Synthesis, antibacterial activities, and 3d-qsar of sulfone derivatives containing 1, 3, 4-oxadiazole moiety

Article abstract of DOI:10.1111/cbdd.12181

A series of sulfone derivatives containing 1, 3, 4-oxadiazole moiety were prepared and evaluated for their antibacterial activities by the turbidimeter test. Most compounds inhibited growth of Ralstonia solanacearum (R. solanacearum) from tomato and tobacco bacterial wilt with high potency, among which compounds 5a and 5b exhibited the most potent inhibition against R. solanacearum from tomato and tobacco bacterial wilts with EC₅₀ values of 19.77 and 8.29 μg/mL, respectively. Our results also demonstrated that 5a, 5b, and a number of other compounds were more potent than commercial bactericides Kocide 3000 and Thiodiazole Copper, which inhibited R. solanacearum from tomato bacterial wilt with EC₅₀ values of 93.59 and 99.80 μg/mL and tobacco bacterial wilt with EC₅₀ values of 45.91 and 216.70 μg/mL, respectively. The structure-activity relationship (SAR) of compounds was studied using three-dimensional quantitative structure-activity relationship (3D-QSAR) models created by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) based on compound bioactivities against tomato and tobacco bacterial wilts. The 3D-QSAR models effectively predicted the correlation between inhibitory activity and steric-electrostatic properties of compounds.

Full text of DOI:10.1111/cbdd.12181

Received Date : 02-May-2013
Revised Date : 18-Jun-2013
Accepted Date : 26-Jun-2013
Article type
: Research Article
Synthesis, Antibacterial Activities, and 3D-QSAR of Sulfone
Derivatives Containing 1, 3, 4-Oxadiazole Moiety
Pei Li1, #, Juan Yin1, #, Weiming Xu1, Jian Wu1, Ming He1, Deyu Hu1, Song Yang1,
Baoan Song1,*
1 State Key Laboratory Breeding Base of Blue Pesticide and Agricultural Bioengineering,
Key Laboratory of Blue Pesticide and Agricultural Bioengineering, Ministry of Education,
Research and Development Center for Fine Chemicals, Guizhou University, Huaxi District,
Guiyang 550025, China
*Corresponding author: Baoan Song, E-mail:songbaoan22@yahoo.com
# First two authors have contributed equally to the manuscript.
This article has been accepted for publication and undergone full peer review but has
not been through the copyediting, typesetting, pagination and proofreading process
which may lead to differences between this version and the Version of Record. Please
cite this article as an 'Accepted Article', doi: 10.1111/cbdd.12181
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E-mails: pl19890627@126.com (Pei Li) yinjuan2009292018@126.com (Juan Yin
xuweiming2009@163.com (Weiming Xu) jianwu2691@yahoo.com.cn (Jian Wu
hmher@126.com (Ming He fcc.dyhu@gzu.edu.cn (Deyu Hu) yangsdqj@126.com (Song
Yang
)
)
)
)
Abstract: A series of sulfone derivatives containing 1, 3, 4-oxadiazole moiety were prepared
and evaluated for their antibacterial activities by the turbidimeter test. Most compounds
inhibited growth of Ralstonia solanacearum (R. solanacearum) from tomato and tobacco
bacterial wilt with high potency, among which compound 5a and 5b exhibited the most potent
inhibition against R. solanacearum from tomato and tobacco bacterial wilts with EC50 values
of 19.77 and 8.29 μg/mL, respectively. Our results also demonstrated that 5a, 5b, and a
number of other compounds were more potent than commercial bactericides Kocide 3000 and
Thiodiazole Copper, which inhibited R. solanacearum from tomato bacterial wilt with EC50
values of 93.59 and 99.80 μg/mL and tobacco bacterial wilt with EC50 values of 45.91 and
216.70 μg/mL, respectively. The structure–activity relationship of compounds was studied
using three-dimensional quantitative structure–activity relationship (3D-QSAR) models
created by comparative molecular field analysis (CoMFA) and comparative molecular
similarity index analysis (CoMSIA) based on compound bioactivities against tomato and
tobacco bacterial wilts. The 3D-QSAR models effectively predicted the correlation between
inhibitory activity and steric–electrostatic properties of compounds.
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Key words: sulfone derivatives, synthesis, antibacterial activities, 3D-QSAR, CoMFA,
CoMSIA
Introduction
Ralstonia solanacearum (R. solanacearum) is a soil-borne plant bacterial pathogen classified
as one of the world’s most destructive plant pathogens due to its lethality, wide host range,
and broad geographic distribution. Smith proved in 1896 that bacterial wilts in many
important crops such as potato, tomato, and eggplant were caused by R. solanacearum. R.
solanacearum was also found to cause Granville wilt in tobacco in 1908 (1 – 2). R.
solanacearum, a complex species belonging to the β-proteobacteria family, is currently
treated by traditional bactericides such as inorganic bactericides (e.g. copper formulations).
These traditional bactericides have only limited effectiveness and may induce resistance in
host tobacco and tomato plants, resulting in insufficient disease control and huge economic
loss. Pathogenic plant bacteria cause billions of dollars of economic loss worldwide each year
and the search of more effective antibacterial agents remains a major challenge to be tackled
in pesticide research (3).
Sulfone derivatives containing 1, 3, 4-oxadiazole moieties display a broad spectrum of
bioactivities including antibacterial (4), antifungal (5), insecticidal (6), herbicidal (7),
anticancer (8), anti-HIV-1 (9), antihepatitis (10), antitumor (11), and anti-inflammatory (12)
activities. Over the past few years, the synthesis and study of bioactivity of sulfone
derivatives have attracted considerable attention (4 – 12). We previously demonstrated that a
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series of new 2-(methyl/ethylsulfonyl)-1, 3, 4-oxadiazole sulfone derivatives displayed potent
antibacterial activities at 200 μg/mL (13). Specifically, compounds 5′c, 5′h, 5′i, and 5′j
(Figure 1) showed half-maximal effective concentrations (EC50) of 39.8, 60.3, 47.9, and
32.1 µg/mL in in vitro control of tobacco bacterial wilt caused by R. solanacearum. Field
trials demonstrated that 5′j achieved better control against tobacco bacterial wilt than
commercial bactericide Saisentong. The structure–activity relationship (SAR) derived from
antibacterial activities of compounds showed that compounds had potent antibacterial
activities when the electron withdrawing group at the 3- or 4-position of phenyl was attached
to the 5-position of oxadiazole (13). In our previous study, we only reported and discussed
compound activities in control of R. solanacearum caused bacterial wilt in tobacco with very
limited SAR interpretation. To continue our efforts in developing highly active and readily
available bacteria inhibitors, we aimed to design and synthesize a series of methyl(ethyl)
sulfone derivatives containing 1, 3, 4-oxadiazole moiety in the present study and create
three-dimensional (3D) quantitative SAR (QSAR) models based on compound antibacterial
activities to guide design and synthesis of more potent compounds. QSAR analysis such as
comparative molecular field analysis (CoMFA) (14) has been successfully used to guide the
design of new bioactive molecules (15) although it has not yet been used to design sulfone
derivatives as antibacterial agents. Our bioassay results showed that some of the newly
synthesized compounds demonstrated significant activities in controlling bacterial wilt in
tobacco and tomato. Encouraged by these results, we performed 3D-QSAR analysis on the
sulfone derivatives using CoMFA and comparative molecular similarity index analysis
(CoMSIA). The effects of steric, electrostatic, hydrophobic, and hydrogen-bonding
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interactions on compound activities against bacterial wilt in tobacco and tomato were
determined with 3D-QSAR analysis conducted on conformationally restrained compounds
including 24 newly designed and synthesized (compounds 5a – 5x) and 12 previously
reported sulfone derivatives (compounds 5aa – 5al) (13, 16, 17). Predictive 3D-QSAR
models for compound design and antibacterial activity forecast were also established using
the SYBYL multifit molecular alignment rule. The conventional noncross-validated
correlation (r2) and cross-validated coefficients (q2) reached 0.961 and 0.905 in CoMFA and
0.977 and 0.907 in CoMSIA for tobacco bacteria wilt control, and 0.969 and 0.961 in CoMFA
and 0.971 and 0.909 in CoMSIA for tomato bacteria wilt control, respectively. The CoMFA
and CoMSIA models were comparable to multifit-derived models in terms of relative
descriptor field contributions and partial least-square (PLS) contour maps. It was noted that
CoMSIA 3D-QSAR models, in which hydrogen-bonding interactions made large
contributions to compound inhibitory activity, performed better than CoMFA models. The
CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic,
lipophilic, and hydrogen-bonding potential surface maps were integrated to construct a
binding mode for active site binding of sulfone inhibitors in tobacco bacterial wilt and tomato
bacterial wilt. To our knowledge, the present work is the first report of sulfone compounds
containing 1, 3, 4-oxadiazole moieties with potent controlling effect against tomato bacterial
wilt with 3D-QSAR analysis. Further field studies on biological efficacy, crop safety, and
toxicity of compounds 5a, 5b, and 5al as potential bactericides are in progress following
regulation guidelines of pesticide registration in China.
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Materials and Methods
Instruments
Melting points were determined using an XT-4 binocular microscope from Beijing Tech
Instrument Co. (Beijing, China) and left uncorrected. 1H and 13C NMR spectra were recorded
on a JEOL ECX 500 NMR spectrometer operated at room temperature and 500 MHz using
CDCl3 as a solvent and TMS as an internal standard. IR spectra were recorded with KBr on a
Bruker VECTOR 22 spectrometer. Elemental analysis was performed on an Elemental
Vario-III CHN analyzer. Reactions were monitored by thin-layer chromatography analysis on
silica gel GF254. All reagents were of analytical grade or chemically pure. The physical and
spectral data of compounds 5aa - 5ah were reported in reference 13. The synthetic method
and characterization data of 5ai, 5aj, and 5ak were provided in reference 16 and the
characterization data of 5al were provided in reference 17.
General Synthetic Procedure
Compounds 5a to 5x (2-(methyl/ethylsulfonyl)-5-substituted-1, 3, 4-oxadiazole) were
synthesized as shown in Scheme 1 using previously described methods (13, 16 – 18). The
compounds were synthesized from substituted benzoic acid in five steps including
esterification, hydrazidation, cyclization, thioetherification, and oxidation.
Preparation of 2-(Methyl/ethylsulfonyl)-5-substituted-1, 3, 4-oxadiazole (5)
2-Substituted methylthio-5-substituted-1, 3, 4-oxadiazole (0.02 mol), acetic acid (10 mL), and
KMnO4 (0.024 mol) were added to a 50 mL three-neck round-bottom flask equipped with a
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magnetic stirrer. Reaction mixtures were stirred for 15 min at room temperature. The
unreacted potassium permanganate was deoxidized by adding 10% sodium bisulfite solution
in distilled water and reaction mixtures were subsequently dried under vacuum. Compounds
5a – 5x were obtained by recrystallized from ethanol. The physical characteristics, IR, 1H
NMR, 13C NMR, and elemental analysis data for all synthesized compounds were reported in
Supporting Information. Representative data for 5a – 5x were shown below.
In Vitro Antibacterial Activities
Sulfone derivatives containing 1, 3, 4-oxadiazole moiety were evaluated for their antibacterial
activities against tobacco bacterial wilt and tomato bacterial wilt in vitro by the turbidimeter
test as described previously (19). In measurement of antibacterial activity in tobacco bacterial
wilt, approximately 40 μL of solvent NB (1.5 g beef extract, 2.5 g peptone, 0.5 g yeast
powder, 5.0 g glucose, and 500 mL distilled water; pH 7.0 – 7.2) containing tobacco bacterial
wilt was added to 5 mL of solvent NB containing test compound, negative control
(dimethylsulfoxide (DMSO)), or positive control (Kocide 3000 or Thiodiazole Copper). The
inoculated test tubes were incubated at 30 1° C with continuous shaking at 180 rpm for
24 h. The growth of bacterial culture was monitored on a spectrophotometer by measuring
optical density at 600 nm (OD600) and calculated by the equation: turbiditycorrected values
ODbacterial wilt − ODno bacterial wilt. The percent inhibition was calculated by
=
I (%) = (Ctur − Ttur)/Ctur × 100, where Ctur was the corrected turbidity value of bacterial culture
treated with NB containing negative control (DMSO), and Ttur was the corrected turbidity
value of bacterial culture treated with NB containing test compound or positive control.
Antibacterial activity in tomato bacterial wilt was measured similarly in solvent SM (5.0 g
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peptone, 2.5 g glucose, 0.5 g casein acid hydrolysate, 500 mL distilled water; pH 7.0 – 7.2)
with the same negative and positive controls. EC50 values of compounds against tobacco
bacterial wilt and tomato bacterial wilt were calculated with SPSS 17.0 software.
Datasets of QSAR
In the present work, EC50 values of 36 sulfone derivatives containing 1, 3, 4-oxadiazole
moiety (Scheme 1, Table 1, and Table 2) against tobacco bacterial wilt and tomato bacterial
wilt were determined. pEC50 values were calculated from EC50 values and presented in
Tables 1 and 2. CoMFA and CoMSIA were performed using a training set of 26 randomly
selected compounds (asterisk labeled in Tables 3 and 4). The 10 compounds not selected
were included in the testing set.
Molecular Modeling and Alignment
Molecular modeling was performed using SYBYL 7.3 software from Tripos Inc. (St. Louis,
MO, USA). All molecules were built with the SKETCH option in SYBYL under default
settings. Energy minimizations were carried out using the Gasteiger–Hückel charge, Tripos
force field, and Powell conjugate gradient algorithm with a convergence criterion of
0.005 kcal/mol·Å as previously described (20). pEC50 values of compounds were calculated
as the negative logarithm of their EC50 values (μM) and were presented in Tables 1 and 2.
3D structures were aligned on common substructures and conformations of 5a and 5b, the
most potent compound against tomato bacterial wilt and tobacco bacterial wilt, respectively.
Compounds 5a and 5b were used as template molecules for CoMFA and CoMSIA models,
respectively. The alignment results were shown in Figure 2.
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Partial Least-Squares (PLS) Analysis
The 3D-QSAR was derived using PLS analysis in which molecules were placed in a
rectangular grid and interaction energies between a probe atom and all compounds were
computed at surrounding points using a volume-dependent lattice with a 2.0 Å grid spacing
(default in SYBYL) to improve the signal-to-noise ratio (21). CoMFA and CoMSIA
descriptors were used as independent variables and pEC50 values were presented as dependent
variables in PLS regression analysis. 3D-QSAR was thereby carried out in two steps using the
PLS technique. First, the performance of models was indicated by leave-one-out
cross-validation and the optimal number of components (ONC) was determined by the
highest cross-validated coefficient q2 (20 – 27). The noncross-validated correlation coefficient
r2, standard error of estimate (SEE), and F-test were calculated from the second run
performed without cross-validation using ONC determined in the first run (23 – 24). Contour
maps of CoMFA and CoMSIA were subsequently generated with PLS coefficients as well as
standard deviations of the corresponding CoMFA and CoMSIA descriptor values.
Results and Discussion
Synthesis
Compounds 5a to 5x were synthesized from substituted benzoic acids as depicted in Scheme
1 in five steps including esterification, hydrazidation, cyclization, thioetherification, and
oxidation. Key intermediates 2-thiol-5-substituted-1, 3, 4-oxadiazoles (3) were prepared by
cyclization of substituted phenylhydrazide and carbon disulfide in the presence of potassium
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hydroxide in ethanol under reflux conditions. The success of this reaction depended on
complete removal of water after the reaction was completed because cyclization may fail in
the presence of trace amounts of water. Oxadiazoles produced from cyclization reactions were
subsequently converted to thioether derivatives by thioetherification. Finally, target sulfone
compounds were produced by oxidation of thioethers. The physical characteristics, IR, 1H
NMR, 13C NMR, and elemental analysis data for all synthesized compounds were reported in
Supporting Information.
In Vitro Antibacterial Activities
The inhibitory effect of compounds on the growth of R solanacearum was studied as
described in materials and methods and compound EC50 values for tobacco and tomato
bacterial wilts were presented in Table 1 and 2, respectively. A significant number of
compounds inhibited R solanacearum growth in tobacco and tomato bacterial wilts with high
potency. Specifically, compounds 5a, 5b, 5g, 5q, 5ae, 5ai, and 5al inhibited R solanacearum
growth in tobacco bacterial wilt in vitro with EC50 values of 21.10, 8.29, 21.02, 24.44, 18.40,
22.54, and 16.55 μg/mL, respectively. Meanwhile, compounds 5a, 5g, 5aa, and 5ai inhibited
R solanacearum growth in tomato bacterial wilt with EC50 values of 19.77, 28.90, 31.04, and
33.74 μg/mL, respectively. Among compounds designed and synthesized in the present study,
5a and 5b exhibited the most potent inhibition of R solanacearum growth in tomato bacterial
wilt and tobacco bacterial wilt with EC50 values of 19.77 and 8.29 μg/mL, respectively. Our
results also demonstrated that 5a, 5b, and a number of other compounds were more potent
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than the commercial agent Kocide 3000 and Thiodiazole Copper, which inhibited R
solanacearum growth in tomato bacterial wilt with EC50 values of 93.59 and 99.80 μg/mL and
tobacco bacterial wilt with EC50 values of 45.91 and 216.70 μg/mL, respectively.
Performance of 3D-QSAR Models
CoMFA and CoMSIA are powerful tools used to create 3D-QSAR models based on data from
known active molecules. 3D-QSAR models were created by CoMFA and CoMSIA based on
experimental EC50 values of the 26 compounds in the training set. Predicted pEC50 values of
compounds in both the training set and the testing set were presented together with their
experimental pEC50 values in Tables 3 and 4 and correlations between predicted and
experimental pEC50 in CoMFA and CoMSIA models were presented in Figures 3 and 4.
Overall, predicted pEC50 values were very close to the corresponding experimental values for
compounds in both the training set and the testing set as shown in Tables 3 and 4. The mostly
linear correlations in Figures 3 and 4 demonstrated high predictive power of these models.
Calculated statistical parameters of CoMFA and CoMSIA models were shown in Table 5. r2,
SEE, and F values were generated by noncross-validated PLS analyses repeated with the
optimum number of components determined by cross-validated analyses. Our modeling
results indicated that CoMFA and CoMSIA models built for tobacco bacterial wilt performed
better than those for tomato bacterial wilt. In CoMFA and CoMSIA models for tobacco
bacterial wilt, the cross-validated coefficients q2 were 0.905 with five ONC and 0.907 with
six ONC, respectively and r2, SEE, and F values were 0.961 and 0.977, 0.109 and 0.087, and
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99.808 and 132.649, respectively. The relative contributions to bioactivity from steric and
electrostatic fields in the CoMFA model were 0.818 and 0.182, respectively, suggesting that
bioactivity was mainly determined by steric interactions. On the other hand, the calculated
contributions from steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor
fields in the CoMSIA model were 14.6%, 37.5%, 45.4%, 0%, and 2.5%, respectively,
suggesting that electrostatic and hydrophobic interactions made major contributions to
bioactivity.
In models built for tomato bacterial wilt, the cross-validated coefficient q2, noncross-validated
correlation coefficient r2, SEE, and F were 0.937 with four ONC, 0.969, 0.066, and 163.664,
respectively in the CoMFA model and 0.909 with six ONC, 0.971, 0.067, and 104.523,
respectively in the CoMSIA model. Calculated field contributions in the same type of model
were similar between tomato and tobacco bacterial wilts. In models for tomato bacterial wilt,
the relative contributions from steric and electrostatic fields in the CoMFA model were 0.853
and 0.147, respectively and the relative contributions from steric, electrostatic, hydrophobic,
H-bond donor, and H-bond acceptor fields in the CoMSIA model were 14.3%, 35.5%, 46.3%,
0%, and 4.0%, respectively.
Graphical Interpretation of CoMFA and CoMSIA Models
CoMFA and CoMSIA contour maps are useful tools to help interpret key structural features
responsible for activity. We used contour maps to analyze how different substituents affected
the inhibitory activity of sulfone derivatives.
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CoMFA contour maps of the steric and electrostatic field for activities against tobacco and
tomato bacterial wilts were shown in Figure 5 and 7, respectively. Figure 5b was displayed
with aid of visualization. Green contours in the CoMFA steric field indicated regions where
bulky groups would increase activity whereas yellow contours indicated regions where bulky
groups would decrease activity. Similarly, blue contours in the CoMFA electrostatic field
indicated regions where electron-withdrawing groups would increase activity and red
contours indicated regions where electron-donating groups would increase activity. Contour
maps of the steric field in CoMFA models (Figures 5A and 7A) showed several large regions
of yellow contours near the 2, 3, 4-substituents of the phenyl ring and R2 substituent group,
indicating that bulky groups in these regions were disfavored. Structural features predicted in
these contour maps were in alignment with experimental data showing that compound activity
decreased with increase in the size of R1 and R2 substituent groups. Specifically, molecules
with –H or –F were more potent than those with –Cl or –Br at the position of R1 substituent
group on the phenyl ring as shown in the activity order of 5b > 5c > 5e in Table 1 and 5ab >
5d > 5f in Table 2. Similarly, replacement of methyl group with ethyl at the position of R2
substituent group resulted in decreased activity as shown in the activity order of 5a > 5aa, 5b
> 5ab, and 5c > 5d in Tables 1 and 2. The contour map of electrostatic field in CoMFA
model for tobacco bacterial wilt was illustrated in Figure 5B where a large region of blue
contour was found near the phenyl ring, indicating that electron-withdrawing substituent
groups were favored in that region. Indeed, compounds with –F at R1 position were more
potent than those with −OCH3 or −H at the same position as shown in the activity order of 5b
> 5o, 5ab > 5p, and 5b > 5a in Table 1. The contour map of electrostatic field in CoMFA
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model for tomato bacterial wilt was illustrated in Figure 7B where a large region of blue
contour and two small regions of red contour were found near the 3, 4-substituents of the
phenyl ring, suggesting that compounds with suitable electron-withdrawing substituent
groups in these regions would likely show high antibacterial activity.
CoMSIA contour maps of steric, electrostatic, hydrophobic, and H-bond acceptor field for
tobacco bacterial wilt and tomato bacterial wilt were shown in Figure 6 and 8, respectively,
in which 6b was also displayed with aid of visualization. The steric field map of CoMSIA
showed that bulky groups in regions of green contours would increase activity whereas bulky
groups in regions of yellow contours would decrease activity. Two large regions of yellow
contours were found near the 4-substituent of the phenyl ring and the R2 substituent group in
Figure 6A, suggesting that bulky groups were disfavored in these regions. The contour maps
agreed well with experimental data showing that replacement of –F with –Cl or –Br at the R1
substituent position on the phenyl ring and replacement of methyl with ethyl at the R2
substituent group resulted in decreased activity against tobacco bacterial wilt in the order of
5b > 5c > 5e, 5ab > 5d > 5f, 5a > 5aa, and 5b > 5ab (Table 1). Moreover, a large region of
yellow contour was found near the 3, 4-substituents of the phenyl ring and R2 substituent
group in Figure 8A, suggesting that bulky groups were disfavored in that region, which was
consistent with experimentally observed activity order of 5b > 5c > 5e, 5g > 5h > 5ad, and 5b
> 5ab (Table 2). Electrostatic contour maps of CoMSIA models were displayed in Figures
6B and 8B, in which blue contours indicated regions where electron-withdrawing groups
would increase activity and red contours indicated regions where electron-donating groups
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would increase activity. A blue contour and a red contour were found in the region near the
4-substituent of the phenyl ring in Figure 6B, suggesting that suitable electron-withdrawing
groups at the 4-substituent position of the phenyl ring would increase activity. Meanwhile, a
blue contour and two red contours were found in the region near the 3, 4-substituent of the
phenyl ring in Figure 8B, suggesting that suitable electron-withdrawing groups at the 3,
4-substituent positions of the phenyl ring would increase activity. Hydrophobic contour maps
of CoMSIA models were displayed in Figures 6C and 8C. Yellow and gray contours
indicated regions where hydrophobic and hydrophilic groups were favored, respectively. A
large region of yellow contour was found around the phenyl ring, indicating that hydrophobic
groups were favored in that region, which was consistent with the prediction made from steric
field maps. CoMSIA contour maps of the H-bond acceptor field were shown in Figures 6D
and 8D in which magenta contours near the 2-substituent position of the phenyl ring and 1, 3,
4-oxadiazole ring indicated regions where H-bond acceptor groups would increase activity.
In summary, 24 methyl(ethyl)sulfone derivatives containing 1, 3, 4-oxadiazole moiety were
designed, synthesized, and evaluated for their antibacterial activities against tobacco and
tomato bacterial wilts by the turbidimeter test. Our bioassay results demonstrated that a
significant number of compounds exhibited excellent inhibitory effect against tobacco and
tomato bacterial wilts with potencies higher than those of Kocide 3000 and Thiodiazole
Copper. SAR analysis showed that compounds with small electron-withdrawing groups (e.g.
–F) at the 3- and 4-position of the phenyl ring displayed high antibacterial activities against
tobacco and tomato bacterial wilts. 3D-QSAR models of methyl(ethyl)sulfone derivatives
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were created based on compound antibacterial activities against tobacco and tomato bacterial
wilts. Predictive CoMFA and CoMSIA 3D-QSAR models for conformationally constrained
methyl(ethyl)sulfone derivatives containing 1, 3, 4-oxadiazole moiety were established in
which CoMSIA models performed better than CoMFA models. The 3D-QSAR models
effectively predicted the correlation between antibacterial activity and steric–electrostatic
properties of compounds. The present study provided further insights into the structure-based
design of antibacterial compounds as potential agrochemicals.
Acknowledgment
The authors gratefully acknowledge financial support from the National Key Program for
Basic Research (No.2010CB 126105) and the Key Technologies R&D Program (No.
2011BAE06 B05-6).
Abbreviations
R. solanacearum, Ralstonia solanacearum; IR, Infrared; 1H NMR, 1H Nuclear Magnetic
Resonance; 13C NMR, 13C Nuclear Magnetic Resonance; EC50, half-maximal effective
concentration; 3D-QSAR, three-dimensional quantitative structure–activity relationship;
CoMFA, comparative molecular field analysis; CoMSIA, comparative molecular similarity
index analysis; NB, Nutrient Broth; SM, Semi-selective medium.
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Supporting Information Description
The synthesis procedure and characterization data of compounds 5a to 5x were available free
of charge at http://wileyonlinelibrary.com/onlineopen.
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Figure, Table, and Scheme Legends
Table 1. Inhibition effect of compounds against tobacco bacterial wilt.
Table 2. Inhibition effect of compounds against tomato bacterial wilt.
Table 3. Experimental and predicted pEC50 values against tobacco bacterial wilt.
Table 4. Experimental and predicted pEC50 values against tomato bacterial wilt.
Table 5. Statistical parameters for final CoMFA and CoMSIA models.
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Figure 1. Compounds previously reported against tobacco bacterial wilt.
Figure 2. Alignment of all compounds.
Figure 3. Plots of predicted pEC50 versus experimental pEC50 against tobacco bacterial wilt.
Predicted pEC50 values were calculated for CoMFA model (A) and CoMSIA model (B).
Figure 4. Plots of predicted pEC50 versus experimental pEC50 against tomato bacterial wilt.
Predicted pEC50 values were calculated for CoMFA model (A) and CoMSIA model (B).
Figure 5. CoMFA contour maps of steric (A) and electrostatic (B) fields for activity against
tobacco bacterial wilt.
Figure 6. CoMSIA contour maps of steric (A), electrostatic (B), hydrophobic (C), and
H-bond acceptor (D) fields for activity against tobacco bacterial wilt.
Figure 7. CoMFA contour maps of steric (A) and electrostatic (B) fields for activity against
tomato bacterial wilt.
Figure 8. CoMSIA contour maps of steric (A), electrostatic (B), hydrophobic (C), and
H-bond acceptor (D) fields for activity against tomato bacterial wilt.
Scheme 1. General synthesis route for compounds 5a – 5x.
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Table 1. Inhibition effect of the compounds against tobacco bacterial wilt.
No.
Compound
EC50(μg/mL) pEC50(μM)
Y = Bx + A
R
R1
R2
H
–CH3
–CH3
–CH3
21.10 0.89
8.29 0.56
4.02
4.46
3.33
2.76
2.61
2.35
4.06
3.88
3.26
3.19
3.49
3.82
y = 1.75x + 2.68
y = 2.76x + 2.47
y = 1.21x + 2.47
y = 0.86x + 2.70
y = 0.87x + 2.50
y = 1.21x + 1.18
y = 3.42x + 0.47
y = 2.99x + 0.43
y = 2.06x + 0.52
y = 2.15x + 0.03
y = 1.54x + 2.05
y = 1.68x + 2.32
0.97
0.96
0.98
0.99
0.98
0.98
0.96
0.97
0.99
0.98
0.94
0.95
5a
5b
5c
5d
5e
5f
4-F
4-Cl
4-Cl
4-Br
4-Br
3-F
120.90 2.63
–CH2CH3 471.27 2.83
–CH3
739.62 5.24
–CH2CH3 1420.65 7.76
–CH3
–CH3
21.02 1.53
33.72 1.99
5g
5h
5i
3-Cl
3-Cl
3-Br
2-F
–CH2CH3 147.90 1.89
–CH2CH3 205.39 2.43
5j
–CH2CH3
–CH3
83.28 1.82
38.98 1.06
5k
5l
2-Cl
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2-Cl
2-Br
–CH2CH3 178.43 0.83
–CH2CH3 219.48 3.26
3.18
3.16
3.53
2.98
4.02
3.37
3.30
2.32
2.97
2.41
3.36
2.92
3.78
3.90
3.73
3.76
y = 1.75x + 1.05
y = 1.99x + 0.35
y = 2.27x + 0.74
y = 1.77x + 0.66
y = 1.88x + 2.39
y = 1.74x + 1.42
y = 1.21x + 2.43
y = 1.47x + 0.35
y = 1.66x + 0.85
y = 1.66x + 0.85
y = 1.67x – 0.15
y = 1.88x + 0.48
y = 3.14x − 0.02
y = 1.81x + 2.28
y = 3.09x – 0.30
y = 1.94x + 1.66
0.99
0.98
0.95
0.96
0.97
0.97
0.99
0.95
0.97
0.95
0.96
0.98
0.95
0.88
0.96
0.98
5m
5n
5o
4-OCH3
4-OCH3
3-OCH3
3-OCH3
2-OCH3
2,4-di-Cl
2,3-di-Cl
2,3-di-Cl
3,4-di-Cl
3,4-di-Cl
H
–CH3
74.31 3.55
–CH2CH3 283.42 3.79
5p
5q
5r
–CH3
24.44 2.85
–CH2CH3 113.95 4.21
–CH2CH3 134.44 1.73
–CH2CH3 1448.80 5.57
5s
5t
–CH3
309.66 3.55
5u
5v
–CH2CH3 1203.71 6.75
–CH3
126.00 1.88
5w
5x
–CH2CH3 255.46 2.71
–CH2CH3
–CH2CH3
–CH2CH3
–CH3
39.82 4.91
32.15 2.34
47.90 3.31
52.44 2.05
5aa
5ab
5ac
5ad
4-F
3-F
3-Br
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2,4-di-F
2,4-di-F
–CH3
18.40 0.93
60.30 2.82
4.15
3.66
y = 2.09x + 2.35
y = 3.81x − 1.78
0.99
0.99
5ae
5af
–CH2CH3
3.4-di-OCH
5ag
5ah
–CH3
198.62 2.55
3.16
2.60
y = 1.86x + 0.73
y = 1.27x + 1.35
0.98
0.97
3
3.4-di-OCH
–CH2CH3 742.88 4.21
3
2-F
–CH3
–CH3
–CH3
–CH3
22.54 1.24
75.93 5.25
32.40 1.98
16.55 1.12
45.91 6.65
216.70 5.12
4.03
3.60
3.89
4.05
/
y = 1.91x + 2.42
y = 1.89x + 1.44
y = 1.79x + 2.29
y = 3.33x + 0.25
y = 4.87x − 3.10
y = 1.03x + 2.94
0.98
0.96
0.94
0.96
0.98
0.99
5ai
5aj
5ak
5al
2-Br
2-OCH3
2,4-di-Cl
CK
CK
Kocide 3000
Thiodiazole Copper
/
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Table 2. Inhibition effect of the compounds against tomato bacterial wilt.
No.
Compound
EC50(μg/mL) pEC50(μM)
Y = Bx + A
R
R1
H
R2
–CH3
–CH3
19.77 0.75
40.13 1.23
90.56 2.53
139.37 2.83
153.53 4.54
4.05
3.78
3.45
3.29
3.29
y = 2.90x + 1.24 0.92
y = 1.71x + 2.19 0.98
y = 1.76x + 1.55 0.99
y = 1.61x + 1.53 0.99
y = 1.29x + 2.18 0.99
5a
5b
5c
5d
5e
5f
4-F
4-Cl
4-Cl
4-Br
–CH3
–CH2CH3
–CH3
4-Br
3-F
–CH2CH3
–CH3
228.76 5.22
28.90 1.87
56.99 1.35
115.07 3.11
153.85 4.72
71.09 2.13
49.44 1.89
118.76 3.45
106.71 3.12
89.83 3.57
159.56 5.25
82.23 2.22
145.83 3.18
3.14
3.92
3.66
3.37
3.31
3.67
3.72
3.36
3.47
3.35
3.22
3.49
3.26
y = 1.83x + 0.68 0.99
y = 1.36x + 3.01 0.97
y = 2.44x + 0.73 0.99
y = 1.46x + 1.99 0.99
y = 1.30x + 2.15 0.99
y = 1.55x + 2.31 0.99
y = 2.49x + 0.78 0.96
y = 1.46x + 1.96 0.99
y = 1.60x + 1.75 0.99
y = 1.82x + 1.45 0.98
y = 1.27x + 2.21 0.98
y = 1.90x + 1.35 0.95
y = 1.37x + 2.02 0.99
5g
5h
3-Cl
–CH3
3-Cl
–CH2CH3
–CH2CH3
–CH2CH3
–CH3
5i
5j
3-Br
2-F
5k
5l
2-Cl
2-Cl
–CH2CH3
–CH2CH3
–CH3
5m
5n
5o
5p
5q
5r
2-Br
4-OCH3
4-OCH3
3-OCH3
3-OCH3
–CH2CH3
–CH3
–CH2CH3
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2-OCH3
2,4-di-Cl
2,3-di-Cl
2,3-di-Cl
3,4-di-Cl
3,4-di-Cl
H
–CH2CH3
–CH2CH3
–CH3
83.18 4.75
366.88 4.33
229.55 3.59
505.38 5.79
133.04 4.70
248.04 2.19
31.04 1.82
43.59 2.55
58.80 1.68
81.07 2.25
40.31 4.16
90.69 2.78
253.93 3.27
527.25 6.24
33.74 1.24
65.34 2.55
46.84 2.87
45.47 3.35
93.59 2.17
99.80 4.72
3.51
2.92
3.10
2.78
3.32
3.09
3.88
3.77
3.64
3.57
3.81
3.48
3.05
2.75
3.88
3.66
3.73
3.81
/
y = 1.80x +
0.99
5s
5t
y = 1.60x + 0.90 0.97
y = 1.82x + 0.70 0.99
y = 1.39x + 1.24 0.95
y = 2.91x – 1.23 0.98
y = 1.44x + 1.56 0.99
y = 1.68x + 2.49 0.92
y = 2.54x + 0.84 0.97
y = 2.52x + 0.54 0.97
y = 1.96x + 1.26 0.97
y = 2.03x + 1.75 0.97
y = 1.83x + 1.41 0.98
y = 1.28x + 1.93 0.99
y = 1.17x + 1.81 0.99
y = 2.04x + 1.94 0.97
y = 2.49x + 0.48 0.99
y = 1.85x + 1.90 0.98
y = 1.12x + 3.13 0.99
y = 1.11x + 2.80 0.99
y = 1.03x + 2.94 0.99
5u
–CH2CH3
–CH3
5v
5w
5x
–CH2CH3
–CH2CH3
–CH2CH3
–CH2CH3
–CH3
5aa
5ab
5ac
5ad
5ae
5af
5ag
5ah
5ai
5aj
5ak
5al
CK
CK
4-F
3-F
3-Br
2,4-di-F
2,4-di-F
3.4-di-OCH
–CH3
–CH2CH3
–CH3
3.4-di-OCH –CH2CH3
2-F
–CH3
–CH3
–CH3
–CH3
2-Br
2-OCH3
2,4-di-Cl
Kocide 3000
Thiodiazole Copper
/
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Table 3. Experimental and predicted results of pEC50 against tobacco bacterial wilt.
CoMFA
CoMSIA
No.
Exp a
Pred b
Res c
Pred d
Res
*5a
*5b
*5c
*5d
*5e
*5f
4.02
4.46
3.33
2.76
2.61
2.35
4.06
3.88
3.26
3.19
3.49
3.82
3.18
4.11
4.49
3.16
2.65
2.89
2.38
4.16
3.87
3.36
3.10
3.51
3.67
3.19
-0.09
-0.03
0.17
0.11
-0.28
-0.03
-0.10
0.01
4.15
4.40
3.32
2.83
2.74
2.20
4.16
3.85
3.36
3.16
3.53
3.74
3.25
-0.13
0.06
0.01
-0.07
-0.13
0.15
-0.10
0.03
*5g
*5h
-0.10
0.09
-0.10
0.03
*5i
*5g
*5k
*5l
-0.02
0.15
-0.04
0.08
-0.01
-0.07
*5m
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3.16
3.48
2.96
3.96
3.44
3.36
2.25
2.93
2.43
3.40
2.91
3.60
3.98
3.65
3.70
4.40
0
3.15
3.53
3.04
3.91
3.41
3.37
2.42
3.02
2.53
3.44
2.95
3.66
3.91
3.67
3.65
4.27
0.01
0
*5n
*5o
*5p
*5q
*5r
*5s
5t
3.16
3.53
2.98
4.02
3.37
3.30
2.32
2.97
2.41
3.36
2.92
3.78
3.90
3.73
3.76
4.15
0.05
0.02
0.06
-0.07
-0.06
0.07
0.04
-0.02
-0.04
0.01
0.18
-0.08
0.08
0.06
-0.25
-0.06
0.11
-0.04
-0.07
-0.10
-0.05
-0.12
-0.08
-0.03
0.12
-0.01
0.06
0.11
-0.12
5u
5v
5w
5x
*5aa
*5ab
*5ac
*5ad
5ae
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3.89
3.04
2.53
4.02
3.62
3.86
3.73
-0.23
0.12
0.07
0.01
-0.02
0.03
-0.32
3.77
3.48
2.99
4.02
3.63
3.86
3.91
-0.11
-0.32
-0.39
0.01
3.66
3.16
2.60
4.03
3.60
3.89
4.05
5af
5ag
5ah
*5ai
*5aj
*5ak
5al
-0.03
0.03
0.14
a Experimental pEC50. b Predicted by CoMFA. c Relative error of experimental predicted (a -
b/a - d). d Predicted by CoMSIA. * Sample of the training set.
Table 4. Experimental and predicted results of pEC50 against tomato bacterial wilt.
CoMFA
CoMSIA
No.
Exp a
Pred b
4.01
Res c
0.04
-0.14
0.03
Pred d
Res
*5a
*5b
*5c
4.05
3.78
3.45
4.09
3.87
3.52
-0.04
-0.09
-0.07
3.92
3.42
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