Nature CommunicationsPlant BiologyPeer-reviewed journal article
β-sheet stabilization of the island domain underlies ligand-induced LRR-RP activation of plant immune signaling
Simon SnoeckiD, Lisha ZhangiD, Valentin StuderiD, Gijeong KimiD, Álvaro D. Fernández-FernándeziD, Thorsten NürnbergeriD, Cyril ZipfeliD
Abstract
Leucine-rich repeat (LRR) receptor kinases (RKs) and receptor proteins (RPs) are important classes of plant pattern recognition receptors (PRRs) activating pattern-triggered immunity. While both classical and AI-based structural approaches have recently provided crucial insights into ligand-LRR-RK binding mechanisms, our understanding of ligand perception by LRR-RPs remains limited. In contrast to LRR-RKs, many LRR-RPs typically embed one or more loopout regions in their extracellular domains that are crucial for functionality. Here, we employed an AI-based approach to reveal a novel ligand-binding mechanism shared by the Arabidopsis LRR-RPs RLP23 and RLP42 - the PRRs for the short peptide ligands nlp20 and pg13, derived from NECROSIS- AND ETHYLENE-INDUCING PEPTIDE 1-like proteins (NLPs) and fungal endopolygalacturonases (PGs), respectively. This mechanism relies on a {beta}-strand interaction with the N-terminal part of the island domain (ID) loopout, which adopts an antiparallel {beta}-sheet conformation. Additionally, we investigated the larger and more complex binding interface of RLP32 - the PRR for proteobacterial TRANSLATION INITIATION FACTOR 1 (IF1), a folded protein ligand that requires its tertiary structure for recognition. Finally, we describe a mechanistic role of the ID for co-receptor recruitment conserved across LRR-RPs. Together, our results shed light on the ligand-binding mechanisms and receptor complex formation of this important class of PRRs. This opens avenues for a molecular understanding of the plant-pathogen co-evolution, as well as the engineering of plant immune receptors for crop disease resistance.
- organism: Arabidopsis
- assay: structural biology
- research focus: host-pathogen & immune response
- approach: deep learning
- Citations
- 5
- FWCI
- 2.69
- Field percentile
- top 10%
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