Membrane proteins govern essential cellular processes, including ion transport, signal transduction, and molecular recognition, and collectively represent more than half of all current therapeutic targets. Yet their structural characterization remains challenging due to intrinsic instability, amphipathic surfaces, and conformational heterogeneity. Over the past decade, antibody-based approaches, spanning full-length immunoglobulins, antigen-binding fragments (Fabs), nanobodies, and engineered scaffolds such as designed ankyrin repeat proteins (DARPins), have transformed structural biology by stabilizing dynamic states, augmenting molecular weight for cryo-electron microscopy (cryo-EM), and enabling visualization of previously inaccessible complexes. In parallel, advances in artificial intelligence and machine learning have begun to enhance predictive modeling, accelerate structure determination, and guide rational design of protein-ligand and antibody–antigen interactions. This review examines how antibody engineering and AI-driven computation together are reshaping the landscape of structural biology and therapeutic discovery.
Ashraf et al. (Fri,) studied this question.