Folding scFv–Antigen Complexes at Scale
TL;DR
A scalable bench-marking pipeline is introduced 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 and evaluating model performance in recovering correct scFv–Ag interfaces.
Abstract
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 thera-peutically important antibodies, but large-scale evaluations of structure prediction models on scFv–Ag complexes are largely lacking. We introduce a scalable bench-marking 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 such as 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