Modeling the Binding Affinity of p38α MAP Kinase Inhibitors by Partial Least Squares Regression

Modeling the Binding Affinity of p38a MAPKinase Inhibitors by Partial Least SquaresRegression

Nikita Basant†, Caterina Durante, MarinaCocchi* and M. Cristina Menziani*

Dipartimento di Chimica, Universit� di Modena e Reggio Emilia,ViaCampi 183, 41100 Modena, Italy*Corresponding authors: Maria Cristina Menziani,[email protected]; Marina Cocchi, [email protected]�Present address: Howard University, School of Pharmacy, 2300 4thStreet, NW, Washington, DC 20059, USA.

The p38 mitogen-activated protein kinase is acti-vated by environmental stress and cytokines andplays a role in transcriptional regulation andinflammatory responses. Factors influencing theactivity and selectivity of the p38a mitogen-acti-vated protein kinase inhibitors have been investi-gated in this paper by inspecting the bindingorientation and the possible residue-inhibitorinteractions in the binding site. The binding pat-tern of a set of 45 different inhibitors against p38a

mitogen-activated protein kinase was studiedthrough Molecular Dynamic Simulations of theprotein-inhibitor complexes. Further, Partial LeastSquares regression was used to develop a Quanti-tative Structure Activity Relationship model topredict the binding affinities of ligands. Theselected model successfully predicted the test setwith a Root Mean Square Error of Prediction of1.36. The regression coefficients and the VariableImportance in Projection plots highlighted the res-idue-inhibitor interactions which exhibited thelargest absolute effect on the ligand binding, suchas the van der Waals interaction with LYS50,ILE81, ASP165; electrostatic interactions withSER29, LEU164; hydrogen bonds with MET106;and total energy interaction with SER29 andLEU83.

Key words: inhibitors, molecular dynamic simulations, p38a mito-gen-activated protein kinase, Partial Least Squares regression, Quantita-tive Structure Activity Relationships

Received 11 July 2011, revised 6 May 2012 and accepted for publica-tion 15 May 2012

Signal transduction via mitogen activated protein (MAP) kinasesplays a key role in a plethora of cellular responses (1). Mitogen acti-vated protein kinases constitute an evolutionarily conserved familyof enzymes that form a highly integrated network required to

achieve specialized cell functions controlling cell differentiation, cellproliferation, and cell death. These cytoplasmic proteins modulatethe activities of other intracellular proteins by adding phosphategroups to their serine ⁄ threonine amino acids (2). Hence, MAP kinas-es provide a focal point in order to understand the control of cellularevents by growth factors and stresses. Consequently, selective inhib-itors against specific kinases can be used to treat various disordersas cancer, arthritis, and diabetes. Most of the protein kinase inhibi-tors are small molecules that either interfere with phosphorylationor bind the ATP binding site, an area within the activation loop ofthe MAP kinase in which the dual phosphorylation takes place. Themajor problem associated with ATP-competitive kinase inhibition istarget specificity, as many other enzymes and kinases utilize ATP(3,4). Moreover, the issue of selectivity is complicated by the highdegree of conservation amongst different MAP kinase sub-families.

Five families of MAP kinases have been reported in mammalian cells:extra cellular signal-regulated kinases (ERK1 and ERK2); c-Jun N-ter-minal kinases (JNK1, JNK2, and JNK3); p38 kinase isozymes (p38a,p38b, p38c, and p38d); ERK3 ⁄ ERK4; and ERK5 (5). In general, theERK cascade is activated by growth factors and is critical for cell pro-liferation (6). Conversely, the JNK and p38 pathways are stimulatedby genotoxic agents and cytokines mediating the stress response,growth arrest, and apoptosis (7). The interest in p38 MAP kinase sig-naling pathways is mainly due to the fact that it is implicated innumerous diseases, including inflammation, arthritis and other jointdiseases, septic shock, and myocardial injury. As p38 MAP kinase reg-ulates the production of TNF-a and IL-1, p38 inhibitors are expectedto inhibit not only the production of pro-inflammatory cytokines, butalso their actions, thereby interrupting the vicious cycle that oftenoccurs in inflammatory and immune responsive diseases (8).

The first p38 selective inhibitor, pyrimidylimidazole, SB-203580 wasreported by SmithKline Beecham in 1994, then a plethora of substi-tuent modifications and heterocyclic replacements of the imidazoleand ⁄ or pyridine ring systems have been proposed (9–12). Rationaldrug design favoring p38a target was greatly accelerated by theavailability of p38 X-Ray crystallographic structures and by anincreasingly comprehensive sequence database of protein kinases.Structurally related p38 inhibitors were discovered which main-tained or improved potency, and ⁄ or minimized liabilities present inthe original pyridylimidazole series (13), as well as different classesof p38a inhibitors that are active in vitro and in vivo assays (14).Since the initial discovery of the first prototypical inhibitor, morethan 150 patent applications from at least 30 pharmaceutical com-panies have claimed other different p38 inhibitors (13).

455

Chem Biol Drug Des 2012; 80: 455–470

Research Article

ª 2012 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2012.01419.x

The early discovered inhibitors bind in the ATP-mimetic mode (Type Iinhibitors) targeting the enzyme in its active form, i.e. in an openconformation of the activation loop usually called DFG 'in' based onthe position of the conserved aspartate-phenylalanine-glycine (DFG)at the beginning of the activation loop. (14) Type I inhibitors are usu-ally effective, but their low specificity and selectivity towards thep38 MAP kinase target make them unsuccessful in many instances.The second generation of inhibitors, defined as type II inhibitors, ismore promising from a selectivity point of view. It binds to the samearea occupied by the type I compounds but also extends to the addi-tional hydrophobic pocket which becomes accessible by the flip ofthe DFG-loop to the inactive form, i.e., DFG 'out' (15,16). Moreover,from the analysis of some recently published kinase inhibitors Zucc-otto et al. (17) highlighted an additional binding mode of the so-called type I1 ⁄ 2 inhibitors which can be exploited in the design ofcompounds with the desired activity ⁄ selectivity profile. Type I1 ⁄ 2ligands recognize the enzyme in the DFG 'in' form; bind to the ATPsite like type I compounds, and reach the hydrophobic pocket estab-lishing interactions with those residues characteristic of the type IIinhibitors which are based on the DFG 'out' form.

Here, with the aim of taking into account a wide range of inhibitorsstructural features and providing rationalization of the binding mod-e ⁄ modes of the different inhibitors proposed in the literature, a setof 45 inhibitors diverse in structure and functionalities, exhibiting ahigh range of binding affinities for p38a will be analyzed. The rec-ognition and selective binding of the inhibitor can be described bythe residue-inhibitor interactions occurring in the active site of theprotein. To this aim, a careful analysis of the binding site for eachstudied complex after minimization of the average structure derivedby Molecular Dynamics will be carried out in order to individuatethe amino acid residues that are likely to form the active (ATP) siteor in general binding site, being located in a position of the enzymesuch as to form some significant interactions with the inhibitors.This could help in the design of new selective inhibitors in future,which would not be restricted by the basic inhibitor structures; onthe contrary, the available molecules could be modified, by introduc-ing favorable substituent from the point of view of giving additionalstabilizing interactions.

Materials and Methods

Data setForty-five inhibitors showing diversity in structure, functionality, andbinding modes, i.e., the usual ATP-mimetic ligands along with oth-ers targeting the DFG-out conformation, were employed in thisstudy. The binding affinities of the considered inhibitors, expressedas concentration (nM) of the compound required to inhibit 50% ofthe MAP kinase activity (IC50), are taken from the literature (18–35)and are listed in Table 1.

Modeling of inhibitors and molecular dockingThe 45 inhibitors were constructed using 3D sketcher module of theDiscovery Studio software from Accelrysa and were subjected togeometry optimization and energy minimization by means of theCHARMM force-field (36). Each optimized inhibitor was aligned to

the similar available inhibitor structures in the complexes, whoseX-Ray crystallographic structures were available in the Protein DataBank (37). Then the inhibitors were manually docked into the corre-sponding p38a protein structures listed in Table 1 (after removingof the docked inhibitor and solvent molecules), by considering thecriterium of inhibitor structure similarity as a guide for dockingposes.

Twenty-four residues contribute to constitute the entire surface ofthe binding pocket: VAL27, SER29, TYR32, VAL35, ALA48, LYS50,GLU68, LEU72, ILE81, LEU83, LEU101, VAL102, THR103, HIS104,LEU105, MET106, GLY107, ASP109, ASN112, SER151, ASN152,LEU164, ASP165, GLU175; they were selected in order to determinetheir role in molecular recognition of docked inhibitors, coveringboth Type I and II inhibitor occupancy sites (see Figure S1, Support-ing Information).

Molecular dynamics simulationsThe complexes were subjected to a short minimization procedure (afast sequence of steepest descent and conjugated gradient) usingthe CHARMM force-field (36) in order to remove artifact due to themanual docking. Standard protonation states corresponding to pH 7were assigned to the amino acid residues.

Each complex was then solvated by the TIP3 water moleculesthrough constructing a sphere of 25 � around the binding site ofthe protein using InsightII software by Accelrysa. A CHARMM spher-ical potential was applied to obtain spherical boundary conditionsthat confer the spherical shape to the water molecules. Solventmolecules are subjected to a Langevin dynamics, useful to smooththe effect of the boundary conditions to the limit of the sphere.The SHAKE algorithm was further applied to constrain bonds involv-ing hydrogen (38).

A standard protocol for molecular dynamic simulation of the pro-tein-inhibitor complexes was followed; it is characterized by fourmain steps: i) minimization of the whole system, ii) heating of thewhole system applying protein restrained, followed by a fast mini-mization, iii) equilibration run, and iv) production run. The first stepwas used to remove the close contacts between water molecules,while in the second step an equilibration of the solvent at 300 Kwas performed, keeping the protein restrained such as to movegradually the water molecules surrounding the protein, so that tohave a better distribution of velocities and positions of the latter.After that the whole system was equilibrated for 1 nanosecond,while during the last steps the system was free to move around fordynamic calculations. The time step used is 0.002 ps. The trajecto-ries were analyzed and the interaction energy values for the pro-tein-inhibitor complexes were calculated on the minimized averagestructures.

The ligand-enzyme interactions were quantified in terms of the totalinteraction energy (ETOT) along with their components: van derWaals (EVDW), electrostatic (EEL), and hydrogen bond (EHB) interac-tion energies. Therefore, the computational procedure performed onthe selected inhibitor-protein complexes yielded a dataset of inter-action energy profiles for each of the 45 inhibitors.

Basant et al.

456 Chem Biol Drug Des 2012; 80: 455–470

Table 1: 2D structure, experimental binding affinity data values (IC50, nM) against p38a, identification PDB code (PDB id), and bindingmode of the 45 inhibitors considered in this study

N. Inhibitor structures IC50 p38a (nM) PDB id Inhibitor identifier Type of inhibitor References

1

N

N

N

F

SO

48 1A9U SB203580 Type I 18

2

NN

F

N

N

NH2

N

19 1BL7 SB220025 Type I 18

3

N

N

F

N

NO

N

19 SB242235 Type I 19

4

NH

NH

NN

O

ON

O0.13 1KV2 BIRB796 Type II 20

5Cl

N

N

NN N

N

Cl

0.078 1PMN Type I 21

6Cl

N

N

NHN

N

HN

Cl

4.0 1PMQ Type I 21

PLS Analysis of p38a MAP Kinase Inhibitors

Chem Biol Drug Des 2012; 80: 455–470 457

Table 1: Continued


7

N

NHO 40 000 1PMU Type I 21

8

N N

O 30 000 1PMV Type I 21

9

NH

O

F

N

1800 JMC5 Type I 22

10

NH

O

N

F >10 000 JMC10 Type I 22

11

N

ON

F 450 JMC13 Type I 22

12

N

NO


13

N

N N

F

S

SO

5100 JMC17 Type I 22

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Table 1: Continued


14

N

NN


15

NH

S

NN

N

N

4800 BIO59 Type I 23

16

N N

N

N

F

OO

NH H

295 S-BIO3 Type I 24

17

N N

N

N

F

OO

NH

54 S-BIO4 Type I 24

18

N N

N

N

F

ONH

28 S-BIO5 Type I 24

19

N N

N

N

F

OO

NH H

116 R-BIO3 Type I 24

20

N N

N

N

F

OO

NH

217 R-BIO4 Type I 24



Table 1: Continued


21

N N

N

N

F

OHNH

31 R-BIO5 Type I 24

22

O

OH

NH

NO

NH

N

NHCl

903 BMCL10 Type I 25

23 30 BMCL4 Type I 25

24N

N NH

O OH

H

H

NH

F

40 BMCL11 Type I 26

25N

N NH

O

N

O

NH 37 BMCL13 Type I 26

26 N

N NH

O

N

O

NH

F


Basant et al.


Table 1: Continued


27 N

N NH

O

O


28N

N NH

O

O

NH

F


29 N

N NH

O

O

NH

F


30 N

N NH

O

O


31N

N NH

O

O

NH

F


32

S

NNH

CH3

H3CN

O

F6.4 3C5U 3C5U Type I 27

33

N

SNH

CH3

O

NH

CH3

OHN

CH3

H2C

3.5 3BX5 3BX5 Type I 28

34

O

H3C

O

NH

NH75 3D7Z 3D7Z Type II



Table 1: Continued

N. Inhibitor structures IC50 p38a (nM) PDB id Inhibitor identifier Type of inhibitor References 29

35 O

N

HN OH

H3C

NS

2500 2ZAZ 2ZAZ Type I 30

36

O

N

OHN

CH3H3C

N

N1500 2ZB0 2ZB0 Type II 30

37

O

N N

CH3

ONH

H3C

3000 2ZB1 2ZB1 Type II 30

38

NH

N

NN O

NH

CH2

CH3

HN

NN

O

O

CH3

0.44 3BV2 3BV2 Type II 31

39

NN

N

CH3O

NH

CH3

NH

HN

N

O

O

CH3

0.46 3BV3 3BV3 Type II 31

Basant et al.


Table 1: Continued


40

N

N

NHO

NHH3C

CH3

NNHN

CH3

14 3CG2 3CG2 Type I 32

41

N

NN

NHCH3

OHN

OH3C

O

NH

CH3

CH3

2.2 2RG6 2RG6 Type I 33

42

ONH

OH3C

NH

N

NN O

NH

CH2

CH3CH3CH3

3.1 2RG5 2RG5 Type I 33

43

N

N

N

F

F

O

NO N

O

N

HO

O

13 2QD9 2QD9 Type I 34

44

CH3

NHO

N

N

CH3

0.8 + )0.6 3DS6 3DS6 Type I 35

45

NH

O

CH3

N

N NH

N

O 2.4 € 1.3 3DT1 3DT1 Type I 35



The data collected, consisting of 96 (24 residues · 4 energy valueseach: ETOT, EVDW, EEL, EHB) interaction descriptors for each inhibitor,were subjected to multivariate data analysis to highlight the impor-tant correlation between the measured variables and to build pre-dictive models to estimate the binding affinity.

Data analysisThe interaction-energy dataset obtained for each of the 45 inhibi-tors docked into the p38a MAP kinase protein was analyzed usingPartial Least Squares (PLS) Regression (39). Partial Least Squares isa multivariate calibration technique which establishes a relationshipbetween a set of predictors, X, and a set of responses, Y, by maxi-mizing their covariance. This is achieved in the latent variablesspace by decomposing the predictors and responses' blocks accord-ing to PCA-like models and imposing an inner linear relation amongthe X-scores, T, and Y-scores, U, trough a weights matrix W,which rotates the latent variables in X to maximize the covariancebetween T and U in each dimension. Partial Least Squares can bere-expressed in the form:

Y = XB

where the pseudo-regression coefficients B are calculated as:

B = W (PT W))1 QT

To avoid overfitting, the number of PLS latent variables (PLS compo-nents) has been assessed using Leave-one-out cross validation(LOO-CV) (39).

In this work, the predictors (X) dataset is comprised of 45 inhibitors· 96 interaction variables, whereas, the response vector (y) con-tains the corresponding )Log IC50 values.

The dataset was randomly split into calibration (28 inhibitors · 96variables) and the validation (17 inhibitors · 96 variables) subsets;to include a wider range of )Log IC50 variation in the calibrationset, the most active p38a inhibitor 1PMN (0.078 nM) and one ofthe least active 1PMV (30000 nM) were both included in the cali-bration set.

Data pretreatment. The ETOT and EEL interaction energies show lar-ger scales and variances with respect to EVDW and EHB, whichmeans that the model would be influenced in different extents bythe two couples of energy descriptors. To overcome this withoutintroducing overweight from interaction terms showing very littlevariance, the ETOT and EEL interactions were transformed by takingthe fourth square root of their absolute values. This transformationalso renders more similar variation in the attractive and repulsiveterms within the Electrostatic interactions.

The energy terms were transformed according to:

X 0ij ¼ 0 if Xij ¼ 0

X 0ij ¼4 pXij ; if Xij >0

X 0ij ¼ �4pabsðXij Þ; if Xij <0

where, xij is a generic element of ETOT and EEL variables.

Prior to PLS modeling, the X and y data were also mean centered.

Results and Discussion

The results of the Interaction Energy analysis derived by computa-tional simulations of the complexes and rationalized by means of PLSRegression provide important information on the binding determinants

Figure 1: Plot of the measuredversus predicted values of y (p38amitogen activated protein kinasebinding affinity) for the training(open circles) and test set (blacktriangles) samples. Bisecting line isshown as dashed. Labels are thesame as reported in Table 1, andfor clarity only discussed inhibitorlabels are reported.

Basant et al.


of a set of 45 inhibitors belonging to several structurally unrelatedchemotypes and representative of different binding modes (Table 1).

A three latent variables (LVs) PLS model was selected according tominimum in leave-one-out cross validation error; it shows a RootMean Square Error in fit (RMSEC), Cross-validation (RMSECV), andexternal test set predictions (RMSEP) of 0.5 (0.96) and 1.4 (1.5) and1.36, in terms of binding affinity values ()Log IC50), respectively. Thehigher RMSECV value (especially due to BIRB796 and 2QD9 estima-tion) with respect to RMSEC indicates that many of the molecules inthe training set bear unique information, as it could be expectedgiven their structural diversity. Overall, the model performance couldbe considered satisfactory for identifying, on a pure computationalbasis, the molecules to be worth of further experimental investiga-

tion. In fact, it is worth noting that a high experimental uncertainty isassociated to the IC50 determinations collected in this paper, as theycome from different research laboratories and are measured in differ-ent experimental conditions, sometimes with different protocols.

The experimental (measured) versus predicted binding affinity valuesare plotted in Figure 1, and overall the training set samples (opencircles) lie close to the y = x line, showing a good model fit. TheResiduals versus yexperimental (measured) plot reported in Figure 2can be more informative regarding the model adequacy. If the resid-uals appear to behave randomly (no patterns), the model is ade-quate. The residuals plot for the above PLS model shows a ratherrandom pattern in distribution of the samples showing residuals ofabout one order of magnitude (errors of about 1 in )Log IC50 units)

Figure 2: Y Residuals versus YMeasured plot for Training (opencircles) and Test set (black trian-gles).

Figure 3: The Regression vec-tor plot for the variables; each res-idue is labeled according to thekind of interaction term (TOT, VDW,EL or HB) they refer to.



and also for external predicted samples (test set, black triangles),except for the inhibitors JMC17, JMC13, SB203580, 3C5U, whichare poorly predicted owing to high residual errors. It may be notedthat the worst prediction is mainly for low to inactive inhibitors.Moreover, the worst test set predicted sample, JMC17, resulted tobe an outlier, i.e., when projected on the training set model, it isoutside both the 95% confidence limits in the squares residuals ver-sus Hotelling-T2 plot (not shown), showing that it has a rather dif-ferent behavior from other inhibitors.

An overview of the interaction energy terms contributing to themodel, hence, influential for binding, can be obtained from the PLSregression coefficients plot, shown in Figure 3.

The binding mode of the inhibitors is evaluated on the basis oftheir interactions with the amino acid residues of the binding siteof the protein and measured in terms of the total interaction energy(ETOT) and its corresponding van der Waals (EVDW), electrostaticenergy (EEL) and hydrogen bond (EHB) components.

The magnitude of these energy values highlights the role andimportance of that particular amino acid residue in stabilizing orde-stabilizing the binding of the inhibitor.

From the regression coefficients plot (Figure 3), it is evident thatthe van der Waals and the total interaction energies, in general,show negative regression coefficient values, hence, the higher theenergy values (less negative) for these residues, the higher the IC50

values and the lower the binding affinity; of course the reverseholds, i.e. the most negative the energy values for these residuesthe most stable the complex. Residues LYS50 (EVDW) show the high-est negative regression coefficients and influence the model morecompared to other residues on the positive side of regression coef-ficients plot. In addition to the above residue, SER29 (ETOT), SER29(EEL), ALA48 (EVDW), ILE81 (EVDW), LEU83 (ETOT), LEU105 (EVDW),MET106 (EHB), LEU164 (EEL), LEU164 (EVDW), and ASP165 (EVDW) alsohave negative regression coefficients.

The most active inhibitors, 1PMN and BIRB796, form strong attrac-tive interactions with all the residues having the highest negativeregression coefficients, as shown by the Interaction Energies datavalues listed in Table 2; 3BV2, 3C5U, and SB203580 show slight

repulsion with the total and electrostatic energy terms of SER29(3BV2 and 3C5U), along with LEU83 (repulsive ETOT energy interac-tion with 3C5U and SB203580) and LEU164 (weak repulsive ETOT

energy interaction with 3BV2). On the other hand, JMC13, 1PMU,and 1PMV, i.e., the least active p38a inhibitors form weaker inter-actions or steric hindrance with the above mentioned residues.

In particular, the increased potency of the most active p38a inhibi-tors is achieved by the simultaneous optimization of the hydrogenbond interaction with MET106 and the van der Waals interactionswith the LYS50, ALA48, ILE81, and LEU105 amino acid residues. Onthe contrary, the least active p38a inhibitors 1PMV, 1PMU, JMC17,and JMC13 show weaker HB with MET106, overall, moderate vander Waals interactions with LYS50, LEU105, ILE 81 and ALA48, andrepulsive interactions with LEU164 (EEl). The MET106 HB is the onlyhydrogen bond interaction having relatively stronger interaction

Table 2: Interaction energy (kcal ⁄ mol) contributions of selected p38amitogen activated protein kinase amino acid residues to the binding of representative inhibitors, together with their experimental IC50 (nM)data values

Inhibitors IC50

SER29TOT

SER29EL

ALA48VDW

LYS50VDW

ILE81VDW

LEU83TOT

LEU105VDW

MET106HB

LEU164TOT

LEU164EL

ASP165VDW

1PMN 0.078 )2.18 )1.96 )2.40 )3.57 )2.18 )1.18 )3.02 )6.27 )3.36 )0.16 )1.37BIRB796 0.13 )1.06 )0.97 )2.45 )4.56 )3.55 )0.24 )2.04 )3.46 )4.72 )1.87 )4.173BV2 0.44 0.04 0.26 )2.40 )5.70 )2.75 )0.12 )2.87 )6.42 )1.51 0.62 )3.333C5U 6.4 0.24 0.30 )1.74 )2.67 )2.19 1.04 )2.34 )5.56 )3.36 )1.37 )0.71SB204580 48 )0.46 )0.35 )0.95 )3.10 )1.76 1.55 )1.57 )2.08 )3.17 )1.75 )1.49JMC13 450 )0.65 )0.54 )1.32 )2.18 )1.78 )0.78 )1.35 )2.61 0.53 2.98 )0.24JMC17 5100 0.07 0.18 )1.06 3.94 )1.62 0.15 )1.30 )2.30 0.48 3.18 )1.831PMV 30000 0.02 0.06 )0.94 )1.45 )0.87 1.01 )1.78 )2.93 1.52 2.53 )0.701PMU 40000 )0.77 )0.73 )0.88 )0.38 )0.81 0.07 )2.23 )2.64 )0.07 1.68 )0.94

Figure 4: Inhibitor 1PMN (in orange) and BIRB796 (in cyan) intothe p38a ATP pocket where they interact with MET106 and LYS50(in cyan), ILE81 and LEU83 (in blue), and LEU164 and ASP165 (ingreen). The 2D structures for 1PMN (Upper right hand corner) andBIRB796 (lower right hand corner) are also provided.

Basant et al.


energy, which clearly highlights the important role of this particularinhibitor-amino acid residue interaction in the classes of inhibitorsstudied or p38a MAP kinase.

The interaction of active p38a inhibitors can be better understoodby looking at the orientations of 1PMN and BIRB796 in the bindingpocket of p38a MAP kinase as resulted from the minimized averagestructure obtained from the dynamic run (Figure 4).

The binding pattern of 1PMN and BIRB796 shows the fluorophenylring (1PMN) and the naphthyl ring (BIRB796) lying into the hydro-phobic pocket (represented in blue in Figure 4) constituted by ILE81,LEU83, LEU101, and LEU105 residues. The pyrimidine ring in 1PMN

and methoxy-morpholino ring in BIRB796 form the important hydro-gen bond with MET106. Further, the piperidine ring of 1PMN andpyrozole moiety in BIRB796 lie near the activation loop (in green, inFigure 4) and interact with LEU164 and ASP165.

Further the presence of JMC17 as an outlier inhibitor according tothe residuals plot can be better understood by looking at its bindingpattern in the p38a ATP site (Figure 5).

The JMC17 when binding to the ATP pocket mimics the hydrogenbond interaction with MET106 (2.52 �), which is usually the mostimportant and stabilizing interaction for p38a inhibitor binding.However, the core pyrimidine ring fixes the movement of the adjoin-ing substitutions on the side chains, causing more repulsive interac-tions with residues LEU164 and LYS50, hence, lowering the activityof the inhibitor against the protein and this could be the reason fordistinct binding mode and interactions compared to other inhibitors.Further, the marked inability of the model to fit the data for JMC17(high Q residual and T2 values) could be due to the loss of van derWaals interaction with the LYS50. JMC17 is the only inhibitorshowing repulsive energy value (Table 2) compared to all otherinhibitors (both active and not active against p38a), which showattractive van der Waals interaction with LYS50. Moreover, all theoutliers JM13, SB203580, and 3C5U share the establishment ofrepulsive interactions with LEU83 or LEU164.

The above highlighted variables also show significant VariableImportance in Projection (VIP) values (39). Variable Importance inProjection is a parameter which defines the relative importance ofeach X variable in the PLS model. A variable with a VIP score closeto or >1 can be considered important in a given model. Variableswith VIP scores significantly <1 are less important and might begood candidates for exclusion from the model. Figure 6 shows asignificantly higher VIP score for EVDW energy of LYS50 along withother interactions such as ETOT for ASP165, EVDW for ASP165, EEL

Figure 5: Binding mode of JMC17 in the p38a ATP pocket.

Figure 6: Variable Importancein Projection (VIP) and the variableplot; the amino acid residues withthe highest positive VIP score areimportant for binding affinity ofthe inhibitor against p38a.



and EVDW for LEU164, EVDW for ILE81, ETOT for SER29, EHB forMET106, which also show high VIP scores.

The plot of PLS variables weights, which capture the contributionof each variable in X to model the responses Y, under differentcomponents may further provide information on the relationshipsamong X variables and their correlation pattern. Figure 7 shows thePLS weights plot for the first versus the second component of aPLS model obtained by including only the X variables significantaccording to VIP. Although there is no significant improvement inthe prediction capability of the model with the RMSEP values low-ering from 1.36 to 1.32, as only the VIP variables are plotted, itbecomes easier to study the pattern of the variable and the correla-tion between them. The variables on the lower left quadrant of theplot (Figure 7) are highly correlated with each other and are rele-vant in predicting the dependent variable (pIC50 values). They areinversely related to the dependent variable (most attractive theenergy terms, i.e., the more negative, the higher the binding affin-ity) whereas, the variables occupying the upper right quadrant arerelevant and directly correlated to pIC50 values (meaning that themost active inhibitors show repulsion with these residues, see Dis-cussion below). As evident from the plot, EVDW for LYS50, ASP165,LEU164, EHB for MET106, EEL for LEU164, and ETOT for SER29 andLEU83 are relevant for increasing the binding affinity and futureinhibitors may be designed to improve these interactions. Whereas,variables (ETOT for SER151, TYR32; EEL for SER151; EVDW for VAL35;and EHB for HIS104) occupying the upper right hand quadrant of theplot are characterized by interaction energies values positive or clo-ser to zero, thus registering repulsion, with the most active inhibi-tors. These residues should be considered in the design ofinhibitors for p38a MAP kinase, as they directly influence the bind-ing affinity of the bound inhibitor and could further influence theselectivity. To incorporate this information, modifications of the sidechains of the inhibitors are admitted to further enhance the abovementioned interactions.

Conclusions

The results obtained by the computational approach based onmolecular dynamics analysis of inhibitor-p38a MAP kinase com-plexes used in this study help in providing a better view of thebinding pattern and orientation of the inhibitor in the bindingpocket and of possible favorable substitutions and modificationswhich can be introduced on the molecular scaffolds to induceimproved affinity against the target.

The PLSR approach used to rationalize the data obtained furnishesa QSAR model with good performance in terms of Y-variance cap-tured and low root mean square in cross-validation and externalprediction. The van der Waals interaction with LYS50, ILE81,ASP165; electrostatic interactions with SER29, LEU164; hydrogenbonds with MET106; and total energy interaction with SER29 andLEU83 were highlighted as the important residue interactions toinfluence the strong binding affinity of the inhibitor against p38a.

Hence, the multivariate regression analysis clearly shows theircapability to analyze datasets generated on ligand-residue interac-tions, by extracting factors significant for the increased bindingaffinity of the inhibitors for the target along with highlighting mole-cules that have a binding orientation different from others.

Acknowledgments

N.B. Thankfully acknowledges the award of Italian Government Fel-lowship for the Doctoral Research Program.

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Note

aAccelrys Inc., San Diego, CA, USA: Accelrys Inc. Available at:http://accelrys.com/products/.

Supporting Information

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

Figure S1. The p38a MAP Kinase binding pocket: the 24 amino-acid residues considered to constitute the surface of the bindingpocket are highlighted in stick and colored in blue.

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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Modeling the Binding Affinity of p38α MAP Kinase Inhibitors by Partial Least Squares Regression

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