1Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia2Department of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
Checking for direct PDF access through Ovid
Motivation:Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.Results:We compiled two sets of AMP and non-AMP peptides, separated into three categories—antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools—ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP—we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks.Contact:email@example.com or firstname.lastname@example.orgSupplementary information:Supplementary data are available at Bioinformatics online.