Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure-activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.
Keywords: antimicrobial peptides; database screening; databases; design; machine learning methods; prediction.
Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.