Title : A new approach that focuses on the basal immunity mechanism to slow down the development of antimicrobial resistance in plants
Abstract:
Antimicrobial resistance (AMR) has been on the rise in both plants and animals. A continuous use of antimicrobial agents such as pesticides against plant pathogens creates selection pressure for the pathogens to mutate and evade the toxic effect. It prompts more usage of the pesticides to ensure crop productivity, which results in a vicious cycle of higher pesticide doses and microbial survival. The ever-increasing amount and number of pesticides released into the environment has put a question mark on the environmental and health safety of the practice. By using a bioinformatics approach, we developed a pipeline that can identify peptides from a large proteome database potentially displaying properties of innate immunity. The latter is a basal form of defense that provides the first layer of protection. We analyzed pea proteomics data to generate possible fragments with 18 amino acids. For the pipeline, different algorithms or programs were combined. Duplicated fragments were removed by using the CD-Hit algorithm. The sequences were evaluated through five antimicrobial activity predictors: sense the moment, DBAASP, CAMP, ADAM and AxPep. The positively charged residue distribution, α-helix formation, and hydrophobicity of selected peptides were manually evaluated to further reduce the number. The top ten candidates from the curated list were evaluated for their toxicity against fungal, oomycete, and bacterial pathogens, which are the causative agents for many diseases in commercial crops. Depending on the pathogen, the peptides showed IC50 values in the range of 1–20 μM, which could be suitably deployed to manage the diseases through eco-friendly means.
