ORIGINAL PAPER

Pharmacophore modeling, 3D-QSAR, and docking studyof pyrozolo[1,5-a]pyridine/4,4-dimethylpyrazolone analoguesas PDE4 selective inhibitors

Naga Srinivas Tripuraneni1 & Mohammed Afzal Azam1

Received: 20 June 2015 /Accepted: 9 October 2015# Springer-Verlag Berlin Heidelberg 2015

Abstract Phosphodiesterases 4 enzyme is an attractive targetfor the design of anti-inflammatory and bronchodilator agents.In the present study pharmacophore and atom based 3D-QSAR studies were carried out for pyrozolo[1,5-a]pyridine/4,4-dimethylpyrazolone analogues. A five pointpharmacophore model was developed using 52 moleculeshaving pIC50 values ranging from 9.959 to 3.939. The bestpredictive pharmacophoric hypothesis AHHRR.3 was charac-terized by survival score (2.944), cross validated (r2=0.8147),regression coefficient (R2=0.9545) and Fisher ratio (F =173)with 4 component PLS factor. Results explained that one hy-drogen bond acceptor, two aromatic rings and two hydropho-bic groups are crucial for the PDE4 inhibition. The dockingstudies of all selected inhibitors in the active site of PDE4showed crucial hydrogen bond interactions with Asp392,Asn395 Tyr233, and Gln443 residues. The pharmacophoricfeatures R15 and R16 exhibited π-π stacking with His234,Phe414, and Phe446 residues. The generated model was fur-ther validated by carrying out the decoy test. The binding freeenergies of these inhibitors in the catalytic domain of 1XMUwere calculated by the molecular mechanics/generalized Bornsurface area VSGB 2.0 method. The results of molecular dy-namics simulation confirmed the extra precision docking-predicted priority for binding sites, the accuracy of docking,

and the reliability of active conformations. Pyrozolo[1,5-a]pyridine/4,4-dimethylpyrazolone analogues in this studyshowed lower binding affinity toward PDE3A in comparisonto PDE4. Outcomes of the present study provide insight indesigning novel molecules with better PDE4 inhibitoryactivity.

Keywords Docking study .Molecular dynamics .

Pharmacophore hypotheses . Phosphodiesterase 4B .

Pyrozolopyridine

Introduction

Type IV cyclic nucleotide phosphodiesterases (PDE4s) arepredominantly expressed enzymes in inflammatory and im-mune cells [1, 2] and play a dominant role in regulation ofcyclic adenosine monophosphate (cAMP) signaling in cells[3]. PDE4s are widely distributed in macrophages and neutro-phils [3–5] and found to be involved in mediating the inflam-mation [6]. However, it is a known fact that PDE3 enzyme isalso expressed in inflammatory cells, the contribution of thisenzyme to the regulation of inflammatory cell function is alsobeing explored [7]. In recent years, targeting PDE 4 and/or 3has been the focus for the development of drugs that couldprove beneficial with the potential to treat respiratory diseases,largely by virtue of their anti-inflammatory (PDE4) and bi-functional bronchodilator/anti-inflammatory (PDE3/4) effects[8, 9]. With the advancements in proteomics, several high-resolution, three-dimensional co-crystal structures of PDE4catalytic domains were investigated with different inhibitorswhich further widened the scope in understanding the inhibi-tor binding as well as the critical function of divalent metalions [10, 11].

Electronic supplementary material The online version of this article(doi:10.1007/s00894-015-2837-4) contains supplementary material,which is available to authorized users.

* Mohammed Afzal Azamafzal9azam@hotmail.com

1 Department of Pharmaceutical Chemistry, J.S.S. College ofPharmacy (Constituent College of JSS University, Mysore),Udhagamandalam 643001, Tamil Nadu, India

J Mol Model (2015) 21:289 DOI 10.1007/s00894-015-2837-4

The catalytic domain of PDE4 enzyme comprises 17 α-helices and can be divide into three subdomains; a bivalentmetal-binding pocket (M pocket), which forms a complexwith the phosphate moiety of cyclic AMP; a pocket containingglutamine (Q pocket), which forms hydrogen bonds with thenucleotide (purine) moiety of cyclic AMP, and a solvent pock-et (S pocket) [12]. The catalytic domain has a cavity of ap-proximately 20 Å by 10 Å and is approximately 15 Å deep.Zn2+ and Mg2+ metal ions located at the wider side of theactive site assume near octahedral coordination geometryand have a role in cAMP hydrolysis [13]. For binding to theactive site, PDE4 inhibitors share two common features: aplanar ring structure that is held in place within the enzymeactive site by a pair of hydrophobic residues (hydrophobicclamp), and the formation of one or two hydrogen bonds be-tween the inhibitor and an invariant purine-selective gluta-mine residue of the PDE4 active site [13].

PDE4 enzyme has drawn considerable attention in recentyears as a promising therapeutic target to combat diseasessuch as schizophrenia, psoriasis, asthma, chronic obstructivepulmonary disease (COPD), inflammatory bowel disease, andrheumatoid arthritis [14–17]. However, intensive research ef-forts [18–27] for the development of effective and selectivePDE4 inhibitors have not achieved much success yet. Forexample, the dose limiting side effects of nausea, emesis,and headache [28, 29] potentially limit the utility of thesePDE4 inhibitors. The side effects may be due to the sequenceand structural similarity within the catalytic domains of thePDE subtypes [30, 31].

Recently, computational methods like docking,pharmacophore modeling, and molecular dynamics simula-tion were used successfully by Li et al. [32] to discoversubfamily-selective PDE4B inhibitors from natural products.Four potential PDE4B-selective inhibitors, identified by vir-tual screening process showed better binding free energy com-pared to the roflumilast. The pharmacophore model of theseinhibitors consisted of six features: one hydrogen bond donor,four hydrogen bond acceptors, and one aromatic ring. In an-other effort, Shubina et al. [33] successfully utilized multiplecomputational methods together to identify highly activePDE4B-inhibitors from less active and inactive ones. The bestpharmacophore model obtained by Phase consists of two hy-drogen bond acceptor sites, one aromatic ring, and one hydro-phobic area. In order to gain further insights into the structuraland chemical features required for PDE4 inhibitory activity,we developed a robust ligand-based 3D-pharmacophore hy-pothesis using PHASE (v4.0, Schrödinger 2014–3) [34, 35].The pharmacophore hypothesis obtained from thepharmacophoric points was used to derive a pharmacophore-based 3D-QSAR model. Molecular docking studies of thedata set molecules was performed using Glide, version 6.4[36]. The receptor-inhibitors complex binding free energieswere calculated by molecular mechanics-generalized Born-

surface area (MM-GBSA) method using Prime (v3.7Schrödinger 2014–3) [37]. The molecular dynamics (MD)simulation was also carried out for the PDE4 (PDB-ID:1XMU) and ligand 14 complex using the Desmond module(v3.9) [38]. These results should serve as a guideline in de-signing more potent and selective PDE4 inhibitors.

Computational methods

Data set

In the present study, a series of 52 molecules containingpyrozolo[1,5-a]pyridine/4,4-dimethylpyrazolone scaffoldswith pIC50 values ranging from 9.959 to 3.939 for PDE4 wereretrieved from literatures [24–27] (Supplementary Table 1).Molecules in the data set share a common assay procedure[39]. Prior to the study, the inhibitory potencies of the dataset molecules reported as IC50 in micromolar were convertedinto their respective pIC50 values. The structural features ofthe molecules in the training set provided the template to con-struct the QSAR model and those of the test set moleculeswere used for the internal validation. The training and testset encompasses all the possible structurally diversified mol-ecules which have more than 3 log differences in their exper-imentally reported PDE4 inhibitory activity.

Energy optimization and conformation generation of thedata set molecules was performed using optimized potentialfor liquid simulations (OPLS-2005) force field [40] ofLigPrep (v3.1, Schrödinger 2014–3). The shape screening isa fast and efficient tool for shape based alignment of structuresbymaximizing the intersection of their van derWaals volumes[35, 41, 42]. We applied shape screening (Phase v4.0) to flex-ibly align the selected inhibitors using the most active inhib-itor 14 as template and keeping pharmacophore type volumescoring as default parameter. Further the default settings wereused, with a maximum of ten conformations per rotatablebonds and a maximum of 100 conformers were generated.Conformations of amide bonds were varied and atmost onealignment per ligand was retained. The average alignmentscore for all 52 flexible superposition is 0.79 and the medianis 0.76.

Pharmacophore hypothesis generation and validation

The pharmacophore hypothesis for PDE4 inhibitory mole-cules was generated using Phase (v4.0, Schrödinger 2014–3)[34, 35]. The partial least square (PLS) approach was used tocorrelate the biological activity with the 3D descriptors. Anactivity threshold was assigned such that the molecules withpIC50 greater than 7.0 were considered active and those hav-ing pIC50 below 6.0 were considered inactive, while the rest ofthe molecules were considered moderately active. After

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assigning the activity thresholds, pharmacophoric sites weregenerated considering the structural features of set of activemolecules. The pharmacophoric hypotheses were generatedwith the criteria that the selected variant must match 25 activesites. In the generated hypotheses (Tables 1 and 2), five siteswere found to be common to all selected molecules. Scoreswere computed for both actives and inactives using defaultparameters for the alignment of the site points and vectors,selectivity, number of ligands matched, volume overlap, andrelative conformational energy and activity. The survival scorewas kept as criterion to ascertain the quality of alignment andpharmacophore matching tolerance was set to 1 Å. Among thegenerated models, AHHRR.3 with 4 component PLS factorwas characterized as the best model comprising respectively38 (73 %) and 14 (27 %) molecules in training and test sets(Supplementary Table 1).

3D-QSAR study

In the present study, atom based three dimensional quantita-tive structure activity relationship (3D-QSAR) models, whichare more useful in explaining the structure-activity relation-ship was generated by dividing the data set into training andtest sets in a random manner. The generated commonpharmacophore hypothesis AHHRR.3 was further used tobuild a 3D-QSAR model by atom based 3D-QSAR approachwhich takes into account all the atoms of the molecules notmerely the pharmacophoric sites. We generated an atom based3D-QSARmodel by keeping 1 Å grid spacing with maximumfour component PLS factor. For the generation of QSARmod-el we then aligned inactive or moderately active (non-modeled) molecules in the data set on the basis of matchingwith at least five of the pharmacophoric features. Increase inthe number of PLS factors did not improve the model predic-tive ability and model statistics. Further, we visualized themolecules under study with generated 3D-QSAR model toexplore the nexus between structure and activity.

Validation of 3D-QSAR model

In the present study, the hypothesis AHHRR.3 was character-ized as the best hypothesis with a survival score of 2.944 andthe best cross validated R2 (Q2) 0.8147. The generated modelproduced good regression coefficient (0.954), Pearson-r(0.9064), and F value (173) (Tables 1 and 2). The distanceand angles between different sites of AHHRR.3 is presentedin Fig. 1a and b (Table 3), respectively. The pharmacophoricmodel was further validated by its accuracy in prediction oftraining set ligands activity. The predicted PDE4 inhibitoractivity of training set ligands exhibited a high correlation(R2) of 0.954 with observed PDE4 inhibitor activity (Fig. 2).The predictability and validity of hypothesis AHHRR.3 isfurther supported by high cross validation coefficient (Q2=0.8147) and F value 173 with smaller P values (1.218e-21)which implies a greater degree of confidence in the model.

Decoy test

Several enrichment descriptors were defined to assess the re-liability and quality of a 3D-QSARmodel [43]. To validate themodel, 1000 decoy molecules was retrieved from theSchrödinger data base and screened with the generated 4-point hypothesis. Enrichment factor (EF) and robust initialenhancement (RIE) were calculated to benchmark the reliabil-ity of the model and for accurate ranking of compounds [44].For the better assessment of generated pharmacophore modela receiver operating characteristic curve (ROC) was plottedusing the scores of top ranked active molecules as the thresh-old (Supplementary Fig. 1).

Homology modeling

The complete amino acid sequence of the target protein hu-man PDE3A was retrieved from UniProt sequence database(Accession No. Q14432) in FASTA format (http://www.uniprot.org/uniprot/). On the Prime HomologyModelingpanel, the sequence was entered in the text and position-specific iterated BLAST (basic local alignment search tool)homology search was performed taking the non-redundantprotein sequence database of NCBI as default, to identifystructure templates. Chain A of the 2.4 Å resolution crystalstructure of human PDE3B (PDB-ID: 1SO2) was selected asthe template based on the identity of 70 %, 0 % gap and 84 %of positives. The template was then aligned to the query se-quence of 360 amino acids using PrimeSTA. Later energybased model building was performed using prime. Generatedmodel was further refined, which comprised loop refinementand energy minimization using OPLS 2005 force field inPrime (v3.7, Schrödinger 2014–3) [37]. This model was opti-mized prior to docking using the protein preparation wizard inSchrödinger suite 2014–3 [45]. Then the amino acid residues

Table 1 Results of 3D-QSAR modeling fortraining and testmolecules

Statistical parameters Model

# of training set molecules 38

# of test set molecules 14

# of PLS factors 4

SD 0.3725

R2 0.9545

F-value 173

P-value 1.218e-21

RMSE 0.54

Q2 0.8147

Pearson-r 0.9064

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that line the potential ligand binding region were identifiedusing Sitemap with default parameters and receptor grid ofthe protein was generated.

Ligand preparation and docking studies

The X-ray crystal structure of catalytic domain of PDE4Benzyme in complex with roflumilast (PDB ID: 1XMU,

resolution 2.3 Å), retrieved from the protein data bank, wasoptimized using the protein preparation wizard [45] availablein Schrödinger suite 2014–3. Hydrogens and charges wereadded using the OPLS 2005 force field [40], and hydrogenswere refined using restrained minimization. Prime (v3.7) [37]was used for the side-chain refinement and to build breakspresent in the structure. Ligands were desalted and tautomerswere generated. The OPLS-2005 force field was used for the

Table 2 Summary of atom-based 3D-quantitative structure activity relationship results

S. No. Hypothesis Survival score SD R2 F P Stability RMSE Q2 Pearson-r

1. AHHRR.3 2.944 0.372 0.954 173 1.218e-21 0.728 0.54 0.8147 0.9064

2. AAARR.2 2.323 0.615 0.721 120 1.731e-08 0.640 0.60 0.6432 0.8550

3. ADHRR.1 2.098 0.535 0.680 134 1.428e-14 0.600 0.65 0.6240 0.8200

SD: standard deviation of the regression; R2 : for the regression; F: variance ratio; P: significance level of variance ratio; RMSE: root mean square error;Q2 : cross validated R2 for the predicted activities; Pearson-r: correlation between the predicted and observed activity for the test set

Fig. 1 a) Distances between the sites of 5-point pharmacophorehypothesis that generated best 3D-QSAR model; Green representshydrophobic feature, brown color rings represents the aromatic rings,pink color represents the electron acceptor feature b) Angles between

the sites of generated 5-point pharmacophore hypothesis c) Overlay ofactive molecules over the generated 5-point pharmacophore hypothesisd) Overlay of inactive molecules over the generated 5-pointpharmacophore hypothesis

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geometric optimization. Generation of multiple ligand ioniza-tion states and tautomeric forms at pH 7.0±0.2 was completedusing Epik (v2.9, Schrödinger 2014–3) [46]. Additional set-tings for both protein and ligand preparation utilized defaultparameters. The ligand-receptor interactions and the dockedpose of the ligands are of importance to quantify the strengthof association of ligands with receptor [47]. The dockingstudy into the catalytic domains of 1XMU and homologymodel of PDE3Awas performed for the entire data set mole-cules in flexible mode using Glide (v6.4, Schrödinger 2014–3) [36] with enabling the Bwrite XP descriptor information^(Supplementary Table 2).

MM-GBSA calculations

The docked poses were minimized using the local optimiza-tion feature in Prime (v3.7, Schrödinger 2014–3) and bindingfree energies of complex were computed using MM-GBSAcontinuum solvent model (Supplementary Table 2) which

incorporates the OPLS-2005 force field [40], VSGB solventmodel [48], and rotamer search algorithms from Prime.

Molecular dynamics (MD) simulations

Molecular dynamics simulation was performed for the protein1XMUwith ligand 14 using the Desmondmodule (v3.9) [38].The nonbonded models were used for the metal ions [49]. Theprotein was solvated in a three centered water model usingTIP3P solvent model in an orthorhombic box. The total num-bers of atoms in solvated protein structure for the MD simu-lations are more than 39,500 (including 11,360 water mole-cules) whereas the total number of atoms of 1XMU with thesubstrate was approximately 5310. The protein was placed at adistance of 10 Å from the edge of the box. The system wasneutralized by the addition of salt at 0.15 M concentrationwhich includes sodium and chloride ions. Energy minimiza-tion of the system was done using OPLS-2005 force field.LBFGS minimization was performed with three vectors andten steepest descent until a gradient threshold 25 kcal mol1-

Å1- was reached. The maximum iterations during minimiza-tionwere 2000 and convergence threshold was kept at 1.0 kcalmol1- Å1-. A molecular dynamics simulation was run for 10 nswith isotropic coupling of Nose-Hoover chain thermostat andMartyna-Tobias-Klein barostat [50] methods. Equilibriumphase was attained by heating the system to 300 K with arelaxation time of 1 ps. Smooth particle mesh Ewald [51]method was used at a tolerance of 1e-09 for long range elec-trostatic interactions and a cut-off radius of 9 Å was selectedfor short range electrostatic interactions. 3D-structures andtrajectories were visually inspected using the Maestro graph-ical interface. Root mean square deviations (RMSDs) from theinitial structures were calculated using superposition option inMaestro. An average structure obtained from the last 250 ps ofMD simulationwas refined bymeans of 1000 steps of steepestdescent followed by conjugate gradient energy minimization.Themaximum number of cycle of minimization was 5000 and

Table 3 Distances and angles between the pharmacophoric sites ofAHHRR.3

Site 1 Site 2 Distance Å Site 1 Site 2 Site 3 Angle (°)

A4 H11 9.042 H11 A4 H12 33.4

A4 H12 10.315 H11 A4 R15 18.1

A4 R15 7.517 H11 A4 R16 34.6

A4 R16 7.113 H12 A4 R15 15.4

H11 H12 5.694 H12 A4 R16 2.8

H11 R15 3.01 R15 A4 R16 16.5

H11 R16 5.147 A4 H11 H12 85.7

H12 R15 3.661 A4 H11 R15 50.9

H12 R16 3.231 A4 H11 R16 51.7

R15 R16 2.138 H12 H11 R15 35

R15 H11 R16 0.8

A4 H12 H11 60.9

Fig. 2 Scatter plot of observedvs. predicted activities bestpredictive atom based QSARmodel for (a) training set (b) testset with the best fit line [y=0.87x+0.91 (R2=0.82)]

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the convergence criterion for the energy gradient was 0.001 kJmol1- Å1-.

Results and discussion

The best 5-point AHHRR.3 common pharmacophore modelwas generated based on 38 active compounds (pIC50≥7.5)from the data set. The variant with a survival score of 2.944,best cross validated R2 0.8147, regression coefficient 0.9949,Pearson-r 0.9105, and F value 173 was selected to be thecommon pharmacophore hypothesis. The fitness score of allligands were checked with the pharmacophore modelAHHRR.3 (Supplementary Table 3). The shape screening(phase v4.0) was used to flexibly align the ligand structures[35, 41, 42]. Further, these aligned molecules were used forthe 3D-QSAR model building. As shown in Fig. 1c and d,active inhibitors of the training set overlayed well on the gen-erated pharmacophore model (AHHRR.3) whereas inactiveinhibitors mapped the same pharmacophoric features less pre-cisely. It is evident from result that all generated hypothesesencompass hydrogen bond acceptor and aromatic ring groupssuggesting an important role in the inhibition of the PDE4enzyme.

Decoy test

To validate the discriminatory ability of the AHHRR.3 model,we retrieved 1000 decoy molecules from the Schrödinger da-tabase. AHHRR.3 model was able to find 100 % of activecompounds in the hit list. We calculated RIE for the generatedmodels to estimate the contribution of the active moleculesranking in the enrichment. For the hypothesis AHHRR.3,RIE value was 8.24 indicating the superiority of thepharmacophore model ranking over random distribution. An-other reliable metrics to evaluate the performance of thepharmacophore model is the AUC of the ROC curve. Thepresent model consummates a markdown AUC of 0.95 and0.99 ROC (Table 4).

Analysis of hydrogen bond donor effects

A better visualization of ligand receptor interactions are in-ferred by 3D-QSAR analysis. The results permit to visualizethe favored region as blue cubes and the unfavorable regions

as red cubes. The contour cubes for hydrogen bond donoreffects are visualized at a positive regression coefficient of +0.0035 and negative regression coefficient of −0.0035(Fig. 3). The blue cubes around the seventh position of R15and R16 pharmacophoric region indicated the preference ofhydrogen bond donor groups at this position. This assumptionis supported by visualizing the molecule 1 (pIC50 9.569), 14(pIC50 9.951) and 15 (pIC50 9.721) having –NH substituent onseventh position of the pyrazolo[1,5-a]pyridine skeleton. Thepresence of an unfavorable hydrogen bond donor substituentfeature is inferred by the red cubes around the third position ofthe R15 pharmacophoric feature is supported by a remarkabledecrease in PDE4 inhibitory activity of 20 (pIC50 3.943) bythree-fold.

Analysis of hydrophobic effects

The QSAR visualization to study the hydrophobic featureswere visualized at a positive regression coefficient of +0.065and negative regression coefficient of −0.065. The presence ofblue cubes around thiomethyl substituent present at the sev-enth position of the pyrazolo[1,5-a]pyridine ring (R15 andR16 pharmacophoric feature) indicated the preference of hy-drophobic substituents for hydrophobic interactions withPDE4 enzyme (Fig. 4a and b). Adding to this assumption,molecules 1 (pIC50 9.569), 14 (pIC50 9.951), and 50 (pIC50

9.114) having methyl substituent on seventh position (H1pharmacophoric feature) possess high inhibitory potency onPDE4. The red cubes around CF3 substituent present at thesecond position of pyrazolo[1,5-a]pyridine ring (R15 and R16pharmacophoric feature) (H11 pharmacophoric feature) indi-cated that hydrophobic groups are not favorable at this posi-tion and this is evident by the fact that ligands 20 (pIC50

3.943) and 33 (pIC50 3.93), having hydrophobic groups at thisposition (Fig. 4b), exhibited less affinity for PDE4 enzyme.The blue cubes around the -NHCO- linker indicated the pref-erence of bulky hydrophobic groups and this is supported by

Table 4 Enrichment data for the generated models

S. No Pharmacophore model ROC RIE AUC

1. AHHRR.3 0.99 8.24 0.95

2. AAARR.2 0.90 4.94 0.91

3. ADHRR.1 0.97 8.24 0.95

Fig. 3 Pictorial representation of the cubes generated using the 3D-QSAR model and visualized in the context of hydrogen bond donoreffects: compounds 1 (orange, 9.569), 14 (green, 9.951), and 15 (gray,9.721). The pIC50 values of the compounds are given in the parentheses

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the QSAR visualization of the most potent molecule 14possessing 3,5-dichloropyridine moiety linked to thepyrazolo[1,5-a]pyridine ring. The above results indicated thathydrophobic substituent at the second and seventh positionsof pyrazolo[1,5-a]pyridine ring is favorable for the PDE4 in-hibitory activity.

Analysis of electron-withdrawing effects

The electron withdrawing effects of the generated model wasvisualized at positive regression coefficient of +0.05 and neg-ative regression coefficient of −0.05. The presence of redcubes at seventh position of the pyrazolo[1,5-a]pyridine ring(R16 pharmacophoric feature) indicated that electron with-drawing group is unfavorable around this position (Fig. 5aand b). The blue cubes around the pyridine ring attached tothe R16 pharmacophoric feature indicated the preference ofelectron withdrawing group at this position. This is evident bythe high potency of inhibitors 1 (pIC50 9.569) and 14 (pIC50

9.951) and 50 (pIC50 9.114) possessing 2,3-dichlorobenzenemoiety attached to the pyrazolo[1,5-a]pyridine ring whencompared with 52 (pIC50 6.824) which is devoid of additionalelectron-withdrawing substituent on the pyridine ring(Fig. 5a). However, replacement of pyridine moiety presenton the fifth position of the pyrazolo[1,5-a]pyridine ring withdihydropyridazinone moiety lowered the PDE4 inhibitory ac-tivity in 19 (pIC50 4.398), 20 (pIC50 3.943), and 32 (pIC50

3.939). Presence of red cubes around the seventh position ofthe pyrazolo[1,5-a]pyridine ring indicated that electron with-drawing substituent is unfavorable at this position (Fig. 5b).

Analysis of positive ionic effects

Positive ionic effects are visualized at positive coefficientthresholds of +0.004 and negative coefficient thresholds of−0.004. The substitution of positive ionic groups is unfavor-able around the first position of the pyridine ring (Fig. 6).

Fig. 4 Pictorial representation of the cubes generated using the 3D-QSAR model and visualized in the context of hydrophobic interactions.(a) compounds 1 (orange, 9.569), 50 (gray, 9.114), and 14 (green, 9.951)

(b) compound 33 (green, 3.93), 20 (orange, 3.943). The pIC50 values ofthe compounds are given in the parentheses

Fig. 5 Pictorial representation ofthe cubes generated using the 3D-QSAR model and visualized inthe context of electronwithdrawing features. (a)compounds 1 (orange, 9.569), 14(green, 9.951), and 50 (gray,9.114) (b) compound 20 (orange,3.943) and 33 (cyan blue, 3.93).The pIC50 values of thecompounds are given in theparentheses

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Moreover, data set molecules considered for this study did notexhibit any other positive ionic substituent effect.

Analysis of ligand-receptor docking studies

A comparison of different docking poses from pyrozolo[1,5-a]pyridine/4,4-dimethylpyrazolone analogues (compound 1–52) exhibited hydrogen bonding network with Asp392,Asn395 Tyr233, and Gln443, and these residues belongs tothe amino acid stretch that forms the catalytic domain of thePDE4 enzyme. In general, R15 and R16 pharmacophoric fea-tures of compounds exhibited hydrophobic interactions withTyr233, Pro396, Trp406, Ile410, Phe414, and Phe446 resi-dues. Interestingly, our Glide XP-docking mode also exposedπ-π stacking of R15 and R16 pharmacophoric features withHis234, Phe414, and Phe446 (Fig. 7a also SupplementaryFig. 3 and 4), which is an attractive, noncovalent interactionsbetween aromatic rings and also play an important role instabilization of inhibitor at the active site. Figure 7a showsthe docked pose of the most active compound 14 within theactive site of PDE4. Precisely, N2 of pyrazolopyridine ringaccepts a hydrogen bond from the backbone NH (rN2⋯NH)of Gln443 (2.01 Å). A hydrogen bonding was observed be-tween linker (NH-C=O) NH and backbone C=O (C=O-NH⋯H-O-H⋯O=C) of Asp392 through water bridge(2.82 Å) and hydroxyl (−C=O-NH⋯O-H(H)⋯OH) groupof Tyr233 (2.73 Å). The pyrozolo[1,5-a]pyridine ring of 14

showed π-π stacking with Phe446 and Phe414 residueswhereas the pyridine ring of the same compound formedπ-π stacking with His234 residue. It is evident from thedocking results that R15 and R16 pharmacophoric featuresof inhibitor 14 are sandwiched between Tyr233, Pro396,Ile410, Phe414, Phe446, and Leu393 residues in the hydro-phobic pocket extending at the catalytic region near theGln443 residue. Thus, it appears that the additional hydropho-bic interactions with these residues are largely responsible forthe high binding affinity that most inhibitors display. Thepharmacophoric feature A4 is directed into the metal bindingcatalytic domain in close proximity to both the Zn2+ andMg2+

cations and such an orientation would block the approach ofcAMP to the catalytic domain and forms the basis forinhibiting PDE4. For the highly active inhibitor 14, regionPro396-Phe446 became more stable as evident by the hydro-gen bonding and hydrophobic interactions in Fig. 7a (alsoSupplementary Fig. 2) whereas region Asp346-Arg380 be-came more flexible and no interactions were observed in thisregion. This is in good agreement with the earlier studies [32],and suggests that the region Pro396-Phe446 is more favorablefor inhibitors binding to PDE4. Moreover, region Leu400–Ile450 has been known to be involved in hydrophobic andhydrogen bonding interactions between PDE4 and inhibitors[52, 53]. However, Glide scores (Gscore) showed statisticallyfair correlation coefficient of 0.41 and 0.61 with Glide energyand Glide emodel, respectively. The evaluation of moleculardocking with a related post-scoring approach, free bindingenergy MM-GBSA [45] for 1XMU and homology model ofPDE3A is reported (Supplementary Table 2). In the presentstudy, the accuracy of the docking procedure is determined byexamining how closely the lowest energy poses (binding con-formation) predicted by the object scoring function, Glidescore (Gscore) almost resembles an experimental bindingmode as determined by X-ray crystallography (Supplementa-ry Fig. 5) [12]. We observed a very similar orientation be-tween the conformation of the inhibitors 14, 15, and 17 upondocking with the co-crystal structure of PDE4B (PDB-ID:1XMU) roflumilast (Supplementary Fig. 5). Further, Glidescores andMM-GBSAvalues obtained after docking of inhib-itors into the catalytic pocket of PDE3A was compared withthat of the PDE4B (Supplementary Table 2). In general, activeinhibitors in this study showed lower binding affinity towardPDE3A in comparison to the PDE4B. In accordance with theprevious study [33] we also observed poor correlation of ex-perimental pIC50-values with Glide scores (r2=0.095) andPrime MM-GBSA values (r2=0.091).

Molecular dynamics study

Proteins, under physiological conditions are macromole-cules that are dynamic in nature. The structure of the pro-tein and its interactions with its physiological environment

Fig. 6 Pictorial representation of the cubes generated using the 3D-QSAR model and visualized in the context of positive ionic features ofcompound 14 (pIC50 9.959)

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especially ligands are governed by this dynamic process.As shown in Fig. 7b, the RMSD values of 1XMU backboneand ligand are 1.8 and 0.9 Å, respectively for the MD tra-jectories (also Supplementary Fig. 8) indicating that thebackbone of the protein did not dramatically change ingoing from its crystal structure to the protein structure inwater. MD analysis of the inhibitor 14/1XMU complexshowed almost the same modes of hydrogen bond interac-tions as predicted by the docking study. Inhibitor 14 exhib-ited hydrogen bonding and hydrophobic interactions withthe stable region Pro396-Phe446 (Supplementary Fig. 9–11) whereas region Asp346-Arg380 was found to be moreflexible and no hydrogen bond and hydrophobic interac-tions were observed in this region. This is in good

agreement with the earlier studies [32]. The 14/1XMUc omp l e x e n c omp a s s t h r e e h y d r o g e n b o n d s ,pyrazolopyridine ring N2 with H of Gln443 side chain,linker NH-C=O⋯H-O-H⋯O=C of Asp392 backbonethrough water bridge and linker –NH-C=O⋯-H-O-H⋯OH group of Tyr233 backbone through water bridge,respectively in approximately 69, 60, and 63 % of the MDsimulation trajectory (Supplementary Fig. 11) was ob-served which is complementary with the docking analysis.All the predicted hydrogen bonds (compound 14/1XMUcomplex) were restored in energy minimized complexstructure and it is evident that those atoms which lost thehydrogen bonding interaction during MD simulationscould still be involved in electrostatic interactions. Most

Fig. 7 (a) Binding mode ofcompound 14 in the catalyticpocket of 1XMU (b). Representsthe RMSD (Å) of the simulatedpositions of 1XMU backboneatoms from those in the initialstructure

J Mol Model (2015) 21:289 Page 9 of 12 289

of the hydrogen bonds detected during MD simulationswere formed with amino acid residues located within theM pocket and such hydrogen bonds probably reflect thehigher conformational freedom of amino acids near the Mpocket than that of amino acids in the S pocket. More pre-cisely, the pyrazolo[1,5-a]pyridine moiety is sandwichedbetween the conserved residues Tyr233, Tyr403, Ile410,and Phe446 with which it makes closer hydrophobic con-tacts throughout the simulation trajectory (0.00 through10.00 ns, Supplementary Fig. 9 and 10). This is also inagreement with the docking (Supplementary Fig. 2) and3D-QSAR study (Fig. 4a and b) results. Due to the highhydrophobic character of the binding pocket, it is not sur-prising that hydrophobic substituent on pyrazolo[1,5-a]pyr-idine moiety will probably enhance the fitting of ligand inthe active site. Our MD simulation study also exposed π-πstacking of R16 pharmacophoric feature with Tyr233 (Sup-plementary Fig. 11), which plays an important role in sta-bilization of inhibitor at the active site. Further, similarorientation was observed between the superposition of theconformations of 14 after MD simulation and best XP-docking pose (RMSD: 0.59 Å); conformations of 14 afterMD simulation and pose of the AHHRR.3 model (RMSD:0.73 Å); conformations of 14 best XP-docking pose and3D-QSAR pose (RMSD: 0.69 Å) (Supplementary Fig. 7).

Conclusions

In the present study, a combined computational approach wasapplied to gain insight into the structural basis and selectivitymechanism for the selective PDE4 inhibitors. We developed ahighly predictive five point 3D-QSAR model using 52 mole-cules. The five point pharmacophore hypothesis consists ofone hydrogen bond acceptor, two hydrophobic features andtwo aromatic rings and it provides insights into the structuralrequirements for the design of selective PDE4 inhibitors. Mo-lecular mechanics based MM-GBSA scoring methods haveseen an upsurge in popularity and compared to the dockingscoring functions provide improved correlation between cal-culated binding affinities and experimental data [54]. In thepresent study, results obtained with MM-GBSA procedureprovided more superior correlation between calculated bind-ing free energies and biological activity of a diverse set ofPDE4 inhibitors when compared to the docking scoring func-tions. Finally, a MD simulation analysis was carried out toidentify the interactions of compound 14 with amino acidresidues in the catalytic pocket of PDE4 (PDB-ID: 1XMU)and it showed similar interactions as predicted by the XP-docking. The developed atom based 3D-QSAR model pro-vides clues for the possible structural modifications and bind-ing interactions and it will be of interest for the developmentof more potent and selective PDE4 inhibitors.

Acknowledgments We would like to thank Science and EngineeringResearch Board (SERB), Government of India for the financial support(No. SR/SO/HS-0264/2012).

Disclosure The authors declare no conflict of interest in this work.

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