Translational potential of AI-driven de novo protein binders for crop protection and improvement

The agricultural sector faces mounting challenges from climate change and crop losses caused by biotic and abiotic stresses, with traditional breeding and chemical controls offering limited solutions. Advances in artificial intelligence (AI) have enabled the de novo design of protein binders exhibiting high specificity and stability, presenting new opportunities for crop protection and stress tolerance. Here we review core AI-driven methodologies—including diffusion models and protein language models—that facilitate the rational design of mini-proteins targeting pathogen effectors and plant immune components, demonstrating potential for precise molecular interventions to enhance disease resistance and abiotic stress resilience. These approaches have the potential to simplify or navigate differently the regulatory complexities often associated with transgenic organisms. While traditional delivery systems still facing a critical challenge, emerging cell-penetrating peptide (CPP) systems offer a non-viral, targeted solution for protein translocation, potentially simplifying regulatory pathways by enabling transient expression without genomic integration. While promising, successful agricultural application requires expanding plant-specific structural data and developing compatible delivery systems. This emerging paradigm integrates computational protein design with plant biotechnology, offering a transformative strategy for sustainable crop improvement amid global food security challenges.