{
  "id": 19411970,
  "title": "Cross-state representation learning enables prospective discovery of dynamic protein allosteric sites",
  "url": "https://urgent.news/2026/11/10/cross-state-representation-learning-enables-prospective-discovery-of",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-10T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.09.765238v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Allosteric regulation offers multifunctional induction in therapeutic interventions, yet conformational plasticity hinders computational identification of allosteric sites. Machine learning models rely on static holo (ligand-bound) structures, generalizing poorly to apo or substrate-bound states where binding pockets are transient or cryptic. We present AsBench2, a cross-state site-recognition…",
  "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."
}