A uniform in vitro efficacy dataset to guide antimicrobial peptide design

Abstract

Antimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data is now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms spanning gram positive, gram negative, fungal, and mycobacterial origin. We draw inferences from the results of 600 individual MIC assays, and discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.