Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions
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Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.
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AF models have been used extensively with single-particle cryo-EM data.
“AF models have also been used extensively together with single particle cryo-EM data60,76,95,96”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [95]
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Hoskins, J., Khoo, Y., Mickelin, O., Singer, A., & Wang, Y. (2026) · SIAM Journal on Imaging Sciences · doi:10.1137/24m1699644 · Cited by 1 · see citing works
Sato, K., Kanaoka, Y., Tsukazaki, T., Uchihashi, T., & Mori, T. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00142 · Cited by 1 · see citing works
OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow
Xu, G., Luo, Z., Zhou, R., Wang, Q., & Ma, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad365 · Cited by 1 · see citing works
Lai, J., Burley, S. K., & Duarte, J. M. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae111 · Cited by 1 · see citing works
From sequence to structure to mechanism to phenotype: The new frontiers of structural biology
Pastore, A., & Shakhnovich, E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102674 · Cited by 1 · see citing works
Zhou, M., Wang, T., Xu, K., Wang, H., Li, Z., Zhang, W., & Wang, Y. (2024) · Frontiers of Environmental Science & Engineering · doi:10.1007/s11783-024-1851-0
Artificial intelligence and deep learning in electron microscopy
Treder, K. P., Huang, C., Kim, J. S., & Kirkland, A. I. (2025) · Advances in imaging and electron physics · doi:10.1016/bs.aiep.2025.01.002
Zhang, C., Condon, A., & Duc, K. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.02.641021
Arora, S., Pan, V. S.-H., Kumar, S., Singh, S. K., Chauhan, U., Alhalabi, W., Arya, V., Alsulami, B. S., Hsu, C., Chui, K. T., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-18065-z
Ion channels: Medicinal chemistry and modeling interface
Galatsis, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00080-7
Sherman, M., & Orlova, E. V. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.06.010
Machine Learning Applications in Vaccine Design
Parvathy, P. S., Kumar, V. A., Biswas, R., & Mohan, C. G. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00682
- Authors
- Giri, N., Roy, R. S., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 79
- Pages
- 102536
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC10023387
- OpenAlex
- W4319723617
- Cited by
- 59
- References
- 55
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 17 · 2023: 13
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Structural Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (DBI1759934, IIS1763246)
- U.S. Department of Energy (DE\u2013SC0021303, DE\u2013SC0020400, DE\u2013AR0001213)
- National Institutes of Health (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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- cryo-EM density maps, protein structure reconstruction, deep learning, protein structure prediction, protein complexes, training data quality, AlphaFold, protein sequences, multi-source data fusion
- MeSH terms
- Deep Learning, Amino Acid Sequence, Models, Molecular, Protein Conformation, Proteins, Cryoelectron Microscopy
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