Folding scFv--Antigen Complexes at Scale

Accurate modeling of antibody-antigen (Ab-Ag) complexes is central to biologic development, yet the reliability and failures of modern Ab-Ag folding pipelines remain poorly characterized. Single-chain variable fragments (scFvs) are therapeutically important antibodies, but large-scale evaluations of structure prediction models on scFv-Ag complexes are largely lacking. We introduce a scalable benchmarking pipeline that generates large ensembles of scFv-Ag structure predictions by cofolding a curated subset of 3,800 Ab-Ag complexes from SAbDab using multiple state-of-the-art models under diverse inference-time settings. The resulting dataset, SCALE (scFv-Ag CompLex Ensembles) includes standardized scFv-Ag sequences and around 200,000 predicted complexes spanning different models, sampling strategies, and auxiliary inputs. Using SCALE, we evaluate model performance in recovering correct scFv-Ag interfaces and assess the ability of existing confidence metrics to select the best structure from prediction ensembles. We find that while confidence scores effectively distinguish easy from hard scFv-Ag complexes, they often fail to identify the highest-quality interface for a given target. Further analysis shows that near-correct interfaces typically appear in ensembles but at low frequency, and inference-time choices like sampling, recycling, and using evolutionary or structural information are crucial for accurate scFv-Ag complex predictions. Dataset and analysis code are available at https://huggingface.co/datasets/ravishah1/SCALE