bioRxivMicrobiologyPreprint
Ipflufenoquin, Novel Dihydroorotate Dehydrogenase Inhibitor: Efficacy Against Strawberry Diseases and Docking Simulations via AlphaFold2 and Autodock Vina
Sun-Gyu ChoiiD, Doeun SoniD, Hyeong-Rok JangiD, Soyoon ParkiD, Haifeng LiuiD, Hyeon-gyeong LeeiD, Hyunkyu SangiD
Abstract
In 2023, fungal diseases, including Botrytis cinerea (gray mold), were isolated from various strawberry fields in Korea. Ipflufenoquin, a novel class 2 dihydroorotate dehydrogenase (DHODH) inhibitor, was applied to plant pathogenic fungal species. EC50 values indicated that ipflufenoquin achieved high sensitivity in B. cinerea while maintaining broad-spectrum activity against other fungi. Fruit and greenhouse trials confirmed its robust efficacy in gray mold diseases, including those resistant to conventional fungicides like pyraclostrobin, fluxapyroxad, and benomyl. However, the ipflufenoquin application was less effective against fungal pathogens like blossom blight and Rhizopus rot, causing shifts in the fungal community and allowing less sensitive species to emerge. Structural prediction using Alphafold2 and molecular docking simulation showed ipflufenoquin was bound to the quinone binding tunnel in DHODH and highlighted the correlation between protein-ligand binding affinity and fungicide susceptibility, with species showing higher affinities being more sensitive. This study is the first to report sensitivity of strawberry fungal species to ipflufenoquin, and this fungicide is recommended in strawberry farms against gray mold disease. Additionally, we conducted in silico predictions of fungicide sensitivity across various plant pathogenic fungi using Alphafod2 and Autodock Vina. This establishes a foundation for future computational analyses to predict the effectiveness of fungicides, offering important baseline data to enhance fungicide management strategies.
- organism: plant (other)
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: protein structure prediction
- approach: deep learning
- approach: mechanistic / dynamical model
- Citations
- 3
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Action mechanism of a novel agrichemical quinofumelin against Fusarium graminearum
Qian Xiu, Xiaoru Yin, … Yabing Duan·bioRxiv·Jan 13, 2025
- Fungicide resistance characterised across seven modes of action in a Botrytis cinerea isolated from Australian vineyards
Lincoln Harper, Scott Paton, … Francisco J. López-Ruiz·Pest Management SciencePeer-reviewed·Dec 2, 2021
- Antifungal activity of Ocotea odorifera (Vell.) Rowher, Ocotea puberula (Rich.) Nees and Cinnamodendron dinisii Schwanke essential oils
P. Mezzomo, T. L. Sausen, … R. L. Cansian·bioRxiv·Nov 29, 2019
- Exploring Effector Protein Dynamics and Natural Fungicidal Potential in Rice Blast Pathogen Magnaporthe oryzae
Jannatul Ferdausi, Tanjin Barketullah Robin, … Mohammad Shahadat Hossain·bioRxiv·Jul 4, 2024
- An Indole Dimer Antifungal Metabolite from a Rice Seed Endophyte Inhibits Ergosterol Biosynthesis in Fungal Pathogens
Santosh Kumar Jana, Supriya Bhunia, … Sukhendu Mandal·Applied and Environmental MicrobiologyPeer-reviewed·Apr 20, 2026
- In Silico Screening of Neem-Derived Phytochemicals Targeting the Avr1 (SIX4) Effector Protein of Fusarium oxysporum for Fusarium Wilt Management
Raihana Sultana, Rofiqul Islam Nayem, Md. Mahidul Islam Masum·bioRxiv·Dec 26, 2025