Fungtion: A Server for Predicting and Visualizing Fungal Effector Proteins.

fungal effector prediction machine learning pre-trained protein language models protein sequence analysis web server

Journal

Journal of molecular biology
ISSN: 1089-8638
Titre abrégé: J Mol Biol
Pays: Netherlands
ID NLM: 2985088R

Informations de publication

Date de publication:
01 Sep 2024
Historique:
received: 21 02 2024
revised: 11 05 2024
accepted: 13 05 2024
medline: 6 9 2024
pubmed: 6 9 2024
entrez: 5 9 2024
Statut: ppublish

Résumé

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/.

Identifiants

pubmed: 39237206
pii: S0022-2836(24)00208-0
doi: 10.1016/j.jmb.2024.168613
pii:
doi:

Substances chimiques

Fungal Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

168613

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jiahui Li (J)

Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.

Jinzheng 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.

Wei Dai (W)

Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.

Christopher Stubenrauch (C)

Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.

Robert D 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.

Jiawei 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: jwang@ebi.ac.uk.

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Classifications MeSH