FungiGuard: identification of plant antifungal peptides with artificial intelligence
Year: 2026
Type: article
Abstract: Antifungal peptides (AFPs) are crucial for plant defense against biotic stress. Yet, no artificial intelligence tool specifically classifies plant AFPs. To fill this gap, we develop FungiGuard, which integrates Random Forest, Long Short-Term Memory, and attention mechanisms to identify AFPs using functionally annotated plant small peptides. FungiGuard outperforms existing generalized AFP model in classifying plant AFPs, and detects candidate AFPs in Arabidopsis, wheat, rice, and maize. It also discovers novel AFPs through randomly generated sequences. Experimental validation confirms the antifungal activity of candidate AFP against Botrytis cinerea. This tool deepens plant AFP understanding and facilitates novel AFP discovery.
Source: Genome biology
Authors: Xiang Li, Yitian Fang, You Wu, Xiang Yu
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Microbiology
Field: Immunology and Microbiology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $5,490
OpenAlex ID: W7127978219