Rational Design of Chitin Deacetylase Inhibitors for Sustainable Agricultural Use Based on Molecular Topology

Abstract

Fungicide resistance is a major concern in modern agriculture; therefore, there is a pressing demand to develop new, greener chemicals. Chitin is a major component of the fungal cell wall and a well-known elicitor of plant immunity. To overcome chitin recognition, fungal pathogens developed different strategies, with chitin deacetylase (CDA) activity being the most conserved. This enzyme is responsible for hydrolyzing the N-acetamido group in N-acetylglucosamine units of chitin to convert it to chitosan, a compound that can no longer be recognized by the plant. In previous works, we observed that treatments with CDA inhibitors, such as carboxylic acids, reduced the symptoms of cucurbit powdery mildew and induced rapid activation of chitin-triggered immunity, indicating that CDA could be an interesting target for fungicide development. In this work, we developed an in silico strategy based on QSAR (quantitative structure-activity relationship) and molecular topology (MT) to discover new, specific, and potent CAD inhibitors. Starting with the chemical structures of few carboxylic acids, with and without disease control activity, three predictive equations based on the MT paradigm were developed to identify a group of potential molecules. Their fungicidal activity was experimentally tested, and their specificity as CDA inhibitors was studied for the three best candidates by molecular docking simulations. To our knowledge, this is the first time that MT has been used for the identification of potential CDA inhibitors to be used against resistant powdery mildew strains. In this sense, we consider of special interest the discovery of molecules capable of stimulating the immune system of plants by triggering a defensive response against fungal species that are highly resistant to fungicides such as powdery mildew.

Keywords: QSAR; crop protection; fungicide resistance; molecular topology; pest control; pesticide design; powdery mildew.

Figure 1

Computational strategy followed for the discovery of new fungicidal hits based on MT using LDA and MLRA.

Figure 2

Example of TI values for active (A) and inactive (I) molecules in the DF₁ training set.

Figure 3

Discriminant function 1 (DF₁) fungicidal distribution diagram (FDD). Blue peaks represent the CDA inhibitor distribution, while orange peaks the inactive.

Figure 4

Example of TI values for active (fungicidal activity) and inactive (no fungicidal activity) molecules in the logInh % training set.

Figure 5

Example of TI values for active and inactive molecules in the logInh % training set.

Figure 6

Discriminant function 2 (DF₂) fungicidal distribution diagram (FDD). Blue peaks represent the distribution of CDA inhibitors, while orange peaks are the ones of non-CDA inhibitors.

Figure 7

Final selection of potential CDA inhibitors for further experimental validation based on the inhibition of P. xanthii development.

Figure 8

Fungicidal effect of the selected compounds against the cucurbit powdery mildew P. xanthii.

Figure 9

Suppression of fungicidal effects of the selected compounds in CmCERK1-silenced melon plants. (A) Suppression of powdery mildew symptoms in P. xanthii-infected CmCERK1-silenced melon plants treated with the selected compounds. (B) Reduction of oxidative burst (ROS production) after treatment with the selected compounds of CmCERK1-silenced melon plants infected with P. xanthii.

Figure 10

Molecular docking interaction between fungal CDA and potential fungicidal CDA inhibitors: VS#2-2 (A), VS#2-1(B), and VS#2-3 (C).

Figure 11

Molecular docking interaction between fungal CDA and known CDA inhibitors: EDTA, (GlcNAc)₂, lactic acid, and propionic acid.

Figure 12

New CDA inhibitors designed through the MT approach.