AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria.

ABSTRACT:

Introduction

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 for predicting and selecting AMPs active against plant pathogenic bacteria.

Methods

N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluat

SUBMITTER: Shao J 

PROVIDER: S-EPMC10955071 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature