Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning
Downy mildew (caused by <i>Plasmopara viticola</i>) and gray mold (caused by <i>Botrytis cinerea</i>) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that ca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Microorganisms |
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| Online Access: | https://www.mdpi.com/2076-2607/11/5/1341 |
| _version_ | 1797598931812614144 |
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| author | Junrui Zhang Sandun D. Fernando |
| author_facet | Junrui Zhang Sandun D. Fernando |
| author_sort | Junrui Zhang |
| collection | DOAJ |
| description | Downy mildew (caused by <i>Plasmopara viticola</i>) and gray mold (caused by <i>Botrytis cinerea</i>) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target for quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single active site, the risk of developing resistance to these fungicides is deemed high. Consequently, using a combination of fungicides is considered an effective way to reduce the development of QoI resistance. Currently, there is little information available to help in the selection of appropriate fungicides. This study used a combination of in silico simulations and quantitative structure–activity relationship (QSAR) machine learning algorithms to screen the most potent QoI-based fungicide combinations for wild-type (WT) and the G143A mutation of fungal cytochrome b. Based on in silico studies, mandestrobin emerged as the top binder for both WT <i>Plasmopara viticola</i> and WT <i>Botrytis cinerea</i> cytochrome b. Famoxadone appeared to be a versatile binder for G143A-mutated cytochrome b of both <i>Plasmopara viticola</i> and <i>Botrytis cinerea.</i> Thiram emerged as a reasonable, low-risk non-QoI fungicide that works on WT and G143A-mutated versions of both fungi. QSAR analysis revealed fenpropidin, fenoxanil, and ethaboxam non-QoIs to have a high affinity for G143A-mutated cytochrome b of <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i>. Above-QoI and non-QoI fungicides can be considered for field studies in a fungicide management program against <i>Plasmopara viticola</i>- and <i>Botrytis cinerea</i>-based fungal infections. |
| first_indexed | 2024-03-11T03:28:38Z |
| format | Article |
| id | doaj.art-9fa2223c9e8b4abbaee9a1ae812c485a |
| institution | Directory Open Access Journal |
| issn | 2076-2607 |
| language | English |
| last_indexed | 2024-03-11T03:28:38Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj.art-9fa2223c9e8b4abbaee9a1ae812c485a2023-11-18T02:35:23ZengMDPI AGMicroorganisms2076-26072023-05-01115134110.3390/microorganisms11051341Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine LearningJunrui Zhang0Sandun D. Fernando1Biological and Agricultural Engineering Department, Texas A&M University, College Station, TX 77843-2117, USABiological and Agricultural Engineering Department, Texas A&M University, College Station, TX 77843-2117, USADowny mildew (caused by <i>Plasmopara viticola</i>) and gray mold (caused by <i>Botrytis cinerea</i>) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target for quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single active site, the risk of developing resistance to these fungicides is deemed high. Consequently, using a combination of fungicides is considered an effective way to reduce the development of QoI resistance. Currently, there is little information available to help in the selection of appropriate fungicides. This study used a combination of in silico simulations and quantitative structure–activity relationship (QSAR) machine learning algorithms to screen the most potent QoI-based fungicide combinations for wild-type (WT) and the G143A mutation of fungal cytochrome b. Based on in silico studies, mandestrobin emerged as the top binder for both WT <i>Plasmopara viticola</i> and WT <i>Botrytis cinerea</i> cytochrome b. Famoxadone appeared to be a versatile binder for G143A-mutated cytochrome b of both <i>Plasmopara viticola</i> and <i>Botrytis cinerea.</i> Thiram emerged as a reasonable, low-risk non-QoI fungicide that works on WT and G143A-mutated versions of both fungi. QSAR analysis revealed fenpropidin, fenoxanil, and ethaboxam non-QoIs to have a high affinity for G143A-mutated cytochrome b of <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i>. Above-QoI and non-QoI fungicides can be considered for field studies in a fungicide management program against <i>Plasmopara viticola</i>- and <i>Botrytis cinerea</i>-based fungal infections.https://www.mdpi.com/2076-2607/11/5/1341cytochrome bQoI fungicidesfungicide resistanceQSARmachine learninggrapes |
| spellingShingle | Junrui Zhang Sandun D. Fernando Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning Microorganisms cytochrome b QoI fungicides fungicide resistance QSAR machine learning grapes |
| title | Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning |
| title_full | Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning |
| title_fullStr | Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning |
| title_full_unstemmed | Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning |
| title_short | Identification of Fungicide Combinations Targeting <i>Plasmopara viticola</i> and <i>Botrytis cinerea</i> Fungicide Resistance Using Machine Learning |
| title_sort | identification of fungicide combinations targeting i plasmopara viticola i and i botrytis cinerea i fungicide resistance using machine learning |
| topic | cytochrome b QoI fungicides fungicide resistance QSAR machine learning grapes |
| url | https://www.mdpi.com/2076-2607/11/5/1341 |
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