Discovery of Novel Succinate Dehydrogenase Inhibitors by the

Subscriber access provided by UB + Fachbibliothek Chemie | (FU-Bibliothekssystem)

Article

Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in silico Library Design and Pharmacophore Mapping Ting-Ting Yao, Shaowei Fang, Zhongshan Li, Douxin Xiao, Jingli Cheng, Huazhou Ying, Yongjun Du, Jinhao Zhao, and Xiaowu Dong J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b00249 • Publication Date (Web): 30 Mar 2017 Downloaded from http://pubs.acs.org on April 1, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 30

Journal of Agricultural and Food Chemistry

1

Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in

2

silico Library Design and Pharmacophore Mapping

3 4

Ting-Ting Yao,† Shao-Wei Fang,† Zhong-Shan Li,† Dou-Xin Xiao,† Jing-Li Cheng,†

5

Hua-Zhou Ying, ‡Yong-Jun Du,† Jin-Hao Zhao*, † and Xiao-Wu Dong*, ‡

6

†Institute of Pesticide and Environmental Toxicology, Ministry of Agriculture Key Lab

7

of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou

8

310029, P. R. China

9

‡

ZJU-ENS Joint Laboratory of Medicinal Chemistry, Zhejiang Province Key

10

Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences,

11

Zhejiang University, Hangzhou, 310058, P. R. China

12 13

*Institute of Pesticide and Environmental Toxicology, Zhejiang University, Kaixuan

14

Road 268, Hangzhou, 310029, P. R. China. Tel (Fax): 086-571-86971923. E-mail:

15

[email protected] (Jin-Hao Zhao).

16

*College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, P. R.

17

China. Tel (Fax): 086-571-88981051. E-mail: [email protected] (Xiao-Wu Dong).

18

1 ACS Paragon Plus Environment


19

ABSTRACT

20

Succinate dehydrogenase (SDH) has been demonstrated as a promising target for

21

fungicide discovery. Crystal structure data has indicated that the carboxyl “core” of

22

current SDH inhibitors contributed largely to their binding affinity. Thus, identifying

23

novel carboxyl “core” of SDHI inhibitors would remarkably improve the biological

24

potency of current SDHI fungicides. Herein, we report the discovery and optimization

25

of novel carboxyl scaffold of SDH inhibitor via the integration of in silico library

26

design and a highly specific amide feature-based pharmacophore model. To our

27

delight, a promising SDH inhibitor A16c (IC50 = 1.07 µM) with novel

28

pyrazol-benzoic scaffold was identified, which displayed excellent activity against

29

Rhizoctonia solani (EC50 = 11.0 µM), and improved potency against Sclerotinia

30

sclerotiorum (EC50 = 5.5 µM) and Phyricularia grisea (EC50 = 12.0 µM) in

31

comparison with the positive control thifluzamide, with EC50 values of 0.09, 33.2 and

32

33.4 µM, respectively. The results showed that our virtual screening strategy could

33

serve as a powerful tool to accelerate the discovery of novel SDH inhibitors.

34

KEYWORDS: succinate dehydrogenase inhibitors, in silico library, amide

35

feature-based pharmacophore model, hit-to-lead optimization, molecular modeling

36


Page 2 of 30

Page 3 of 30


37

INTRODUCTION

38

Succinate dehydrogenase (SDH, EC 1.3.5.1, also known as complex II), which

39

catalyzes the oxidation of succinate to fumarate in mitochondrial matrix, is the only

40

enzyme complex simultaneously involved in respiration chain and Krebs cycle.1-3 Due

41

to its crucial role in life processes, SDH has been particularly appreciated as a

42

promising target for agrochemical discovery. To date, 19 structural diverse SDHI

43

fungicides have been successfully developed and shown potential for the plant

44

protection. All of these SDHI fungicides share a prototypical pharmacophoric scheme,

45

which consists of a conserved amide function, a structurally diverse carboxyl “core”

46

and an amine moiety (Scheme 1).4, 5 According to co-crystal structure of SDH from

47

porcine heart,6 avian7 and E. coli,8,

48

ubiquinone binding site (Q-site), and contributes predominantly to the binding affinity

49

of SDHI fungicides. However, current efforts mainly focused on the modification of

50

amine part of SDHI fungicide.10-14 Therefore, we envisioned that identification and

51

optimization of novel carboxyl “core” would be of great interest in pursuit of highly

52

potent SDH inhibitors.

9

the carboxyl “core” buries deeply into the

53

In the pesticide/drug discovery campaign, virtual screening (VS) has gained much

54

attention and successfully identified novel scaffolds against different targets.15-20 The

55

virtual screening approaches are historically branched into two categories:

56

ligand-based 21-25 and structure-based VS 26, 27. In case of SDH inhibitors, the absence

57

of SDH crystal structure from fungi has limited the application of structure-based VS

58

approach. In order to take advantages of rich structural information of commercial



59

SDHI fungicides, a ligand-based pharmacophore mapping strategy was employed.

60

Considering the conserved amide function of current SDHI fungicides, a customized

61

“amide” feature was incorporated for the first time to improve the specificity of our

62

VS strategy.28 Additionally, in silico library, which was built on the basis of specific

63

starting pharmacophore and geared toward particular molecular target, is a

64

complementary tool of VS techniques. Over the past decades, combination of virtual

65

screening and iterative in silico library design has been successfully applied to

66

accelerate drug discovery process.29, 30 To the best of our knowledge, such a hybrid

67

approach has not been disclosed in agrochemical discovery so far. Therefore,

68

combining virtual screening and iterative in silico library design for the development

69

of novel SDH inhibitors would still be of great interest.

70

Inspired by aforementioned reasons, herein, we present the discovery and structure

71

optimization of novel carboxyl “core” of SDH inhibitor by the integration of amide

72

feature-based pharmacophore mapping and in silico library design. To our delight, a

73

highly potent SDH inhibitor with novel pyrazol-benzoic “core” was successfully

74

obtained, which displayed excellent activity against R. solani, and improved

75

fungicidal activity against S. sclerotiorum and P. grisea in comparison with

76

commercial product thifluzamide. Furthermore, the binding modes of newly obtained

77

pyrazol-benzoic SDH inhibitors were also explored by molecular docking, providing

78

useful information for future design of SDHI fungicides.

79

MATERIALS AND METHODS

80

Pharmacophore Model Generation and Validation. Ten commercial SDHI


Page 4 of 30

Page 5 of 30


81

fungicides were selected as the training set to establish the pharmacophore model

82

based on their SDH inhibitory activities and structural diversities.31 The HipHop

83

algorithm implemented in Discovery studio 2.5 (DS 2.5, Accelrys Inc., San Diego)

84

was utilized to establish the common feature pharmocophore model. Based on an

85

analysis of the chemical features present in the training set structures, five features

86

were selected, including “amide” (Am), hydrogen bond acceptor (HBA), hydrogen

87

bond donor (HBD), hydrophobic (H), and aromatic ring (AR) features. Owing to the

88

absence of “amide” feature in the dictionary of DS, the “amide” feature was

89

customized by replacing the negative ionizable feature employing the Customize

90

Pharmacophore Features tool. The best conformation generation method was

91

employed, and other parameters were set at the default values. The crystal-bound

92

conformation of flutolanil (entry code: 4YXD) was directly utilized for model

93

building. For the calculation step, the Principal and MaxOmitFeat values were set to 2,

94

0 for compounds with the IC50 < 20 µM, 1, 1 for compounds with the 20 µM < IC50
50 µM. Ten pharmacophore models

96

were generated, and the best one was selected as Am-based pharmacophore model

97

based on the rank values. Subsequently, a shape constraint was generated based on the

98

volume of flutolanil and merged with the chemical features of initial model. Besides,

99

a pharmacophore model was simultaneously constructed, in which the “amide”

100

feature was represented by hydrogen-bond acceptor feature (named as HBA-based

101

pharmacophore). The sensitivity of established pharmacophore models was evaluated

102

by screening a test set database of 25 known active SDH inhibitors (Supporting



103

information, Figure S2) and 1250 decoys generated by the DUD-E database.32

104

In Silico Library Design. The virtual combinatorial library design was constructed

105

with the Enumerate Library by Reaction protocol embedded in DS 2.5. A total of 2243

106

commercially available acids and/or acid halides were collected as the building blocks

107

for in silico library construction. The reagents containing diacids or other functions

108

that may interfere with the reaction scheme were removed. These available building

109

blocks were then combined with aniline to enumerate the in silico amide library

110

(Supporting information, Figure S3). The obtained library was prepared with the

111

Prepare Ligand module to remove the duplicated molecules and generate 3D

112

conformations. The resulting structures were further minimized with CHARMm

113

forcefield converging to a RMS gradient of 0.01.

114

Chemistry. All reagents and solvents were purchased from commercial vendors and

115

used without further purification. Reactions were monitored by thin-layer

116

chromatography (TLC). Target compounds were purified by column chromatography

117

using silica gel. 1H NMR (500 MHz) and 13C NMR (125 MHz) spectra were recorded

118

at a Bruker AVANCE III spectrometer in CDCl3 or DMSO-d6 solution, with SiMe4

119

(TMS) serving as the internal standard. Chemical shift values (δ) were listed in parts

120

per million (ppm). MS data were performed on an Agilent 6530 Accurate-Mass

121

Q-TOF and QUATTRO PREMIER XE employing the electrospray ionization (ESI)

122

method. The melting points were determined on an X-4 binocular microscope melting

123

point apparatus and were uncorrected. The preparation of target amides was

124

performed according to the reported method,33 and the detailed procedure and


Page 6 of 30

Page 7 of 30


125

characterization data are supplied in the Supporting Information.

126

Fungicidal Assay. Rhizoctonia solani, Sclerotinia sclerotiorum, Phyricularia grisea

127

were provided by the Institute of Pesticide and Environment Toxicology, Zhejiang

128

University. The fungicidal activities of the synthetic compounds were tested in vitro

129

against three fungi using a mycelia growth inhibition method.14 Each compound was

130

dissolved in DMSO to prepare the 10 mg/mL stock solution. Compounds were

131

initially tested at a concentration of 100 mg/L. In the precision antifungal test, the 10

132

mg/mL stock solution was diluted to 100, 50, 25, 12.5, 6.25, 3.12, 1.56 mg/L, and the

133

above experiments were repeated for three replicates. The commercial SDHI

134

fungicide thifluzamide served as positive control. The EC50 values were calculated

135

using SPSS Statistics v17.0.

136

The in vivo fungicidal activity of the target compound was carried on rice (Oryza

137

sativa cv CO-39) leaves.34 Conidia harvested from 10-day-old cultures on CM plates

138

were suspended in 0.2% (w/v) gelatin solution to ensure 1 × 105 conidia/mL.

139

Appropriate amounts of the test samples, including target compound and positive

140

control thifluzamide, were dissolved in DMSO and then suspended in the conidia

141

dilution at the concentration of 200 and 50 mg/L. A 20 µL droplet of conidia dilution

142

was deposited onto the upper side of the cut leaves maintained on 4% (w/v) water

143

agar plates (distilled water was used). The leaves were observed for disease lesions

144

after 96 h of incubation at 25 °C.

145

Enzyme assay. The mitochondrial respiratory complex II from porcine heart was

146

isolated essentially as reported.35 The enzymatic activities were assessed as previously



147

described.36, 37 On the basis of the measured molar extinction coefficient, the change

148

of absorbance was converted to product concentration variation and make a linear

149

fitting of time. Here, the slope would stand for the enzymatic reaction velocity.

150

Compared with the control sample, the inhibition rates of the tested compounds were

151

measured.38,39 Inhibitory rates were further used for half-inhibitory concentration

152

(IC50) calculation using SigmaPlot 8.0 software.

153

Homology Modeling and Molecular Docking. According to the X-ray crystal

154

structures of SDH from prokaryotes and eukaryotes,6-9 the ubiquinone binding site

155

(Q-site) was formed by the residues from B, C and D subunits of SDH. Therefore, B,

156

C and D subunits of Rhizoctonia solani SDH (RsSDH) were built (the detailed

157

procedure was supplied in the Supporting Information). The constructed homology

158

model of RsSDH was used for the subsequent docking study. The structures of small

159

molecules were optimized with the Ligand Minimization protocol. Molecular docking

160

study was performed by the Libdock module implemented in DS 2.5. The active site

161

of SDH was derived from the copied ligand flutolanil in RsSDH model. Ten random

162

conformations were generated for each ligand. The rest of the parameters were set to

163

the default values. The optimal pose was selected in terms of docking score and visual

164

inspection.

165

RESULTS AND DISCUSSION

166

Pharmacophore Model Generation and Validation. A ligand-based pharmacophore

167

model for SDH inhibitors was developed using the HipHop algorithm of DS 2.5.

168

Model generation was based on the structural information of ten commercial SDHI


Page 8 of 30

Page 9 of 30


169

fungicides (Figure 1). It was observed that all the compounds of training set shared an

170

essential common amide functionality. With the aim to improve the sensitivity of

171

pharmacophore model, a customized “amide” feature was generated using DS 2.5.

172

Thus, ten pharmacophore models were constructed, and the best one was selected on

173

the basis of rank values (Supporting information, Table S1). Furthermore,

174

pharmacophore query with no spatial restriction may return hits that are too

175

voluminous to fit into the ligand binding site.21 Thus, a shape constraint based on the

176

co-crystal conformation of flutolanil was added to the selected model in order to

177

further improve its selectivity. As shown in Figure 2, the amide feature-based

178

pharmacophore model (Am-based pharmacophore) consisted of a ring aromatic

179

feature, an “amide” feature, two hydrophobic features and a shape query. To

180

determine the contribution of “amide” feature to the selectivity of pharmacophore

181

model, we simultaneously established a HBA-based pharmacophore model, in which

182

the “amide” feature was replaced by a hydrogen bond acceptor (HBA) feature

183

according to the previous co-crystal study (Supporting information, Figure S1).6-8

184

The discriminatory power of the constructed pharmacophore models was evaluated

185

by screening test set, which contains 25 structurally diverse SDH inhibitors

186

(Supporting information, Figure S2) and 1250 confusing decoys derived from the

187

DUD-E database. The enrichment factor (EF) was calculated for each pharmacophore

188

model (Figure 3). At 3%, 5% and 10% of the test set screened, the EF values of

189

Am-based pharmacophore model were 33.6, 20.2 and 10.0, respectively, which were

190

significantly higher than the corresponding values of HBA-based pharmacophore



191

model. The results demonstrated that the introduction of customized “amide” feature

192

significantly improved the sensitivity and specificity. The Am-based pharmacophore

193

model without shape constraint was slightly less sensitive with the EF values of 26.8,

194

17.8 and 9.6, respectively, which indicated that the addition of shape constraint to

195

original model exhibited an improved performance.

196

In Silico Library Design and Virtual Screening. Revealed by crystal structure

197

information from porcine heart, avian and E. coli, the carboxyl “core” of SDHI

198

fungicides contributed predominantly to their binding affinity. To efficiently identify

199

novel carboxyl “core” within this chemical space, an in silico library approach was

200

undertaken. On the basis of prototypical pharmacophore of current SDHI fungicides

201

(Scheme 1), we designed an in silico library based on an aniline template linked to the

202

amide function. Surveying commercially available acids or acyl halides, an exhaustive

203

enumeration of aniline-based amide products was performed. With the aid of amide

204

feature-based pharmacophore model, the in silico library was virtually screened,

205

which led to a list of 200 compounds mapping the pharmacophore features. Then,

206

cluster analysis divided these hits into ten groups according to their structural

207

diversity. Considering the fitvalue, chemotype and synthetic feasibility, 16 broadly

208

representative compounds were submitted for chemical synthesis and biological

209

assays (Figure 4).

210

Chemical Synthesis and Biological Evaluation. The selected VS hits were

211

successfully synthesized and characterized using 1H NMR,

212

spectral data. In order to set up a faster and cheaper model for screening the activity


13

C NMR and HRMS

Page 10 of 30

Page 11 of 30


213

of VS hits, they were initially submitted to fungicidal assay against three

214

representative plant pathogens R. solani, S. sclerotiorum and P. grisea at the

215

concentration of 100 mg/L. For compounds that showed more than 50% inhibition

216

against all the tested fungi, the EC50 values were determined.

217

Among the sixteen synthetic VS hits, eight compounds exhibited above 50%

218

inhibitory activities against S. sclerotiorum, R. solani and P. grisea at the

219

concentration of 100 mg/L (Figure 5). The hit rate was 50%, suggesting the robust

220

enrichment of our virtual screening strategy. Therefore, the EC50 values of these

221

compounds were further determined and provided in Table 1. To our delight, A16 was

222

found to be superior to thifluzamide against S. sclerotiorum (EC50 = 8.1 µM) and P.

223

grisea (EC50 = 21.7 µM), and promising inhibitory activity against R. solani (EC50 =

224

16.5 µM). In addition, the enzyme inhibition was of great importance to understand

225

the fungicidal activity of a compound. Therefore, A16 was further evaluated for its in

226

vitro enzymatic inhibition against SDH. As shown in Figure 6 (B), A16 with IC50

227

value of 1.30 µM displayed excellent inhibition against SDH, which was comparable

228

to thifluzamide (IC50 = 0.16 µM).

229

Novelty and Pesticide-likeness Analysis. To evaluate the structural novelty of

230

identified potent VS hits with respect to current SDHI fungicides, the pairwise

231

Tanimoto similarity indices were reported in Table 1. Typically, 0.7 was defined as a

232

cutoff value, and a similarity index over 0.7 indicated a similar structural feature.40

233

The results showed that all the similarity coefficients were below 0.7, demonstrating

234

that the potent VS hits were structurally distinct from current SDHI fungicides.



235

Moreover, all the potent hits satisfied the pesticide-likeness rules,41 which indicated

236

their favorable physicochemical properties and structural features (Supporting

237

information, Table S2). Considering the excellent fungicidal activities, enzymatic

238

inhibition, structural novelty and pesticide-likeness, herein, compound A16 was

239

selected as a lead candidate for further optimization.

240

Hit-to-Lead Optimization. To probe the structure-activity relationship (SAR) of

241

pyrazol-benzoic acid scaffold and discover more potent SDH inhibitors, a second

242

iteration of in silico library was established as follows: (a) replacement of aniline with

243

more rigid (i.e. naphthylamine) or flexible amine (i.e. benzylamine); (b) modification

244

of amide function by the addition of small substituent (i.e. alky, alkoxy and

245

cycloalkoxy moiety) to the nitrogen atom. (c) introduction of various electronic

246

donating or withdrawing substituents to the ortho-, meta- or para- position of aniline;

247

For more efficient rational design strategy for SDH inhibitors, the follow up library

248

was mapped by the Am-based pharmacophore model, and 15 derivatives were finally

249

selected for synthesis and biological evaluation.

250

The EC50 values of these 15 derivatives were determined and provided in Table 2,

251

and the preliminary SAR was discussed based on the experimental data. We initially

252

focused on the substitution of aniline moiety with long flexible amine. Astonishingly,

253

most substitutions did not augment the fungicidal activities, including benzylamine

254

(A16l), pyridylmethylamine (A16n), phenylethanamine (A16o). On the contrary, the

255

rigid naphthalene substituted derivative (A16i) exhibited the improved potency with

256

EC50 values of 3.8, 13.8 and 11.8 µM against S. sclerotiorum, R. solani and P. grisea.


Page 12 of 30

Page 13 of 30


257

Moreover, modification of the amide function led to the significant decrease of

258

inhibition (i.e. A16j, k, m), which indicated the importance of conserved amide

259

function to the biological activity of SDHI fungicides. Subsequently, we examined the

260

influence of the modification at the ortho, meta and para position of aniline. Among

261

the simple substituted phenyl analogues, the ortho and meta-substituted aniline

262

moiety was not favorable for the fungicidal activities against R. solani and P. grisea,

263

including A16a, d, e, g, h (EC50: 57.8 ~ 213.4 and 32.4 ~ 111.2 µM, respectively). To

264

be of interest, the electron withdrawing para-chlorine, A16c (EC50 = 5.5 and 12.0 µM)

265

and electron donating para-methoxyl, A16f (EC50 = 4.9 and 5.6 µM) displayed the

266

improved fungicidal activities against S. sclerotiorum and P. grise. These results

267

demonstrated the importance of substituted aniline moiety towards the fungicidal

268

activity of A16 derivatives.

269

Enzymatic inhibition and in vivo fungicidal activity. Among the derivatives tested

270

for antifungal activity in vitro, A16c exhibited the highest activities against S.

271

sclerotiorum, R. solani and P. grisea with EC50 values of 5.5, 11.0, 12.0 µM,

272

respectively. Therefore, it was further evaluated for the enzymatic inhibition and in

273

vivo fungicidal activity of A16c. As shown in Figure 6 (C), A16c exhibited promising

274

inhibitory activity against SDH with a slightly improved IC50 value of 1.07 µM. The

275

in vivo fungicidal activity of A16c was provided in Figure 7. The negative control

276

(pathogen only) showed severe blast 4 days after inoculation. To our delight, A16c

277

afforded a good preventative effect against P. grise, showing no significant difference

278

from that of the positive control thifluzamide at 200 mg/L. These results demonstrated



279

the practical potential of this novel leading compound for crop protection.

280

Binding Mode Analysis. In order to elucidate the mechanism of newly identified

281

SDH inhibitors and explain the SARs in details, docking studies were performed. Due

282

to the absence of crystal structure of SDH from fungi, a homology model of RsSDH

283

was built and validated (Supporting information, Figure S4). Subsequently,

284

representative derivatives A16c and A16m were docked into the active site of RsSDH,

285

respectively. As shown in Figure 8 (A), the pyrazol-benzoic “core” of A16c buried

286

into the Q-site, which was involved in the π-cation interaction with Arg358, and π-π

287

interaction with His249. Besides, the amide function of A16c formed a key hydrogen

288

bond with Tyr206, orientating the para-chlorine substituted phenyl moiety towards

289

the entrance of Q-site. The favorable interaction between the pyrazol-benzoic “core”

290

and Q-site reasonably accounted for its strong fungicidal activities. As depicted in

291

Figure 8 (B), compared with A16c, the introduction of extended benzylamine moiety

292

and modification of amide function resulted in huge conformational change of A16m.

293

Due to the blocking of Phe342 at the entrance of Q-site, the amide function of A16m

294

could not form stable hydrogen bond with Tyr206, thus leading to the worse

295

fungicidal activities. These results confirmed the rationality of our initial hypothesis

296

and prompted us to develop more potent SDH inhibitors along the same strategy.

297

In summary, we described the generation and application of an amide-feature based

298

pharmacophore model for SDH inhibitors. The sensitivity of pharmacophore model

299

was remarkably improved by incorporating a customized “amide” feature.

300

Subsequently, pharmacophore-based virtual screening of in silico library led to the


Page 14 of 30

Page 15 of 30


301

discovery of eight potent hits. Structural analysis of these potent VS hits demonstrated

302

their novelty and

303

pharmacophore model, structural optimization of the lead candidate A16 was

304

performed using a second iteration of in silico library. Thus, it allowed us to develop a

305

highly efficient SDH inhibitor with excellent fungicidal activities against S.

306

sclerotiorum, R. solani, P. grisea and enzymatic inhibition. These results

307

demonstrated that the integration of in silico library design and amide feature-based

308

pharmacophore mapping could serve as an efficient tool for the identification and

309

hit-to-lead optimization of novel SDH inhibitors.

pesticide-likeness.

Guided

by the amide

feature-based

310 311

SUPPORTING INFORMATION DESCRIPTION

312

Comparison of Am-based and HBA-based pharmacophore model; 25 known active

313

SDH inhibitors for model validation; Reaction scheme for the library enumeration;

314

Homology model of RsSDH and the Ramachandran plot; Features, rank values and

315

max fit features for the Am-based pharmacophore models; Pesticide-likeness values

316

of potent VS hits; Experimental details and analytical data for the target compounds;

317

Computational protocol for the homology modeling.

318



319

REFERENCES

320

1.

321

Rev. Biochem. 2003, 72, 77-109.

322

2.

323

structure of mitochondrial respiratory membrane protein complex II. Cell 2005, 121,

324

1043-1057.

325

3.

326

Leger, C.; Byrne, B.; Cecchini, G.; Iwata, S. Architecture of succinate dehydrogenase

327

and reactive oxygen species generation. Science 2003, 299, 700-704.

328

4.

329

the next-generation succinate dehydrogenase inhibitor fungicides. Phytopathology

330

2013, 103, 880-887.

331

5. Xiong, L.; Shen, Y. Q.; Jiang, L. N.; Zhu, X. L.; Yang, W. C.; Huang, W.; Yang, G.

332

F. Succinate dehydrogenase: an ideal target for fungicide discovery. ACS Sym. Ser.

333

2015, 1204, 175-194.

334

6. Inaoka, D. K.; Shiba, T.; Sato, D.; Balogun, E. O.; Sasaki, T.; Nagahama, M.; Oda,

335

M.; Matsuoka, S.; Ohmori, J.; Honma, T.; Inoue, M.; Kita, K.; Harada, S. Structural

336

insights into the molecular design of flutolanil derivatives targeted for fumarate

337

respiration of parasite mitochondria. Int. J. Mol. Sci. 2015, 16, 15287-15308.

338

7. Huang, L. S.; Sun, G.; Cobessi, D.; Wang, A. C.; Shen, J. T.; Tung, E. Y.; Anderson,

339

V. E.; Berry, E. A. 3-nitropropionic acid is a suicide inhibitor of mitochondrial

340

respiration that, upon oxidation by complex II, forms a covalent adduct with a

Cecchini, G. Function and structure of complex II of the respiratory chain. Annu.

Sun, F.; Huo, X.; Zhai, Y.; Wang, A.; Xu, J.; Su, D.; Bartlam, M.; Rao, Z. Crystal

Yankovskaya, V.; Horsefield, R.; Tornroth, S.; Luna-Chavez, C.; Miyoshi, H.;

Sierotzki, H.; Scalliet, G. A review of current knowledge of resistance aspects for


Page 16 of 30

Page 17 of 30


341

catalytic base arginine in the active site of the enzyme. J. Biol. Chem. 2006, 281,

342

5965-5972.

343

8.

344

of Escherichia coli succinate: quinone oxidoreductase with an occupied and empty

345

quinone-binding site. J. Biol. Chem. 2009, 284, 29836-29846.

346

9. Ruprecht, J.; Iwata, S.; Rothery, R. A.; Weiner, J. H.; Maklashina, E.; Cecchini, G.

347

Perturbation of the quinone-binding site of complex II alters the electronic properties

348

of the proximal 3Fe-4S iron-sulfur cluster. J. Biol. Chem. 2011, 286, 12756-12765.

349

10. Xiong, L.; Li, H.; Jiang, L. N.; Ge, J. M.; Yang, W. C.; Zhu, X. L.; Yang, G. F.

350

Structure-based discovery of potential fungicides as succinate ubiquinone

351

oxidoreductase inhibitors. J. Agr. Food Chem. 2017, 65, 1021-1029.

352

11. Li, S.; Li, D.; Xiao, T.; Zhang, S.; Song, Z.; Ma, H. Design, synthesis, fungicidal

353

activity, and unexpected docking model of the first chiral boscalid analogues

354

containing oxazolines. J. Agr. Food Chem. 2016, 64, 8927-8934.

355

12. Wen, F.; Jin, H.; Tao, K.; Hou, T. Design, synthesis and antifungal activity of

356

novel furancarboxamide derivatives. Eur. J. Med. Chem. 2016, 120, 244-251.

357

13. Xiong, L.; Zhu, X. L.; Gao, H. W.; Fu, Y.; Hu, S. Q.; Jiang, L. N.; Yang, W. C.;

358

Yang, G. F. Discovery of potent succinate-ubiquinone oxidoreductase inhibitors via

359

pharmacophore-linked fragment virtual screening approach. J. Agr. Food Chem. 2016,

360

64, 4830-4837.

361

14. Ye, Y. H.; Ma, L.; Dai, Z. C.; Xiao, Y.; Zhang, Y. Y.; Li, D. D.; Wang, J. X.; Zhu,

362

H. L. Synthesis and antifungal activity of nicotinamide derivatives as succinate

Ruprecht, J.; Yankovskaya, V.; Maklashina, E.; Iwata, S.; Cecchini, G. Structure



363

dehydrogenase inhibitors. J. Agr. Food Chem. 2014, 62, 4063-4071.

364

15. Harada, T.; Nakagawa, Y.; Ogura, T.; Yamada, Y.; Ohe, T.; Miyagawa, H. Virtual

365

screening for ligands of the insect molting hormone receptor. J. Chem. Inf. Model.

366

2011, 51, 296-305.

367

16. Wilton, D. J.; Harrison, R. F.; Willett, P.; Delaney, J.; Lawson, K.; Mullier, G.

368

Virtual screening using binary kernel discrimination: analysis of pesticide data. J.

369

Chem. Inf. Model. 2006, 46, 471-477.

370

17. Liu, J.; Liu, M.; Yao, Y.; Wang, J.; Li, Y.; Li, G.; Wang, Y. Identification of novel

371

potential β-N-acetyl-D-hexosaminidase inhibitors by virtual screening, molecular

372

dynamics simulation and MM-PBSA calculations. Int. J. Mol. Sci. 2012, 13,

373

4545-4563.

374

18. Lavecchia, A.; Di Giovanni, C.; Cerchia, C.; Russo, A.; Russo, G.; Novellino, E.

375

Discovery of a novel small molecule inhibitor targeting the frataxin/ubiquitin

376

interaction via structure-based virtual screening and bioassays. J. Med. Chem. 2013,

377

56, 2861-2873.

378

19. Siddiquee, K.; Zhang, S.; Guida, W. C.; Blaskovich, M. A.; Greedy, B.; Lawrence,

379

H. R.; Yip, M. L. R.; Jove, R.; McLaughlin, M. M.; Lawrence, N. J.; Sebti, S. M.;

380

Turkson, J. Selective chemical probe inhibitor of Stat3, identified through

381

structure-based virtual screening, induces antitumor activity. P. Natl. Acad. Sci. USA

382

2007, 104, 7391-7396.

383

20. Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo vadis, virtual screening?

384

a comprehensive survey of prospective applications. J. Med. Chem. 2010, 53,


Page 18 of 30

Page 19 of 30


385

8461-8467.

386

21. Onnis, V.; Kinsella, G. K.; Carta, G.; Jagoe, W. N.; Price, T.; Williams, D. C.;

387

Fayne, D.; Lloyd, D. G. Virtual screening for the identification of novel nonsteroidal

388

glucocorticoid modulators. J. Med. Chem. 2010, 53, 3065-3074.

389

22. Goracci, L.; Deschamps, N.; Randazzo, G. M.; Petit, C.; Passos, C. D. S.; Carrupt,

390

P. A.; Simoes-Pires, C.; Nurisso, A. A rational approach for the identification of

391

non-hydroxamate HDAC6-selective inhibitors. Sci. Rep-UK 2016, 6, 1-12.

392

23. Waltenberger, B.; Wiechmann, K.; Bauer, J.; Markt, P.; Noha, S. M.; Wolber, G.;

393

Rollinger, J. M.; Werz, O.; Schuster, D.; Stuppner, H. Pharmacophore modeling and

394

virtual screening for novel acidic inhibitors of microsomal prostaglandin E-2

395

synthase-1 (mPGES-1). J. Med. Chem. 2011, 54, 3163-3174.

396

24. Peterson, Y. K.; Wang, X. S.; Casey, P. J.; Tropsha, A. Discovery of

397

geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of

398

quantitative

399

experimental validation. J. Med. Chem. 2009, 52, 4210-4220.

400

25. Yao, T. T.; Cheng, J. L.; Xu, B. R.; Zhang, M. Z.; Hu, Y. Z.; Zhao, J. H.; Dong, X.

401

W. Support vector machine (SVM) classification model based rational design of novel

402

tetronic acid derivatives as potent insecticidal and acaricidal agents. RSC Adv. 2015, 5,

403

49195-49203.

404

26. Park, S. J.; Kim, Y. G.; Park, H. J. Identification of RNA pseudoknot-binding

405

ligand that inhibits the -1 ribosomal frameshifting of SARS-coronavirus by

406

structure-based virtual screening. J. Am. Chem. Soc. 2011, 133, 10094-10100.

structure-activity

relationship

modeling,


virtual

screening,

and


407

27. Bayry, J.; Tchilian, E. Z.; Davies, M. N.; Forbes, E. K.; Draper, S. J.; Kaveri, S. V.;

408

Hill, A. V. S.; Kazatchkine, M. D.; Beverley, P. C. L.; Flower, D. R.; Tough, D. F. In

409

silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity

410

in vaccination. P. Natl. Acad. Sci. USA 2008, 105, 10221-10226.

411

28. Hou, X.; Du, J.; Liu, R.; Zhou, Y.; Li, M.; Xu, W.; Fang, H. Enhancing the

412

sensitivity of pharmacophore-based virtual screening by incorporating customized

413

ZBG features: a case study using histone deacetylase 8. J. Chem. Inf. Model. 2015, 55,

414

861-871.

415

29. Xing, L.; McDonald, J. J.; Kolodziej, S. A.; Kurumbail, R. G.; Williams, J. M.;

416

Warren, C. J.; O'Neal, J. M.; Skepner, J. E.; Roberds, S. L. Discovery of potent

417

inhibitors of soluble epoxide hydrolase by combinatorial library design and

418

structure-based virtual screening. J. Med. Chem. 2011, 54, 1211-1222.

419

30. Getlik, M.; Smil, D.; Zepeda-Velazquez, C.; Bolshan, Y.; Poda, G.; Wu, H.; Dong,

420

A.; Kuznetsova, E.; Marcellus, R.; Senisterra, G.; Dombrovski, L.; Hajian, T.; Kiyota,

421

T.; Schapira, M.; Arrowsmith, C. H.; Brown, P. J.; Vedadi, M.; Al-awar, R.

422

Structure-based optimization of a small molecule antagonist of the interaction

423

between WD repeat-containing protein 5 (WDR5) and mixed-lineage leukemia 1

424

(MLL1). J. Med. Chem. 2016, 59, 2478-2496.

425

31. Zhu, X. L.; Xiong, L.; Li, H.; Song, X. Y.; Liu, J. J.; Yang, G. F. Computational

426

and experimental insight into the molecular mechanism of carboxamide inhibitors of

427

succinate-ubquinone oxidoreductase. Chemmedchem 2014, 9, 1512-1521.

428

32. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking.


Page 20 of 30

Page 21 of 30


429

J. Med. Chem. 2006, 49, 6789-6801.

430

33. Thiede, S.; Wosniok, P. R.; Herkommer, D.; Schulz-Fincke, A. C.; Gütschow, M.;

431

Menche, D. Total synthesis of Leupyrrin B1: a potent inhibitor of human leukocyte

432

elastase. Org. Lett. 2016, 18, 3964-3967.

433

34. Liu, X. H.; Lu, J. P.; Zhang, L.; Dong, B.; Min, H.; Lin, F. C. Involvement of a

434

Magnaporthe grisea serine/threonine kinase gene, MgATG1, in appressorium turgor

435

and pathogenesis. Eukaryot. Cell 2007, 6, 997-1005.

436

35. King, T. E. Preparations of succinate-cytochrome c reductase and the cytochrome

437

b-c1 particle, and reconstitution of succinate-cytochrome c reductase. Methods

438

Enzymol. 1967, 10, 216−225.

439

36. Fisher, N.; Bourges, I.; Hill, P.; Brasseur, G.; Meunier, B. Disruption of the

440

interaction between the Rieske iron-sulfur protein and cytochrome b in the yeast bc1

441

complex owing to a human disease associated mutation within cytochrome b. Eur. J.

442

Biochem. 2004, 271, 1292−1298.

443

37. Fisher, N.; Brown, A. C.; Sexton, G.; Cook, A.; Windass, J.; Meunier, B.

444

Modeling the Qo site of crop pathogens in Saccharomyces cerevisiae cytochrome b.

445

Eur. J. Biochem. 2004, 271, 2264−2271.

446

38. Cheng, H.; Shen, Y. Q.; Pan, X. Y.; Hou, Y. P.; Wu, Q. Y.; Yang, G. F. Discovery of

447

1,2,4-triazole-1,3-disulfonamides as dual inhibitors of mitochondrial complex II and

448

complex III. New J. Chem. 2015, 39, 7281-7292.

449

39. Xiong, L.; Zhu, X. L.; Shen, Y. Q.; Wishwa, W. K. W. M.; Li, K.; Yang, G. F.

450

Discovery of N-benzoxazol-5-yl-pyrazole-4-carboxamides as nanomolar SQR



Page 22 of 30

451

inhibitors. Eur. J. Med. Chem. 2015, 95, 424-434.

452

40. Sorna, V.; Theisen, E. R.; Stephens, B.; Warner, S. L.; Bearss, D. J.; Vankayalapati,

453

H.;

454

N'-(1-phenylethylidene)-benzohydrazides as potent, specific, and reversible LSD1

455

inhibitors. J. Med. Chem. 2013, 56, 9496-9508.

456

41. Hao, G.; Dong, Q.; Yang, G. A comparative study on the constitutive properties of

457

marketed pesticides. Mol. Inform. 2011, 30, 614-622.

Sharma,

S.

High-throughput

virtual

screening


identifies

novel

Page 23 of 30


Figure captions Scheme 1. Representative SDHI fungicides and their prototypical pharmacophore. Figure 1. Training set compounds used for pharmacophore model generation. Figure 2. The space organization of Am-based pharmacophore model (A) and the most potent inhibitor 1 (thifluzamide) mapped to the pharmacophore model (B). The pharmacophore features are colored with cyan (“amide” feature), orange (aromatic ring) and blue (hydrophobic group). Figure 3. ROC plot (A) and enrichment factor (B) for Am-based and HBA-based pharmacophore models validation. Figure 4. Structures of synthetic virtual screening hits A1~A16. Figure 5. Fungicidal activities of the synthetic VS hits against S. sclerotiorum, R. solani and P. grisea at 100 mg/L (asterisk indicated all inhibitions against the tested fungi above 50%). Figure 6. The concentration-dependent inhibition of SDH for thifluzamide (A), A16 (B) and A16c (C). Figure 7. In vivo activity of compound A16c against P. grisea. Figure 8. The binding mode of A16c (A) and A16m (B).



Page 24 of 30

Table 1. Experimentally determined fungicidal activities (EC50) against S. sclerotiorum, R. solani and P. grisea of eight potent VS hits. EC50 (µM)a similarityb

No.

a

S. sclerotiorum

R. solani

P. grisea

thifluzamide

33.2 ± 0.8

0.09 ± 0.01

33.4 ± 4.2

A1

14.0 ± 0.1

406.1 ± 8.9

232.5 ± 18.4

0.32

A4

102.9 ± 8.4

235.3 ± 31.8

152.3 ± 16.2

0.45

A5

60.2 ± 1.0

111.3 ± 1.1

161.8 ± 2.5

0.43

A8

54.4 ± 0.1

132.7 ± 6.5

533.9 ± 41.8

0.42

A9

78.2 ± 4.9

523.3 ± 11.0

> 800

0.28

A12

71.7 ± 4.7

340.7 ± 32.0

622.0 ± 9.6

0.51

A14

176.8 ± 4.4

551.8 ± 2.9

167.9 ± 6.8

0.43

A16

8.1 ± 0.1

16.5 ±1.0

21.7 ± 0.5

0.43

Values are the mean ± standard deviation (SD) of three replicates. bPairwise

Tanimoto similarity indices based on the FCFP_6 fingerprints for each inhibitor with current SDHI fungicides.


Page 25 of 30


Table 2. Chemical structures and fungicidal activities against S. sclerotiorum, R. solani and P. grisea of A16 derivatives.

EC50 (µM)a No.

R S. sclerotiorum

R. solani

P. grisea

92.3 ± 8.6

213.4 ± 2.8

32.6 ± 1.0

4.3 ± 0.1

15.1 ± 0.1

37.2 ± 0.7

A16c

5.5 ± 0.4

11.0 ± 2.1

12.0 ± 0.3

A16d

4.0 ± 0.1

57.8 ± 3.5

111.2 ± 2.6

A16e

43.0 ± 2.5

206.3 ± 7.0

32.4 ± 2.1

A16f

4.9 ± 0.2

50.6 ± 1.8

5.6 ± 0.4

A16g

3.8 ± 0.2

127.8 ± 1.0

33.0 ± 0.8

A16h

4.3 ± 0.2

19.7 ± 3.3

71.5 ± 10.1

A16i

3.8 ± 0.1

13.8 ± 0.1

11.8 ± 0.4

A16j

31.0 ± 1.2

37.5 ± 0.8

28.1 ± 0.5

A16k

116.7 ± 22.3

61.4 ± 2.4

34.0 ± 1.4

A16l

37.8 ± 0.4

39.5 ± 0.2

47.4 ± 0.5

A16m

27.5 ± 2.7

16.0 ± 0.5

32.3 ± 3.4

A16n

175.8 ± 17.6

> 500

43.5 ± 0.9

26.5 ± 2.2

24.4 ± 0.1

147.1 ± 2.2

A16a H N

A16b

A16o

Cl

H N Cl

a

Values are the mean ± standard deviation (SD) of three replicates.



Scheme 1.

Figure 1.


Page 26 of 30

Page 27 of 30


Figure 2.

Figure 3.

O

O

O

O

O N H

N H

N N

A2

A1

A3

A4

O

O

N H

N H

N 8 H

A6

A5

O O

O

O

F3C

O

O O

N H

N H

N H

N 4 H

N H OH

Cl

N H

N 9 H

H3C

NH

A8

A7 N

N

A10 O

O O

N

N

HN

A13

A12

A11

Br

O

N

HN

Cl

A9

O

N

HN Cl

Cl

A14

O

N N

HN

A15

Figure 4.


A16


S. sclerotiorum R. solani P. grisea

120 110 100

% inhibition at 100 mg/L

Page 28 of 30

90 80 70 60 50 40 30 20 10 0

A1

∗

A2

A3

A4 ∗

A5 ∗

A6

A7

A8 ∗

A9 A10 A11 A12 A13 A14 A15 A16 ∗ ∗ ∗ ∗

Figure 5.

Figure 6.

Figure 7


Page 29 of 30


Figure 8.



Graphic for table of contents


Page 30 of 30

Discovery of Novel Succinate Dehydrogenase Inhibitors by the

Recommend Documents