Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins.
J Mol Biol
; 436(17): 168613, 2024 Sep 01.
Article
en En
| MEDLINE
| ID: mdl-39237206
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/.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Proteínas Fúngicas
/
Biología Computacional
Idioma:
En
Revista:
J Mol Biol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Australia