Overall performance and taxonomic bias of antimicrobial peptide prediction tools in invertebrates

Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here, we tested if there is a taxonomic bias in prediction power in 8 tools with a total of 15 prediction algorithms, in 19 invertebrate taxa. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups, based on which we provide guidance in choosing the adequate prediction tool for specific taxa.