DEVELOPMENT OF PHARMACOPHORE MODEL FOR S. AUREUS DNA GYRASE INHIBITORS

7/30/2019 DEVELOPMENT OF PHARMACOPHORE MODEL FOR S. AUREUS DNA GYRASE INHIBITORS

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Int. J. Pharm. Biosci. Technol.

Lele et al Pg. 34

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International Journal of Pharma Bioscience and Technology; Volume 1, Issue 1, May 2013, Pg 34-49

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DEVELOPMENT OF PHARMACOPHORE MODEL FOR S. AUREUS DNA

GYRASE INHIBITORS

Arundhati C. Lele, Nilesh R. Tawari, Mariam S. Degani*

Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Sciences and Technology,Institute of Chemical Technology, N. P. Marg, Matunga, Mumbai 400019, India

Corresponding Author*

E-mail address- [email protected]

ABSTRACT:

Pharmacophore mapping studies were undertaken for a series of molecules belonging to indazole,pyrazole and their congeners as inhibitors of S. Aureus DNA gyrase. A four point pharmacophore withtwo aromatic rings (R), one lipophilic/hydrophobic group (H) and one positive ionic feature (P) aspharmacophoric features was developed. The pharmacophore hypothesis yielded a statisticallysignificant 3D-QSAR model, with correlation coefficient of r2 = 0.681 for training set molecules. Themodel so generated showed good predictive power with correlation coefficient for an external test set of fifteen molecules. The geometry and features of the pharmacophore will be useful for ligand baseddesign of potential DNA gyrase inhibitors.

Key words: Pharmacophore, 3D-QSAR, DNA gyrase, S. Aureus.

INTRODUCTION

Antimicrobial resistance is a growing threatworldwide. Staphylococcus aureus infections are amajor cause of morbidity and mortality incommunity and hospital settings.[1] Theemergence of methicillin resistant and vancomycin resistant strains of S. Aureus represents an enormous threat to public health

with MRSA accounting for as many as 50% of infections.[2] S. Aureus strains that combine

resistant and virulent genes have become a majortreatment problem in Europe and the U.S.[3]Hence there is an ever increasing need for thedevelopment of newer antibiotics to treat S. aureus infections.

DNA gyrase is a prokaryotic specific type ΙΙ topoisomerase and has attracted considerableattention from the pharmaceutical industry eversince the discovery of Nalidixic acid (DNA gyraseinhibitor) in the early 1960’s. Other well-knownDNA gyrase inhibitors include quinolones,coumarins and cyclothialidines.[4] Bacterial DNA

gyrase is involved in the vital processes of DNA replication, transcription, and recombination andcatalyzes the ATP-dependent introduction of

negative supercoils into bacterial DNA as well asthe decatenation and unknotting of DNA. [5]

Ligand based drug design approaches likepharmacophore mapping and QSAR can be usedin drug discovery in several ways; therationalization of activity trend in molecules understudy, to predict the activity for new compounds,for database search studies in search of new hitsand to identify important features for activity. This

paper describes the development of a robustligand based 3D- pharmacophore hypothesisusing Pharmacophore Alignment and ScoringEngine (PHASE) for S. aureus DNA gyrase. Ligandsselected for the development of thepharmacophore model and the subsequentgeneration of atom based 3D-QSAR wereindazoles and pyrrazoles analogues acting as DNA gyrase inhibitors and the alignment obtained fromthe generated pharmacophoric points is used toderive an atom based 3D-QSAR model. Thepharmacophore thus developed impartsinformation about important features for DNA

gyrase inhibitors and the geometry has the abilityto mine 3D virtual databases of drug like

Research ArticleReceived: 20 April 2013, Accepted: 29 April 2013



Int. J. Pharm. Biosci. Technol .

Lele et al Pg. 35

molecules. Furthermore, the contours generatedfrom QSAR studies highlight the structural featuresrequired for DNA gyrase inhibitory activity andare useful for further design of more potentinhibitors.

MATERIALS AND METHODS

Biological data

A set of 63 compounds shown in Table 1(a) to

Table 1(m), belonging to the family of indazoleand pyrrazole analogues reported as DNA gyraseinhibitors [6-10] were used for the pharmacophoregeneration. The negative logarithm of themeasured IC50 value as pIC50 was used in the 3D-

QSAR study. The complete set was divided into atraining set (48 compounds) and a test set (15compounds) using randomization as well aschemical and biological diversity.

Table 1. Structures and the inhibitory concemtrations of the set of molecules utilized for generation

of pharmacophore model.

Table 1(a) [6]

No Structure MIC (µg/ml)

1. 64

2.

Novobiocin

0.25

Table 1(b)

General structure 1.[6]

No. R1 MIC (µg/ml)

3. 32

4.16




Lele et al Pg. 36

5.

HNNH

O

OH3C

64

6.

HNNH

O

OH3C

64

7. 32

8. 64

9 32

10. 32

11. 16

12. 16

13.

O

O

H3C

HN

4

Table 1(c)

General structure 2 [6]




Lele et al Pg. 37

Structure No. R1 MIC (µg/ml)

14. 64

15. 128

16. 32

17. 4

Table 1(d) [7]

Structure No. Structure MIC (µg/ml)

18. 4

19. 4

20. 1

21. 1




Lele et al Pg. 38

Table 1(e)


Structure No. R3 MIC (µg/ml)

22.

(E Stereochemistry)

4

23.

(Z Stereochemistry)

4

24.

(E Stereochemistry)

8

25.

(Z Stereochemistry)

4

Table 1(f)


Structure No. R MIC (µg/ml)

26. 2

27. 8




Lele et al Pg. 39

28. 2

29. 2

30. 32

31. 16

32. 4

33. 1

34.

Sparfloxacin

0.125

Table 1(g)

General Structure 5 [8]




Lele et al Pg. 40

Structure No. R MIC (µg/ml)

35. 2

36. 128

37. 32

38. 4

Table 1(h)


Structure No. R1 R2 MIC (µg/ml)

39. H 64

40. H 64

48. CH3 32

42. 4




Lele et al Pg. 41

43. 4

44. 4

45. 4

46. 1

Table 1(i)


Structure No. R1 R2 MIC (µg/ml)

47. CH3 64

48. 8




Lele et al Pg. 42

Table 1(j) [10]

Structure No. Structure MIC (µg/ml)

49. 64

Table 1(k) General Structure 8 [10]

Structure No. R1 H-R2 MIC (µg/ml)

50. 4

51. 2

52. 16

53. 2

54. 4

55. 64

56 2




Lele et al Pg. 43

Table 1(L)


Structure No. R1 H-R2 MIC (µg/ml)

57.4

58. 8

59. 4

Table 1(m)


Structure No. X R1 H-R2 MIC

(µg/ml)

60. O3- Cl

4

61. O3- Cl

2

62. NH3- Cl

2

63. NH3- Cl

2




Lele et al Pg. 44

Generation of Common PharmacophoreHypothesis (CPH)

The pharmacophore hypothesis and alignmentbased on it was carried out using PHASE, version2.0, Schrödinger, LLC, New York, NY, 2005

installed on AMD Athelon workstation.[11] Thestructures were imported from the project table inthe “Develop Pharmacophore Hypothesis” paneland geometrically refined (cleaned) usingLigprep. Conformations were generated byMCMM/LMOD method using maximum of 2000steps with distance dependent dielectric solventmodel and OPLS-2005 force field. All theconformers were subsequently minimized usingtruncated Newton conjugate gradient minimizationup to 500 iterations. For each molecule a set of conformers with maximum energy difference of 30kcal/mol relative to the global energy minimumwere retained. A redundancy check of 2 Å in theheavy atom positions was applied to removeduplicate conformers. Pharmacophore features;hydrogen bond acceptor (A), hydrogen bonddonor (D), hydrophobic group (H), negativelycharged group (N), positively charged group (P),aromatic ring (R), were defined by a set of 4chemical structure patterns as SMARTS queriesand assigned one of three possible geometries,which define physical characteristics of the site:

Point—the site is located on a single atom in theSMARTS query.

Vector—the site is located on a single atom in theSMARTS query, and assigned directionalityaccording to one or more vectors originating fromthe atom.

Group—the site is located at the centroid of agroup of atoms in the SMARTS query. For aromaticrings, the site is assigned directionality, definedby a vector that is normal to the plane of the ring.

Active and inactive thresholds were applied to thedataset (Table 1.) to yield 11 actives and 12inactives which were used for the pharmacophore

generation and subsequent scoring. Commonpharmacophoric features were then identifiedfrom a set of variants, a set of feature types thatdefine a possible pharmacophore, using a treebased partitioning algorithm with maximum treedepth of 4. The final size of the pharmacophorebox was 1Å to optimize the number of finalcommon pharmacophore hypotheses (CPH).These CPHs were examined using a scoringfunction to yield the best alignment of the activeligands using overall maximum RMSD value of 1.2 Å with default options for distance tolerance. Thequality of alignment was measured by a survivalscore, which was defined using followingequation. [12]

S= WsiteSsite + W vecS vec + W volS vol + WselSsel + Wrewm

Where W’s are weights and S’s are scores

Ssite represents alignment score, the root meansquare deviation in the site point position.

S vec represents vector score, and averages cosineof the angles formed by corresponding pairs of vector features in the aligned structures.

S vol represents volume score based on overlap of Van der Waals models of non hydrogen atoms ineach pair of structures.

Ssel represents selectivity score, and accounts forwhat fractions of molecules are likely to match thehypothesis regardless of their activity towardsreceptor.

Wsite, W vec, W vol, Wrew have default value of 1.0

while Wsel has default value of 0.0.In the hypothesis generation default values havebeen used. Wrew

m represents reward weightsdefined by m-1 where m is the number of activesthat match the hypothesis.

The assessment of the generated CPHs wasperformed by correlating the observed andestimated activity for the internal set of 48 trainingmolecules and external set of 15 test moleculesusing partial least square analysis. The partialleast square (PLS) regression was carried outusing PHASE with maximum of 4 PLS factors usingpharmacophore based model, with grid spacing of 1Å. The CPH with best predictive value andsignificant statistical data was chosen foralignment of molecules and used for further 3D-QSAR studies.

QSAR Model Building

A training set of 48 molecules were selectedrandomly, incorporating biological and chemicaldiversity, and were used to generate atom-basedQSAR models for all hypotheses using a gridspacing of 1.0 Ǻ. Models containing four or more

PLS factors tended to fit the pIC50 values beyondtheir experimental uncertainty and thus, only one,two and three factor models were considered.Each of these models were validated using anexternal test set of fifteen molecules which werenot considered during model generation.

RESULTS AND DISCUSSION

Pharmacophore models containing three and foursites were generated using a terminal box size of 1 Å with twelve highly active molecules selected [6-10] belonging to indazole, pyrazole and their

congeners, using a tree based partition algorithm.The three featured CPHs were rejected, as they




Lele et al Pg. 45

were unable to define the complete binding spaceof the selected molecules. A total of 10,487probable four featured CPHs belonging to seventypes HHPR, AHPR, HHRR, AHHR, AHRR, AHHP andHPRR were subjected to stringent scoring function

analysis, with respect to actives using defaultparameters for site, vector, and volume.

Hypotheses emerging from this process weresubsequently scored with respect to the seveninactives, using a weight of 1.0. The hypotheseswhich survived the scoring process were used tobuild an atom-based QSAR model. The summary

of statistical data of the best CPHs labeled CPH1 toCPH6 with their survival scores is listed in Table 2.

Table 2. Summary of QSAR results for six best CPHs with survival scoresCPH1

(HPRR)

CPH2

(HHPR)

CPH3

(HHRR)

CPH4

(AHHR)

CPH5

(AHRR)

Survival score 3.136 3.136 3.092 3.026 2.872

SD 0.422 0.444 0.372 0.443 0.386

r 2 0.681 0.647 0.729 0.648 0.699

F 31.3 26.9 35.1 27.1 33.4

P 5.413e-11 4.949e-10 3.686e-11 4.429e-10 2.644e-11

RMSE 0.452 0.466 0.486 0.471 0.501

Q2 0.525 0.510 0.449 0.4842 0.414

Pearson-R 0.741 0.720 0.754 0.736 0.646

SD = Standard deviation of the regression, r 2 = Value of r2 for the regression, F = Variance ratio P = Significance level of variance ratio, RMSE = Root-mean-square error, Q2 = Value of Q2 for the

predicted activities, Pearson-R = Correlation between the predicted and observed activity for the test set.

Consistent external predictivity was observed forCPH1 for each combination as compared to others.The CPH1 showed a statistically significant r 2 valuefor training set (0.681), excellent predictive powerwith Q2 of 0.525 and a Pearson-R value of 0.741.Hence, the hypothesis CPH1 with two aromaticring (R), one lipophilic/hydrophobic group (H)and one positive ionic feature (P) aspharmacophoric features was retained for furtherstudies. Actual and predicted values of the training

set and test set molecules are given in Table 3.Figure 1 shows the alignment of the moleculesunder study along with CPH1 while Figure 2 showsthe gemotry of the pharmacophore generated. Thedistances and angles between thepharmacophoric features are depicted in Figure3(a) and 3(b) respectively. Figures 4(a) and Figure4(b) shows the graphs of actual v/s. predictedactivity for training and test set molecules.

Table 3. Actual and predicted values of the dataset

Structure

No.

Biological

activity

Predicted

activity

Structure

No.

Biological

activity

Predicted

activity

1 -2.283 -2.34 33 -0.359 -0.87

2 0.389 0.31 34 0.497 0.98

3 -1.995 -1.80 35 -0.669 -1.28

4 -1.626 -1.76 36 -2.599 -1.49

5 -2.166 -1.56 37 -1.994 -1.53

6 -2.166 -1.92 38 -1.094 -1.51

7 -1.894 -1.72 39 -2.302 -2.34

8 -2.195 -1.83 40 -2.363 -2.299 -1.866 -1.72 41 -1.964 -2.25




Lele et al Pg. 46

10 -1.866 -1.56 42 -1.013 -1.11

11 -1.551 -1.50 43 -0.969 -1.11

12 -1.577 -1.56 44 -0.99 -1.04

13 -0.949 -1.42 45 -0.966 -0.96

14 -2.211 -1.80 46 -0.359 -0.9915 -2.474 -1.41 47 -2.265 -2.06

16 -1.874 -1.84 48 -1.267 -0.97

17 -0.937 -1.22 49 -2.283 -2.34

18 -0.949 -1.57 50 -0.969 -0.93

19 -0.937 -1.38 51 -0.668 -0.93

20 -0.359 -0.87 52 -1.571 -1.25

21 -0.345 -0.90 53 -0.68 -0.73

22 -0.991 -1.41 54 -1.011 -1.30

23 -0.991 -1.10 55 -2.138 -1.25

24 -1.273 -0.99 56 -0.654 -0.8625 -0.972 -1.47 57 -0.969 -1.12

26 -0.57 -0.93 58 -1.312 -0.98

27 -1.267 -0.87 59 -0.955 -1.13

28 -0.646 -0.90 60 -0.968 -1.14

29 -0.66 -0.87 61 -0.653 -1.21

30 -1.867 -0.97 62 -0.668 -0.98

31 -1.561 -0.97 63 -0.654 -1.35

32 -0.966 -1.07

Fig. 1. CPH1 based alignment DNA gyraseinhibitors.

Fig. 2. Geometry of the pharmacophore- P9 isthe positive ionic feature represented as bluesphere; R11 and R12 are the aromatic ring

features represented in orange torus and H4 is

the hydrophobic feature represented in greensphere.




Lele et al Pg. 47

Fig. 3(a) CPH1 features and distances.

Fig. 3(b) CPH1 features and angles.

Fig. 4(a). Scatter plots for the predicted and

experimental pIC50 values for the DNA gyraseQSAR model applied to the training set.

Fig. 4(b). Scatter plots for the predicted and

experimental pIC50 values for the DNA gyrase

QSAR model applied to the test set.

The pharmacophore contains two aromatic ring (R)features mapping on the pyrazole heterocycle andbenzene ring, one positive ionic feature mappingon one of the nitrogens in the piperazine ring andone lipophilic/ hydrophobic feature mapping onthe para-chloro atom in the distal part of molecule.When the QSAR model was visualized in thecontext of one of the most active molecule(Structure 21) blue cubes, favorable for theactivity, were observed in context of thepharmacophoric features thus corroborating thefindings of pharmacophore as observed in Fig. 5.

Fig. 5. QSAR model visualization for DNAgyrase B of S. aureus.

CONCLUSION

The current work involves development of predictive pharmacophore hypothesis (CPH) for S.

aureus DNA gyrase inhibitors using PHASE and

performing the QSAR analysis of the model. BestCPH was selected primarily on the basis of itsinternal and external predictive capabilities. The




Lele et al Pg. 48

generated CPH consists of two aromatic rings, onelipophilic/hydrophobic group and one positiveionic feature. The pharmacophore hypothesisyielded statistically significant 3D-QSAR model.The developed pharmacophore model will aid in

the rational design of DNA gyrase inhibitors.

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ACKNOWLEDGEMENTS

Author A. C. Lele is thankful to Council of Scientificand Industrial Research, Delhi, India and N. R.Tawari is thankful to Department of Biotechnology,Delhi, India for financial assistance.

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Lele et al Pg. 49

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Lele, A. C., Tawari, N. R., & Degani, M. S.

(2013). Development of pharmacophoremodel for S. Aureus DNA-gyrase inhibitors. International Journal of Pharma Bioscience and Technology , 1(1), 34–49.

Elsevier Harvard style

Lele, A.C., Tawari, N.R., Degani, M.S., 2013.Development of pharmacophore model for S. Aureus DNA-gyrase inhibitors. Int. J. Pharm.Biosci. Technol. 1, 34–49.

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Lele AC, Tawari NR, Degani MS. Developmentof pharmacophore model for S. Aureus DNA-

gyrase inhibitors. Int. J. Pharm. Biosci.Technol. 2013;1(1):34–49.

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DEVELOPMENT OF PHARMACOPHORE MODEL FOR S. AUREUS DNA GYRASE INHIBITORS

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