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Deep learning methods in protein structure prediction
Open accessAbstract
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
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Cited by
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429
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High-throughput prediction of protein conformational distributions with subsampled AlphaFold2
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196
Lindorff-Larsen, K., & Kragelund, B. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167196 · Cited by 85
Szikszai, M., Magnus, M., Sanghi, S., Kadyan, S., Bouatta, N., & Rivas, E. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168552 · Cited by 38
Metadata
- Authors
- Torrisi, M., Pollastri, G., & Le, Q.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2020
- Volume
- 18
- Pages
- 1301-1310
- ISSN
- 2001-0370 (ISSN-L)
- OpenAlex
- W3000982932
- Cited by
- 284
- References
- 21
- FWCI
- 12.65
- Type
- journal article
- Language
- en
- Open access
- gold
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