# AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface

Article · Homma F, Huang J, van der Hoorn RAL

- Archive id: doi:10.1038/s41467-023-41721-9
- Published: 2023-09-27
- Venue: Nature communications
- DOI: https://doi.org/10.1038/s41467-023-41721-9
- PMID: 37758696
- PMCID: PMC10533508
- Citations: 151
- Source URL: https://doi.org/10.1038/s41467-023-41721-9
- PDF: https://europepmc.org/articles/PMC10533508/pdf
- Provenance: europepmc

## Abstract

Adapted plant pathogens from various microbial kingdoms produce hundreds of unrelated small secreted proteins (SSPs) with elusive roles. Here, we used AlphaFold-Multimer (AFM) to screen 1879 SSPs of seven tomato pathogens for interacting with six defence-related hydrolases of tomato. This screen of 11,274 protein pairs identified 15 non-annotated SSPs that are predicted to obstruct the active site of chitinases and proteases with an intrinsic fold. Four SSPs were experimentally verified to be inhibitors of pathogenesis-related subtilase P69B, including extracellular protein-36 (Ecp36) and secreted-into-xylem-15 (Six15) of the fungal pathogens Cladosporium fulvum and Fusarium oxysporum, respectively. Together with a P69B inhibitor from the bacterial pathogen Xanthomonas perforans and Kazal-like inhibitors of the oomycete pathogen Phytophthora infestans, P69B emerges as an effector hub targeted by different microbial kingdoms, consistent with a diversification of P69B orthologs and paralogs. This study demonstrates the power of artificial intelligence to predict cross-kingdom interactions at the plant-pathogen interface.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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