{
  "abstract": "Mapping the multi-mutant fitness landscape is vital to protein engineering, but is challenging due to the vast combinatorial sequence space awaiting exploration. A central difficulty lies in the accurate and efficient modeling of non-additive epistatic effects among individual mutations, which partially arise from the physical inter-residue interactions prescribed by the protein structure. Existing fitness predictors usually perform well on single mutants but become less powerful for higher-order mutants, due to the lack of explicitly considering the relationship between sequence, structure and function of the target protein. Here, we present an end-to-end framework named Cerebra-Epistasis, which couples a single-sequence structure predictor that explicitly endows the structure awareness beyond the conventional sequence-fitness mapping with a downstream fitness prediction network that deliberately models the non-linear epistatic effects beyond the traditional additive terms. When evaluated across diverse assays, Cerebra-Epistasis outperforms the other state-of-the-art baselines in multi-mutant fitness prediction, with enhanced advantage over increasing mutation orders. Moreover, our special design on the epistasis modeling allows reliable extrapolation from low-order mutant data to unseen higher-order combinations, enabling one-shot inference of the overall mutation atlas from the starting sequence, a benefit that supposedly introduces three orders of magnitude acceleration in the landscape-scale prediction.",
  "abstract_shown": true,
  "affiliations": [
    "Zhongnan University of Economics and Law"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "W. Wang"
    },
    {
      "name": "Z. Yu"
    },
    {
      "name": "E. Yang"
    },
    {
      "name": "Z. Shi"
    },
    {
      "name": "S. Yu"
    },
    {
      "name": "J. Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Xu"
    },
    {
      "name": "H. Gong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/71gsahxjqzwfj.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.753701",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753701v1.full",
  "id": "71gsahxjqzwfj",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753701v1",
  "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": "W7214071265",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753701v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/71gsahxjqzwfj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:24Z",
      "native_id": "10.64898/2026.09.24.753701",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.753701v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "From single-sequence structure prediction to protein fitness landscape through a composable, epistasis-aware mutation atlas",
  "title_text": "From single-sequence structure prediction to protein fitness landscape through a composable, epistasis-aware mutation atlas",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:24Z",
  "url": "https://research.draconitic.com/paper/71gsahxjqzwfj",
  "venue": "bioRxiv",
  "versions": []
}