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DESIGNING
OF
NOVEL
PEPTIDE
DEFORMYLASE
INHIBITORS
COMPARATIVE MOLECULAR FIELD ANALYSIS
Itishree Shantanu Vaidya1*, Kaumudi Sumedh Puranik2, Krishnacharya Akamanchi2
1
Dr. L.H. Hiranandani College of Pharmacy, CHM Campus, Ulhasnagar, District: Thane,
Maharashtra PIN: 421003
2
Pharmaceutical Division, Institute of Chemical Technology, Matunga, Mumbai-400019
Address for correspondence:
*Dr. (Mrs.) Itishree Vaidya, Dr. L.H. Hiranandani College of Pharmacy, CHM Campus,
Ulhasnagar, District: Thane, Maharashtra PIN: 421003
Tel: 0251-2561463/ 9819287323
Tel Fax: 0251-2561341
Email: [email protected]
BY
A
ABSTRACT
Peptide deformylase (PDF) represents the most promising bacterial target in the search of novel
antibiotics lacking cross resistance to existing drugs. PDF inhibitors reported till dates are peptide
and non-peptide inhibitors. To figure out the structural requirements of PDF inhibitors belonging to
non-peptidic class and to optimize their Escherichia coli peptide deformylase inhibitory activity
comparative molecular field analysis(CoMFA) on twenty low molecular weight -Sulfonyl and
sulfinylhydroxamic acid derivatives was performed.
A CoMFA model showed considerable
internal as well as external predictive ability (r2cv = 0.707, r2pred = 0.826). CoMFA study helped in
designing novel PDF inhibitors and their PDF inhibitory activities are also predicted.
KEYWORDS
Peptide deformylase, non-peptide inhibitors, hydroxamic acid, antibacterial, comparative molecular
field analysis
INTRODUCTION
The emergence of antibiotic-resistant bacteria has created an urgent demand for new antibacterial
agents with novel mechanisms of action. Bacterial genomics has revealed a plethora of previously
unknown targets of potential use in the discovery of novel antibacterial design. The vital features
for the identification of good target agreed are i) present in most human pathogens (wide –spectrum
effects), ii) absent from human cells, iii) part of an essential pathway in the pathogens, iv) not be
inhibited by widely used antibiotics, v) easy to assay invitro and invivo, vi) highly specific for the
pathogen and non-toxic to humans and vi) not result in resistance or bypass processes if inactivated.
Peptide deformylase (PDF; EC 3.5.1.31) fulfils the criteria listed above and is likely the most
attractive bacterial target to deliver the next class of novel antibacterial drugs. 1-4
The protein synthesis processes for bacterial and mammalian cells are very similar. Both utilize the
same amino acids and codons and share the same mechanism for elongation. However, a major
difference between bacterial protein synthesis and mammalian cytosol protein synthesis is the use of
formylmethionine as the initiator. Unlike cytosol protein synthesis in mammalian cells, which is
initiated with methionine, protein synthesis in bacteria is initiated with N-formylmethionine which
is generated through enzymatic transformylation of methionyl-tRNA by formylmethionine tRNA
transferase. PDF is a bacterial metalloenzyme and is responsible for incorporating or removing
Formyl group into bacterial methioninepolypeptide chain5. The N-formyl methionine of the
nascent protein in bacteria is removed by the sequential action of PDF and a methionine amino
peptidase in order to afford the mature protein. This formylation-deformylation cycle is essential for
bacterial growth and is conserved among all studied bacterial species. Previous reports indicate that
this cycle is not required for mammalian cells. The specific bacterial requirement for PDF in protein
synthesis provides a rational basis for selectivity, making it an attractive drug discovery target. PDF
is essential for bacterial survival; either deletion of def gene or treatment with a PDF inhibitor
prevents bacterial growth.6- 8
Rational and design strategy:
Using mechanistic information about the reaction catalyzed by PDF, together with an understanding
of the general principles of metalloprotease inhibition, others have constructed several chelatorbased inhibitor libraries with
chelating pharmacophore element that can bind to the metal ion at
the active centre of PDF, the N-alkyl group mimics the methionine side chain, and domains of the
inhibitor that can provide additional binding energy, selectivity, and favourable pharmacokinetic
properties9-12.
With the available 3D-QSAR facilities in house we have decided to explore structural requirements
of the PDF inhibitors. In the present investigation our interest was to develop a Comparitive
Molecular Field Analysis (3D-CoMFA) for reported PDF inhibitors, which would give us better
insight for designing of newer PDF inhibitors. To fulfil this objective, Tripos’s SYBYL 6.6
program was used to develop 3D-QSAR model.
Dataset:
A set of 20 compounds belonging to -Sulfonyl and sulfinylhydroxamic acid derivatives
were selected for study from literature13. Table I gives biological activities of these compounds. The
biological activity expressed as log BA, where BA is IC50 for PDF of Escherichia coli and used as a
dependent variable.
pIC50 = -log10 IC50
where IC50 is millimolar concentration of the inhibitor producing 50% inhibition
Method:
All computational studies were performed on Silicon Graphics INDY R5000 workstation using
SYBYL6.6 molecular modelling software from Tripos Inc., St. Louis, MO. 14 The 3D structures of
molecules were built from fragments in SYBYL database. Each structure was fully geometry
optimized using standard Tripos force field with distance dependent dielectric function and distance
dependent dielectric function and 0.001Kcal/mol energy gradient convergence criterion. Partial
atomic charges were computed by semiempirical molecular orbital method using MOPAC 6.0
program. The charges were computed using the PM3 model Hamiltonian. The minimized structures
were subjected to systematic search routine of all rotatable bonds in 10, increment from 0 to 360.
Conformational energies were computed with electrostatic term, the lowest energy structure finally
minimized was used in superimposition. The most active compound 12 was selected for this
purpose. Development of predictive 3D-QSAR predictive model is essentially alignment sensitive
that defines the putative pharmacophore of the series of ligands under investigation. Each analogue
was aligned to the template molecule 12 by Atom fit method in SYBYL.
CoMFA analysis:
CoMFA steric and electrostatic field energies were calculated using the sp3 carbon probe atom
using a Vander Wall’s radius of 1.52 A. The energies were truncated to ±30Kcal/mol and the
electrostatic contributions were ignored at the lattice intersection with maximal steric interaction.
The CoMFA fields generated were automatically scaled by CoMFA-STD method in SYBYL.
Partial least squares analysis (PLS):
The CoMFA descriptors served as independent variable(X )and pIC50 as dependent
variable(Y) in PLS regression analysis for deducing 3-D QSAR models. PLS analyses were
performed following CoMFA standard implementation in SYBYL. Normally cross validation is
used to check the predictivity of the derived model. The results of the analysis correspond to the
regression equation with thousands of coefficients. The predictive values of the model were
evaluated using Leave-one-out (LOO) cross validation method. The number of components leading
to the highest cross validated r2 and lowest standard error of prediction (SEP) was set as optimum
number of components (Nc) in PLS analysis. For all conventional analyses (no cross-validation) the
minimum sigma stanadard deviation threshold was set to 2.0 Kcal/mol. The cross validated r2 is
defined as
r2cv= 1-PRESS / Σ(Y- Ymean)2
where PRESS= Σ(Y- Ypred)2
Ymean: Mean biological activity, and Ypred: Predicted biological activity
Predictive ability of CoMFA:
It is determined from test set molecules that were not included in training set. These molecules were
aligned and their activities were predicted by PLS analysis. The predictive r2 (r2pred) is defined as:
r2pred = (SSD-PRESS)/SSD
Where, SSD: sum of squared deviations between the biological activity of test set and mean activity
of training set molecules
PRESS: Sum of squared deviation between actual and predicted activities of test set molecules.
Figure 1 gives predicted activity for the compounds belonging to training set and Figure 2 gives the
predicted activity for test set molecules. Test set comprised of compounds namely 10, 14, 15 and 20
and rest as training set.
CoMFA Contour maps:
In order to visualize the derived 3D QSAR model, CoMFA steric and electrostatic fields from PLS
analysis, contour maps of the product of standard deviation associated with the CoMFA column and
coefficient at each lattice point were generated. The contour maps are plotted as percentage
contribution to the QSAR equation and are associated with the difference in the biological activity.
The contour maps generated gives important ideas about the effect of substituent on various
positions in the compound.
RESULTS AND DISCUSSION
The model generated by CoMFA is summarized in table 2. The model was analyzed for the
predictive ability for the training set as well as test set molecules. Final model was selected
primarily based on the values of better cross-validated r2 and predictive r2 (all of these values are
highlighted in table 2.)
In this CoMFA analysis both steric and electrostatic fields contribute to QSAR equation by 63.9%
and 36.1% respectively, suggesting variation in the biological activity of compounds is dominated
by differences in steric interactions. Figure 3, 4 and 5 give contour maps for steric, electrostatic
fields for most active (compound 12) and steric fields for least active (compound 6) compound in
the series respectively. Bulky substituent in the region shaded yellow is likely to decrease biological
activity. It is observed from figure 5 that Compound 6 containing R1 substituent as hexyl is least
active because its hexyl chain is placed in sterically unfavourable region. Also the activity of these
inhibitors is strongly influenced by nature of substituent R2. Green contours are exactly located
where R2 substituent extends in the space clear from figure 3. Really substituent R2 occupies the
same position in the enzyme as methionine side chain of the natural substrate. The most active
member of the series compound 12 with R1 as 4-AcNH-phenyl mimic the peptide backbone of the
substrate and projects in sterically favoured region as seen from figure 3. The sulfone oxygen of the
inhibitor 12 is oriented exactly in the high red contour density suggesting it is involved in bonding
interaction with enzyme.
The results of the model are found to be predictive and can be used to design novel PDF inhibitors.
Based on literature on PDF inhibitors and our current CoMFA studies for PDF inhibitors and we
have designed molecules and three of those designed NCEs are shown in figure 6. A current
CoMFA study is used to predict their activities are given in table III. Out of the three compounds
designed compound III is showing highest PDF inhibitory activity. Steric bulk for III and II are
comparable and more than compound I. But compound III is electronically also in favourable
region because of position of hydroxyl group.
CONCLUSION:
CoMFA analysis of twenty -sulfonyl and sulfinylhydroxamic acid derivatives produced
good model with high predictive abilities (r2cv = 0.707, r2pred = 0.826). The contour diagrams
obtained from CoMFA field contribution are mapped back onto structural features accounting for
activity trends among the inhibitors. On the basis of spatial arrangement of field contribution novel
molecules are designed and predicted for PDF inhibitory activity. The information is very crucial in
the design and development of PDF inhibitors as antibacterial agents with reduced resistance.
Considering that the predictivity of the model these molecules are good enough to be synthesized
and tested for their activity to validate the model.
ACKNOWLEDGEMENTS
Computational studies were carried out in University Institute of Chemical Technology, University
of Mumbai, Matunga, Mumbai-400019. The authors gratefully acknowledge support from the
University Grants Commission (UGC), New Delhi under its DSA and COSIST programmes. IV
thanks UGC for the award of senior research fellowship.
REFERENCES
1. Giglione C, Pierre M and Meinnel T Peptide deformylase as a target for new generation,
broad spectrum antimicrobial agents Molecular microbiology, 2000, 36(6),1197-1206
2. Johnson KW, Lofland D, Moser HE PDF inhibitors: An emerging class of antibacterial
drugs. Current drug targets-Infectious Disorders 2005,5,39-52
3. Verma SK, Jat RK, Nagar N, A novel antibacterial target: peptide deformylase.
Pharmacophore 2011,2(2),114-123
4. Jain R, Chen D, White RJ, Patel DV, Yuan Z Bacterial peptide deformylase inhibitors: A
new class of antibacterial agents. Current Medicinal Chemistry 2005, 12,1607-1621
5. Becker, A, Schlichtings, I., Kabsch, W., Schultz, S. Wagner, A. Structure of peptide
deformylase and identification of substrate binding site. Journal of Biological Chemistry
1998,11413-11416
6.
Chen DZ, Patel DV, Hackbarth CJ, Wang W etal. Actinonin, a naturally occurring
antibacterial agent is a potent deformylase inhibitor. Biochemistry 2000, 39, 1256-1262
7. Nguyen KT, Hu X, Colton C, Chakrabarti R. Characteriastion of human peptide
deformylase: Implication for antibacterial drug design. Biochemistry 2003, 42, 9952-9958
8. Yuan Z, Trias J, White R. Deformylase as a novel antibacterial target Drug Discovery Today
2001, 6(18), 954-961
9. Chen D, Hackbarth C, Ni ZJ , Wu C, Wang W, Jain R, He Y etal. Peptide deformylase
inhibitors as antibacterial agents: Identification of VRC3375 as a proline-3-alkylsuccinyl
hydroxamate derivative by using an integrated combinatorial and medicinal chemistry
approach . Antimicrobial Agents and Chemother. 2004, 48(1), 250-261
10. Hu X, Nguyen KT, Verlinde CLM, Hol WG, Pei D. Structure-based design of macrocyclic
inhibitor for peptide deformylase. J. Med. Chem. 2003,46, 3771-3774
11. Boularot A, Giglione C, Petit S, Duroc Y etal. Discovery and refinement of new structural
class of potent peptide deformylase inhibitors. J. Med. Chem. 2007, 50 (1), 10-20
12. Flipo M, Beghyn T, Charton J, Leroux VA. A library of novel hydroxamic acids targeting
the metallo-protease family: Design, parallel synthesis and screening Bioorg. Med. Chem.
2007,15,63-76
13. Apfel, C., Banner, DW, Bur D, Dietz M., Hubschwerlen, HL, Locher H, Page M etal.
Hydroxamic acid derivatives as potent peptide deformylase inhibitors and antibacterial
agents. J. Med.Chem,2000,43, 2324-2331
14. SYBYL Molecular Modeling System ,version 6.6; Tripos Inc., St. Louis, MO 63144-2913
Table 1: Dataset of -Sulfonyl and sulfinylhydroxamic acid derivatives
(O)n
R3
S
NHOH
R1
R2
O
Sr.No.
R1
R2
R3
n
IC50(E.coli
PDF) µM
Log1/IC50
1.
Phenyl
Propyl
H
2
0.160
0.7959
2.
Phenyl
Butyl
H
2
0.035
1.456
3.
Phenyl
Pentyl
H
2
0.140
1.854
4.
Phenyl
Phenyl
H
2
0.160
0.7959
5.
Phenyl
2-furanyl
H
2
0.094
1.027
6.
Hexyl
Butyl
H
2
0.530
0.2757
7.
Cyclohexyl
Butyl
H
2
0.230
0.6383
8.
Benzyl
Butyl
H
2
0.190
0.7212
9.
2-Naphthyl
Butyl
H
2
0.023
1.638
10.
4-MeOC6H4
Butyl
H
2
0.031
1.5086
11.
3-MeOC6H4
Butyl
H
2
0.086
1.0655
12.
4-AcNHC6H4
Butyl
H
2
0.011
1.9586
13.
4-BrC6H4
Butyl
H
2
0.120
0.9208
14.
Phenyl
Butyl
H
1
0.100
1
15.
4-MeOC6H4
Butyl
H
1
0.099
1.004
16.
3-MeOC6H4
Butyl
H
1
0.094
1.0269
17.
2-Naphthyl
Butyl
H
1
0.064
1.1938
18.
4-BrC6H4
Butyl
H
1
0.210
0.6778
19.
Phenyl
Butyl
OH
1
0.280
0.5528
20.
Phenyl
Phenyl
H
2
0.016
1.7959
Table 2: Summary of CoMFA results
Parameter
Value
r2cv
0.707
Number of components (Nc)
2
Standard error of predictions (SEP)
0.342
r2ncv
0.988
F
92.87
S (steric)
63.9
E (electrostatic)
36.9
r2pred
0.826
Table 3: Predicted PDF inhibitory activities for proposed inhibitors
Predicted
Log1/IC50
Predicted IC50 for
PDF of
E.coli (M)
Compound I
0.9618
0.109
Compound II
1.2155
0.061
Compound III
1.3061
0.0494
Figure 1: Graph showing predicted activities vs. actual activities for the training set
Predicted Activity
Training Set
3
2.5
2
1.5
1
0.5
0
0
0.5
1
1.5
Actual Activity
2
2.5
Figure 2: Graph showing predicted activities vs. actual activities for the test set
Predicted Activity
Test Set
2.5
2
1.5
1
0.5
0
0
0.5
1
1.5
Actual Activity
2
2.5
Figure 3: Stereo view of CoMFA steric contour plot with most active compound 12
( green polyhedra: sterically favored; yellow polyhedra: sterically disfavored )
Figure 4: Stereo view of CoMFA electrostatic contour plot with most active compound 12
( blue polyhedra: electropositive groups favored ; red polyhedra: electronegative groups favored
Figure 5: Stereo view of CoMFA steric contour plot with least active compound 6
( green polyhedra: sterically favored; yellow polyhedra: sterically disfavored )
Figure 6: Novel proposed PDF inhibitors
OH
O
NH
NHOH
O
Compound I
OH
O
O
NH
O
Compound II
NH
NHOH
OH
O
Compound III
NHOH