{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Xingyu Chen"
    },
    {
      "name": "Kai Steffen Stroh"
    },
    {
      "name": "Jan Erzberger"
    },
    {
      "name": "Florian Stengel"
    },
    {
      "name": "Riccardo Pellarin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a6mtkfjezz9wt.bib",
  "cited_by": 2,
  "doi": "10.1016/j.sbi.2026.103237",
  "fulltext_url": null,
  "id": "a6mtkfjezz9wt",
  "issns": [
    "0959-440X"
  ],
  "landing_url": "https://hal.science/hal-05850652v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.bbm"
    ],
    [
      "hal",
      "2.sdv.bbm.bs"
    ],
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7133188105",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/a6mtkfjezz9wt.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:20:53Z",
      "native_id": "hal-05850652",
      "source": "hal",
      "url": "https://hal.science/hal-05850652v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "hal:2.sdv.bbm.bs",
      "subfield": "structural-biology"
    },
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    }
  ],
  "title": "Interpreting chemical crosslinks: Score-based approaches and deep neural networks",
  "title_text": "Interpreting chemical crosslinks: Score-based approaches and deep neural networks",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:12Z",
  "url": "https://research.draconitic.com/paper/a6mtkfjezz9wt",
  "venue": "Current Opinion in Structural Biology",
  "versions": []
}