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Updated: Jun 14, 2025

Unravelling the Function of a Bacterial Effector from a Non-cultivable Plant Pathogen Using a Yeast Two-hybrid Screen
Published on: January 20, 2017
Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins
Jiahui Li1, Jinzheng Ren2, Wei Dai1
1Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.
Abstract:
Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/.
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