{
  "annotation_id": "db47130e-0a05-421a-a6f9-ba24d7bb63a1",
  "date_created": "2026-03-02T18:01:31.875000Z",
  "date_modified": "2026-03-02T18:01:31.875000Z",
  "file_hash": "a171d52e3362250d8b74c0e67f0d5d135bc8e983cbf17ba1ec568729f7d97920",
  "private": false,
  "record": {
    "abstract": "It has recently been discovered that many biological systems, when\nrepresented as graphs, exhibit a scale-free topology. One such system is the\nset of structural relationships among protein domains. The scale-free nature of\nthis and other systems has previously been explained using network growth\nmodels that, while motivated by biological processes, do not explicitly\nconsider the underlying physics or biology. In the present work we explore a\nsequence-based model for the evolution protein structures and demonstrate that\nthis model is able to recapitulate the scale-free nature observed in graphs of\nreal protein structures. We find that this model also reproduces other\nstatistical feature of the protein domain graph. This represents, to our\nknowledge, the first such microscopic, physics-based evolutionary model for a\nscale-free network of biological importance and as such has strong implications\nfor our understanding of the evolution of protein structures and of other\nbiological networks.",
    "arxiv_id": "q-bio/0407026",
    "authors": [
      "Eric J. Deeds",
      "Eugene I. Shakhnovich"
    ],
    "categories": [
      "q-bio.PE",
      "q-bio.MN"
    ],
    "doi": "10.1529/biophysj.104.051433",
    "title": "The Emergence of Scaling in Sequence-based Physical Models of Protein   Evolution",
    "url": "https://arxiv.org/abs/q-bio/0407026"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "42257b69-3a18-4700-87d0-2fdbdf4b4af0",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}