{
  "abstract": "Motivation\n\nDetermining the three-dimensional (3D) structure of proteins is a cornerstone of structural biology, providing essential insights into their underlying biological mechanisms and functional roles. Cryogenic electron microscopy (cryo-EM) single-particle analysis allows researchers to investigate biomolecules at near-atomic resolutions, unraveling the structure of biomolecular complexes. However, reconstructing high-fidelity 3D volumes from extremely noisy projections, particularly under the interference of structural heterogeneity, remains a challenging task. Traditional Fourier-domain methods often suffer from frequency-dependent signal attenuation and uneven information distribution. While recent deep-learning-based frameworks attempt to predict high-frequency components directly in frequency space, they are highly susceptible to noise and often introduce artifacts. State-of-the-art (SOTA) real-space method circumvents Fourier-domain defects, yet remains constrained by the inherent spectral bias of multi-layer perceptrons, as fixed positional encoding fails to sufficiently capture intricate high-frequency details.Results\n\nWe propose cryoTriplane, a real-space 3D homogeneous reconstruction network that utilizes a hybrid spatial representation to adaptively capture fine structural details. By combining fixed positional encoding for noise-robust structural initialization with structure-aware positional encoding for high-frequency refinement, our method achieves 3D reconstructions that are simultaneously globally consistent and locally fine-grained. Comprehensive qualitative and quantitative evaluations across five simulated and four real-world datasets demonstrate that cryoTriplane significantly outperforms existing methods in resolving fine structural features. Furthermore, validation through downstream atomic modeling underscores cryoTriplane's ability to recover high-quality density maps. Specifically, our method achieves sequence coverage improvements of 4.6% and 5.5% on the EMPIAR-10005 and EMPIAR-10492 datasets over the SOTA approach, respectively.Availability and implementation\n\nOur code is available at https://github.com/chengyr1999/CryoTriplane. The real datasets used in this study are publicly available from EMPIAR.",
  "abstract_shown": true,
  "affiliations": [
    "Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Research Center for Mathematics and Interdisciplinary Sciences, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao 266237, China.",
    "College of Medical Information and Engineering, Ningxia Medical University, Yinchuan 750004, China.",
    "School of Computer Science and Technology, Shandong University, Qingdao 266237, China.",
    "School of Medical Technology, Beijing Institute of Technology, Beijing 100190, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yiran Cheng",
      "orcid": "0000-0001-5311-4560"
    },
    {
      "affs": [
        0
      ],
      "name": "Bintao He",
      "orcid": "0000-0001-8811-5029"
    },
    {
      "affs": [
        2
      ],
      "name": "Qiong Zeng"
    },
    {
      "affs": [
        3
      ],
      "name": "Fa Zhang",
      "orcid": "0000-0002-2081-9369"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Renmin Han",
      "orcid": "0000-0003-4761-6526"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e344mqswa3za6.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag754",
  "fulltext_url": "https://europepmc.org/articles/PMC13690421",
  "id": "e344mqswa3za6",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42870393",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7221762495",
  "pdf_url": "https://europepmc.org/articles/PMC13690421?pdf=render",
  "pmcid": "PMC13690421",
  "pmid": "42870393",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/e344mqswa3za6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42870393",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42870393"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag754",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag754/8914810"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "CryoTriplane: triplane-based neural radiance field representation for high-resolution cryo-EM 3D homogeneous reconstruction.",
  "title_text": "CryoTriplane: triplane-based neural radiance field representation for high-resolution cryo-EM 3D homogeneous reconstruction.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/e344mqswa3za6",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}