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AlphaFold2 and its applications in the fields of biology and medicine

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Yang, Zhenyu, Zeng, Xiaoxi, Zhao, Yi, Chen, Runsheng·2023·Signal Transduction and Targeted Therapy
DOI 10.1038/s41392-023-01381-z
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Abstract

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

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Cited by

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Metadata

Authors
Yang, Z., Zeng, X., Zhao, Y., & Chen, R.
Venue
Signal Transduction and Targeted Therapy
Publisher
Springer Science and Business Media LLC
Published
2023-03-14
Volume
8
Issue
1
Pages
115-115
ISSN
2059-3635 (ISSN-L)
DOI
10.1038/s41392-023-01381-z
OpenAlex
W4324143928
Cited by
577
References
54
FWCI
43.71
Type
journal article
Language
en
Open access
gold
Licence

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.