论文库

原文摘要

Proximity-inducing drugs, including PROTACs, represent a promising therapeutic modality. Their activity is driven by the geometry of the assembled ternary complex, which determines whether the target is ubiquitinated. Co-folding models perform poorly in these cases due to scarcity of training data and the dynamic nature of such complexes. Structural proteomics can supply the missing geometric information and steer the model towards an accurate prediction. However, current models accept only positive signals, while a differential structural mass-spectrometry experiment also reports negative signals. We introduce AIMS-Fold, an inference-time guidance layer built on Boltz-2 that requires no retraining and accepts repulsive constraints alongside attractive ones. We tested AIMS-Fold on 28 PROTAC, glue and antibody complexes that Boltz-2 fails to predict. From only five generated models, guidance recovers the correct conformation in 64% of cases. The gap does not close with scale: across 100 models and 20 seeds, unguided Boltz-2 reaches 14% while AIMS-Fold reaches 86%. Repulsive constraints alone recover the correct conformation on some systems, occasionally exceeding the matched attractive constraints. Taken together, these results demonstrate the strength of utilizing the full structural proteomics signal, positive and negative, to steer generative models toward the correct conformation, addressing a central problem in rational degrader design.