Beyond Static Structures: New Opportunities to Model the Dynamic Interactions of Cell‐Surface Proteins

Recent advances in AI-driven structure prediction have transformed structural biology but remain limited for cell-surface proteins mediating transient, context-dependent interactions. Multidomain architectures, flexible linkers, and coupled cis- and trans-interactions challenge current models, leading to inaccurate predictions for key adhesion molecules. These limitations highlight the need to incorporate cellular context, conformational dynamics, and multivalent interactions. Emerging strategies, including MD-informed machine learning, generative models, and multiscale simulations integrated with experimental restraints, offer promising solutions. Together, these approaches aim to bridge static structure prediction with dynamic, physiologically relevant modeling of protein interactions at cell-cell interfaces.