Biomedicine Discovery Institute, Monash University, VIC 3800, Australia; Centre to Impact AMR, Monash University, VIC 3800, Australia.
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.
J Mol Biol. 2024 Sep 1;436(17):168613. doi: 10.1016/j.jmb.2024.168613. Epub 2024 May 20.
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/.
真菌病原体通过分泌操纵植物-宿主防御的效应子对植物健康构成重大威胁。然而,识别效应子蛋白仍然具有挑战性,部分原因是它们缺乏常见的序列基序。在这里,我们引入 Fungtion(真菌效应子预测),这是一个利用混合框架来准确预测和可视化真菌效应子的工具包。通过将从预训练的蛋白质语言模型中学习到的全局模式与来自已知效应子的精炼信息相结合,Fungtion 实现了最先进的预测性能。此外,我们开发的交互式可视化功能使研究人员能够探索预测和已知效应子之间的序列和高级关系,促进效应子功能的发现、注释和关于植物-病原体相互作用的假设制定。我们预计 Fungtion 将成为寻求深入了解真菌效应子功能的生物学家和旨在开发未来真菌效应子预测方法的计算生物学家的宝贵资源:https://step3.erc.monash.edu/Fungtion/。