Co-folding model guided by structural proteomics

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. Across four PROTAC ternary complexes and one antibody-antigen complex, guidance recovers conformations that the unguided model does not reach, even when generating a hundred unguided models. 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.