Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship

Antifungal
peptides are effective, biocompatible, and biodegradable,
and thus, they are promising to be the next generation of drugs for
treating infections caused by fungi. The identification processes
of highly active peptides, however, are still time-consuming and labor-intensive.
Quantitative structure–activity relationships (QSARs) have
dramatically facilitated the discovery of many bioactive drug molecules
without a priori knowledge. In this study, we have
established an effective QSAR protocol for screening antifungal peptides.
The screening protocol integrates an accurate antifungal peptide classification
model and four activity prediction models against specified target
fungi. A demonstrative application was performed on more than three
million candidate peptides, and three outstanding peptides were identified.
The whole screening took only a few days, which was much faster than
our previous experimental screening works. In conclusion, the protocol
is useful and effective for reducing repetitive laboratory efforts
in antifungal peptide discovery. The prediction server (antifungal
Web server) is freely available at www.chemoinfolab.com/antifungal.