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The impact of AlphaFold2 one year on

Nature Methods volume 19, pages 15–20 (2022)Cite this article

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The greatly improved prediction of protein 3D structure from sequence achieved by the second version of AlphaFold in 2020 has already had a huge impact on biological research, but challenges remain; the protein folding problem cannot be considered solved. We expect fierce competition to improve the method even further and new applications of machine learning to help illuminate proteomes and their many interactions.

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Fig. 1: Distribution of average confidence scores for AlphaFold2 models of human proteins with and without homologs available in the PDB.
Fig. 2: Entry Q99558 (MAP3K14) from the EBI AlphaFold Database.
Fig. 3: Ramachandran plots of the ϕ,ψ main chain torsion angles for experimentally determined and AlphaFold2-derived protein structures.

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Acknowledgements

The authors acknowledge help from R. Laskowski, who generated Fig. 3. J.M.T. acknowledges funding from EMBL.

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  1. Department of Computer Science and Department of Structural and Molecular Biology, University College London, Gower Street, London, UK

    David T. Jones

  2. European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK

    Janet M. Thornton

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Correspondence to David T. Jones or Janet M. Thornton.

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Jones, D.T., Thornton, J.M. The impact of AlphaFold2 one year on. Nat Methods 19, 15–20 (2022). https://doi.org/10.1038/s41592-021-01365-3

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