The 2020 Critical Assessment of protein Structure Prediction (CASP) marked a significant advance. The machine learning method AlphaFold predicted the structure of most target proteins to an accuracy assessors called “competitive with experiment”. Here, I discuss the impact of improved protein structure prediction, highlighting exciting research areas and remaining challenges.
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References
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Akdel, M. et al. A structural biology community assessment of AlphaFold 2 applications. Preprint at bioRxiv https://doi.org/10.1101/2021.09.26.461876 (2021).
Mosalaganti, S. et al. Artificial intelligence reveals nuclear pore complexity. Preprint at bioRxiv https://doi.org/10.1101/2021.10.26.465776 (2021).
Bludau, I. et al. The structural context of PTMs at a proteome wide scale. Preprint at bioRxiv https://doi.org/10.1101/2022.02.23.481596 (2022).
van Kempen, M. et al. Foldseek: fast and accurate protein structure search. Preprint at bioRxiv https://doi.org/10.1101/2022.02.07.479398 (2022).
Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2021).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Humphreys, I. R. et al. Computed structures of core eukaryotic protein complexes. Science 374, eabm4805 (2021).
Burke, D. F. et al. Towards a structurally resolved human protein interaction network. Preprint at bioRxiv https://doi.org/10.1101/2021.11.08.467664 (2021).
Acknowledgements
The author thanks J. Jumper, C. Meyer, P. Kohli and D. Hassabis for their suggestions and comments.
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Tunyasuvunakool, K. The prospects and opportunities of protein structure prediction with AI. Nat Rev Mol Cell Biol 23, 445–446 (2022). https://doi.org/10.1038/s41580-022-00488-5
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DOI: https://doi.org/10.1038/s41580-022-00488-5
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