The transformative power of structural predictions with AI in plant science Structural predictions with AI in plant science Joy Chenqu Lyu and Renier A. L. van der Hoorn

Abstract

Title: SUMMARY Since the introduction of various structural prediction programs, the emerging transformative power of these technologies in plant science is apparent. Not only programs like AlphaFold but also RoseTTAFold, Chai-1 and Boltz suddenly enable plant scientists to predict structures with high confidence. This ability has facilitated the discovery of novel protein functions inspired by structural homology and provided novel insights into how proteins evolved from ancestral folds. Prediction of protein oligomers and their interactions with lipids was crucial for studying immune receptors that assemble into resistosomes, while prediction of peptide-protein interactions has enabled the engineering of broad-range cell surface receptors. In silico screens for novel protein interactions identified novel autophagy receptors and inhibitors of immune hydrolases. More discoveries will soon follow with the development of new tools to predict and analyse structures. These and many other recent discoveries highlight the transformative power of structural predictions with artificial intelligence in plant science.