{
  "id": 2311594,
  "title": "Dynamics-aware geometric learning predicts disease-associated molecular perturbations",
  "url": "https://urgent.news/2026/08/20/dynamics-aware-geometric-learning-predicts-disease-associated",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-20T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.17.745323v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Missense mutations and post-translational modifications (PTMs) are major molecular perturbations that reshape protein function but are traditionally studied independently. Current computational approaches largely rely on sequence conservation or static structural features, limiting our understanding of how perturbations alter intrinsic protein dynamics. We present DynGeo-Pheno, a unified…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}