Peptides with antimicrobial activity can be a good resource to tackle antimicrobial resistance in plants and animals. A case study with peptides identified from the Pisum sativum L. protein database

Antimicrobial resistance has been on the rise in both plants and animals. A continuous use of antimicrobial agents (AMA) such as pesticides and antibiotics against pathogens creates selection pressure for the pathogens to mutate and evade the toxic effect. It prompts more usage of the AMA for their effectiveness, which results in a vicious cycle of higher doses, mutations, and microbial survival. The ever-increasing amount and number of pesticides released into the environment have put a question mark on the environmental and health safety of the practice. Similarly, in animals the reliance alone on antibiotics may not be sustainable. By using a bioinformatics approach, we developed a pipeline that can identify peptides from a large proteome database that potentially display 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 crops and animals. Depending on the pathogen, the peptides showed IC50 values in the range of 1–20 µM.