AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
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Abstract
Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15 N- 1 H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
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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 [30]
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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
“…use of AlphaFold models in molecular replacement can be further enhanced by downweighting or removing low-confidence regions 23 . https://doi.org/10.1038/s41589-024-01638-w Another burgeoning area where AlphaFold models are integrated with experimental data is solution-state NMR 15,27,28,30,81–85 .”
BackgroundInfluential
- Authors
- Tejero, R., Huang, Y. J., Ramelot, T. A., & Montelione, G. T.
- Affiliations
- Universitat de València (ES)
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Tejero, Roberto
- Montelione, Gaetano T.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2022-06-13
- Volume
- 9
- Pages
- 877000-877000
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC9234698
- OpenAlex
- W4282840217
- Cited by
- 56
- References
- 64
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 16 · 2023: 15 · 2022: 8
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01-GM120574 R35-GM141818)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
- Keywords
- AlphaFold, AlphaFold2, small proteins, protein structure prediction, NMR solution structure, structural validation, residual dipolar couplings, chemical shift prediction, CASP
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