{
  "id": 6857193,
  "title": "Predicting Capsid Protein Binding Sites in Single-Stranded RNA Viruses Using Machine Learning from Local Geometric Features",
  "url": "https://urgent.news/2026/09/11/predicting-capsid-protein-binding-sites-in-single-stranded-rna",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-11T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750674v1?rss=1"
  },
  "original_language": "en",
  "account": "Predicting capsid protein binding sites in ssRNA viruses relies heavily on computational methods due to labor-intensive experimental techniques. Researchers present a sequence-based framework that combines RNA tertiary modeling, geometric feature extraction, and machine learning. The Qbeta bacteriophage serves as a proof-of-concept system for benchmarking against experimentally identified binding and non-binding RNA fragments. A neural network trained on geometric descriptors shows high accuracy in distinguishing binding sites, achieving an AUC of 0.88. Applying the model to AlphaFold-predicted RNA structures also maintains predictive performance, though failed predictions suggest additional dynamic factors beyond static conformations influence viral genome packaging. This study offers insights into RNA-capsid interactions and lays groundwork for applying the approach to other ssRNA viruses.",
  "summary": "Selective recognition of viral RNA by capsid proteins is essential for genome packaging during the assembly of single-stranded RNA (ssRNA) viruses. However, identification of capsid protein binding sites in the RNA genome remains challenging because current experimental techniques are labor-intensive and low-throughput, motivating the development of computational approaches. Here, we present a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Scientific Reports",
        "title": "Predicting water hyacinth expansion and identifying environmental drivers in Lake Tana using machine learning",
        "url": "https://urgent.news/2026/09/09/predicting-water-hyacinth-expansion-and-identifying-environmental",
        "published": "2026-09-09T00:00:00.000Z"
      }
    ]
  },
  "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."
}