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Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins.
Li, Jiahui; Ren, Jinzheng; Dai, Wei; Stubenrauch, Christopher; Finn, Robert D; Wang, Jiawei.
Afiliación
  • Li J; Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.
  • Ren J; Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia; College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2600, Australia.
  • Dai W; Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.
  • Stubenrauch C; Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.
  • Finn RD; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Electronic address: rdf@ebi.ac.uk.
  • Wang J; Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. Electronic address: jwa
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/.
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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

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