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Cryo-EM in drug discovery: achievements, limitations and prospects
Open accessAbstract
Cryo-electron microscopy (cryo-EM) of non-crystalline single particles is a biophysical technique that can be used to determine the structure of biological macromolecules and assemblies. Historically, its potential for application in drug discovery has been heavily limited by two issues: the minimum size of the structures it can be used to study and the resolution of the images. However, recent technological advances — including the development of direct electron detectors and more effective computational image analysis techniques — are revolutionizing the utility of cryo-EM, leading to a burst of high-resolution structures of large macromolecular assemblies. These advances have raised hopes that single-particle cryo-EM might soon become an important tool for drug discovery, particularly if they could enable structural determination for 'intractable' targets that are still not accessible to X-ray crystallographic analysis. This article describes the recent advances in the field and critically assesses their relevance for drug discovery as well as discussing at what stages of the drug discovery pipeline cryo-EM can be useful today and what to expect in the near future.
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Cites
Crystal Structure of Rhodopsin: A G Protein-Coupled Receptor
Palczewski, K., Kumasaka, T., Hori, T., Behnke, C. A., Motoshima, H., Fox, B. A., Trong, I. L., Teller, D. C., Okada, T., Stenkamp, R. E., et al. (2000) · Science · doi:10.1126/science.289.5480.739 · Cited by 5,637
RELION: Implementation of a Bayesian approach to cryo-EM structure determination
Scheres, S. H. W. (2012) · Journal of Structural Biology · doi:10.1016/j.jsb.2012.09.006 · Cited by 5,446
Crystal structure of the β2 adrenergic receptor–Gs protein complex
Rasmussen, S. G. F., DeVree, B. T., Zou, Y., Kruse, A. C., Chung, K. Y., Kobilka, T. S., Thian, F. S., Chae, P. S., Pardon, E., Calinski, D., et al. (2011) · Nature · doi:10.1038/nature10361 · Cited by 3,009
Cryo-EM structures of tau filaments from Alzheimer’s disease
Fitzpatrick, A. W. P., Falcon, B., He, S., Murzin, A. G., Murshudov, G., Garringer, H. J., Crowther, R. A., Ghetti, B., Goedert, M., & Scheres, S. H. W. (2017) · Nature · doi:10.1038/nature23002 · Cited by 2,061
Linking Crystallographic Model and Data Quality
Karplus, P. A., & Diederichs, K. (2012) · Science · doi:10.1126/science.1218231 · Cited by 1,781
Kühlbrandt, W. (2014) · Science · doi:10.1126/science.1251652 · Cited by 1,325
Twenty years on: the impact of fragments on drug discovery
Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W., & Jhoti, H. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.109 · Cited by 1,000
Structure of Rhombohedral 2 Zinc Insulin Crystals
ADAMS, M. J., BLUNDELL, T. L., DODSON, E. J., DODSON, G. G., VIJAYAN, M., BAKER, E. N., HARDING, M. M., HODGKIN, D. C., RIMMER, B., & SHEAT, S. (1969) · Nature · doi:10.1038/224491a0 · Cited by 503
Structure of a yeast activated spliceosome at 3.5 Å resolution
Yan, C., Wan, R., Bai, R., Huang, G., & Shi, Y. (2016) · Science · doi:10.1126/science.aag0291 · Cited by 261
Cryo-EM structures and atomic model of the HIV-1 strand transfer complex intasome
Passos, D. O., Li, M., Yang, R., Rebensburg, S. V., Ghirlando, R., Jeon, Y., Shkriabai, N., Kvaratskhelia, M., Craigie, R., & Lyumkis, D. (2017) · Science · doi:10.1126/science.aah5163 · Cited by 202
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Carabet, L. A., Rennie, P. S., & Cherkasov, A. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms20010120 · Cited by 172
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de Oliveira, T. M., van Beek, L., Shilliday, F., Debreczeni, J. É., & Phillips, C. (2021) · SLAS DISCOVERY · doi:10.1177/2472555220960401 · Cited by 73
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Dang, X., Guelen, L., Lutje Hulsik, D., Ermakov, G., Hsieh, E. J., Kreijtz, J., Stammen-Vogelzangs, J., Lodewijks, I., Bertens, A., Bramer, A., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2285285 · Cited by 43
Harnessing systematic protein–ligand interaction fingerprints for drug discovery
Zhao, Z., & Bourne, P. E. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.07.004 · Cited by 41
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Rennie, M. L., Arkinson, C., Chaugule, V. K., & Walden, H. (2022) · Science Advances · doi:10.1126/sciadv.abq6353 · Cited by 40
DeepSS2GO: protein function prediction from secondary structure
Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26
Gomes, D. E. B., Yang, B., Vanella, R., Nash, M. A., & Bernardi, R. C. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c05869 · Cited by 18
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Orasch, O., Weber, N., Müller, M., Amanzadi, A., Gasbarri, C., & Trummer, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23137033 · Cited by 17
High-resolution real-space reconstruction of cryo-EM structures using a neural field network
Huang, Y., Zhu, C., Yang, X., & Liu, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00870-2 · Cited by 9
Xu, Z., Sinha, A., Pandya, D. N., Schnicker, N. J., & Wadas, T. J. (2025) · RSC Chemical Biology · doi:10.1039/d4cb00267a · Cited by 5
Metadata
- Authors
- Renaud, J.-P., Chari, A., Ciferri, C., Liu, W.-T., Rémigy, H.-W., Stark, H., & Wiesmann, C.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2018-06-08
- Volume
- 17
- Issue
- 7
- Pages
- 471-492
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2805746983
- Cited by
- 461
- References
- 13
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- green
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