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 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 () is freely available at www.chemoinfolab.com/antifungal.
References
Nucleic Acids Res. 2021 Jan 8;49(D1):D288-D297
[PMID: 33151284]
Anal Chim Acta. 2021 Jan 15;1142:169-178
[PMID: 33280694]
