{
  "id": 6239092,
  "title": "AtlasFold: Protein structure prediction with metagenomic-scale language models",
  "url": "https://urgent.news/2026/09/07/atlasfold-protein-structure-prediction-with-metagenomic-scale",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-07T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.04.749352v1?rss=1"
  },
  "original_language": "en",
  "account": "Protein language models (PLMs) trained on evolutionary sequences can predict protein structures without the need for multiple-sequence alignments (MSAs). The Atlas model family, an open and trainable system, encompasses protein language modeling, monomer folding, and protein-complex prediction. AtlasLM-3B is a 3 billion-scale language model trained with masked language modeling on around 1.56 billion sequences, including metagenomic data. This model surpasses the similarly sized ESM2-3B in unsupervised contact prediction.\n\nAtlasFold, which builds on these representations, predicts all-atom protein structures and attains state-of-the-art accuracy among PLM-based folding models. By fine-tuning AtlasFold for protein-complex prediction, AtlasFold-M, or AtlasFold for Multimer prediction, is created. This protein-specific folding architecture allows for fast, memory-efficient inference with AtlasFold and AtlasFold-M. The training code, data, stage checkpoints, and model weights are released under the MIT License, providing a foundation for advancing PLM-based protein structure prediction.",
  "summary": "Protein language models (PLMs) trained on evolutionary sequences learn representations that encode protein structure, enabling direct structure prediction without multiple-sequence alignments (MSAs). Here we present the Atlas model family, an open and trainable system spanning protein language modeling, monomer folding, and protein-complex prediction. AtlasLM-3B is a 3B-scale language model…",
  "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."
}