Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins

Area of Science:

• Plant pathology
• Mycology
• Bioinformatics

Background:

• Fungal pathogens threaten plant health by secreting effector proteins.
• Identifying these effectors is difficult due to a lack of conserved sequence motifs.

Purpose of the Study:

• Introduce Fungtion, a novel toolkit for predicting and visualizing fungal effectors.
• Facilitate the discovery, annotation, and hypothesis generation of effector functions.

Main Methods:

• Developed a hybrid framework combining protein language models and known effector data.
• Implemented state-of-the-art prediction algorithms.
• Created interactive visualizations for exploring effector relationships.

Main Results:

• Achieved state-of-the-art prediction performance for fungal effectors.
• Enabled exploration of sequence- and high-level relationships between predicted and known effectors.
• Provided a valuable resource for biological and computational research.

Conclusions:

• Fungtion is a powerful tool for identifying fungal effectors and understanding plant-pathogen interactions.
• The toolkit supports deeper insights into fungal effector functions.
• It serves as a resource for developing future effector prediction methodologies.