bioRxivMolecular BiologyPreprint
Receptor-ligand interactions in plant inmate immunity revealed by AlphaFold protein structure prediction
Li Wang, Yulin JiaiD, Osakina Aron, Kenneth M. OlseniD, Yixiao HuangiD, Melissa H. JiaiD, Sathish K. PonniahiD, Rodrigo PedrozoiD, Camila Primieri NicolliiD, Jeremy D. EdwardsiD
Abstract
One of the common mechanisms to trigger plant innate immunity is recognition of pathogen avirulence gene products directly by products of major resistance (R) genes in a gene for gene manner. In the USA, the R genes, Pik-s, PiKh/m, and Pi-ta, Pi-39(t), and Ptr genes have been effectively deployed to prevent the infections of M. oryzae races, IB49, and IC17 for some time. Pi-9 is only recently being deployed to provide overlapped and complimentary resistance to Magnaporthe oryzae races IB49, IC17 and IE1k in the USA. Pi-ta, Pi-39(t), Pi9 are major nuclear binding site-leucine rich (NLR) proteins, and Ptr is an atypical R protein with 4 armadillo repeats. AlphaFold is an artificial intelligence system that predicts a protein 3D structure from its amino acid sequence. Here we report genome sequence analyses of the effectors and avirulence (AVR) genes, AVR-Pita and AVR-Pik, and AVR-Pi9, in 3 differential M. oryzae races. Using AlphaFold 2 and 3 we find strong evidence of direct interactions of products of resistance genes Pi-ta and Pik with M. oryzae avirulence (AVR) genes, AVR-Pita and AVR-Pik respectively. We also found that AVR-Pita interacts with Pi-39(t) and Ptr, and Pi9 interacts with both AVR-Pi9 and AVR-Pik. Validation of direct interactions of two pairs of R and AVR proteins supported a direct interaction mechanism of plant innate immunity. Detecting interaction of both Ptr and Pi39(t) with AVR-Pita, and Pi-9 with both AVR-Pi9 and AVR-Pik, revealed a new insight into recognition of pathogen signaling molecules by these host R genes in triggering plant innate immunity.
- organism: plant (other)
- assay: protein structure
- research focus: protein structure prediction
- research focus: cell-cell communication
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 8
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