Learning the language of plant immunity: opportunities and challenges for AI-assisted modelling of fungal effector x host protein complexes.

IF 4.4 2区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Computational and structural biotechnology journal Pub Date : 2025-07-01 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.06.048

C Verdonk, K K Gagalova, S Raffaele, M C Derbyshire

{"title":"Learning the language of plant immunity: opportunities and challenges for AI-assisted modelling of fungal effector x host protein complexes.","authors":"C Verdonk, K K Gagalova, S Raffaele, M C Derbyshire","doi":"10.1016/j.csbj.2025.06.048","DOIUrl":null,"url":null,"abstract":"<p><p>Phytopathogenic fungi cause substantial crop losses worldwide. They secrete proteins called effectors, which enable infection through interactions with diverse host proteins. These interactions are fundamentally important to plant disease and its practical control. New artificial intelligence (AI) techniques can predict many individual protein structures to near experimental accuracy. Although these techniques also predict protein complexes, they are not as accurate as single-protein models. Use of AI to study interactions between fungal pathogen effectors and plant proteins is currently limited. However, despite some challenges, early adoption of AI has highlighted its potential. General improvements in AI-assisted protein complex modelling may create more opportunities in future. This review focuses on recent research using AI to study the interactions between fungal effectors and plant proteins, outlining challenges and emerging opportunities.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2881-2889"},"PeriodicalIF":4.4000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270735/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2025.06.048","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Phytopathogenic fungi cause substantial crop losses worldwide. They secrete proteins called effectors, which enable infection through interactions with diverse host proteins. These interactions are fundamentally important to plant disease and its practical control. New artificial intelligence (AI) techniques can predict many individual protein structures to near experimental accuracy. Although these techniques also predict protein complexes, they are not as accurate as single-protein models. Use of AI to study interactions between fungal pathogen effectors and plant proteins is currently limited. However, despite some challenges, early adoption of AI has highlighted its potential. General improvements in AI-assisted protein complex modelling may create more opportunities in future. This review focuses on recent research using AI to study the interactions between fungal effectors and plant proteins, outlining challenges and emerging opportunities.

查看原文本刊更多论文

学习植物免疫语言：真菌效应物x宿主蛋白复合物人工智能辅助建模的机遇和挑战

植物病原真菌在世界范围内造成了大量的作物损失。它们分泌一种叫做效应物的蛋白质，这种蛋白质通过与不同的宿主蛋白质相互作用而使感染成为可能。这些相互作用对植物病害及其实际控制至关重要。新的人工智能（AI）技术可以以接近实验的精度预测许多单个蛋白质结构。虽然这些技术也可以预测蛋白质复合物，但它们不如单蛋白质模型准确。利用人工智能研究真菌病原体效应物与植物蛋白之间的相互作用目前是有限的。然而，尽管存在一些挑战，人工智能的早期采用突显了它的潜力。人工智能辅助蛋白质复合物建模的总体改进可能会在未来创造更多的机会。本文综述了近年来利用人工智能研究真菌效应物与植物蛋白之间相互作用的研究，概述了面临的挑战和新的机遇。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

9.30

自引率

3.30%

发文量

540

审稿时长

6 weeks

期刊介绍： Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology