Construction of antimicrobial peptide-drug combination networks from scientific literature based on a semi-automated curation workflow.

Considerable research efforts are being invested in the development of novel antimicrobial therapies effective against the growing number of multi-drug resistant pathogens. Notably, the combination of different agents is increasingly explored as means to exploit and improve individual agent actions while minimizing microorganism resistance. Although there are several databases on antimicrobial agents, scientific literature is the primary source of information on experimental antimicrobial combination testing. This work presents a semi-automated database curation workflow that supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Currently, the database contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms. Likewise, researchers may look into indirect network associations and identify new potential combinations to be tested. The database is available without charges.Database URL: http://sing.ei.uvigo.es/antimicrobialCombination/.