{
  "abstract": "Accurate segmentation of protein secondary structures in cryo-electron microscopy (cryo-EM) maps is crucial for protein tracing and atomic model building, yet remains challenging at intermediate resolutions. Although deep learning architectures have recently been applied to this task, there remains a lack of standardized evaluation protocols and systematic resolution-stratified analyses. Consequently, it remains unclear how segmentation performance varies across finer resolution ranges within the intermediate-resolution regime and how different volumetric segmentation architectures compare under controlled experimental conditions. To address these gaps, we present a nonredundant, resolution-stratified benchmark dataset and a controlled evaluation framework for protein secondary-structure segmentation in intermediate-resolution cryo-EM maps. The dataset comprises 972 nonredundant protein-chain samples from 495 cryo-EM protein structures resolved at 4.5–8.0 Å, with voxel-level annotations for helices, sheets, and non-periodic secondary-structure (NPSS) regions. The dataset is stratified into three resolution ranges─4.5–5.5, 5.5–6.5, and 6.5–8.0 Å─to enable systematic analysis of resolution-dependent segmentation performance. Using this benchmark, we evaluate five representative convolutional and transformer-based volumetric segmentation architectures─U-Net, Residual U-Net, Attention U-Net, UNETR, and Swin-UNETR─under standardized preprocessing, training, and evaluation protocols. Residual U-Net achieves the highest macro-averaged foreground (Macro-FG) F1 score in all three resolution bins, while helices are segmented more accurately than sheets and NPSS regions. All five architectures achieve their highest Macro-FG F1 scores in the highest-resolution bin, whereas performance varies between the two lower-resolution bins across architectures and secondary-structure classes. Overall, this benchmark enables fine-grained analysis of resolution-dependent segmentation performance, supports reproducible method development, and facilitates controlled comparison of future cryo-EM secondary-structure segmentation approaches.",
  "abstract_shown": true,
  "affiliations": [
    "Georgia Southern University , , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Md Ehashan Rabbi Pial"
    },
    {
      "affs": [
        0
      ],
      "name": "Farhan Noor Dehan"
    },
    {
      "affs": [
        0
      ],
      "name": "Salim Sazzed",
      "orcid": "0000-0002-8552-5337"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b609jp8v25yjq.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c06887",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13717664/?tool=EBI",
  "id": "b609jp8v25yjq",
  "issns": [
    "2470-1343"
  ],
  "landing_url": "https://pubs.acs.org/acsodf/article/11/42/65152/5452270/Deep-Learning-for-Protein-Secondary-Structure",
  "language": "eng",
  "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",
      "Article"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
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  "openalex": "W7222019707",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c06887",
  "pmcid": "PMC13717664",
  "pmid": "42959586",
  "published": "2026-10-15",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/b609jp8v25yjq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:39Z",
      "native_id": "10.1021/acsomega.6c06887",
      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/11/42/65152/5452270/Deep-Learning-for-Protein-Secondary-Structure"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
      "native_id": "PMC:PMC13717664",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13717664"
    },
    {
      "first_seen": "2026-10-31T12:21:46Z",
      "native_id": "MED:42959586",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42959586"
    },
    {
      "first_seen": "2026-11-02T12:21:56Z",
      "native_id": "PMC13717664",
      "source": "pmc",
      "url": "https://doi.org/10.1021/acsomega.6c06887"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "Deep Learning for Protein Secondary-Structure Segmentation in Intermediate-Resolution Cryo-EM Maps: A Resolution-Stratified Benchmark Dataset and Controlled Evaluation",
  "title_text": "Deep Learning for Protein Secondary-Structure Segmentation in Intermediate-Resolution Cryo-EM Maps: A Resolution-Stratified Benchmark Dataset and Controlled Evaluation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:56Z",
  "url": "https://research.draconitic.com/paper/b609jp8v25yjq",
  "venue": "ACS Omega",
  "versions": []
}