Allelic variants of the NLR protein Rpi-chc1 differentially recognise members of the<i>Phytophthora infestans</i>PexRD12/31 effector superfamily through the leucine-rich repeat domain

M, Lopez; Nijenhuis,; Kodde,; Kamoun, Sophien; Salehian,; Schenstnyi,; Stam,; Lokossou,; Haliem, Abd'el; Visser, Joost; Vossen,

doi:10.1101/2020.12.29.424692

Cited by 1 publication

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“…NLR and effector protein sequences, from 93 known interactions previously identified by Kourelis et al (2021), were downloaded from the Uniprot online database chacoense (Rpi-chc1.2_543-5; QZA82918.1) and S. tuberosum (rpi-tub1.3_RH89-039-16; QZA82920.1) were downloaded from Uniprot. Amino acids in these sequences were manually altered according to experimentally tested mutations from Monino-Lopez et al, (2021) Additionally, two P. infestans PexRD31 sequences (XP_002897637 and XP_002897638) were used for investigating interactions with Rpi-chc1.2 and rpi-tub1.3 (Monino-Lopez et al, 2021).…”

Section: Nlr and Effector Sequencesmentioning

confidence: 99%

What NLR you recognizing? Predicted binding affinities- and energies may be used to identify novel NLR-effector interactions

Fick,

Swart

et al. 2024

Preprint

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Nucleotide binding Leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity in plants following pathogen infection. Advances in genome sequencing have led to the identification of a myriad NLRs in numerous agriculturally important plant species. However, deciphering which NLR proteins recognize specific pathogen effectors remains a challenge. Predicting NLR-effector interactions in silico would provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using Alphafold2-Multimer for all experimentally validated NLR-effector interactions. Binding affinities- and energies were predicted using 97 machine learning models from Area-affinity. We show that predicted structures with an AlphaFold confidence score > 0.42 have acceptable accuracy, and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol, depending on the Area-Affinity model used. For 2427 forced NLR-effector complexes, these estimates showed larger variability, enabling the identification of novel NLR-effector complexes with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for true interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all plant-pathogen interactions. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for providing resistance against plant pathogens, advancing our understanding of plant immunity.

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Section: Nlr and Effector Sequencesmentioning

confidence: 99%