Peptide based crop disease control

Crop pathogens destroy up to 40% of potential yields and cost over $100B annually. Chemical control methods are expensive and time consuming to develop, and may have safety concerns. Biochemical control has the advantages of allowing for the rapid design of products with new modes of action, and of inherent safety that reduces the time and cost required for regulatory approval. The Innatrix team has previously designed a peptide that binds to a specific protein target in several oomycete species, and interferes with the pathogens’ ability to infect grapevine, potato, and tomato. We now aim to dramatically improve the efficacy, field stability, and manufacturing costs of this biological approach to pathogen control. Using machine learning and other computational methods, we will identify new protein targets in oomycete pathogens that are essential for infection. We will use a CRISPR gene knockout approach to confirm that each putative target is in fact essential. Using machine learning-assisted computer modeling, we will design peptides to bind specifically to the targets. We will emphasize cyclic peptides, which are expected to require fewer sprayings per season because of improved stability compared to linear peptides. We will use greenhouse experiments to confirm efficacy and stability, and laboratory experiments to confirm the mode of action. Simultaneously we will develop a low-cost method of manufacturing cyclic peptides.