AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria - Shao, Jonathan; Zhao, Yan; Wei, Wei; Vaisman, Iosif I. (Frontiers in Microbiology, 2024)
Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models based on N-gram representations of peptide sequences and reduced amino acid alphabet to predict and select AMPs active against plant pathogenic bacteria. The developed models are evaluated using a cross-validation technique on the training set, along with an independent validation set. The prediction accuracies of the reported models range from 0.72 to 0.91, demonstrating a promising way to accurately identify AMPs. The models are applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. A subset of the predicted AMPs is selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models. To facilitate broader accessibility our model is publicly available on the AGRAMP (Agricultural N-grams Antimicrobial Peptides) server. The described models would contribute to the development of effective AMP based strategies for plant disease management in agricultural and environmental settings.