Deep learning-driven protein binder design for crop improvement

Deep learning (DL) now enables the end-to-end design of protein binders-proteins that bind to specific targets-to precisely modulate protein-protein interactions (PPIs). Models and tools such as BindCraft, AlphaFold, RoseTTAFold, RFdiffusion, and ProteinMPNN predict the structures of these binders and their targets, generate binder sequences, and refine their binding interfaces with increasing accuracy. Most progress so far has been in the therapeutics field, where de novo protein binders are now common targets for design and testing. In plant biology, however, applications remain early and scattered, and clear, crop-focused guidance that links design choices to practical testing is limited. This review explores the potential of protein binders by outlining an end-to-end pipeline for DL-driven protein binder design in plants. The pipeline covers target selection in key pathways for disease resistance, stress tolerance, and plant development; protein binder generation using current DL tools; and tiered in silico screening of the best binders using interface quality and stability metrics. We connect these steps to laboratory practice, including initial binding assays, production in suitable systems, and early in planta evaluation before stable transformation. Current constraints are also discussed, including uncertainty in affinity prediction and the limited structural information available for many plant proteins, as well as practical approaches to mitigate risk during target selection. Together, this plant-focused synthesis illustrates how DL-driven protein binder design can be applied to crop engineering and highlights the work still required to move from early demonstrations of success cases to robust agricultural use.