{
  "abstract": "Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Life Sciences"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "B. P. Behera"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Dixit"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8q1amv9gag695.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.764833",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764833v1.full",
  "id": "8q1amv9gag695",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764833v1",
  "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/10.64898/2026.11.06.764833v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8q1amv9gag695.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:32Z",
      "native_id": "10.64898/2026.11.06.764833",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764833v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction",
  "title_text": "StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction",
  "type": "preprint",
  "updated_at": "2026-11-09T12:20:32Z",
  "url": "https://research.draconitic.com/paper/8q1amv9gag695",
  "venue": "bioRxiv",
  "versions": []
}