AI-enabled protein design facilitates future plant research and crop breeding

Artificial intelligence (AI) is poised to reshape the research paradigm of the life sciences by rapidly advancing the adoption of protein language models and their derivative tools. These technologies are increasingly being applied to protein structure prediction, function analysis, and protein design throughout the life sciences, and have only recently begun to gain attention within the plant science community. Moreover, while the era of AI-driven bio-breeding is on the horizon, it remains largely in the proof-of-concept stage. Therefore, there is a pressing need not only to outline the fundamental principles, models, and tools in this rapidly evolving field, but also to explore their potential applications in plant research and crop breeding. This review begins by introducing general principles and widely used models for protein understanding and generation, supported by illustrative case studies that highlight how these tools are advancing fundamental plant research. For instance, the analyses of 2 maize (Zea mays) genes demonstrate how a structure-aware interpretation of the relationships between mutations and protein function enables more precise hypothesis generation and facilitates experimental validation. Subsequently, the review presents generic AI-enabled protein engineering strategies and pipelines, including rational, semi-rational, refactoring, and de novo design, tailored to diverse protein engineering objectives. These approaches aim to create artificial variants and synthetic proteins with improved or novel functions to foster innovation in crop breeding. Finally, the significant challenges of applying protein design in plants are discussed, particularly in light of the limited availability of experimentally resolved protein structures and the inherent complexity of plant biological systems.