{
  "abstract": "Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only static structures. Here, we present ESMDynamic, a deep learning model that predicts residue-residue contact dynamics directly from protein sequence. Built on the ESMFold architecture and trained on conformational variability from experimental structure ensembles and molecular dynamics (MD) simulations, ESMDynamic predicts dynamic contact probabilities, contact occupancy fraction, and coarse-grained kinetics of contact formation and dissociation across multiple temperature conditions. On large-scale MD benchmarks (mdCATH and ATLAS), ESMDynamic matches or outperforms state-of-the-art ensemble prediction methods (AlphaFlow, ESMFlow, BioEmu) while requiring orders-of-magnitude less computation. We demonstrate generalization to diverse systems, including membrane transporters, a de novo designed protein, and a homodimer complex. We show that predicted dynamic contacts enable automated selection of collective variables for Markov state model construction. Applied to the human proteome, ESMDynamic generates predictions for over 18,000 proteins, enabling large-scale analysis of conformational variability. Overall, ESMDynamic provides a scalable, sequence-based representation of protein dynamics to inform simulation, analysis, and design workflows.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA",
    "University of Illinois at Urbana-Champaign",
    "Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA",
    "Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. diwakar@illinois.edu",
    "Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. diwakar@illinois.edu",
    "Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. diwakar@illinois.edu",
    "Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA. diwakar@illinois.edu",
    "Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. diwakar@illinois.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Diego E. Kleiman",
      "orcid": "0000-0002-3833-5872"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Jiangyan Feng",
      "orcid": "0000-0003-0292-6758"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhengyuan Xue"
    },
    {
      "affs": [
        3,
        4,
        5,
        6,
        7,
        1
      ],
      "name": "Diwakar Shukla",
      "orcid": "0000-0003-4079-5381"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bpa9bg2655dap.bib",
  "cited_by": 7,
  "doi": "10.1038/s41467-026-76361-2",
  "fulltext_url": null,
  "id": "bpa9bg2655dap",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://doi.org/10.1038/s41467-026-76361-2",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W4413478036",
  "pdf_url": null,
  "pmcid": "PMC12393515",
  "pmid": "40894558",
  "published": "2026-08-11",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/bpa9bg2655dap.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:48Z",
      "native_id": "W4413478036",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41467-026-76361-2"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Protein Structure and Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences",
  "title_text": "ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T13:20:30Z",
  "url": "https://research.draconitic.com/paper/bpa9bg2655dap",
  "venue": "Nature Communications",
  "versions": []
}