A labeled dataset for AI-based cryo-EM map enhancement
Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002570 |
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| author | Nabin Giri Xiao Chen Liguo Wang Jianlin Cheng |
| author_facet | Nabin Giri Xiao Chen Liguo Wang Jianlin Cheng |
| author_sort | Nabin Giri |
| collection | DOAJ |
| description | Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate model building. While various methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps: regression maps capturing idealized density distributions, binary classification maps distinguishing structural elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label maps compared to experimental maps. This resource bridges the gap between structural biology and artificial intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing cryo-EM density maps. |
| format | Article |
| id | doaj-art-e9a4142e5c7e48be9f1d3dcdc3dff1ce |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-e9a4142e5c7e48be9f1d3dcdc3dff1ce2025-08-20T02:43:50ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01272843285010.1016/j.csbj.2025.06.041A labeled dataset for AI-based cryo-EM map enhancementNabin Giri0Xiao Chen1Liguo Wang2Jianlin Cheng3Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, MO, USA; NextGen Precision Health Institute, University of Missouri, Columbia, 65211, MO, USAComputer Science Department, Hamilton College, Clinton, 13323, NY, USALaboratory for BioMolecular Structure, Brookhaven National Laboratory, Upton, 11973, NY, USAElectrical Engineering and Computer Science, University of Missouri, Columbia, 65211, MO, USA; NextGen Precision Health Institute, University of Missouri, Columbia, 65211, MO, USA; Corresponding author at: Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, MO, USA.Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate model building. While various methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps: regression maps capturing idealized density distributions, binary classification maps distinguishing structural elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label maps compared to experimental maps. This resource bridges the gap between structural biology and artificial intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing cryo-EM density maps.http://www.sciencedirect.com/science/article/pii/S2001037025002570Cryo-EMCryo-EM map enhancementProtein structureDataset |
| spellingShingle | Nabin Giri Xiao Chen Liguo Wang Jianlin Cheng A labeled dataset for AI-based cryo-EM map enhancement Computational and Structural Biotechnology Journal Cryo-EM Cryo-EM map enhancement Protein structure Dataset |
| title | A labeled dataset for AI-based cryo-EM map enhancement |
| title_full | A labeled dataset for AI-based cryo-EM map enhancement |
| title_fullStr | A labeled dataset for AI-based cryo-EM map enhancement |
| title_full_unstemmed | A labeled dataset for AI-based cryo-EM map enhancement |
| title_short | A labeled dataset for AI-based cryo-EM map enhancement |
| title_sort | labeled dataset for ai based cryo em map enhancement |
| topic | Cryo-EM Cryo-EM map enhancement Protein structure Dataset |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025002570 |
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