Co-folding model guided by structural proteomics

Researchers have developed AIMS-Fold, a guided-diffusion framework that integrates structural proteomics data from XL-MS and HDX-MS to improve protein complex structure prediction. The model outperforms purely computational state-of-the-art models like Boltz-2 on challenging induced proximity targets, offering a powerful approach for structure-based drug design of antibodies and PROTACs.

arXiv:2605.26192v1 Announce Type: new Abstract: Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conformational state of protein complexes, critical for protein design and induced proximity modalities such as antibodies and PROTACs. While structural proteomics techniques like Cross-Linking Mass Spectrometry XL-MS and Hydrogen-Deuterium Exchange HDX-MS offer valuable spatial and dynamic insights, integrating these sparse, heterogeneous measurements into these models remains an open challenge. Here, we bridge this gap by combining structural proteomics data with the rich biophysical priors learned by pretrained diffusion models. We introduce AIMS-Fold, an inference-time guided-diffusion framework that actively steers the generative sampling trajectory using differentiable physical potentials derived from XL-MS spatial restraints and HDX-MS solvent accessibility profiles. We demonstrate that these structural methods individually enhance predictive accuracy, and their integration yields synergistic improvement. Crucially, by leveraging these experimental restraints, AIMS-Fold achieves higher accuracy on challenging induced proximity targets than purely computational, unguided state-of-the-art models like Boltz-2. This establishes our framework as a powerful, integrative computational approach for the structure based drug design of induced proximity drugs. Evaluation code will be made publicly available upon publication.