AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function
Open accessAbstract
The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts the 3D structure of two thirds of the human proteome. Its predictions can be used directly as structural models or indirectly as aids for experimental structure determination using X-ray crystallography, CryoEM or NMR spectroscopy. Nevertheless, AlphaFold 2 can neither afford insight into how proteins fold, nor can it determine protein stability or dynamics. Rare folds or minor alternative conformations are also not predicted by AlphaFold 2 and the program does not forecast the impact of post translational modifications, mutations or ligand binding. The remaining third of human proteome which is poorly predicted largely corresponds to intrinsically disordered regions of proteins. Key to regulation and signaling networks, these disordered regions often form biomolecular condensates or amyloids. Fortunately, the limitations of AlphaFold 2 are largely complemented by NMR spectroscopy. This experimental approach provides information on protein folding and dynamics as well as biomolecular condensates and amyloids and their modulation by experimental conditions, small molecules, post translational modifications, mutations, flanking sequence, interactions with other proteins, RNA and virus. Together, NMR spectroscopy and AlphaFold 2 can collaborate to advance our comprehension of proteins.
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Membrane proteins make up less than 3% of PDB structures, so AF2's training set was strongly biased toward soluble proteins.
“membrane proteins represent less than 3% of total structures in the PDB15, meaning that the training set used by AF2 was highly biased towards soluble proteins39”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [15]
AF models are integrated with solution-state NMR data.
“Another burgeoning area where AF models are integrated with experimental data is solution-state NMR15,27,28,30,81–85.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [15]
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BackgroundInfluentialAdvancing membrane-associated protein docking with improved sampling and scoring in Rosetta
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- Authors
- Laurents, D. V.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Corresponding author
- Laurents, Douglas V.
- ORCID
- Laurents, Douglas V.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2022-05-17
- Volume
- 9
- Pages
- 906437-906437
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC9152297
- OpenAlex
- W4280548378
- Cited by
- 95
- References
- 90
- Citations by year
- 2026: 21 · 2025: 23 · 2024: 20 · 2023: 28 · 2022: 3
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministerio de Ciencia y Tecnología
- 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, NMR spectroscopy, protein folding, protein dynamics, intrinsically disordered regions, biomolecular condensates, amyloid, post-translational modification, protein stability, protein-ligand binding, protein-protein interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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