Directed Evolution of a Plant Immune Receptor for Broad Spectrum Effector Recognition.

Rapid development of immune receptors that protect crops from emerging pathogens is a critical challenge . While novel immune receptors that recognize previously undetected pathogen effectors could provide protection against a wider range of pathogens, engineering such receptors has been constrained by the low throughput and speed of testing. We established yeast surface display as a high throughput platform to recapitulate plant immune receptor-ligand interactions and evolve new binding capabilities.

Combining Directed Evolution with Machine Learning Enables Accurate Genotype-to-Phenotype Predictions.

Linking sequence variation to phenotypic effects is critical for efficient exploitation of large genomic datasets. Here we present a novel approach combining directed evolution with protein language modeling to characterize naturally-evolved variants of a rice immune receptor. Using high-throughput directed evolution, we engineered the rice immune receptor to bind and recognize the fungal proteins Avr-PikC and Avr-PikF, which evade detection by currently characterized alleles.