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

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

Abstract readReview

In one paragraph

Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

TheForestry research · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

C VerdonkCentre for Crop and Disease Management, Curtin University, Perth, Australia.

K K GagalovaCentre for Crop and Disease Management, Curtin University, Perth, Australia.

S RaffaeleUniversité de Toulouse, INRAE, CNRS, Laboratoire des Interactions Plantes Micro-organismes Environnement (LIPME), Castanet-Tolosan, France.

M C DerbyshireCentre for Crop and Disease Management, Curtin University, Perth, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI structure predictionEffectorPlant pathogenPlant receptorProtein interactionProtein structure

Identifiers

PMID40687999

DOI10.1016/j.csbj.2025.06.048

PMCPMC12270735

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.