{
  "id": 2728237,
  "title": "PAM-DB: Revealing Protein Activation Mechanisms for Next-Generation Rational Drug Discovery",
  "url": "https://urgent.news/2026/08/22/pam-db-revealing-protein-activation-mechanisms-for-next-generation",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-22T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.20.745895v1?rss=1"
  },
  "original_language": "en",
  "account": "PAM-DB: Unveiling Protein Activation Mechanisms for Next-Generation Drug Discovery\n\nTraditional rational drug design primarily utilizes computational methods to model binding thermodynamics and static shapes of target proteins, mainly in their inactive states. However, the crucial kinetic parameters that influence experimental efficacy - such as catalytic turnover and signaling potency - are determined by molecular interactions with transition states, intermediate states, and the entire range of conformations along the least free-energy activation pathway. This lack of dynamic information has significantly constrained the predictive power and success rate of conventional structure-based approaches.\n\nThe PAM-DB database systematically maps the complete activation trajectories of pharmacologically relevant targets, including transition states, intermediate states, and all connecting conformational ensembles. This resource provides several advantages for drug discovery: it allows for rational targeting of previously undruggable proteins, facilitates biased agonism/antagonism design, reveals cryptic allosteric sites in inactive conformations, identifies novel transient pockets along the activation route, rationalizes the mechanisms of existing drugs, predicts mutational effects on activation barriers, and prospectively forecasts drug resistance and off-target liabilities.\n\nThe utility of PAM-DB is demonstrated through case studies, and guidelines for integration into existing discovery pipelines are provided. For more detailed information, visit the database website at https://www.momedpamdb.com/en.",
  "summary": "Current rational drug design relies predominantly on computational (CADD/AIDD) methods that model binding thermodynamics and static conformations of target proteins, primarily in their inactive states. However, the kinetic parameters that govern experimental efficacy-such as catalytic turnover and signaling potency-are determined by molecular interactions with transition states (TS), intermediate…",
  "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."
}