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The case for post-predictional modifications in the AlphaFold Protein Structure Database

Nature Structural & Molecular Biology volume 28, pages 869–870 (2021)Cite this article

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To the editor — AlphaFold2 has arrived to change workflows in structural biology, for good. However, the algorithm does not account for essential modifications that affect protein structure and function, which gives us only part of the picture. Here we discuss how this omission can be addressed in a relatively straightforward manner, which leads to a complete structural prediction of complex biomolecular systems.

The recent release of the AlphaFold Protein Structure Database1 by DeepMind and EMBL-EBI marks a major breakthrough in structural biology, as it makes available to the scientific community worldwide highly accurate structural predictions for 20,000 proteins from humans and proteins from 20 other biologically relevant organisms that include Escherichia coli. Like many scientists that work on macromolecular structure, we are genuinely excited about this development, yet we feel that there is a non-negligible potential for misinterpretation of its content in its current form. In particular, the protein-only predictions in the AlphaFold database means that cofactors and, most importantly, co- and post-translational modifications are understandably — owing to the scope of the technique — excluded. Among the most relevant co- and post-translational modifications is protein glycosylation — relevant and very visible, as recent studies of the dynamics of a fully glycosylated SARS-CoV-2 spike protein illustrate2,3. Indeed, between 50% and 70% of those 20,000 predicted human proteins are believed to be glycosylated4, but none of this is yet visibly highlighted on the database. Detailed information on the likelihood of modifications is readily available through AlphaFolds’s links to Uniprot (https://www.uniprot.org), and thus we strongly encourage the users of this fantastic new resource to check the information available on Uniprot before downloading a model.

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Fig. 1: Grafting an N-glycan onto an AlphaFold model.

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Acknowledgements

H.B. is funded by The Royal Society grant RGF/R1/181006. J.A. is the Royal Society Olga Kennard Research Fellow award ref. UF160039. C.A.F. is funded by the Irish Research Council (IRC) Government of Ireland Postgraduate Scholarship Programme. Data and methods are available at https://doi.org/10.5281/zenodo.5290624

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  1. York Structural Biology Laboratory, Department of Chemistry, University of York, York, UK

    Haroldas Bagdonas & Jon Agirre

  2. Department of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland

    Carl A. Fogarty & Elisa Fadda

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  1. Haroldas Bagdonas
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  2. Carl A. Fogarty
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Correspondence to Elisa Fadda or Jon Agirre.

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Bagdonas, H., Fogarty, C.A., Fadda, E. et al. The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nat Struct Mol Biol 28, 869–870 (2021). https://doi.org/10.1038/s41594-021-00680-9

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