{
  "abstract": "Accurate prediction of mutation-induced protein stability responses, including changes in folding free energy and melting temperature, is critical for protein engineering. Existing models represent these related but non-equivalent measurements separately and often focus on single mutations. Here we present OmegaTherm, a sequence-based framework that jointly learns both stability responses for single and multiple mutations. OmegaTherm introduces a shared protein language model to capture transferable mutation context, measurement-specific encoders to preserve measurement identity, and paired-measurement-informed distillation to learn complementary knowledge from heterogeneous data. An antisymmetric prediction architecture further enforces physical consistency between forward and reverse mutations. Across independent benchmarks, OmegaTherm achieves state-of-the-art performance in quantitative prediction, mutation classification and protein-level ranking. Beyond benchmark prediction, OmegaTherm-derived stability landscapes correlate strongly with fitness landscapes from deep mutational scanning, enrich favorable variants and reveal function-associated mutation-sensitive regions. These results establish unified representation learning as an effective strategy for integrating related but non-equivalent biochemical measurements.",
  "abstract_shown": true,
  "affiliations": [
    "Central South University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "W. Wang"
    },
    {
      "name": "Y. Zhou"
    },
    {
      "name": "W. Yu"
    },
    {
      "name": "Y. Wu"
    },
    {
      "name": "Q. Yang"
    },
    {
      "name": "R. Zheng"
    },
    {
      "name": "X. Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Li"
    },
    {
      "name": "Y. Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2gekn62cdasw2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.753709",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753709v1.full",
  "id": "2gekn62cdasw2",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753709v1",
  "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",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7214767503",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753709v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2gekn62cdasw2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:02Z",
      "native_id": "10.64898/2026.09.23.753709",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753709v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Unified modeling of mutation-induced protein stability responses with OmegaTherm",
  "title_text": "Unified modeling of mutation-induced protein stability responses with OmegaTherm",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:02Z",
  "url": "https://research.draconitic.com/paper/2gekn62cdasw2",
  "venue": "bioRxiv",
  "versions": []
}