide (PDB code: 2BIL) and PIM1−MX in the substrate binding site. (D) The sequence comparison of pimtide, Pim1 phosphorylation sites of BAD and eIF4B.

binding to both sites simultaneously. Unfortunately, we cannot directly decompose the binding contribution of each individual site based on our biochemical studies. However, the poorer electron density and higher B-factors of the MX at the substrate binding site might suggest the less thermodynamically stable binding interactions with less significant contribution to the PIM1 inhibition. We expect that our study may provide the structural and mechanistic basis to inspire the novel PIM1 kinase inhibitor development by targeting the substrate binding site. Novel Mechanism of Action Implicated through Targeting PIM1 Kinase. To gain deeper insights into the mechanism of action of MX, we examined the changes of phosphorylation level of Ser112 on BAD protein by MX in two representative human leukemia cell lines, K562 and MV4-11. BAD phosphorylation by PIM1 results in inhibition of its proapoptotic activity.42 MX, acting exactly like other potent PIM1 kinase inhibitors, can dose-dependently inhibit the BAD phosphorylation in both K562 cells and MV4-11 cells (Figure 6). The K562 cell line was reported to be less sensitive to PIM1 inhibitors.43 Correspondingly, MX inhibited the BAD phosphorylation in K562 cells at micromolar concentrations (Figure 6A). The MV4-11 cell line was more sensitive in responding of PIM1 kinase inhibitors,43 thus, a significant reduction in BAD phosphorylation was observed in MV4-11 cells treated by MX at concentrations as low as 100 nM (Figure 6B). Meanwhile, MX does not significantly affect the protein levels of total BAD and PIM1 under these effective concentrations. Thus, our cellular phosphorylation assays indicate that MX directly blocks PIM1 enzymatic activity in intact cells. Surprisingly but interestingly, the low nanomolar PIM1 inhibition activity of MX might contribute to its anticancer efficacy and specificity in vivo. First, elevated levels of PIM1 kinase were discovered in several types of hematologic malignancies and solid cancers, especially in AML and prostate

inhibit protein kinase at low nanomolar activity. We expect that an understanding of the structural basis of promiscuity of MX may facilitate the design of more selective MX analogues. Strikingly, our solved crystal complex structure also reveals a novel mode of PIM1 inhibition, where another MX molecule binds to an additional binding site adjacent to the one that binds to the ATP site (Figures 4A and 5A), and contributes to PIM1 inhibition by substrate-competitive fashion (Figure 5B). This additional binding site overlaps with a high-affinity PIM1 peptide substrate (designated as pimtide) binding site (Figure 5C).40 The pimtide-binding site is directly involved in recognition of phosphorylation site in Bcl-2-associated death promoter (BAD), eukaryotic translation initiation factor 4B (eIF4B), and other PIM1 substrate proteins containing the consensus motif (Figure 5D).41 MX forms strong polar interactions with several charged residues in this site, including Lys169, Asp170, Asp234, Asp239, Glu243, and Glu247. It also interacts favorably with several hydrophobic residues, including Val206, Tyr207, Ile230, and Ile240 (Figure 5A). It is wellknown that the substrate-binding interface is less conserved than the ATP site, and inhibitors targeting this region may hold promise for improving the specificity while reducing side effects. To further define the inhibitory mechanism of MX, we determined the catalytic velocities of PIM1 with respect to either ATP or substrate with the presence of different concentrations of MX. The measured Km values with respect to substrate are 0.84, 1.01, and 1.68 μM in the presence of 0, 50, and 100 nM MX, respectively; which clearly demonstrates a substrate-competitive inhibitory mechanism (Figure 5B). Similarly, MX also competes with ATP with increased Km values in responding to increased concentrations of MX (Figure S4 in Supporting Information). These biochemical results strongly support our structural observation that MX acts as a dual ATP-competitive and substrate-competitive inhibitor by F



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intercalation and Topo II inhibition, we computationally profiled MX across our data set containing 143 kinases in active conformation. By only testing the predicted top five kinases, we successfully identified MX as low nanomolar inhibitor against PIM1 kinase and further validated its PIM1 inhibitory activity in cancer cells. Thus we hypothesize that PIM1 kinase inhibition might also contribute to MX’s therapeutic efficacy and specificity, and it represents a verifiable working hypothesis to stimulate further experimental studies to elucidate molecular mechanisms of MX. As the pharmacologist and Nobel laureate Sir James Black once said, “The most fruitful basis for the discovery of a new drug is to start with an old drug.”52 Our findings might shed light on the mechanism of action of MX in clinical applications. For example, it might be logical to combine MX with other targeted anticancer agents whose antitumor activity is limited by PIM1 expression. Moreover, the structural characteristics of MX are distinct from those of other PIM1 inhibitors, and may provide a novel scaffold for designing PIM1 inhibitors. In particular, the novel binding mode of MX revealed by our crystallographic study may suggest a new direction for developing specific PIM1 inhibitors targeting the substrate binding site.

Figure 6. MX inhibits the phosphorylation of Bad at Ser112 in K562 and MV4-11 cell lines. (A) Immunoblot analysis of PIM1 in K562 cell line treated with 0.1% vehicle DMSO alone, 0.1, 0.3, 1, 3, 10, 30 μM MX as well as 10 μM SGI-1776 as positive control. Cell were harvested after 12 h and lysed. (B) Immunoblot analysis of PIM1 in MV4-11 cell line treated with 0.1% vehicle DMSO alone, 10, 30, 100 nM MX as well as 100 nM SGI-1776 as positive control. Cell were harvested after 12 h and lysed.

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EXPERIMENTAL SECTION

Kinase Structure Analysis and Inhibitor Selection. The kinase sequences were retrieved from the Protein Data Bank (PDB), and subsequently aligned with the human kinome sequences (http://www. kinase.com/) using BLAST (version 2.2.17).53 If sequence identity of a PDB structure was larger than 80% compared to any human kinase sequence, and the sequence length of the structure was larger than 150 residues, the kinase was selected. This led to a total of 195 unique kinases with 2,646 single-chain structures. Three key regulatory elements in kinase domain have been commonly used to distinguish the active conformation from the inactive state, including DFG-motif flipping, αC-helix displacement, and conformational change of the activation loop (Figure S1 in the Supporting Information).54 Accordingly, approximately 70% of single-chain kinase structures were classified in active conformation by satisfying the structural characteristics of at least two regulatory elements; the rest were intermediate and inactive structures (Figure S2 in the Supporting Information). Next, the redundant single-chain structures of single kinase were clustered by structural variations of 13 conserved ATP binding-site residues using the NMRCLUSTER program,55 and the most representative one from the largest cluster was selected as the target structure (Figure S2C in the Supporting Information). Finally, we compiled a total of 159 unique kinases with a single target structure in the active conformation. Among them, 143 kinases were included in a most recent kinase inhibitor profiling campaign, where we identified 17 well-known kinase inhibitors with at least one available cocrystal structure in active conformation (Figure 1).18 Thus, we assessed our computational protocol in reproducing the experimental binding profiles of these 17 kinase inhibitors against 143 protein kinases in active conformation, for a total of 2,431 binding interactions. Modeling Kinase−Inhibitor Complex Structure. We modeled the initial kinase−inhibitor complex structures by comparative docking strategy as previously reported.10 The cocrystal structure with the best binding affinity was chosen as the template structure for each inhibitor. The inhibitor-bound template structure was superimposed on each target structure using the UCSF Chimera program.56 The coordinates of the inhibitor were transferred from the template structure to the target kinase, and the steric clashes between inhibitor and binding-site residues were identified using Chimera. Next, the generated complex structure was submitted to the MM-GB/SA refinement and rescoring procedure. The binding-site residues within 12 Å of the inhibitor were subjected to side chain predictions and energy minimizations using the Protein Local Optimization Program (PLOP) with an all-atom molecular-mechanics (MM) force field and a generalized Born surface

cancer.44−47 Second, PIM1 kinase inhibitors have been extensively studied as aggressive prostate cancer and AML treatment.48,49 Third, the intracellular concentrations of MX were reported to be 200−300 times higher than in plasma in AML patients.50 Therefore, the anticancer activity of MX might lie in its capacity of simultaneously targeting multiple cellular targets. In addition, MX was approved for the treatment of relapsing−remitting multiple sclerosis (MS), secondary progressive MS, and progressive-relapsing MS. Recently, PIM1 kinase inhibitor has demonstrated the efficacy in murine models of MS.51 Thus, we hypothesize that the PIM1 inhibition might also contribute to MX’s therapeutic effects in the treatment of MS.

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CONCLUSION The rational design of selective kinase inhibitors remains a great challenge. Here we have developed a physics-based computational procedure to efficiently model the kinase selectivity at the kinome-wide scale. We evaluated 17 therapeutically important kinase inhibitors against 143 protein kinases, with the assumptions that an inhibitor binds to different kinases in the same binding mode, and the same kinase binds with different inhibitors in the same active conformational state. Thus, the relative binding free energy calculation via MM-GB/SA scoring method may capture kinase inhibitor selectivity due to the cancellation of certain free energy components. Encouragingly, our approach is adept in predicting the binding profiles for selective kinase inhibitors. Nevertheless, a more accurate approach is desirable for distinguishing the binding complexes with similar binding affinities. Currently, our approach is limited to treating kinase inhibitors with available cocrystal structure, however, we expect that our approach can be easily adopted to study structural analogues of cocrystal ligands, which may provide practical value in designing new selective kinase inhibitors. To predict the potential novel target for the FDA-approved chemotherapy drug MX, originally known to act through DNA G



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area (GB/SA) implicit solvent model with variable internal dielectric constant.57,58 The binding energy was computed by subtracting the energies of the optimized free ligand in solution and the free protein in solution from the optimized ligand−protein complex’s energy in solution, accounting for protein−ligand interaction energy, desolvation energies of ligand and protein, and ligand strain energy.11,14,59 Quantifying Agreement between Computation and Experiment. In order to quantify the agreement between the Kd values and our prediction, two parameters were calculated: the enrichment factor of top 20% retrieved targets as a function of the percentage of targets in the database selection (EF20) and the predictive index (PI), a sum over all pairwise ligand comparisons introduced by Pearlman.19 The predictive index (PI) is defined as

PI =

∑j > i ∑i wijcij ∑j > i ∑i wij

⎧1 ⎪ ⎪ cij = ⎨− 1 ⎪ ⎪0 ⎩

if if if

with

wij = |E(j) − E(i)|

Then the cells were lysed and purified with Ni affinity column (GE Lifescience). After the protease cleavages His-tag on the overnight at 4 °C, the digested PIM1 was loaded directly onto a HiTrap Q column (GE Lifescience) for anion-exchange chromatography. The fractions containing PIM1 kinase were further loaded onto a pre-equilibrated Superdex-200 column (GE Lifescience) followed by elution using buffer (20 mM Tris pH 8.0, 200 mM NaCl, 2 mM CaCl2, 2 mM MgCl2, 5 mM DTT).69 The purity of PIM1 kinase was characterized using SDS−PAGE. HTRF Kinase Assay. The Ser/Thr KinEase assay kit for Ser/Thr kinases 3 (CisBio) was used to measure the half-maximum inhibitory concentration (IC50) of PIM1 kinase according to the manuscript. The HTRF kinase assay was conducted after optimizing the ATP, enzyme, and substrate concentrations. The assay was carried out in 10 μL of kinase reaction buffer containing 50 mM HEPES (pH 7.0), 0.02% NaN3, 0.01% BSA, 0.1 mM orthovanadate, 5 mM MgCl2, 1 mM DTT, 5.8 nM PIM1 protein, 100 μM ATP, and 1 μM biotin labeled substrate at RT for 30 min in the presence or absence of inhibitor. The reactions were allowed to stand at room temperature for 1 h and then read in a time-resolved fluorescence detector (Envision, Perkin-Elmer) at 615 and 665 nm simultaneously. The ratio between the signals of 615 and 665 nm was used in the calculation of the IC50 value. To determine the kinetics binding model of MX on PIM1 on the substrate binding site, initial rates were obtained by keeping a constant PIM1 concentration of 0.2 μM and varying the substrate concentration ranging from 0.15 to 10 μM in the presence of MX at a concentration of 0, 50, and 100 nM, respectively. In a similar way for the ATP kinetic study of PIM1, the initial rates were measured by changing the ATP concentration from 50 to 1000 μM in the presence of MX at a concentration of 0, 25, and 50 nM, respectively. The Km and Vmax values were determined with the Michaelis−Menten equation fits and Lineweaver−Burk plot in GraphPad Prism 5.0. X-ray Cocrystal Structure Determination. The crystal of MX bound PIM1 kinase was obtained by soaking. Hanging drop vapor diffusion method was applied to crystallize the apo-PIM1 by mixing 1 μL of protein (10 mg/mL) solution and 1 μL of well buffer at 291 K. PIM1 crystallized in well buffer containing 0.4 M potassium sodium tartrate tetrahydrate after 7 days. For inhibitor soaking, apo-crystal was transferred to a 2 μL drop of well buffer plus 0.2 mM MX, soaked for 1 h. Then the MX concentration was further increased to 0.5 mM for soaking overnight. The soaked crystal was cyroprotected by the well buffer plus about 25% (v/v) glycerol. A data set was collected using the Rigaku X-ray generator (Cu Kα, 007HF) with the RAXIS IV++ detector. Diffractions were integrated and scaled by Denzo and Scalepack programs. The structure was solved by molecular replacement in Phaser with the apo PIM1 structure (PDB ID: 1YWV38) as search model. Ligand fitting and model adjustment were carried out in Coot, and the model was refined in Refmac5.70 All molecular structure graphic figures were made with PyMOL.71 Cellular Phosphorylation Assays. The human leukemia cell lines K562 and MV4-11 were obtained from the American Type Culture Collection (Manassas, VA) and cultured in the recommended medium. K562 cells were cultured in RPM1640 plus 10% fetal bovine serum (Invitrogen, Carlsbad, CA). MV4-11 cells were cultured under the same conditions in Iscove’s modified Dulbecco’s medium (IMDM) plus 10% fetal bovine serum. Cells were seeded at 2−10 × 105 cells/5 mL/dish. After treatment with compounds for 12 h, the cells were scraped into cold PBS, pelleted, and resuspended in 100 μL of ice cold cell lysis buffer supplemented with protease inhibitors. The lysates were normalized by total protein content and analyzed by immunoblotting with the following antibodies: monoclonal anti-PIM1 (Epitomics), monoclonal anti-h-actin antibody (Abcam), polyclonal anti-BAD (Cell Signaling Technology), and monoclonal anti-phosphoBAD S112 (Cell Signaling Technology).

and

[E(j) − E(i)]/[P(j) − P(i)] < 0 ⎫ ⎪ ⎪ [E(j) − E(i)]/[P(j) − P(i)] > 0 ⎬ ⎪ ⎪ [P(j) − P(i)] = 0 ⎭

where the P(i) is the calculated binding score and E(i) is the log of experimental pKd. This index reflects the consistency between the experiment and prediction, and it ranges from +1 to −1. 1 indicates perfect prediction, and 0 indicates completely random prediction. Similar to the definition of enrichment in ligand docking method, the enrichment factor reflects the ability of our computational method to find true positives throughout the background database compared with random selection.60 The EF20 is calculated as = (number of true targets in subset/number of targets in subset)/(number of true targets/ number of kinases). The experimental binding Kd value of 100 nM was chosen as cutoff to discriminate target or nontarget. Molecular Dynamics-Based MM-PB/SA Calculations. The kinase−inhibitor complex structure generated after MM-GB/SA refinement was used as starting structure for the MD simulations. MD simulations and energy calculations were performed using AMBER10.0 with the AMBER99SB protein force field.61,62 The ligand parameters were assigned using the general atom force field (GAFF)63 and the AM1-BCC charge scheme.64,65 All system setups were performed in the TLEAP module, and MD simulations were carried out in the SANDER module. For each complex system, three stages of minimization were performed in the gas phase, followed by the addition of a 30 Å water cap based on the geometric center of the binding site. After 200 ps of equilibration of solvents, a production run of 5 ns was performed at 300 K with a time step of 2.0 fs. All residues including solvents beyond 12 Å of the ligand were fully frozen. 100 snapshots from the last 1 ns of the trajectory were extracted for energy calculation. The interaction energy between receptor and ligand as well as internal energy was calculated with the SANDER module. For the solvation energy, the polar contribution was calculated using PB/ SA66 with PARSE radii while the nonpolar part was estimated proportional to the solvent-accessible surface area.67 Primary Kinase Profiling. The inhibition potential of MX on the predicted top five kinase targets (i.e., CK2a2, PIM1, CHK2, BTK, and AKT1) was tested using P33 radiolabeled kinase assay (Reaction Biology Corp., Malvern, PA).36 Each kinase activity assay was performed in duplicate with a MX concentration of 1 μM and an ATP concentration of 10 μM. The testing compound MX was obtained from Sigma-Aldrich, with guaranteed purity >98%. Recombinant PIM1 Kinase Expression and Purification. The cDNA of PIM1 kinase (29−313) was amplified by transfer-PCR (TPCR)68 using Ultimate ORF clone IOH14704 (Invitrogen Corp., Carlsbad, CA) as a template. The forward and reverse primers are 5′GAAGTGCTGTTCCAGGGCCCACATATGGCTAAGGAGAAGGAGCCCCTG-3′ and 5′-ATCTCAGTGGTGGTGGTGGTGGTGCTCGAGCTATTTGCTGGGCCCCGG-3′. The gene was cloned into modified pET28a vector and verified by sequencing. The PIM1 construct was expressed in Escherichia coli strain BL21 Star (DE3) pLysS in LB media with 0.5 mM IPTG induction for 15 h at 18 °C.

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ASSOCIATED CONTENT

* Supporting Information S

The details for kinase structure classification, and the criteria to determinate the kinase active conformation. The RMSDs of H



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kinase inhibitors from different cocrystal structures, and the comparisons of experimental binding profiles and prediction results in several failure cases. The kinetic study of the interaction of ATP with PIM1 in the presence of MX. The sequence alignment of ATP site for 5 representative kinases against PknB. The activity profile of five randomly chosen kinases using radiolabeled kinase assay. The PIM1−MX complex crystallization, data collection, and structure determination. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 86-10-80720645. Fax: 86-10-80720813. E-mail: [email protected]. Author Contributions

X.W. and W.Z. contributed equally. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the anonymous reviewers for their constructive comments for improving the manuscript. Computational support was provided by the Supercomputing Center of Chinese Academy of Sciences (SCCAS). We gratefully thank Luyao Ma for computer support. We thank Pan Chen at NIBS for advice on Western blot experiment. We also thank Dr. Andrew Christofferson for proofreading. We acknowledge financial support from the Chinese Ministry of Science and Technology “973” Grant 2011CB812402 (to N.H.).

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ABBREVIATIONS USED PIM, proviral integration site in Moloney murine leukemia virus; MX, mitoxantrone; Topo II, type II topoisomerase; EGFR, epidermal growth factor receptor; MAPK, mitogenactivated protein kinase; PknB, protein kinase B; SRE, splicing regulatory element; BAD, Bcl-2-associated death promoter; eIF4B, eukaryotic translation initiation factor 4B; PLOP, Protein Local Optimization Program; MD, molecular dynamics; PME, particle mesh Ewald; RMSD, root-mean-square deviation; MM-GB/SA, molecular mechanics-generalized Born/surface area; MM-PB/SA, molecular mechanics-Poisson−Boltzmann/surface area; HTRF, homogeneous timeresolved fluorescence; TPCR, transfer-PCR; MS, multiple sclerosis

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