{
  "abstract": "Backbone amide order parameters (S2) describe protein motions on the picosecond-to-nanosecond time scale at residue resolution. However, NMR measurement and calculation from atomistic molecular dynamics (MD) trajectories are time-consuming. We developed GeoDSSOP (Geometry-aware Deep Sequence–Structure Order-Parameter Predictor) to predict backbone N–H S2 from one amino-acid sequence and one corresponding structure. GeoDSSOP combines frozen ESM-2 residue embeddings with a geometric vector perceptron-style encoder of a residue graph. A gated residual module fuses the sequence and structure representations, and a dilated temporal convolutional network gives a bounded prediction for each residue. We trained these models on 3,490 proteins with MD-iRED labels or S-OPPE pseudo-labels. The two label sources were weighted equally, and homologous proteins were kept in the same data partition. The model gave a protein-macro PCC of 0.8250 on a protected and homology-separated test set of 136 MD-labelled proteins, and 0.8424 on a historical experimental NMR benchmark of ten proteins that was not used to train OPPE, S-OPPE, or GeoDSSOP. In a matched three-run ablation, adding the structure pathway increased the mean protein-macro PCC on MD-iRED validation from 0.7220 to 0.8026. This result shows that one protein structure provides useful information beyond the sequence pathway. Label-source ablations also showed domain-specific performance and asymmetric transfer. Balanced dual-source training gave the best overall performance on the mixed-domain development set. GeoDSSOP can rapidly produce residue-level fast-dynamics profiles from a sequence and one available structure. It does not require a new MD simulation or a multi-conformer structure ensemble, but it remains a statistical predictor and does not replace experiment or trajectory-based analysis.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging",
    "National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences",
    "University of Chinese Academy of Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wanjia Tang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qianqian Wang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiongjie Xiao"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xu Zhang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Bin Jiang",
      "orcid": "0000-0002-1450-2269"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Maili Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ze66sndpegem.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008246/v1",
  "fulltext_url": null,
  "id": "7ze66sndpegem",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008246/v1",
  "language": null,
  "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": "W7207593404",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008246/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/7ze66sndpegem.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008246",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008246/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Protein Structure and Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "GeoDSSOP: Geometry-aware prediction of protein backbone order parameters from sequence and structure",
  "title_text": "GeoDSSOP: Geometry-aware prediction of protein backbone order parameters from sequence and structure",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/7ze66sndpegem",
  "venue": "ChemRxiv",
  "versions": []
}