Identifying antimicrobial peptides in genomes using machine learning

Legana Fingerhut used machine learning to improve predictions of antimicrobial peptides (AMPs) from protein sequences. Her associated framework was the first to specifically address the problem of identifying AMPs from whole-genome data. Her work leads to improved workflows for identifying novel AMPs which advances our understanding of the innate immune system

ampir: an R package for fast genome-wide prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are the key components of the innate immune system that protect against pathogens, regulate the microbiome and are promising targets for pharmaceutical research. Computational tools based on machine learning have the potential to aid discovery of genes encoding novel AMPs but existing approaches are not designed for genome-wide scans. To facilitate such genome-wide discovery of AMPs we developed a fast and accurate AMP classification framework, ampir. ampir is designed for high throughput, integrates well with existing bioinformatics pipelines, and has much higher classification accuracy than existing methods when applied to whole genome data. Availability and implementation ampir is implemented primarily in R with core feature calculation methods written in C++. Release versions are available via CRAN and work on all major operating systems. The development version is maintained at https://github.com/legana/ampir