Antifungal peptides (AFPs) are emerging as promising
candidates
for advanced antifungal therapies because of their broad-spectrum
efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of
an efficient and well-validated quantitative assessment of antifungal
activity. This study introduced an AFP design approach that leverages
an innovative quantitative metric, named the antifungal index (AFI),
through a three-step process, i.e., segmentation,
single-point mutation, and global multipoint optimization. An exhaustive
search of 100 putative AFP sequences indicated that random modifications
without guidance only have a 5.97–20.24% chance of enhancing
antifungal activity. Analysis of the search results revealed that
(1) N-terminus truncation is more effective in enhancing antifungal
activity than the modifications at the C-terminus or both ends, (2)
introducing the amino acids within the 10–60% sequence region
that enhance aromaticity and hydrophobicity are more effective in
increasing antifungal efficacy, and (3) incorporating alanine, cysteine,
and phenylalanine during multiple point mutations has a synergistic
effect on enhancing antifungal activity. Subsequently, 28 designed
peptides were synthesized and tested against four typical fungal strains.
The success rate for developing promising AFPs, with a minimal inhibitory
concentration of ≤5.00 μM, was an impressive 82.14%.
The predictive and design tool is accessible at .