We present AF3-TurboAb, a scalable framework that makes a repertoire-scale antibody-antigen complex structural decoding routine for antibody engineering. By eliminating preprocessing bottlenecks, AF3-TurboAb enables end-to-end complex modeling in 0.5 minutes per seed on a single GPU while preserving near-experimental interface fidelity, as validated on ~1000 posttraining Protein Data Bank (PDB) benchmarks and 12 experimentally determined cryo–electron microscopy nanobody-antigen structures. Applying this capability to 275,371 immunization-derived antigen-specific nanobodies produced 28,013 high-confidence complex predictions, substantially expanding the structural landscape of antibody recognition. The resulting atlas reveals hundreds of previously unmapped epitopes, extensive coverage of solvent-exposed surfaces, and recurrent affinity hotspots enriched in aromatic and charged residues. Despite wide sequence diversity, we observe structural convergence at shared epitopes and consistent physicochemical and geometric features that complement and extend existing PDB entries. We demonstrate translational utility by (i) designing durable (escape-proof), multiepitope neutralizers against highly evolved viruses, (ii) identifying cross-species and glycoform-specific binders to a cancer checkpoint, and (iii) enabling near-real-time in silico binder triage. The models and metadata will be shared for community use, establishing repertoire-scale structural decoding as a practical design modality that transforms the scale and speed of structure-guided antibody engineering.
Sang et al. (Fri,) studied this question.