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Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures
Open accessPreprint — published as 10.1038/s41597-024-03299-9 · Paper page
Abstract
The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. It is larger and of higher quality than any existing, publicly available dataset. We trained and tested deep learning models on Cryo2StructData to make sure it is ready for the large-scale development of AI methods for building atomic models from cryo-EM density maps.
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Metadata
- Authors
- Giri, N., Wang, L., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Brookhaven National Laboratory (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- Scientific Data
- Publisher
- openRxiv
- Published
- 2023-06-15
- Volume
- 11
- Issue
- 1
- Pages
- 458-458
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC11074267
- OpenAlex
- W4396660070
- Cited by
- 17
- References
- 1
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 4
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Scientific Data (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (DEAC05- 00OR22725, 2308699, 00OR22725, R01GM093123)
- U.S. Department of Energy (AC05-00OR22725, DESC0021303, DE--AC05--00OR22725, DE-AC05?, under Contract DE-AC05-00OR22725, DESC0020400, 00OR22725)
- National Institutes of Health (DE-AC05-00OR22725)
- Office of Science (DEAC05- 00OR22725, AC05-00OR22725)
- National Institute of General Medical Sciences (R01GM093123, R01GM146340)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- cryo-EM density maps, protein structure prediction, deep learning, single-particle cryo-electron microscopy, macromolecular complexes
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