Activity Prediction and Structural Insights ofExtracellular Signal-Regulated Kinase 2 Inhibitorswith Molecular Dynamics Simulations

Alberto Del Rio, Benedetta Frida Baldi andGiulio Rastelli*

Dipartimento di Scienze Farmaceutiche, Universit� di Modena eReggio Emilia, Via Campi 183 41100 Modena, Italy*Corresponding author: Giulio Rastelli, giulio.rastelli@unimore.it

A computational application to predict, probe andinterpret the activities of a series of congenericcompounds inhibiting extracellular signal-regu-lated kinase 2 protein kinase is presented. Thestudy shows that molecular dynamics coupled withmolecular mechanics Poisson–Boltzmann solventaccessible surface area free energy estimation is asuitable tool for investigating the experimentalbinding activities of ligands to protein kinases.Computed and experimental binding activitieswere found to be significantly correlated. More-over, the interpretation of the X-ray co-crystalstructure in conjunction with computationalresults shows that the hinge region of the proteininsure the principal binding site via multiple hydro-gen bonding interactions, whereas fine-modulationof biological activities along the series is accom-plished through the combination of weak andstrong interactions that compete with water. Theseare located in the substituent moieties of theligands interfacing with the DFG motif, the sugarregion and the hydrophobic pocket of extracellularsignal-regulated kinase 2. The study suggests thata wider interaction framework that is well beyondthe hinge region is required to predict and rational-ize at molecular level the experimental biologicalactivities of congeneric compound series.

Key words: anticancer drugs, binding free energy, ERK2, MM-PBSA, molecular dynamics, protein kinases

Abbreviations: ERK2, extracellular signal-regulated kinase 2;MD, molecular dynamics; MM-PBSA, molecular mechanics Poisson–Boltzmann ⁄ surface area.

Received 15 June 2009, revised 28 August 2009 and accepted forpublication 10 September 2009

Designing small molecule architectures with desired anticanceractivity is a major focus of oncology research. The key to such

success is believed to lie in the concept of targeted therapy that inthe last years has been conveyed toward more and more efficientresearch achievements. The goal of targeted therapy is to selec-tively treat cancer cells without harming healthy tissues by actingon pathways that are unique to cancer cells (1–3). In this context,protein kinases and cancer have been intimately linked for 30 years(1,4–6). Over 518 protein kinases with a central role in cellular sig-naling transduction have been identified to date, and their dysregu-lation has been correlated with a plethora of various diseasesespecially tumorigenesis. Many protein kinase inhibitors have beendiscovered and developed, some of which were approved andentered the clinic (1,2,6–8). However, the road to develop kinaseinhibitors as marketed drugs proved to be difficult, and many inhibi-tors failed late in development. Among all protein kinases, theMAPK cascade comprising RAF, MEKs and ERK plays a pivotal rolein cell proliferation, differentiation, survival and death. Dysregula-tion of these kinases is involved in different cancer pathologies sothat MAPKs are considered suitable targets for antiproliferativetherapies (9). In this context, we focused the attention on the extra-cellular signal-regulated kinase 2 (ERK2) for its crucial biologicalimportance as end-connecting point through different external stim-uli towards important transcription factors such as Myc, CREB andFOXO3a (10). The inhibition of ERK2 is highly desirable consideringthat downstream effects on the pathway should be most profoundby inhibition of ERK pathway in the order ERK > MEK > RAF. Forinstance, the activation of only the 5% of Ras is sufficient, throughamplification of the signal, to induce the full activation of ERK1 ⁄ 2(11). Despite some inhibitors have been reported in the literature,to our knowledge so far, no drugs targeting ERK2 have beenapproved or entered clinical trials. Several high-resolution crystalstructures of ERK2 suitable for structure-based drug design pur-poses have been solved and deposited in the Protein Data Bank inthe recent years. However, while experimental techniques such asX-ray have elucidated the most important anchoring points betweenligands and kinases, the structural features that fine-modulate theobserved biological activity and ⁄ or selectivity are still ratherunclear. In fact, it is quite common to observe congeneric sets ofligands in which hydrogen bond interactions with active site resi-dues are conserved, for example, with the so called 'hinge region',but show a wide spectrum of experimental inhibitory activities.

In order to gain insight on this topic we have carried out a compre-hensive molecular dynamics (MD) and binding free energy investiga-tion on a selected series of pyrazolylpyrrole inhibitors of ERK2taken from the work of Aronov et al. (12), who also solved the

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Research Letter

ª 2009 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2009.00903.x

crystal structures of three members of the series. To the best ofour knowledge, this is the first comprehensive set of congenericcompounds described in the literature that inhibit ERK2 with a vari-ation of biological activity spanning several orders of magnitude. Inaddition, the core scaffold is not structurally related to ATP and istherefore more interesting from a pharmacological point of view.Figure 1 shows the R1- and R2-substituted pyrazolylpyrrole scaffoldand the main ERK2 residues interacting with them as well as theexperimental inhibition constants taken from (12). The inhibitorswere selected on the basis of significant variation in biologicalactivity (Ki ranges from >10 to 0.002 lM), chemical diversity andthe presence of two examples of enantiomeric pairs of compounds(namely 8 ⁄ 9 and 14 ⁄ 15) in order to see if computations were ableto discriminate their activities. Compounds tested as racemateswere discarded, since a defined stereochemistry of the ligand isrequired to compute meaningful free energies and protein–ligandcomplexes. A few redundant compounds exhibiting minor variationsin chemical structure and biological activity with respect to com-pounds already included in our set were also removed. We con-structed a suitable computational model for interpreting the threeco-crystal structures and we complemented the structural analysisby including all the inhibitors of Figure 1. MD simulations and esti-mation of the free energies of binding via molecular mechanicsPoisson–Boltzmann solvent accessible surface area (MM-PBSA)algorithm were carried out with the Amber 9 software package(13). The molecular mechanics (MM) parameters for these mole-cules were assigned with antechamber, divcon and parmcheck mod-ules. The full ligand preparation protocol is available in supportinginformation (Table S1). The protein structures used in our investi-gation were taken from the crystal structure of ERK2 in complexwith compounds 1, 5 and 15 (PDB-id 2OJG, 2OJI and 2OJJ,respectively) (12). The initial orientations of compounds 2–4, 6–7

in ERK2 were obtained by matching the ligands with 5 in 2OJI, andby using the latter receptor structure for calculations. Equally, thereceptor structure of 2OJJ was used as a template for building thecomplexes with the remaining compounds 8–14. Each complex wassolvated with an octahedral box of TIP3P water molecules extend-ing 5 � outside the protein on all sides, resulting in >8200 watersadded. To ensure an adequate sampling of the potential energysurfaces and equilibration of the complexes, these were subjectedto 100 ps constant volume MD and 100 ps constant pressure MDwith constraints, followed by at least 1 ns equilibration withoutconstraints. The convergence toward to the production phase wasassessed by monitoring binding free energies and root meansquares (RMS) deviations of the structures estimated during MDequilibration. Six subsequent and consecutive production runs ofthe length of 1 ns each were performed with coordinates extractedevery 2 ps. The Amber MM-PBSA routine was used to estimateligand-binding affinity from the 6 ns MD production runs. As inmany other MM-PBSA investigations (14–18), the entropic contribu-tion to binding (TDS) was not evaluated for the following reasons.First, even if enthalpy ⁄ entropy compensation is generally importantto understand and reliably predict binding energies (19), we expectthat congeneric compounds such as the ones investigated here mayexhibit similar solute entropy contributions to binding (18); second,huge computational resources are required to perform normal modeanalyses on a sufficiently large ensemble of protein–ligandcomplexes (14,17); third, entropy calculations usually have large

Figure 1: Set of ERK2 inhibitors considered in this study. Thescheme on top shows the main interactions between the ligandsand ERK2. Reported Ki (lM) was obtained by Aronov et al. (12).

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error bars, and the inclusion of inaccurate entropy estimates in DGmay harm more than completely neglecting this term (14,15,20).Thus, while incorporation of solute entropy in binding affinitycalculations remains an important but daunting challenge for whichimprovements are needed in future research, in this applicationentopic terms were not estimated. Finally, hydrogen bond analyseswere performed using ptraj. The full list of settings and parametersfor MD and MM-PBSA is available in Table S2. The binding freeenergy evaluations were performed on an ensemble of ligand–ERK2complexes generated with 6 ns periodic boundary MD simulationsin water. The average free energy results are given in Table 1 alongwith the free energy components and the experimental DG. Bindingfree energies of each inhibitor have been computed according tothe equation DG0calc = DEMM + DGsolv, where DEMM is the mole-cular mechanics contribution expressed as the sum of the electro-static and van der Waals contributions to binding in vacuo, andDGsolv is the solvation free energy contribution to binding expressedas the sum of polar (DGpsolv) and non-polar (DGnpsolv) solvation freeenergies.

Results in Table 1 show that the in vacuo electrostatic and van derWaals contributions to binding explore significant variation alongthe series of compounds, reflecting the importance of the variousmolecular interactions with active site residues. As expected, gas–phase interaction energies (DEMM) are scarcely correlated withexperimental DG (r2 = 0.51) since the inclusion of additional termssuch as desolvation energies are crucial for predicting binding activ-ities in agreement with experiments. The non-polar solvation freeenergies are always negative, that is, favorable to binding. Polarsolvation free energies are positive, that is, the molecules must paya desolvation penalty upon protein binding.

The regression plot of the calculated DG0calc against the experimen-tal DGexp values (Table 1) for the series of the 15 compounds isreported in Figure 2. Compounds 3 and 11 whose experimental Ki

are not defined (>10 and >5 lM, respectively) have been omittedfrom the regression. The results unequivocally show that the corre-lation is very good (r2 = 0.70, S = 0.68, F = 25.9). Therefore, asalready reported in other studies (16,21–23), MD and MM-PBSAprovide reliable ways to predict binding activities of ligands inagreement with experimental data. In our application, the correla-tion holds for a set of structurally related molecules, demonstrating

Table 1: Binding free energy components (kcal ⁄ mol) and SEM calculated with MM-PBSA for the set of compounds of Figure 1

Compound Electrostatica VDWb DEMMc DGnpsolv

d DGpsolve DGsolv

f DG0calcg DGexp

h

1 )29.95 (0.16) )36.90 (0.12) )66.85 (0.16) )4.98 (0.00) 39.52 (0.12) 34.53 (0.12) )32.32 (0.14) )7.712 )36.51 (0.22) )43.56 (0.13) )80.07 (0.20) )5.80 (0.01) 50.02 (0.18) 44.22 (0.18) )35.85 (0.16) )8.713 )85.54 (0.41) )47.60 (0.14) )133.14 (0.41) )6.42 (0.01) 108.64 (0.37) 102.22 (0.37) )30.93 (0.19) >)6.844 )34.81 (0.19) )43.60 (0.14) )78.40 (0.19) )5.97 (0.01) 51.66 (0.16) 45.69 (0.15) )32.72 (0.19) )7.795 )34.34 (0.18) )45.81 (0.13) )80.15 (0.17) )5.95 (0.01) 51.49 (0.12) 45.54 (0.12) )34.61 (0.16) )9.666 )37.78 (0.21) )45.70 (0.13) )83.47 (0.20) )6.19 (0.01) 53.26 (0.16) 47.07 (0.16) )36.40 (0.15) )8.697 )40.00 (0.19) )45.59 (0.13) )85.59 (0.20) )6.12 (0.01) 53.71 (0.20) 47.59 (0.19) )38.00 (0.16) )10.128 )35.47 (0.19) )46.01 (0.15) )81.48 (0.21) )6.19 (0.01) 53.23 (0.20) 47.05 (0.19) )34.43 (0.17) )9.579 )37.52 (0.19) )45.78 (0.14) )83.29 (0.21) )6.18 (0.01) 55.01 (0.16) 48.83 (0.16) )34.47 (0.19) )8.45

10 )42.58 (0.30) )47.86 (0.14) )90.44 (0.29) )6.53 (0.01) 59.78 (0.23) 53.24 (0.23) )37.20 (0.19) )10.2011 )15.90 (0.45) )48.02 (0.14) )63.92 (0.46) )6.37 (0.01) 43.17 (0.46) 36.80 (0.46) )27.12 (0.24) >)7.2512 )45.72 (0.25) )47.25 (0.14) )92.97 (0.23) )6.48 (0.01) 63.09 (0.20) 56.61 (0.20) )36.36 (0.18) )8.6113 )38.81 (0.20) )44.16 (0.13) )82.97 (0.20) )6.36 (0.01) 59.93 (0.17) 53.57 (0.17) )29.40 (0.18) )7.7114 )46.40 (0.27) )45.13 (0.15) )91.53 (0.27) )6.58 (0.01) 62.49 (0.22) 55.92 (0.23) )35.62 (0.18) )9.3315 )47.33 (0.24) )54.00 (0.14) )101.33 (0.24) )6.86 (0.01) 67.93 (0.23) 61.08 (0.22) )40.26 (0.18) )11.90

Energy values correspond to the average values calculated from the 6 ns MD simulations in water, with SEM (rsd ⁄ N1 ⁄ 2) in parentheses.aNon-bonded electrostatic contribution to binding.bNon-bonded van der Waals contribution.cMM gas–phase interaction energy.dNon-polar contribution to solvation free energy according to Gnpsolv = cSASA + b, where c = 0.0072 kcal ⁄ mol �)2 and b = 0 kcal ⁄ mol.ePolar contribution to solvation free energy (PB).fTotal solvation free energy (DGnpsolv + DGpsolv).gEstimated free energies of binding DG0bind= DEMM + DGsolv.hExperimental free energies of binding according to DG = -RTln(1 ⁄ Ki), the experimental Ki data are reported in Figure 1.

Figure 2: Correlation between calculated and experimental freeenergies of binding (kcal ⁄ mol). Theoretical values were obtainedfrom 6 ns MD in water and MM-PBSA free energy analyses, experi-mental values were obtained from the Ki values.

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that the approach is able to discriminate the biological activity ofcongeneric compounds. In particular, we remark that very similarstructures such as the enantiomeric pairs 8 ⁄ 9 and 14 ⁄ 15 are welldiscriminated by the theoretical values and give a faithful represen-tation of the experimental Ki. Moreover, the computational predic-tion for compounds 3 and 11 extrapolated on the trend line ofFigure 2 is compatible with their experimental data of >)6.9 and>)7.3 Kcal ⁄ mol, respectively. Finally, while it is worth noting that,as already reported in many other MM-PBSA applications (16,21–23), the values of calculated and experimental binding free energiesdiffer on an absolute scale (Table 1), the free energy values arehighly correlated (Figure 2). Predicting the absolute values wouldrequire for instance the inclusion of entropic effects (14,19) that, asalready mentioned, are computationally intensive and are beyondthe goals of the present study (see discussion above).

Having obtained a very good correlation between calculated andexperimental free energies, we have derived further straightforwardinformation from our computational models by analyzing structuresand ligand–receptor interactions along the series of compounds. Infact, differently from crystallography, MD simulations allow an effi-cient sampling and monitoring of multiple conformational states ofthe ligand–receptor complexes in water at room temperature.Therefore, they are able to highlight time-dependent structural fea-tures that may provide additional and useful information for drugdesign efforts. Moreover, the application of MD is particularly rele-vant to predict the binding modes of those members of the seriesfor which no crystallographic data are experimentally available. Thestructural analyses results show that the three hydrogen bond inter-actions with Met106, Asp104 and Gln103 belonging to the hingeregion are conserved within the whole series of compounds (Fig-ures S1 and S2) and that the occurrence of these interactions isclose to 100%, that is, they are present for the whole duration ofthe 6 ns MD trajectories (Table S3). Other important but less con-served interactions are present between the carbonyl adjacent tothe pyrrole and the side chain of Lys52 on the b3 sheet as well aswith a water molecule that is firmly located inside the bindingpocket (Figure 1). According to MD the interaction with Lys52,which is an absolutely conserved residue important for a correctorientation of ATP (24), is conserved for all compounds but is pres-ent, on average, for �80% of the simulation time while water isanchored to the carbonyl for �90% of the simulation time(Table S3). Considering that the above-mentioned interactions areconserved for all compounds in the series, we can exclude that thedrop in activity observed for less active compounds, for example,compounds 3 and 11, is due to steric effects or mismatched inter-actions of the R2 substituent that ultimately result in a differentpositioning or displacement of the common scaffold with respect tothe hinge region of ERK2. Rather, these results suggest the impor-tance of other regions of the active site for driving the modulationof inhibitory potencies. Under this hypothesis, differences in experi-mental as well as computed binding energies should be attributedto different local interactions of various R2 substituents in theactive site. A first straightforward observation in this direction con-cerns the two enantiomeric forms 14 and 15. The high eudismicratio suggests that different configurations of the stereogeniccarbon located in R2 have a profound impact on ERK2 binding. Acomparison of the binding mode of the two enantiomeric structures

obtained from MD simulations is shown in Figure 3. The twoligands rearrange to undergo different intermolecular interactionswith ERK2 residues located in the middle of the accessible solventarea of the pocket. In particular, the distomer compound 14 makeshydrogen bonds with Asn152 of the sugar region and Asp165 ofthe DFG motif with interaction frequencies of 83% and 7%, respec-tively. The eutomer 15 (that is also the most potent inhibitor of theseries) is hydrogen bonded to the backbone carbonyl of Ser151 inthe sugar region with a frequency of 50% while for the leftover50% interacts with solvent molecules located into the bindingpocket. The information on the rather conserved interaction withSer151 is particularly relevant considering that the binding mode ofthe crystallographic structure (2OJJ) show an equidistance of thehydroxyl group of the ligand to Ser151 and Asn152. Moreover, con-sistently with the present findings, hydrogen bonding with theSer151 residue of ERK2 was also observed recently with hypothe-mycin, an ERK2 covalent inhibitor (25). The fact that the electro-static contributions to binding are very similar (Table 1) indicatesthat the different hydrogen bonding interactions explored by 14

and 15 cannot explain their different biological activities. Rather,we found that the two enantiomers exhibit a different orientationof their halogen-substituted phenyl rings in the active site, which inturn affects the conformation of the adjacent glycine-rich loop. Thedifferent nature of these interactions is testified by the �9 kcal ⁄mol more favorable van der Waals interaction energy of 15 withrespect to 14 (Table 1) that can be attributed to an optimal loca-tion of the phenyl ring of 15 in the hydrophobic pocket in agree-ment with its observed higher activity.

Similar observations can be performed comparing the binding fea-tures of compounds 4 and 5 depicted by MD. The results showthat compound 4, lacking the methylenic carbon bridging the phenylring and the scaffold, cannot orient the former in the optimal loca-tion already described for 15 (Figure S3). On the contrary, the

A B

Figure 3: (A) Overlay of the two enantiomeric conformation of14 (green) and 15 (blue) as they bind to ERK2. (B) Binding mode of14 and 15 in the ATP-pocket when matching residues located inthe sugar region and b3.

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phenyl ring of the more active compound 5 adopts a similar orien-tation. In agreement with these structural observations, we foundthat the electrostatic contribution to binding is almost identicalwhile the van der Waals contribution is �2 kcal ⁄ mol in favor of 5

(Table 1). Therefore, we can conclude that the possibility to wellaccommodate an aryl ring in the hydrophobic pocket of ERK2 playsan important role for modulation of biological activity. These com-parisons exemplify the importance of considering other regions ofprotein kinase pocket that are different from the hinge region forfine-modulating ligand activities. Along the series of compounds,other comparisons can be performed to corroborate such observa-tions. For instance, compound 10 bearing the chiral hydroxymethylgroup is more active than 5. The presence of such substituentresults in a 10 kcal ⁄ mol gain of in vacuo interaction energy whichis only in part balanced by a stronger desolvation penalty (Table 1).This leads to the prediction that compound 10 is more active inagreement with experiment. Most of the stabilization comes fromthe electrostatic term, reflecting the polar nature of the substituentand the observed hydrogen bonding patterns. It is interesting tonote that the hydroxymethyl substituent has also an effect on theorientation of the aryl group that binds the hydrophobic pocket, asalready noted for other inhibitors. The binding mode (Figure S4)shows a rearrangement of the structure so that the hydroxyl groupof 10 hydrogen bonds with Asp165 (frequency of 54%) of the DFGmotif while compound 5 has not such possibility. Introduction of anegatively charged carboxylate in place of the hydroxymethyl (com-pound 11) has a very negative impact on the electrostatic interac-tion energy (Table 1), which nicely explains the drop in activityobserved for this compound. Vice versa, halogen atoms on the phe-nyl ring increase activity. Compound 15 with both fluorine and chlo-rine is �20-fold more active than the unsubstituted compound 10.The MD models for these compounds show that the two halogensnicely fit into the ERK2 hydrophobic pocket (Figure S5) and createadditional contacts. In a consistent manner, the van der Waalsinteraction energy is �6 kcal ⁄ mol in favor of 15 as opposed to 10

(Table 1). In addition, we found that the presence of the two halo-gens slightly alters the location of the phenyl ring that in turnaffects the position of the connected hydroxymethyl group. In fact,MD predicts that compound 10 interacts mainly with Asp165 ofthe DFG motif while 15 interacts with Ser151 of the sugar region(Figure S5), and that 15 has �5 kcal ⁄ mol more favorable electro-static interaction energy (Table 1). One final comparison can be per-formed between compounds 12 and 13 that differ from a methylsubstituent in the amide. Introduction of the methyl group results ina reduction of electrostatic and van der Waals interaction energies(Table 1). In the case of 13, the methyl group is closer to thehydrophilic residues Ser151 and Asn152 leading to unfavorableinteractions (Figure S6), whereas the unsubstituted amide of 12

interacts favorably with Ser151.

In summary, with MD and MM-PBSA methodology one has a pow-erful tool available to predict the activities of a series of similarligands in complex with a biological target and to rationalize struc-ture–activity relationships. In this study, we have proved that suchcomputational models are suitable for investigating the fine-modula-tion of experimental activities of ligands binding to a target proteinkinase. The application of these techniques to a congeneric set ofinhibitors of ERK2 has shown that computed and experimental

binding activities are significantly correlated. On structural grounds,whereas hydrogen bonding creates the more conserved bindingmotif in the hinge region, the sum of the intermolecular interactionunderlining the inhibition process is also governed by other contri-butions that are located in the hydrophobic pocket next to the cata-lytic loop, in the sugar region and in the DFG motif. Thesestructural insights on ERK2 can be conveniently used for futurestructure-based drug design efforts.

Acknowledgments

Financial support for this project comes from the Associazione Itali-ana per la Ricerca sul Cancro (AIRC) that is gratefully acknowledged.The authors wish to thank CINECA for providing computing resources.

References

1. Fabian M.A., Biggs W.H. III, Treiber D.K., Atteridge C.E.,Azimioara M.D., Benedetti M.G., Carter T.A. et al. (2005) A smallmolecule-kinase interaction map for clinical kinase inhibitors.Nat Biotechnol;23:329–336.

2. Knight Z.A., Shokat K.M. (2005) Features of selective kinaseinhibitors. Chem Biol;12:621–637.

3. Wang Z., Canagarajah B.J., Boehm J.C., Kassis� S., Cobb M.H.,Young P.R. et al. (1998) Structural basis of inhibitor selectivity inMAP kinases. Structure;6:1117–1128.

4. Garber K. (2006) The second wave in kinase cancer drugs. NatBiotechnol;24:127–130.

5. Bridges A.J. (2001) Chemical inhibitors of protein kinases. ChemRev;101:2541–2572.

6. Bikker J.A., Brooijmans N., Wissner A., Mansour T.S. (2009)Kinase domain mutations in cancer: implications for small mole-cule drug design strategies. J Med Chem;52:1493–1509.

7. Fedorov O., Marsden B., Pogacic V., Rellos P., Muller S., BullockA.N. et al. (2007) A systematic interaction map of validatedkinase inhibitors with Ser ⁄ Thr kinases. Proc Natl Acad SciUSA;104:20523–20528.

8. Noble M.E., Endicott J.A., Johnson L.N. (2004) Protein kinaseinhibitors: insights into drug design from structure. Sci-ence;303:1800–1805.

9. Margutti S., Laufer S.A. (2007) Are MAP kinases drug targets?Yes, but difficult ones. Chem Med Chem;2:1116–1140.

10. Yang W., Dolloff N.G., El-Deiry W.S. (2008) ERK and MDM2 preyon FOXO3a. Nat Cell Biol;10:125–126.

11. Hallberg B., Rayter S.I., Downward J. (1994) Interaction of Rasand Raf in intact mammalian cells upon extracellular stimula-tion. J Biol Chem;269:3913–3916.

12. Aronov A.M., Baker C., Bemis G.W., Cao J., Chen G., Ford P.J.et al. (2007) Flipped out: structure-guided design of selectivepyrazolylpyrrole ERK inhibitors. J Med Chem;50:1280–1287.

13. Case D.A., Cheatham T.E. III, Darden T., Gohlke H., Luo R., MerzK.M. Jr et al. (2005) The Amber biomolecular simulation pro-grams. J Comput Chem;26:1668–1688.

14. Rastelli G., Del Rio A., Degliesposti G., Sgobba M. (2009) Fastand accurate predictions of binding free energies using

Del Rio et al.

634 Chem Biol Drug Des 2009; 74: 630–635

MM-PBSA and MM-GBSA. J Comput Chem; doi: 10.1002 ⁄ jcc.21372.

15. Gilson M.K., Zhou H.X. (2007) Calculation of protein-ligand bind-ing affinities. Annu Rev Biophys Biomol Struct;36:21–42.

16. Lyne P.D., Lamb M.L., Saeh J.C. (2006) Accurate prediction ofthe relative potencies of members of a series of kinase inhibi-tors using molecular docking and MM-GBSA scoring. J MedChem;49:4805–4808.

17. Brown S.P., Muchmore S.W. (2007) Rapid estimation of relativeprotein-ligand binding affinities using a high-throughput versionof MM-PBSA. J Chem Inf Model;47:1493–1503.

18. Wang J., Morin P., Wang W., Kollman P.A. (2001) Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBOderivatives and predicting the binding mode to HIV-1 RT of efavi-renz by docking and MM-PBSA. J Am Chem Soc;123:5221–5230.

19. Lafont V., Armstrong A.A., Ohtaka H., Kiso Y., Amzel L.M., FreireE. (2007) Compensating enthalpic and entropic changes hinderbinding affinity optimization. Chem Biol Drug Des;69:413–422.

20. Weis A., Katebzadeh K., Soderhjelm P., Nilsson I., Ryde U.(2006) Ligand affinities predicted with the MM ⁄ PBSA method:dependence on the simulation method and the force field.J Med Chem;49:6596–6606.

21. Rastelli G., Degliesposti G., Del Rio A., Sgobba M. (2009) Bind-ing estimation after refinement, a new automated procedure forthe refinement and rescoring of docked ligands in virtual screen-ing. Chem Biol Drug Des;73:283–286.

22. Sun H., Jiang Y.J., Yu Q.S., Luo C.C., Zou J.W. (2008) Effect ofmutation K85R on GSK-3beta: molecular dynamics simulation.Biochem Biophys Res Commun;377:962–965.

23. Ferrari A.M., Degliesposti G., Sgobba M., Rastelli G. (2007) Vali-dation of an automated procedure for the prediction of relativefree energies of binding on a set of aldose reductase inhibitors.Bioorg Med Chem;15:7865–7877.

24. Robinson M.J., Harkins P.C., Zhang J., Baer R., Haycock J.W.,Cobb M.H. et al. (1996) Mutation of position 52 in ERK2 createsa nonproductive binding mode for adenosine 5¢-triphosphate.Biochemistry;35:5641–5646.

25. Rastelli G., Rosenfeld R., Reid R., Santi D.V. (2008) Molecularmodeling and crystal structure of ERK2-hypothemycin complexes.J Struct Biol;164:18–23.

Supporting Information

Additional Supporting Information may be found in the online ver-sion of this article:

Figure S1. Atom type exemplification for compound 15.

Figure S2. Molecular interactions of the common scaffold withthe hinge region of ERK2.

Figure S3. (A) Overlay of compounds 5 (green) and 4 (brown) asthey bind ERK2. (B) Binding mode of 4 and 5 in the ATP bindingsite.

Figure S4. (A) Overlay of compounds 5 (olive) and 10 (rose) asthey bind ERK2. (B) Binding mode of 5 and 10 in the ATP bindingsite.

Figure S5. (A) Overlay of compounds 10 (green) and 15 (violet)as they bind ERK2. (B) Binding mode of 5 and 10 in the ATP bind-ing site.

Figure S6. (A) Overlay of compounds 12 (sky blue) and 13

(magenta) as they bind ERK2. (B) Binding mode of 12 and 13 inthe ATP binding site.

Table S1. Modification of force field parameters for compound15

Table S2. Molecular dynamics and MM-PBSA parameters andsettings

Table S3. Percentage of presence (occupancy) of hydrogen bond-ing interactions between ligands 1–15 and the hinge residues, theconserved water molecule and the catalytic lysine residues

Please note: Wiley-Blackwell is not responsible for the content orfunctionality of any supporting materials supplied by the authors.Any queries (other than missing material) should be directed to thecorresponding author for the article.

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