NMR hawk‐eyed view of AlphaFold2 structures
Open accessAbstract
Abstract The prediction of the three‐dimensional (3D) structure of proteins from the amino acid sequence made a stunning breakthrough reaching atomic accuracy. Using the neural network‐based method AlphaFold2, 3D structures of almost the entire human proteome have been predicted and made available ( https://www.alphafold.ebi.ac.uk ). To gain insight into how well AlphaFold2 structures represent the conformation of proteins in solution, I here compare the AlphaFold2 structures of selected small proteins with their 3D structures that were determined by nuclear magnetic resonance (NMR) spectroscopy. Proteins were selected for which the 3D solution structures were determined on the basis of a very large number of distance restraints and residual dipolar couplings and are thus some of the best‐resolved solution structures of proteins to date. The quality of the backbone conformation of the AlphaFold2 structures is assessed by fitting a large set of experimental residual dipolar couplings (RDCs). The analysis shows that experimental RDCs fit extremely well to the AlphaFold2 structures predicted for GB3, DinI, and ubiquitin. In the case of GB3, the accuracy of the AlphaFold2 structure even surpasses that of a 1.1 Å crystal structure. Fitting of experimental RDCs furthermore allows identification of AlphaFold2 structures that are best representative of the protein's conformation in solution as seen for the EF hands of the N‐terminal domain of Ca2+‐ligated calmodulin. Taken together, the analysis shows that structures predicted by AlphaFold2 can be highly representative of the solution conformation of proteins. The combination of AlphaFold2 structures with RDCs promises to be a powerful approach to study structural changes in proteins.
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NMR structural ensembles provide a unique validation metric for AF2 because AF2's training PDB subset excluded NMR data.
“NMR structural ensembles offer a unique validation metric to assess the accuracy of predicted AlphaFold2 models since AlphaFold2 was trained on a subset of the PDB that excluded NMR data1,27–30”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [29]
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- Authors
- Zweckstetter, M.
- Affiliations
- German Center for Neurodegenerative Diseases (DE)
- Corresponding author
- Zweckstetter, Markus
- ORCID
- Zweckstetter, Markus
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2021-09-10
- Volume
- 30
- Issue
- 11
- Pages
- 2333-2337
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC8521308
- OpenAlex
- W3198769796
- Cited by
- 47
- References
- 18
- Citations by year
- 2025: 6 · 2024: 9 · 2023: 12 · 2022: 17 · 2021: 3
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DZNE Pub (submittedVersion)
- Funders
- H2020 European Research Council (787679)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold2, protein structure prediction, residual dipolar couplings, solution structure, globotriaosylceramide, DINI, ubiquitin, backbone conformation
- MeSH terms
- Models, Molecular, Proteins, Protein Folding, Nuclear Magnetic Resonance, Biomolecular
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