A network perspective on antimicrobial peptide combination therapies: the potential of colistin, polymyxin B and nisin

- Antimicrobial combinations involving polymyxin B, colistin (polymyxin E) or nisin are described.
- Combination reconstruction relies on literature mining and manual expert curation.
- Network analysis enables a bird's-eye view of current research trends.
- The networks are available at http://sing-group.org/antimicrobialCombination/.

Antimicrobial combinations involving antimicrobial peptides (AMPs) attract considerable attention within current antimicrobial and anti-resistance research. The objective of this study was to review the available scientific literature on the effects of antimicrobial combinations involving colistin (polymyxin E), polymyxin B and nisin, which are US Food and Drug Administration (FDA)-approved AMPs broadly tested against prominent multidrug-resistant pathogens. A bioinformatics approach based on literature mining and manual expert curation supported the reconstruction of experimental evidence on the potential of these AMP combinations, as described in the literature. Network analysis enabled further characterisation of the retrieved antimicrobial agents, targets and combinatory effects. This systematic analysis was able to output valuable information on the studies conducted on colistin, polymyxin B and nisin combinations. The reconstructed networks enable the traversal and browsing of a large number of agent combinations, providing comprehensive details on the organisms, modes of growth and methodologies used in the studies. Therefore, network analysis enables a bird's-eye view of current research trends as well as in-depth analysis of specific drugs, organisms and combinatory effects, according to particular user interests. The reconstructed knowledge networks are publicly accessible at http://sing-group.org/antimicrobialCombination/. Hopefully, this resource will help researchers to look into antimicrobial combinations more easily and systematically. User-customised queries may help identify missing and less studied links and to generate new research hypotheses.