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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

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Journal of Agricultural and Food Chemistry

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Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in

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silico Library Design and Pharmacophore Mapping

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

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

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

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

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310029, P. R. China

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‡

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

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

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

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

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

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[email protected] (Jin-Hao Zhao).

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

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

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1 ACS Paragon Plus Environment


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ABSTRACT

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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KEYWORDS: succinate dehydrogenase inhibitors, in silico library, amide

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

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INTRODUCTION

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

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

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

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

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

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fungicides have been successfully developed and shown potential for the plant

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

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

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

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

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

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

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

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

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potent SDH inhibitors.

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the carboxyl “core” buries deeply into the

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

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

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virtual screening approaches are historically branched into two categories:

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

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

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



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

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

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

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

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starting pharmacophore and geared toward particular molecular target, is a

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

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

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

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

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

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of novel SDH inhibitors would still be of great interest.

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

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

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

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

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

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

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

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

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useful information for future design of SDHI fungicides.

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MATERIALS AND METHODS

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Pharmacophore Model Generation and Validation. Ten commercial SDHI


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

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

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

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

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

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

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

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

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customized by replacing the negative ionizable feature employing the Customize

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Pharmacophore Features tool. The best conformation generation method was

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

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

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

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

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

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

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

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

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

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pharmacophore). The sensitivity of established pharmacophore models was evaluated

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



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

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

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

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

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

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

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

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

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

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conformations. The resulting structures were further minimized with CHARMm

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forcefield converging to a RMS gradient of 0.01.

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

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

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

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

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

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

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

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

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

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

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


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characterization data are supplied in the Supporting Information.

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Fungicidal Assay. Rhizoctonia solani, Sclerotinia sclerotiorum, Phyricularia grisea

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

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

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

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

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

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

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above experiments were repeated for three replicates. The commercial SDHI

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

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using SPSS Statistics v17.0.

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

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

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

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

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

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

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

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

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after 96 h of incubation at 25 °C.

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

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



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

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

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

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

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

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(IC50) calculation using SigmaPlot 8.0 software.

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

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

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

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

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

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

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molecules were optimized with the Ligand Minimization protocol. Molecular docking

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

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

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

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

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inspection.

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RESULTS AND DISCUSSION

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Pharmacophore Model Generation and Validation. A ligand-based pharmacophore

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The discriminatory power of the constructed pharmacophore models was evaluated

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

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

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

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

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

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significantly higher than the corresponding values of HBA-based pharmacophore



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

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

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

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

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original model exhibited an improved performance.

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

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

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fungicides contributed predominantly to their binding affinity. To efficiently identify

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

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

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

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

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

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

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

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

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

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representative compounds were submitted for chemical synthesis and biological

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assays (Figure 4).

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Chemical Synthesis and Biological Evaluation. The selected VS hits were

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successfully synthesized and characterized using 1H NMR,

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


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C NMR and HRMS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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to thifluzamide (IC50 = 0.16 µM).

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

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

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

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

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

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



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

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their favorable physicochemical properties and structural features (Supporting

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

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

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selected as a lead candidate for further optimization.

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

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

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

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

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

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

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

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

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

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selected for synthesis and biological evaluation.

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

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

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

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most substitutions did not augment the fungicidal activities, including benzylamine

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

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

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


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

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

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

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

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

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

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

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

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

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

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demonstrated the importance of substituted aniline moiety towards the fungicidal

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activity of A16 derivatives.

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

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

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

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

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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pharmacophore model for SDH inhibitors. The sensitivity of pharmacophore model

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

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


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

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their novelty and

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pharmacophore model, structural optimization of the lead candidate A16 was

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

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highly efficient SDH inhibitor with excellent fungicidal activities against S.

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

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

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

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hit-to-lead optimization of novel SDH inhibitors.

pesticide-likeness.

Guided

by the amide

feature-based

310 311

SUPPORTING INFORMATION DESCRIPTION

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

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

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

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

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

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Computational protocol for the homology modeling.

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High-throughput

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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).



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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 ... - ACS Publications

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