Related Experiment Video
Updated: May 9, 2025
10:19
Screening and Identification of RNA Silencing Suppressors from Secreted Effectors of Plant Pathogens
Published on: February 3, 2020
6.2K
In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.
Alicia Fick1,2, Jacobus Lukas Marthinus Fick3, Velushka Swart1,2
1Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, Gauteng, South Africa.
The Plant Journal : for Cell and Molecular Biology
|April 30, 2025
Summary
Predicting plant Nucleotide-binding leucine-rich repeat (NLR) protein interactions with pathogen effectors is now possible. This study developed a computational method to identify key NLRs for plant immunity, aiding resistance strategies.
Area of Science:
- Plant molecular biology
- Computational biology
- Immunology
Background:
- Nucleotide-binding leucine-rich repeat (NLR) proteins are vital for plant immunity.
- Identifying specific NLR-effector interactions is crucial but challenging.
- Advancing plant defense mechanisms requires understanding NLR function.
Purpose of the Study:
- To develop and validate an in silico method for predicting plant NLR-effector interactions.
- To identify novel NLR-effector interactions with high accuracy.
- To create a resource for streamlining plant immunity research.
Main Methods:
- Predicted NLR-effector complex structures using AlphaFold2-Multimer.
- Assessed binding affinities and energies with 97 machine learning models.
- Developed an Ensemble machine learning model for novel interaction prediction.
Main Results:
- AlphaFold2-Multimer structures showed acceptable accuracy for NLR-effector interactions.
- Validated interactions had specific binding affinity and energy ranges.
- Identified novel NLR-effector interactions with 99% accuracy.
Conclusions:
- The developed in silico method accurately predicts NLR-effector interactions across pathosystems.
- Specific binding energy changes are likely required for NLR activation.
- The NLR-Effector Interaction Classification (NEIC) resource will accelerate plant immunity research.
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