ORIGINAL PAPER

A flexible-protein molecular docking study of the bindingof ruthenium complex compounds to PIM1, GSK-3β,and CDK2/Cyclin A protein kinases

Yingting Liu & Neeraj J. Agrawal & Ravi Radhakrishnan

Received: 18 October 2011 /Accepted: 30 July 2012 /Published online: 29 August 2012# Springer-Verlag 2012

Abstract We employ ensemble docking simulations tocharacterize the interactions of two enantiomeric forms ofa Ru-complex compound (1-R and 1-S) with three proteinkinases, namely PIM1, GSK-3β, and CDK2/cyclin A. Weshow that our ensemble docking computational protocol ade-quately models the structural features of these interactions anddiscriminates between competing conformational clusters ofligand-bound protein structures. Using the determined X-raycrystal structure of PIM1 complexed to the compound 1-R asa control, we discuss the importance of including the proteinflexibility inherent in the ensemble docking protocol, for theaccuracy of the structure prediction of the bound state. Acomparison of our ensemble docking results suggests thatPIM1 and GSK-3β bind the two enantiomers in similar fash-ion, through two primary binding modes: conformation I,which is very similar to the conformation presented in theexisting PIM1/compound 1-R crystal structure; conformationII, which represents a 180° flip about an axis through the NHgroup of the pyridocarbazole moiety, relative to conformationI. In contrast, the binding of the enantiomers to CDK2 is foundto have a different structural profile including a suggestedbound conformation, which lacks the conserved hydrogenbond between the kinase and the ligand (i.e., ATP, staurospor-ine, Ru-complex compound). The top scoring conformationof the inhibitor bound to CDK2 is not present among the top-scoring conformations of the inhibitor bound to either PIM1

or GSK-3β and vice-versa. Collectively, our results helpprovide atomic-level insights into inhibitor selectivity amongthe three kinases.

Keywords Small molecular kinase inhibitor .

Protein kinase . Inhibitor selectivity . Ruthenium-basedorganometalic compound .Molecular dynamics simulation .

Molecular docking . Protein flexibility .

Ensemble molecular docking

Introduction

Protein kinases—one of the most important targets in thecurrent cancer therapy—are the largest enzyme family in-volved in cell signal transduction [1, 2]. They are encodedby approximately 2 % of eukaryotic genes and more than500 protein kinases have been identified based on humangenome sequencing [3] and biochemical studies. Proteinkinases catalyze the transfer of the γ-phosphate group froman ATP molecule to tyrosine, serine or threonine residues inproteins. This process plays an essential role in regulatingmany fundamental cellular processes [4]. Constitutive orinappropriate activation of protein kinases are seen in avariety of cancers and small molecule inhibitors aredesigned to target/inhibit kinase signaling in such scenarios[5]. Understanding the inhibition and phosphorylation ofprotein kinases is significant in guiding cancer therapy.The kinases studied in this work—GSK3-β, CDK2, andPIM1—are all well-established drug targets.

A pharmacophore model of the ATP-binding site of pro-tein kinases [6] is depicted in Fig. 1. One way to inhibitabnormal kinase activity is to design small molecule inhib-itors to bind into the ATP binding cleft, block ATP bindingand therefore inhibit phosphorylation. The success of small-molecule ATP-competitive inhibitors such as imatinib

Y. Liu : R. RadhakrishnanDepartment of Bioengineering, University of Pennsylvania,Philadelphia, USA

N. J. Agrawal : R. Radhakrishnan (*)Department of Chemical and Biomolecular Engineering,University of Pennsylvania,210 S. 33 Street, 240 Skirkanich Hall,Philadelphia, PA 19104, USAe-mail: rradhak@seas.upenn.edu

J Mol Model (2013) 19:371–382DOI 10.1007/s00894-012-1555-4

(Gleevec) for the treatment of chronic myeloid leukemia(CML) and gastrointestinal stromal tumors (GIST) con-firmed that this strategy is indeed effective [7]. In spite ofthe success of Gleevec, the design of ATP-competitiveinhibitors that are selective (specific) for a particular kinaseappears to be quite challenging due to the conserved natureof the protein structures.

X-ray crystal structures for a range of kinases are avail-able in the Protein Data Bank (PDB), providing a structuralbasis for understanding kinase inhibition and facilitatingstructure-guided design of kinase-specific inhibitors. Thecatalytic clefts of protein kinases usually include two hy-drophobic pockets (Hyp1 and Hyc1), an adenine region,‘sugar’ pocket and the phosphate groove (Fig. 1). Theadenine region contains the two key hydrogen bonds formedby the interaction of the N-1 and N-6 amino groups of theadenine ring with the backbone NH and carbonyl groups ofthe adenine anchoring hinge region of the protein kinase.Many potent inhibitors use at least one of these hydrogenbonds. The hydrophobic pocket is not used by ATP, but isexploited in the design of most kinase inhibitors. It plays animportant role in inhibitor selectivity, and its size is differentin active and inactive kinase states. The hydrophobic chan-nel opens to solvent and is not used by ATP and hence canbe exploited in the design of inhibitors. The phosphatebinding region offers little opportunity in terms of inhibitorbinding affinity due to high solvent exposure. However, itcan be utilized in improving selectivity.

As the overall protein fold is conserved in the kinasefamily, a major challenge is to understand the molecularbasis for inhibitor sensitivity and to design small-moleculecompounds that are highly selective (specific) for the tar-geted protein kinase. Progress towards the design of selec-tive small-molecule kinase inhibitors based on theexploitation of distinct features presented by ATP-bindingsite has been reviewed recently [8, 9]. The vast majority ofspecific enzyme inhibitors are small organic molecules thatgain their specificity by a combination of weak interactions,including hydrogen bonding, electrostatic contacts, and

hydrophobic interactions. Recently, a novel strategy hasbeen introduced for the design of small-molecule enzymeinhibitors by using substitutionally inert organometallicscaffolds [10, 11], based on the hypothesis that complement-ing organic elements with a metal center may provide newopportunities for building three-dimensional structures withunique and defined shapes. In particular, a class of half-sandwich ruthenium complex compounds based on the scaf-fold shown in Fig. 2 have been reported to show highaffinities and promising selectivity profiles for proteinkinases and lipid kinases [12–14].

The new ruthenium complex compounds are designed tomimic the shape of staurosporine—a well-known proteinkinase inhibitor—by replacing the indolocarbazole alkaloidscaffold with metal complexes in which the structural fea-tures of the indolocarbazole heterocycle is retained. Theruthenium metal center plays a structural role by organizingthe organic ligands in three-dimensional space. As shown inFig. 2, the coordination geometry around the ruthenium ispseudo-octahedral, formed by the pyridocarbazole ligand,the CO group oriented perpendicular to the pyridocarbazoleplane, and the cyclopentadiene (Cp) moiety. The compoundwas designed to bind with the kinase by forming hydrogenbonds to the backbone residues at the hinge region of thekinases. However, unlike staurosporine, which is a nonspe-cific nanomolar inhibitor for most protein kinases, theseruthenium half-sandwich compounds show remarkable se-lectivity profiles. In particular, profiling the racemic mixtureof (R/S)-1 against more than 50 protein kinases in vitroshows the high selectivity of this class of compounds forPIM1 (IC5003nM, 100 μm ATP) and GSK-3β (IC500

50nM, 100 μm ATP) [12]. Interestingly, the phylogenetical-ly and structurally closely related cyclin-dependent kinases(CDKs) are not significantly inhibited, with IC5003μM(100 μm ATP) for the CDK2/cyclin A complex [12].

Sequence alignment analysis (Table 1) shows a highdegree of conservation among the ATP binding pocket

Fig. 1 Pharmacophore model of the ATP-binding site of proteinkinases. ATP is in red. Also depicted are the hydrophobic pocket, thehydrophobic channel, the hinge region, and the phosphate bindingregion

Fig. 2 Chemical structure of ruthenium complex compound 1. Left R-enantiomer, right S-enantiomer

372 J Mol Model (2013) 19:371–382

residues of the three kinases. Superposition of the kinaseactive sites using the α-carbon atoms of the residues aroundthe ATP pocket also shows that the structure of the bindingsite is highly similar among the three kinases (Fig. 3). De-spite these similarities, there are differences in specific ami-no acid positions (noted in Table 1 and labeled in Fig. 3),which possibly have a bearing on the differences in thebinding interactions, and hence inhibitor selectivity.

Although it is of great interest to understand the interactionof ruthenium compounds with the three protein kinases inatomic details, [15, 16] only one crystal structure, i.e., PIM1bound with (R)-1, is currently available. It is not clear how theother enantiomer, (S)-1 is bound to PIM1. The bound con-formations of both (R)-1 and (S)-1 are not known from experi-ments either. Thus, in this article, we modeled the interactionsbetween the ruthenium scaffold and three protein kinases,PIM1, GSK-3β and CDK2/cyclin A, targeting the selectivityprofile of the compound.

Molecular docking is used frequently to predict ligandbinding in the absence of ligand-bound crystal structuresand functional affinity data [17, 18]. While ligand flexibilityis accounted for in most docking programs, many treat theprotein as a rigid body. However, in practical scenarios inwhich the receptor structure is derived either from an exper-imentally determined structure of the apo-kinase or from astructure where the kinase is complexed with a differentligand, the rigid-receptor docking often fails to predict prop-er bound conformation. Hence, efforts have been made to

account for protein flexibility [19, 20]. In this article, weapplied the ensemble docking procedure to predict boundconformations of ruthenium compounds (R/S)-1 against thethree protein kinases PIM1, GSK-3β and CDK2/cyclin A.As we describe in the following sections, our dockingresults reveal the protein–ligand interaction and suggestpossible structural factors that may give rise to differentbinding affinities of the proteins.

Methods

The flow chart in Fig. 4 depicts the ensemble dockingprotocol followed. To summarize, protein conformationsare sampled with all-atom molecular dynamics (MD) simu-lations with explicit solvent. Charges and geometry of theligands are calculated using electronic structure (ab-initio)methods. The ligands were then docked into each of theprotein conformations using the docking program Auto-Dock3.0. The predicted bound conformations of ligand aresubsequently clustered.

Ab-Initio electronic-structure calculations of Ru-complexcompounds

The geometries of both enantiomers of compound 1 wereoptimized using the Gaussian 98 [21] program at the densityfunctional theory (DFT) level using the B3LYP functionalas well as at the Hartree-Fock (HF) [21] level. For thehydrogen, carbon, oxygen and nitrogen, the 6-31 G* basisset was used, while for the Ru atom, the Los Alamos ECP(effective core potential) plus DZ (double zeta) basis set(LanL2DZ [22]) was used. The geometry optimization ofthe molecule returns the minimum-energy geometry of themolecule and the spatial electron-density map at 0 K. Fol-lowing the procedure of Aiken et al. [23], the partial(Mulliken) charges on each atom were inferred from abinitio calculations, which are used to determine the molec-ular descriptors of the inhibitor compound and are usedfurther in docking calculations. The minimum energy ge-ometries obtained using HF and B3LYP methods agreeclosely with each other [root mean squared deviation(RMSD)00.215 Å, see Fig. 5]; however, Mulliken partialcharges differ for these two methods (see Table 2).

Table 1 Sequence alignment of residues around active site of the three protein kinases, PIM1, GSK3, and CDK2

Kinase N-terminal lobe Linker region C-terminal lobe

PIM-1 L44 G45 S46 F49 V52 A65 I104 L120a E121 R122 – P123 E171 L174 I185a D186

GSK-3β I62 G63 N64 F67 V70 A83 V110 L132a D133 Y134 V135 P136 Q185 L188 C199a D200

CDK2 I10 G11 E12 Y15 V18 A31 V64 F80a E81 F82 L83 H84 Q131 L134 A144a D145

a Significantly different residues

Fig. 3 Structural alignment of the ATP binding pockets of three proteinkinases, PIM1 (blue), GSK3 (purple), and CDK2/CYCLIN A (orange)

J Mol Model (2013) 19:371–382 373

Even though a DFT level of theory is considered theappropriate because it includes electron correlation, whichis critical in treating ruthenium interactions correctly, the HFlevel was included for comparison because the rest of thebiomolecular interaction force-field was parameterized atthat level, and the HF is the approach prescribed to param-eterize new interactions. We note that the Ru is fully buriedwithin the cp ring on one side and with the staurosporinegroup on the other and is not exposed to the solvent or theprotein, and hence the partial charges derived at the HF levelwill still be accurate enough to model the protein–ligand

interactions while maintaining compatibility with the rest ofthe biomolecular force-field (see below).

Protein conformation

The native and non-native protein conformations of PIM1kinase were constructed based on two different crystallo-graphic structures from the PDB: (1) the structure of thePIM1/(R)-1 bound complex (PDB ID: 2BZH), and (2) thestructure of PIM1 bound to an ATP analog (PDB ID:1YXT). For the GSK-3β and CDK2/cyclin A systems,conformations derived from crystal complex structures withthe ATP analog (1I09 for GSK-3β and 1QMZ for CDK2/cyclin A ) were employed in the docking simulations. Fourapo-kinase conformations were generated based on thesecrystal structures, by removing ligands and adding hydrogenatoms and other missing residues using the CHARMM [24]biomolecular simulation package.

To generate an ensemble of conformations for each ki-nase, MD simulations for each protein kinase were per-formed with the respective crystal structures serving as thestarting conformation. The proteins were solvated explicitlyusing the TIP3P model for water and neutralized by placingions (sodium and chloride) at an ionic strength of 150 mM.The ions were placed at positions of electrostatic extremapredicted by mean-field Debye-Huckel calculations. Thesolvated models are energy minimized, heated to 300 K,and equilibrated at constant temperature and constant pres-sure (300 K and 1 atm) using the NAMD simulation pack-age [25] in conjunction with the CHARMM27 force-field[26]. All our simulations were performed on a fully periodicsystem, and include long-range electrostatics using the par-ticle mesh Ewald (PME) algorithm [27]. The extent of theequilibration phase was determined by tracking the plateaubehavior of the RMSD of the protein backbone (Cα

Fig. 4 Flow chart of theensemble docking method

Fig. 5 Overlay of the minimum energy geometry of the compound 1-R obtained using HF (red) and B3LYP (blue) methods. For bothmethods, the 6-31 G* basis set was used for the carbon, nitrogen,oxygen and hydrogen atoms, while the LanL2DZ basis set was usedfor the ruthenium atom. The root mean squared deviation (RMSD)between these two geometries is 0.215 Å

374 J Mol Model (2013) 19:371–382

positions) calculated with respect to the starting conforma-tion. For each protein kinase system, 10 ns of dynamics runs

were generated, from which we extracted 100 protein con-formations at uniform intervals from the last 8 ns of the

Table 2 Mulliken charges computed for each atom of compound 1-Rusing HF and B3LYP methods. For both methods, the 6–31 G* basisset was used for carbon, nitrogen, oxygen and hydrogen atoms, whilethe LanL2DZ basis set was used for the ruthenium atom. The figure

depicts the geometry of compound 1-R showing carbon atoms (cyan),oxygen (red), nitrogen (blue), ruthenium (brown) and hydrogen(white ).Indices used in the Table for each atom are also depicted

Index Atom HF B3LYP Atom Label

1 N −0.94 −0.672 C 0.89 0.53

3 O −0.56 −0.41

4 C −0.13 −0.08

5 C −0.03 0.08

6 C 0.34 0.24

7 N −0.94 −0.64

8 Ru 0.54 0.20

9 C 0.41 0.26

10 O −0.30 −0.26

11 C −0.27 −0.13

12 C −0.22 −0.18

13 C −0.20 −0.12

14 C −0.30 −0.19

15 C −0.16 −0.10

16 C −0.06 0.03

17 C 0.32 0.25

18 C −0.26 −0.20

19 C −0.19 −0.13

20 C −0.17 −0.19

21 C −0.25 −0.16

22 C 0.87 0.51

23 O −0.59 −0.44

24 C −0.21 −0.07

25 C 0.00 0.14

26 C 0.35 0.24

27 N −0.78 −0.52

28 C −0.10 −0.15

29 C −0.30 −0.16

30 C 0.12 0.04

31 H 0.42 0.34

32 H 0.23 0.17

33 H 0.22 0.16

34 H 0.23 0.18

35 H 0.23 0.17

36 H 0.25 0.19

37 H 0.20 0.13

38 H 0.19 0.12

39 H 0.19 0.12

40 H 0.26 0.17

41 H 0.23 0.15

42 H 0.22 0.17

43 H 0.28 0.19

J Mol Model (2013) 19:371–382 375

respective trajectories for use in our ensemble docking pro-tocol. In order to make the comparison of different systemseasier, all the protein conformations were aligned with re-spect to the PIM1 coordinates in 2BZH structure based onresidues within 15 Å of the ATP binding site.

Docking protocol

The AutoDock3.0.5 program [28] was used for dockingsimulations. In silico or computational docking is used togenerate a large set of conformations of the receptor-ligandcomplex and to rank them according to their stability; there-fore, an essential component of docking is an algorithm forsearching through conformational space. Awell-appreciatedfact regarding ligand–protein binding interactions is thatboth components flex and adjust to complement each other.As a result, it is necessary to include the flexibility for bothreceptor and ligand. Ligand flexibility is considered explic-itly by AutoDock, while protein flexibility is consideredimplicitly in our MD simulations (described below). Anoth-er essential component of a docking program is a fast yetaccurate method for scoring. In AutoDock, an approximatebinding free energy based on the evaluation of a singlestructure is used as a scoring function, which assumes thatthe binding free energy can be estimated by a linear combi-nation of pairwise terms: ΔG ¼ ΔGvdW þΔGhbond þΔGelec þΔGconform þΔGtor þΔGsol , where the first fourterms are molecular mechanics terms, namely, dispersion/repulsion, hydrogen bonding, electrostatics and deviationsfrom the covalent geometry, the fifth term models the restric-tion of internal rotors, global rotation and translation, and thelast term accounts for desolvation and the hydrophobic effect.Different methods implement different approximations forthese terms. This class of scoring functions is of practicaluse for molecular docking and is computationally tractable.On the other hand, they have limitations in terms of theaccuracy with which they represent the free energy of bindingdue to the various simplifying assumptions.

For each protein structure, the non-polar hydrogen atomsare merged to heavy atoms and Kollman charges and solva-tion parameters are then assigned to the protein atoms usingAutoDockTool (ADT) [29]. Grid maps for each proteinconformation of dimension 126×126×126 points with a gridspacing of 0.184 Å are constructed, encompassing the entireATP binding site. The Lamarckian genetic algorithm (LGA)[28] is applied to explore the conformational space of theligand using the scoring function. In each docking run, theinitial population is set to 50 individuals, the maximumnumber of energy evaluations is set to 108, and the genera-tion of the GA run is set to 10,000; 30 GA runs areperformed for each protein conformation. This choice forthe set of run-parameters is adequate to achieve convergenceof the docking results.

Ligand conformation clustering

Ligand conformations were clustered using the hierarchicalclustering algorithm in Matlab [30]. For each docking run, theRMSD value between each pair of all docking-generatedligand conformations (30 for single conformation dockingwith the crystal structure, and 3,000 for ensemble docking)were calculated and used as the distance matrix to build ahierarchical tree by progressively merging clusters using theunweighted average distance of each cluster. A cut-off RMSDvalue was then used to cluster ligand conformations based onthe hierarchical tree. We used the pairwise RMSD distributionof docked conformation to determine the optimal radius [31].The distribution of pairwise RMSD of all the docked confor-mations are depicted in Fig. 6. The minimum RMSD valuesafter the first peak were chosen as the optimal clusteringradius, namely, 2.0 Å for single conformation docking and2.5 Å for ensemble docking. The conformations with thelowest docked energy in each cluster are reported as thepredicted bound conformations for each system.

Results

Ensemble docking protocol takes into account proteinflexibility and improves prediction of the bound complexstructure

We are interested in predicting and comparing the interactionbetween the Ru-complex compound and three protein kinases,PIM1, GSK-3β and CDK2/cyclin A, to provide insight intothe structural basis of the selectivity profile of the compound.Docking ligands to non-native protein conformations (apo-protein or protein complexed with other ligands) is still achallenging task. To account for protein flexibility, we usedthe ensemble docking protocol described in Fig. 4.

The protocol was first tested using the PIM1/(R)-1 system,where a crystal structure of the exact complex has beensolved. To examine the reliability of different docking proto-cols, we performed two test cases to dock the (R)-1 compoundto the PIM1 protein kinase. The first test was docking to thePIM1 structure (PDB ID: 2BZH) in which (R)-1 was co-crystallized. In the second test, the crystal structure of PIM1(PDB ID: 1YXT) bound with an ATP analog was used in-stead. In each test, both the traditional docking protocol basedon single protein structure and the ensemble-docking (using100 conformations) protocol were applied to generate com-plex structures. The actual complex structure (2BZH) deter-mined from crystallography serves as the “native” pose, or asa reference to evaluate the docking results.

The results shown in Fig. 7 show that the predictedligand-bound structures from single-conformation dockingcan be classified into several clusters with similar docking

376 J Mol Model (2013) 19:371–382

energies. When ensemble docking was used and the flexi-bility of the protein was thus accounted for, more complexconformations were generated and a better discriminationbetween the lowest (dock) energy conformations of differentclusters was observed. Furthermore, conformations similarto the native pose emerge among the top clusters whenranked by dock energy.

All predicted complex structures were superimposed on thenative pose (2BZH), with the closest one from each dockingtask shown in Fig. 8. We also list the parameters of the criticalhydrogen bond between the NH group of the inhibitor and theGLU121 of the protein in Table 3. When single-conformationdocking was used in combination with the exact proteinstructure (2BZH), the results (blue structure) not surprisingly

closely resemble the native pose present in the same crystalstructure. However, when the approximate protein structure(1YXT) was used, the single-conformation docking failed tocorrectly present the critical hydrogen bond in the native pose.In contrast, when ensemble docking was applied on either theexact or the approximate protein structure, reasonable boundconformations (orange and pink structures) with the properhydrogen bond and consistent with the native pose wereamong the sampled poses.

To show how the ensemble of conformations provides amore suitable ligand docking environment, the 100 conforma-tions of the PIM1 ensemble corresponding to non-nativestructure (PDB ID: 1YXT) were aligned together (Fig. 9a).It is evident that the snapshots in our ensemble simulations

Fig. 6a,b Pairwise RMSDdistribution histogram ofdocked conformations forPIM1/(R)-1 system. a Singleconformation docking, bensemble docking

Fig. 7 RMSD relative to thereference structure versus thedocked energy score for eachpredicted conformation ofcompounds (R/S)-1 to twoPIM1 structures, i.e., the nativestructure of PIM1 bound tocompound (R)-1 (PDB ID:2BZH) and the non-nativestructure of PIM1 bound to anATP analog (PDB ID:1YXT).■ Single conformation dockingresults, ○ ensemble dockingresults

J Mol Model (2013) 19:371–382 377

sample kinase flexibility by exploring both backbone and sidechain fluctuations around the crystallographic conformationused as the starting point for MD simulations. In Fig. 9b, theinitial non-native PIM1 conformation (PDB ID: 1YXT), andthe conformation, which binds the inhibitor with the lowestdocking energy are aligned together, and key residues aroundthe binding pocket are depicted. The comparison reveals theshifting of key residues, in particular GLU171, ASP128 andASP186, from the initial non-native structure, and the rear-rangement of the flexible Gly-rich loop (Gly45) to betterposition the CO group and the Cp ring.

As noted in Fig. 8, the non-native PIM1 structure fails toaccount for the conserved hydrogen bond between GLU121:O–(R)-1:H1, which is captured after the slight rearrangementof active-site residues using the ensemble docking protocol.Thus, the implicit protein flexibility in our protocol yields a

better docked score for this conformation. Even though pro-tein kinases are known to assume multiple conformationalstates in a rugged energy landscape [32], which are not ex-haustively sampled in short MD simulations, we find that, bysampling the fluctuations of protein conformation around agiven initial state of our control system, our ensemble protocoldemonstrates clear improvement in the prediction of theligand-bound structure of the complex.

Two dominant conformations are predicted for (R/S)-1to bind with GSK-3β and PIM-1

Figure 7 illustrates that, for both enantiomers, two confor-mations are predicted with relatively lower docking energiescompared to other conformation, with RMSDs of 2 Å and5 Å from the crystallized conformation, respectively. Thetwo structures predicted for both enantiomers are depicted inFig. 10. In all of the conformations, the pyridocarbazolemoiety forms a hydrogen bond between the maleimide NHgroup and the backbone carbonyl oxygen atom of a residuewithin the hinge region (GLU121) of the kinase, whichmimics the hydrogen-bonding pattern of the ATP as wellas staurosporine with the kinase, and is present in the PIM1/(R)-1 crystal structure. One of the two conformations, whichwe denote as conformation I (Fig. 10a,c), is very similar tothe reference structure constructed from crystallography. Inthe second conformation, which we denote as conformationII, the CO and Cp groups occupy opposite (swapped) posi-tions relative to conformation I. Thus the two conformationsI and II are related by a 180° flip around an axis through theNH group of the pyridocarbazole moiety. The existence ofconformation II as a stable structure has not been confirmedthrough crystallographic studies of the PIM1 kinase. How-ever, this conformation is very close to a recent structure ofcompound (S)-2 bound to a lipid kinase (PI3Kγ) [14].Hence, we propose conformation II as a competing alterna-tive bound conformation for the inhibitor bound to theprotein kinase. The same two conformations of compound(R/S)-1 also turn out to be the most dominant bound con-formation with GSK-3β through ensemble docking asdepicted in Fig. 11, making the binding characteristics ofthe (R/S)-1 Ru-compound with GSK-3β similar to thosewith PIM1.

A unique bound conformation of the ruthenium compounddominates its binding to CDK2

Our results for the bound conformations (R/S)-1 to CDK2/cyclin A are in stark contrast to those for PIM1 and GSK-3β. The conformation close to the two conformations forPIM1 and GSK-3β are ranked lower [4th for (R)-1] or notpresent in our prediction [(S)-1]. The disfavorable two con-formations are evidences for unfavorable binding of this

Fig. 8 Predicted bound conformations of the inhibitor with the lowestRMSD to the reference structure from single conformation and ensem-ble conformation docking are aligned together with crystal structure.Blue Single conformation docking of compound (R)-1 to the nativePIM1 structure (PDBID: 2BZH); orange ensemble conformation dock-ing of compound (R)-1 to the PIM1 ensemble of structures based onnative PIM1 structure; tan single conformation docking of compound(R)-1 to the non-native PIM1 structure (PDBID: 1YXT); pink ensem-ble conformation docking of compound (R)-1 to the ensemble of PIM1structures based on non-native PIM1 structure; red crystal structures(PDB ID: 2BZH)

Table 3 Parameters of the hydrogen bond between the NH group ofcompound 1-R and residue GLU121 in PIM1 for the complex con-formations shown in Fig. 8

Structure Distance (Å) Angle (degrees)

Red (crystal structure) 2.0 151.02

Blue (native/single docking) 1.72 140.16

Orange (native/ensemble docking) 1.76 137.39

Tan (nonnative/single docking) 3.55 114.33

Pink (nonnative/ensemble docking) 1.65 135.03

378 J Mol Model (2013) 19:371–382

compound to CDK2/cyclin A in the native conformationobserved in PIM1 and GSK-3β. While conformations I andII are not favorable for CDK2, we found the top-rankedconformation for both enantiomers to be very similar, and

this conformation is unique to CDK2, (i.e., not observed forPIM1 and GSK-3β). Figure 12 depicts the lowest-energyconformations predicted for CDK2/cyclin A. The lowest-rank conformations for both enantiomers are quite similar,

Fig. 9 a Snapshots of PIM1 conformations generated from moleculardynamic (MD) simulations based on the non-native PIM1 structure(PDBID: 1YXT). Snapshots are aligned by overall RMSD, and coloredby atom type. The black conformation is the crystallized non-nativePIM1 conformation. b Protein conformation comparison between the

non-native crystallized structure of PIM1 (PDBID: 1YXT, gray) andthe predicted protein bound conformation with the lowest RMSD to thereference structure using the ensemble conformation dockingcorresponding to this non-native PIM1 system (orange)

Fig. 10 a–d Top two ranks of conformations of compound (R/S)-1 bound to PIM1 predicted by ensemble docking. a, b Compound (R)-1. c, dCompound (S)-1. We denote the similar conformations in a and c as conformation I and those in b and d as conformation II

J Mol Model (2013) 19:371–382 379

showing strikingly that, instead of forming a hydrogen bondwith the hinge region residue, the NH group of the pyridocar-bazole moiety points outside the binding pocket. Moreover,the plane of the Cp ring stacks with the plane of the aromaticPHE80—the gate-keeper residue. The CO group orients intothe small binding pocket formed by ALA144, ASP145,ASN132 and GLN131. In this conformation, even thoughthe compounds appear to fit nicely within the CDK2 bindingsite, there is a distinct lack of a conserved hydrogen bond,which warrants further investigation and experimental valida-tion of the structure of the inhibitor complexed to CDK2.

Discussion

Based on in vitro protein kinase profiling results, it has beendetermined that the racemic mixture of ruthenium-basedorganometalic protein kinase inhibitor scaffold [compound(R/S)-1] prefers to inhibit PIM1, GSK-3β over CDK2/cyclinA, even though the active sites of the three kinases showhigh degree of sequence and structure similarity. Here, wefound that ruthenium-based compounds bind in a similarfashion to PIM1 and GSK-3β but show a novel conforma-tion with marked differences in binding to CDK2/cyclin A.

Fig. 11 a–d Top two ranks of conformations of compound (R/S)-1 bound to GSK3-β predicted by ensemble docking. a, b Compound (R)-1. c, dCompound (S)-1. We denote the similar conformations in a and c as conformation I, and those in b and d as conformation II

Fig. 12 Top conformation predicted for both enantiomers bound to CDK2

380 J Mol Model (2013) 19:371–382

Based on our structural analysis, we suggest the followingexplanations for this selectivity: (1) despite the extensivestructure and sequence homology, CDK2 differs in the ac-tive site at the location of PHE80, which possibly stabilizesa novel bound conformation due to a stacking interactionwith the Cp ring. This conformation also lacks the charac-teristic hydrogen bond between the inhibitor and linkerregion residue of the kinase and may cause a non-preference to the bound conformation. (2) Compared toPIM1 and GSK-3β, it is less preferable for CDK2/cyclinA to bind the inhibitor (especially the S-isomer) in a con-formation that preserves the conserved hydrogen bond. Byaligning the CDK2 structure to PIM1 and GSK-3β, wesuggest that residue PHE80 might play a negative role inpositioning the carboxyl group in the pyridocarbazole plane(Fig. 3). We therefore suggest examining the selectivityprofile of a new compound 2 as shown in Fig. 13, whichmay help to further clarify the possible reason for the dif-ferent binding of compound 1 with GSK-3β and CDK2.

Using the existing crystal structure of PIM1/(R)-1 as acontrol test, we also have shown that by implicitly account-ing for protein flexibility, the ensemble docking protocol isable to help refine the structural features as well as dis-criminate the docked energies associated with bound con-formations of two enantiomeric forms of the inhibitor to thethree kinases. The comparison of results from single pointdocking and ensemble docking demonstrate that the tradi-tional docking method based on a single protein conforma-tion is sensitive to the protein structure used. Although it

generates good docking poses on the exact protein struc-ture, the results become considerably worse on even aslightly different protein structure. The ensemble dockingrepresents a significant improvement in this aspect, yield-ing consistent results regardless of the initial protein struc-ture used. For the cases presented above, in which thenative pose is unknown, the ensemble docking protocol ismore robust and reliable in sampling the true complexstructure.

In our ensemble docking analysis, we employ the lowestdock energy conformation to characterize the clusters, andshow that, for PIM1 and GSK-3β, the ensemble dockingprotocol ranks the native-like conformations as one of thetop clusters. Another popular choice for scoring the con-formations is based on the frequency of an observed cluster[33]. In our case, we find that, based on the dockingenergy, many of the top-ranked clusters would also beidentified by the high-frequency criterion. Hence, the twocriteria tend to yield similar predictions for the top-rankedconformations.

In the ensemble docking protocol employed here, we donot consider the induced effect of the inhibitor to the proteinconformation, i.e., our docking protocol only implicitlyaccounts for protein flexibility. Our assumption is that bysampling the protein kinase in and around its unbound (apo)state, the dynamics of the protein will account for the smallinduced effect and the bound conformations will select forthose protein conformations resulting in lower dock scores.Thus for a good scoring function (with large correlationbetween experimental binding affinity and the computeddocking score on an extensive test set of compounds), theensemble docking is expected to yield conformations withthe lowest binding affinities. We advocate that our ensembledocking approach is a good first step in cases where we havesome knowledge of the protein structure (through experi-mental or modeling schemes), such as the prediction of thebound complex with a new inhibitor scaffold.

We also note the organometalic inhibitor compounds wehave studied are very new, and fully flexible force-fields forthese inhibitors are not yet available. Therefore, our focus inthis article is on the structure prediction of the bound com-plex as opposed to the free energy of binding. Once thestructural aspects are validated, our predictions are thenlogical candidates for a more rigorous evaluation usingcomputationally intensive free energy protocols in fullyatomistic systems [34–38].

Acknowledgments We thank Eric Meggers, Scott Diamond, andMary Pat Beavers for insightful discussions and acknowledge financialsupport from the United States National Science Foundation (Grantnumbers: 0853389, 0853539, 1120901, 1133267, 1244507). Compu-tational resources were provided in part by Extreme Science andEngineering Discovery Environment (XSEDE) under a resource allo-cation grant (MCB06006).

Fig. 13 Structure of suggested new compound 2 proposed for furthertesting. This compound combines the structural features of compound1 and the functionality of staurosporine

J Mol Model (2013) 19:371–382 381

References

1. Blume-Jensen P, Hunter T (2001) Oncogenic kinase signalling.Nature 411(6835):355–365

2. Hunter T (2000) Signaling–2000 and beyond. Cell 100(1):113–1273. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S

(2002) The protein kinase complement of the human genome.Science 298(5600):1912–1934

4. Jorissen RN, Walker F, Pouliot N, Garrett TPJ, Ward CW, BurgessAW (2003) Epidermal growth factor receptor: mechanisms ofactivation and signalling. Exp Cell Res 284(1):31–53

5. Ciardiello F, Vita FD, Orditura M, Tortora G (2004) The role ofEGFR inhibitors in nonsmall cell lung cancer. Curr Opin Oncol 16(2):130–135

6. Fabbro D, Ruetz S, Buchdunger E, Cowan-Jacob SW, Fendrich G,Liebetanz J, Mestan J, O’Reilly T, Traxler P, Chaudhuri B, Fretz H,Zimmermann J, Meyer T, Caravatti G, Furet P, Manley PW (2002)Protein kinases as targets for anticancer agents: from inhibitors touseful drugs. Pharmacol Ther 93(2–3):79–98

7. Buchdunger E, O’Reilly T, Wood J (2002) Pharmacology of imatinib(STI571). Eur J Cancer 38(Suppl 5):S28–36

8. Thaimattam R, Banerjee R, Miglani R, Iqbal J (2007) Proteinkinase inhibitors: Structural insights into selectivity. Curr PharmDes 13(27):2751–2765

9. Noble MEM, Endicott JA, Johnson LN (2004) Protein kinaseinhibitors: Insights into drug design from structure. Science 303(5665):1800–1805

10. Bregman H, Carroll PJ, Meggers E (2006) Rapid access to unex-plored chemical space by ligand scanning around a rutheniumcenter: discovery of potent and selective protein kinase inhibitors.J Am Chem Soc 128(3):877–884

11. Meggers E (2007) Exploring biologically relevant chemical spacewith metal complexes. Curr Opin Chem Biol 11(3):287–292

12. Atilla-Gokcumen GE, Williams DS, Bregman H, Pagano N,Meggers E (2006) Organometallic compounds with biologicalactivity: A very selective and highly potent cellular inhibitor forglycogen synthase kinase 3. Chem Bio Chem 7(9):1443–1450

13. Bregman H, Meggers E (2006) Ruthenium half-sandwich complexesas protein kinase inhibitors: an N-succinimidyl ester for rapid deriva-tizations of the cyclopentadienyl moiety. Org Lett 8(24):5465–5468

14. Xie P, Williams DS, Atilta-Gokcumen GE, Milk L, Xiao M,Smalley KSM, Herlyn M, Meggers E, Marmorstein R (2008)Structure-based design of an organoruthenium phosphatidyl-inositol-3-kinase inhibitor reveals a switch governing lipid kinasepotency and selectivity. Chem Biol 3(5):305–316

15. Schindler T, Bornmann W, Pellicena P, Miller WT, Clarkson B,Kuriyan J (2000) Structural mechanism for STI-571 inhibition ofabelson tyrosine kinase. Science 289(5486):1938–1942

16. Wang ZL, Canagarajah BJ, Boehm JC, Kassisa S, Cobb MH,Young PR, Abdel-Meguid S, Adams JL, Goldsmith EJ (1998)Structural basis of inhibitor selectivity in MAP kinases. StructFold Des 6(9):1117–1128

17. Halperin I, Ma BY, Wolfson H, Nussinov R (2002) Principles ofdocking: an overview of search algorithms and a guide to scoringfunctions. Proteins Struct Funct Bioinf 47(4):409–443

18. Brooijmans N, Kuntz ID (2003) Molecular recognition and dockingalgorithms. Annu Rev Biophys Biomol Struct 32:335–373

19. Wong CF (2008) Flexible ligand-flexible protein docking in pro-tein kinase systems. Biochim Biophys Acta 1784(1):244–251

20. Chaudhury S, Gray JJ (2008) Conformer selection and induced fitin flexible backbone protein-protein docking using computationaland NMR ensembles. J Mol Biol 381(4):1068–1087

21. Szabo A, Ostlund NS (1996) Modern Quantum Chemistry. Dover,Mineola, NY

22. Jensen F (2007) Introduction to computational chemistry. Wiley,Chichester

23. Aikens CL, Laederach A, Reilly PJ (2004) Visualizing complexesof phospholipids with streptomyces phospholipase D by automateddocking. Proteins Struct Funct Bioinf 57(1):27–35

24. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, SwaminathanS, Karplus M (1983) CHARMM—a program for macromolecularenergy, minimization, and dynamics calculations. J Comput Chem4(2):187–217

25. Kale L, Skeel R, Bhandarkar M, Brunner R, Gursoy A, Krawetz N,Phillips J, Shinozaki A, Varadarajan K, Schulten K (1999)NAMD2: Greater scalability for parallel molecular dynamics. JComput Phys 151(1):283–312

26. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, EvanseckJD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D,Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T,Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M,Smith JC, Stote R, Straub J, Watanabe M, Wiorkiewicz-Kuczera J,Yin D, Karplus M (1998) All-atom empirical potential for molecularmodeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616

27. Batcho P, Case DA, Schlick T (2001) Optimized particle-meshEwald / multiple-timestep integration for molecular dynamicssimulations. J Chem Phys 115:4003–4018

28. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, BelewRK, Olson AJ (1998) Automated docking using a Lamarckiangenetic algorithm and an empirical binding free energy function.J Comput Chem 19(14):1639–1662

29. AutoDockTools_ i86Linux2_1.5.2. The Scripps Research Institute.http://autodock.scripps.edu/resources/adt

30. Matlab7.0. The MathWorks. http://www.mathworks.co.uk/31. Kozakov D, Clodfelter KH, Vajda S, Camacho CJ (2005) Optimal

clustering for detecting near-native conformations in protein docking.Biophys J 89(2):867–875

32. Frauenfelder H, Sligar SG, Wolynes PG (1991) The energy land-scapes and motions of proteins. Science 254(5038):1598–1603

33. Wong CF, Kua J, Zhang YK, Straatsma TP, McCammon JA (2005)Molecular docking of balanol to dynamics snapshots of proteinkinase A. Proteins Struct Funct Bioinf 61(4):850–858

34. Gilson MK, Zhou HX (2007) Calculation of protein-ligand bindingaffinities. Annu Rev Biophys Biomol Struct 36:21–42

35. Radmer RJ, Kollman PA (1997) Free energy calculation methods:a theoretical and empirical comparison of numerical errors and anew method for qualitative estimates of free energy changes. JComput Chem 18(7):902–919

36. Kollman PA, Massova I, Reyes C, Kuhn B, Huo SH, Chong L, LeeM, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J,Case DA, Cheatham TE (2000) Calculating structures and freeenergies of complex molecules: combining molecular mechanicsand continuum models. Acc Chem Res 33(12):889–897

37. Kuhn B, Gerber P, Schulz-Gasch T, Stahl M (2005) Validation anduse of the MM-PBSA approach for drug discovery. J Med Chem48(12):4040–4048

38. Shirts MR, Pande VS (2001) Mathematical analysis of coupledparallel simulations. Phys Rev Lett 86(22):4983–4987

382 J Mol Model (2013) 19:371–382