{
  "abstract": "DEAD-box RNA helicases (DDXs) are traditionally known as ATP-dependent motors that unwind double-stranded RNA (dsRNA). Recent experiments, however, show that some DDXs promote dsRNA unwinding even in the absence of ATP, raising a fundamental question about the physical mechanism underlying ATP-independent strand separation. Here, we develop a minimal, physics-based coarse-grained RNA model and incorporate weak, specific interactions between DDX3X and dsRNA, revealing the inherently stochastic nature of unwinding events. The unwinding process must overcome an energy barrier, but thermal fluctuations and entropy gain provide a driving force for RNA remodeling. We identify that dsRNA separation proceeds through rare yet obligatory strand-displacing intermediates facilitated by DDX3X. By combining deep learning-assisted analysis, we further rank the contributions of different entropic components, revealing hydrogen bonding as the dominant factor, followed by base stacking and then the backbone conformation. These findings reveal a previously unrecognized physical mechanism for RNA duplex unwinding and offer an effective framework for studying RNA remodeling kinetics. Author summary: Molecular motors are generally thought to require chemical energy to perform mechanical work. ATP-dependent helicases are a classic example: they use ATP to separate nucleic-acid strands. Recent experiments, however, have challenged this conventional view by showing that DDX3X can promote strand separation without ATP. This raises a fundamental question: if no chemical energy is consumed, what drives the strands apart? We used computer simulations to address this question for DDX3X-mediated dsRNA unwinding in the absence of ATP. We found that strand separation is associated with RNA remodeling. Most fluctuations do not lead to complete separation, but rare fluctuations can drive the RNA from the paired state to an unwound state. We further used machine learning to identify which molecular interactions contribute most strongly to this process. Our results suggest that some molecular machines can promote structural remodeling even without direct chemical energy input, by exploiting the thermal fluctuations and entropy inherent to the molecular system. This provides a possible physical mechanism for ATP-independent activity and broadens our understanding of how molecular machines can function.",
  "abstract_shown": true,
  "affiliations": [
    "National Laboratory of Solid State Microstructures and Department of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China",
    "Hefei National Laboratory, Hefei, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kang Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Chun-Lai Ren",
      "orcid": "0000-0003-0244-082X"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Yu-Qiang Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/25q60h8qz85g5.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014928",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014928",
  "id": "25q60h8qz85g5",
  "issns": [
    "1553-7358",
    "1553-734X"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014928",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Biochemical Simulations"
    ],
    [
      "heading",
      "Biochemistry"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Biology and life sciences"
    ],
    [
      "heading",
      "Chemical Bonding"
    ],
    [
      "heading",
      "Chemistry"
    ],
    [
      "heading",
      "Computational Biology"
    ],
    [
      "heading",
      "Condensed Matter Physics"
    ],
    [
      "heading",
      "DEAD-box"
    ],
    [
      "heading",
      "Entropy"
    ],
    [
      "heading",
      "Enzymes"
    ],
    [
      "heading",
      "Enzymology"
    ],
    [
      "heading",
      "Free Energy"
    ],
    [
      "heading",
      "Helicases"
    ],
    [
      "heading",
      "Hydrogen Bonding"
    ],
    [
      "heading",
      "Macromolecular structure analysis"
    ],
    [
      "heading",
      "Melting"
    ],
    [
      "heading",
      "Molecular biology"
    ],
    [
      "heading",
      "Nucleic acids"
    ],
    [
      "heading",
      "Phase Transitions"
    ],
    [
      "heading",
      "Physical Chemistry"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Physics"
    ],
    [
      "heading",
      "Protein domains"
    ],
    [
      "heading",
      "Proteins"
    ],
    [
      "heading",
      "RNA"
    ],
    [
      "heading",
      "RNA structure"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Thermodynamics"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Biochemical simulations"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Enzymology/Enzymes/Helicases"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Nucleic acids/RNA/RNA structure"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Enzymes/Helicases"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Protein domains/DEAD-box"
    ],
    [
      "plos",
      "/Biology and life sciences/Computational biology/Biochemical simulations"
    ],
    [
      "plos",
      "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/RNA structure"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Physical chemistry/Chemical bonding/Hydrogen bonding"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Condensed matter physics/Phase transitions/Melting"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Free energy"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014928&type=printable",
  "pmcid": "PMC13734570",
  "pmid": "42962055",
  "published": "2026-10-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/25q60h8qz85g5.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:20:55Z",
      "native_id": "10.1371/journal.pcbi.1014928",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014928"
    },
    {
      "first_seen": "2026-11-08T12:24:01Z",
      "native_id": "PMC13734570",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pcbi.1014928"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "plos:enzymology",
      "subfield": "enzymology-metabolism"
    },
    {
      "field": "biochemistry",
      "origin": "heading:Molecular biology",
      "subfield": "molecular-biology"
    },
    {
      "field": "biology",
      "origin": "plos:computational biology",
      "subfield": "bioinformatics"
    },
    {
      "field": "physics",
      "origin": "heading:Condensed Matter Physics",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "plos:thermodynamics",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "ATP-independent entropy-driven dsRNA unwinding by DDX3X revealed by coarse-grained simulations and deep learning",
  "title_text": "ATP-independent entropy-driven dsRNA unwinding by DDX3X revealed by coarse-grained simulations and deep learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:24:01Z",
  "url": "https://research.draconitic.com/paper/25q60h8qz85g5",
  "venue": "PLOS Computational Biology",
  "versions": []
}