Employing AI-guided approaches to discover and engineer plant disease resistance proteins

Plant diseases threaten our food supply. Pathogens can reduce crop yields by 30%, costing
hundreds of billions of dollars. Losses could feed billions. Global trade, climate change, and plant
pathogens' tendency to reduce disease resistance have increased plant disease outbreaks.
Population is expected to reach 9 billion by 2050, and food production must increase by 70% to
prevent global hunger.

Here we propose an AI-guided synthetic biology approach to discover and engineer plant immune
receptors with new disease resistance gene specificities. To this end we formed a partnership with
Resurrect bio, to implement an AI-guided pipeline combined with our expertise in plant
immunology. We will first establish an automated machine learning pipeline to generate digital
libraries of ligand-receptor complexes from plant-pathogen genomes. Next, ligands of interest will
be screened using AF2-multimer to identify candidate receptors, which will be subsequently
validated through genetic and biochemical approaches. We will then transform individual
receptors into plant species that lack them, as a proof of concept for engineering disease
resistance. However, some pathogens encode virulent alleles of the ligands of interest, enabling
them to avoid immune recognition. We will identify such ligands from genomes of the virulent
pathogen strains through genomic surveys and make necessary mutations in the corresponding
receptor interfaces to expand their resistance spectrum. Our business partners, Resurrect Bio,
specialized to discover and amend defeated disease resistance traits will then employ genome-
editing approaches to introduce these traits in the crops of interest