{
  "abstract": "Intrinsically disordered proteins (IDPs) are central to many biological processes, but their function depends on dynamic conformational ensembles rather than a single structure. Despite the impact of artificial intelligence on protein structure prediction, determining these ensembles at atomistic resolution remains challenging. Recently, we introduced bAIes, a framework that leverages the structural information encoded in AlphaFold2 distograms to generate conformational ensembles of IDPs at atomistic resolution. Here, we extend this framework to six state-of-the-art structure prediction tools (AlphaFold2, AlphaFold3, Boltz-1, Boltz-2, ESMFold, and ESMFold2) and systematically compare the structural information encoded in their predicted inter-residue distance distributions. Despite differences between predictors, we show that bAIes generates ensembles with comparable accuracy against experimental NMR and SAXS data, and competitive performance relative to state-of-the-art all-atom simulations and alternative IDP ensemble generation methods. These results demonstrate that bAIes is robust to the choice of structure predictor and that the distance distributions generated by different deep learning architectures contain valuable information about the conformational preferences of disordered proteins. More broadly, our work highlights the potential of combining AI-derived structural information with physics-based modeling to generate atomistic ensembles of intrinsically disordered proteins and regions.",
  "abstract_shown": true,
  "affiliations": [
    "Institut Pasteur"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "V. Schnapka"
    },
    {
      "name": "N. Portal"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Bonomi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eyzy1jxbb7f83.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.10.765624",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.10.765624v1.full",
  "id": "eyzy1jxbb7f83",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.10.765624v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.10.765624v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/eyzy1jxbb7f83.ris",
  "sources": [
    {
      "first_seen": "2026-11-12T00:23:34Z",
      "native_id": "10.64898/2026.11.10.765624",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.10.765624v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Leveraging state-of-the-art AI structure predictors to generate atomic resolution ensembles of intrinsically disordered proteins",
  "title_text": "Leveraging state-of-the-art AI structure predictors to generate atomic resolution ensembles of intrinsically disordered proteins",
  "type": "preprint",
  "updated_at": "2026-11-13T00:23:10Z",
  "url": "https://research.draconitic.com/paper/eyzy1jxbb7f83",
  "venue": "bioRxiv",
  "versions": []
}