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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
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Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine
Erica T Prates1, Omar Demerdash1, Manesh Shah2
1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Computational and Structural Biotechnology Journal
|July 18, 2025
Summary
A new hybrid molecular dynamics/machine learning method accurately predicts binding affinities for large plant signaling molecules like lipo-chitooligosaccharides (LCOs). This advances understanding of plant-microbe interactions and symbiosis.
Area of Science:
- Plant science
- Microbiology
- Computational biology
Background:
- Microbiome assembly influences plant health.
- Microbial signaling molecules mediate plant symbiosis.
- Lipo-chitooligosaccharides (LCOs) are recognized by plant Lysin Motif Receptor-Like Kinases (LysM-RLKs).
Purpose of the Study:
- To develop a predictive method for molecular interactions between large, flexible ligands and plant receptors.
- To overcome challenges in structural characterization and binding affinity prediction for LCOs.
- To advance understanding of endosymbiosis initiation.
Main Methods:
- Developed a hybrid molecular dynamics/machine learning (MD/ML) approach.
- Utilized coarse initial structural models.
- Employed MD-based conformation selection protocol.
Main Results:
- Achieved high accuracy in predicting binding affinity rankings for large, flexible ligands.
- Demonstrated strong alignment with experimental trends for legume LysM-RLKs binding LCOs.
- Provided structural insights into substrate specificity and binding mechanisms.
Conclusions:
- The MD/ML workflow is a powerful method for screening challenging ligand-receptor pairs.
- This approach advances understanding of the molecular basis of plant microbial colonization.
- Enables prediction of molecular interactions crucial for plant health and symbiosis.
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