Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
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Abstract
The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.
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For many targets, AF2 models with predicted disorder may be useful only for assigning likelihood of conformational heterogeneity, not for structure-function analysis.
“for many targets, AF2 models with predicted disorder may not be relevant for structure and function analysis other than for assigning the likelihood for conformational heterogeneity27”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [27]
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 [27]
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 [27]
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Flagged first, then by citations
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AlphaFold2: A Role for Disordered Protein/Region Prediction?
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“…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 .”
BackgroundInfluentialFrom interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
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Novel machine learning approaches revolutionize protein knowledge
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Challenges in bridging the gap between protein structure prediction and functional interpretation
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“…that IDRs that have high pLDDT scores from AlphaFold2 can undergo conditional folding under certain conditions to interact with other proteins [63]. that IDRs that have high pLDDT scores from AlphaFold2 can undergo conditional folding under certain conditions to interact with other proteins [63].”
BackgroundProtein language model identifies disordered, conserved motifs implicated in phase separation
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- Authors
- Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D.
- Affiliations
- University of Toronto (CA)
- Medical University of Graz (AT)
- Corresponding author
- Forman-Kay, Julie D.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2023-10-25
- Volume
- 120
- Issue
- 44
- Pages
- e2304302120-e2304302120
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC10622901
- OpenAlex
- W4387934645
- Cited by
- 240
- References
- 123
- Citations by year
- 2026: 60 · 2025: 71 · 2024: 67 · 2023: 26 · 2022: 16
- FWCI
- 19.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- CIHR (FDN-148375, PJT-148532, Canada Research Chair in Intrinsically Disordered Proteins, Canada Research Chair in Computational Biology, Banting Postdoctoral Fellowship)
- The Hospital for Sick Children (LiUNA! Fellowship for Research Innovation)
- Canada Foundation for Innovation (John R. Evans Leaders Fund)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- intrinsically disordered regions, AlphaFold2, protein structure prediction, pathogenic variants, structural plasticity
- MeSH terms
- Humans, Protein Conformation, Eukaryota, Intrinsically Disordered Proteins
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.
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