Interpreting chemical crosslinks: Score-based approaches and deep neural networks

Chemical cross-linking coupled with mass spectrometry (XL-MS) has become a powerful tool for probing residue-level proximities within macromolecular assemblies. By providing sparse but informative distance restraints, XL-MS can be integrated with electron microscopy and domain-level high-resolution structures to model the architecture of protein complexes. Unlike X-ray crystallography, electron microscopy, or solid-state Nuclear Magnetic Resonance (NMR), XL-MS can be applied under near-physiological conditions, scaled to large modular systems, and performed at higher throughput. In this review, we highlight recent advances in the field, with particular emphasis on the impact of AI-driven structure prediction. As an illustration, we describe a hybrid protocol that combines the Integrative Modeling Platform (IMP) with the deep neural network Chai-1 to dock and refine the helicase Dbp10 on a transient ribosome biogenesis intermediate using XL-MS restraints. • Chemical cross-linking coupled with mass spectrometry (XL-MS) maps residue proximities within protein complexes. • The interpretation of XL-MS data requires probabilistic restraints that account for structural flexibility and data noise. • Deep neural networks (DNNs) incorporate distance information into pair representations to guide structure prediction. • We demonstrated that score-based methods can be effectively combined with DNNs.