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Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in Silico Library Design and Pharmacophore Mapping.

Ting-Ting YaoShao-Wei FangZhong-Shan LiDou-Xin XiaoJing-Li ChengHua-Zhou YingYong-Jun DuJin-Hao ZhaoXiao-Wu Dong
Published in: Journal of agricultural and food chemistry (2017)
Succinate dehydrogenase (SDH) has been demonstrated as a promising target for fungicide discovery. Crystal structure data have indicated that the carboxyl "core" of current SDH inhibitors contributed largely to their binding affinity. Thus, identifying novel carboxyl "core" SDH inhibitors would remarkably improve the biological potency of current SDHI fungicides. Herein, we report the discovery and optimization of novel carboxyl scaffold SDH inhibitor via the integration of in silico library design and a highly specific amide feature-based pharmacophore model. To our delight, a promising SDH inhibitor, A16c (IC50 = 1.07 μM), with a novel pyrazol-benzoic scaffold was identified, which displayed excellent activity against Rhizoctonia solani (EC50 = 11.0 μM) and improved potency against Sclerotinia sclerotiorum (EC50 = 5.5 μM) and Phyricularia grisea (EC50 = 12.0 μM) in comparison with the positive control thifluzamide, with EC50 values of 0.09, 33.2, and 33.4 μM, respectively. The results showed that our virtual screening strategy could serve as a powerful tool to accelerate the discovery of novel SDH inhibitors.
Keyphrases
  • small molecule
  • molecular docking
  • crystal structure
  • high throughput
  • molecular dynamics
  • high resolution
  • deep learning
  • transcription factor
  • mass spectrometry
  • binding protein
  • dna binding
  • artificial intelligence