The DOI matches its Crossref record; 97 of the 103 references it cites were found in a registry or an index; 1 of the 58 sources its claims cite were checked for support.
AlphaFold and Implications for Intrinsically Disordered Proteins
Open accessAbstract
Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the "structures" of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This perspective cautions against over-interpreting AlphaFold’s low-confidence and “unstructured” annotations for intrinsically disordered regions as literal structures or as failures of prediction. It argues that quantitative sequence–ensemble descriptions, not static models, are needed to understand IDR function.
Key findings
- AlphaFold annotated over 98% of human proteome sequences, but roughly 30% of residues have pLDDT scores below 50, consistent with estimates that about 30% of the human proteome is disordered.
- Low-confidence regions largely overlap intrinsically disordered regions (IDRs); the authors argue most are likely genuinely disordered rather than well-folded structures that AlphaFold missed.
- Static AlphaFold structures of IDRs often overestimate radius of gyration relative to SAXS measurements, as shown for p27 CTD and the hnRNP-A1 prion-like low-complexity domain (A1-LCD).
- Quantitative ensembles from atomistic simulations vetted by experiments reveal sequence-specific behavior: p27 CTD behaves on average like a Flory random coil, A1-LCD uses aromatic stickers/spacers, and Httex1 prefers tadpole-like conformations with polyQ radius scaling as n^1/3.
Methods
- Perspective/cautionary note, not primary research; it synthesizes the AlphaFold papers, human proteome annotations, and prior IDP literature.
- Uses illustrative examples—p27 Kip1 CTD, hnRNP-A1 A1-LCD, and huntingtin exon 1 (Httex1)—comparing AlphaFold static structures with SAXS, NMR, smFRET, and atomistic simulations based on the ABSINTH implicit solvation model and forcefield.
- Main analysis is interpretive and qualitative, focused on why static AlphaFold depictions of IDRs lack physical meaning and how sequence–ensemble relationships should instead be quantified.
Limitations
- The paper does not state formal limitations.
- Evident from the text: it is a perspective/opinion piece, not a systematic review, and its examples are drawn largely from the authors’ own work and close colleagues.
- Evident from the text: no new experimental data are reported; the quantitative examples rely on previously published simulations and experiments, and the scope is limited to interpreting AlphaFold annotations for IDRs.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
38 Not checked yet · 9 Own interpretation · 9 No basis given · 5 Own result · 1 Own method · accuracy not yet measured
None of the sources cited for this claim have been checked yet.
In the paper
“Conservative estimates indicate that roughly 30% of sequences, 30residues or longer, drawn at random from the human proteome, are likely to be IDRs. 3,5”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [3] source
- Could not verifycites [5] sourceExperimental — accuracy not yet measured
The Results describe DisProt annotations and note that the dataset is biased toward model organisms, including humans; they do not estimate the prevalence of IDRs in randomly sampled human-proteome sequences. Searches for a 30% estimate and a 30-residue threshold found no passage supporting those figures. (LLM, conf=0.70)
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In the paper
“Integrating SAXS and NMR measurements with simulations, suggests that the ensemble of conformations fluctuate among locally compact regions, globally compact conformations, and expanded conformations.88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“However, the efficiency of T187 phosphorylation does not change monotonically with changes to R g .”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
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In the paper
“Roughly 30% of the residues across predicted structures in the human proteome tend to have pLDDT scores that are less than 50.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“Instead, there is a cryptic motif that is proxi-mal to the primary substrate motif that regulates the efficiency of T187 phosphorylation.64”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [64]
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The R g value we compute using the very low confidence static structure excised from the AlphaFold prediction is considerably higher than values obtained from direct measurements based on SAXS (Figure 2(C)).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“These conformational features are driven by the distributed network of contacts formed between pairs of sequenceproximal or distal aromatic residues and influenced or diluted by the spacer regions that are interspersed between aromatic residues (stickers).88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88] source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The C-terminal domain of p27, p27 CTD , generally shows low pLDDT scores (<70) in the AlphaFold prediction.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This is because, the static AlphaFold structure likely overestimates the radius of gyration of IDRs as measured using small angle X-ray scattering (SAXS) 64 (Figure 2(A)).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [64]
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 10
- The quote only partly states this claim. 2
- Probably not a substantial claim. 9
In the paper
“Modulating the charge patterning changes the mean size of the conformational ensemble monotonically, consistent with SAXS experiments.64”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [64]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“A sizable fraction of the N-terminal region is predicted to adopt a helical structure by Alpha-Fold, and this is consistent with experimental characterizations based on nuclear magnetic resonance (NMR) spectroscopy.85”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Atomistic simulations reproduce the average R g value measured using SAXS.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > DECLARATION OF COMPETING INTEREST
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“This ensemble is sequence-specific 30,31 and the extent of heterogeneity 36 as well as the relative preferences of conformations within the ensemble will depend on the primary sequence, solution conditions, and functional contexts.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [30]
- Not checked yetcites [31]
- Not checked yetcites [36]
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Instead, disorder implies that a diverse conformational ensemble best describes the region of interest.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The N-terminal portion (residues 1-95) folds upon binding to Cdk2/cyclin A and this leads to kinase inhibition.85,86”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
- Not checked yetcites [86]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In the current dataset, long regions with pLDDT < 50 adopt a readily identifiable ribbon-like appearance, and should not be interpreted as structures but rather as a prediction of disorder.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Intrinsically disordered regions often evolve more rapidly than ordered regions and thus alignments of these regions are generally poorer and involve large gaps and extensions of gaps because orthologous IDRs can span vast sequence lengths.9,23,58,60,[68][69][70]”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites 9,23,58,60,[68][69][70]
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Alleviation of steric clashes leads to a steep decrease in the potential energy, and often the local minimum will be situated closest to the region of the steepest drop in energy.72,73”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [72]
- Not checked yetcites [73]
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Over the past two decades, there has been growing recognition that the sequence-structurefunction paradigm must be generalized to account for the role of conformational heterogeneity and conformational dynamics in protein function.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“created quite a stir when it was deployed to predict the structures of 98.5% of proteins in the human proteome.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“leading to the postulate, tested through theory, computations, and experiments, that aromatic residues function as cohesive motifs known as stickers that drive single chain compaction as well as phase separation 88 -the latter through a network of intermolecular interactions.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Under standard conditions used to study and characterize protein structures, IDPs/IDRs are defined by conformational heterogeneity, and the preference for heterogeneity is encoded in their amino acid sequences.11,[29][30][31]”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [11]
- Not checked yetcites [29]
- Not checked yetcites [30]
- Not checked yetcites [31]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The overall number of distinct conformations in the ensemble, the conformational heterogeneity, in terms of features such as the sizes and shapes sampled, and the relative importance of different conformations within the ensemble are defined by a combination of sequence-encoded short-and long-range interactions 37,38 as well as the modulation of these interactions by solution conditions.39,40”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [37]
- Not checked yetcites [38]
- Not checked yetcites [39]
- Not checked yetcites [40]
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Importantly, prediction engines are also available to identify regions that undergo disorder-to-order, order-to-disorder, and disorder-to-disorder transitions upon binding.44”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [44]
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The Pro-rich C-terminal region forms a semi-flexible tail.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Conformational ensembles of the exon 1 encoded region of huntingtin (Httex1)
What the sources say
- Not checked yetcites [94]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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The quote only partly states this claim.
In the paper
“IDPs/IDRs are best defined by a heterogeneous ensemble of conformations.36”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [36]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Taken together with complementary efforts focused on characterizing Httex1 phase behavior, the results obtained from multipronged studies of monomeric Httex1 predict that the growth of the “sticky head” with polyQ length mediates homotypic interactions and modulates heterotypic interactions in the cellular context.94”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Conformational ensembles of the exon 1 encoded region of huntingtin (Httex1)
What the sources say
- Not checked yetcites [94]
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Evidence in favor of this point of view is the successful prediction of natively disordered protein regions by AlphaFold and the correlation between the decrease of pLDDT and propensity to be in a disordered region [25].”
From Using AlphaFold to predict the impact of single mutations on protein stability and function (2023) · cites this paper as [25]
In the citing paper
“like peptides, IDPs and IDRs are best thought of as sampling diverse conformational ensembles rather than a single static conformation42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Ruff et al showed that radius of gyration values of IDPs / IDR containing proteins calculated using static AF2 models significantly deviates from those experimentally obtained by small-angle X-ray scattering (SAXS)42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary studies suggest that the majority of targets with very low-confidence score (pLDDT < 50) assigned by AF2 are likely to be IDPs / IDRs rather than well-folded structures that AF2 fails to predict42–44”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Some groups have used MD simulations to suggest that pLDDT and PAE metrics provide information on dynamics and disorder42,65”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary comparison of SAXS-derived versus AF calculated P(r) curves revealed that for many cases a static AF2 model does not adequately describe solution state structures42,72.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“It is therefore not surprising that it was recently shown [214] that data-driven approaches can hardly predict the structure of so-called intrinsically disordered proteins”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [214]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. Miskei, M., Antal, C., Fuxreiter, M., (2016). FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nucleic Acids Res., 45, D228- D235.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. To ´th-Petro ´czy, A ´., Oldfield, C.J., Simon, I., Takagi, Y., Dunker, A.K., Uversky, V.N., et al., (2008). Malleable machines in transcription regulation: The mediator complex. PLoS Comput. Biol., 4, e1000243”
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Valence and patterning of aromatic residues determine the phase behavior of prion-like domains
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“Improving and analyzing AlphaFold’s performance on IDRs is actively being explored [17, 23, 27].”
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Mészáros, A. (2024) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2024.142
The Impact of Neural Networks in Molecular and Cellular Biology
Guerroudji, W. H., Meddah, F. G., & Saadi, L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4571-0.ch003
From Orphan Genes to Cryptogenic Gene Candidates: Reassessing UniquenessPreprint
Maan, S. K., Butzin, X. Y., Ge, X., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.28.662130
Lauruol, F., Galaup, T., Bourgeois, A., et al. (2025) · mSphere · doi:10.1128/msphere.00287-25
“However, rather than indicating a failure in structure prediction, these low-pLDDT regions likely reflect inherent protein flexibility or disorder (72).”
Liquid–Liquid Phase Separation in Hereditary Hearing Loss
Tao, K., Zong, Y., Liu, X., Shi, X., Zhao, Z., & Sun, Y. (2025) · Neuroscience Bulletin · doi:10.1007/s12264-025-01446-9
“Nevertheless, while targeting LLPS presents a promising avenue for therapeutic intervention, it is crucial to acknowledge the widespread distribution of intrinsically disordered regions [121] within the human proteome, which constitutes over 30% of its total regions.”
Cui, H., Wang, Y., & Li, K. (2025) · International Conference on Scientometrics and Informetrics (ISSI) · doi:10.51408/issi2025_147
Wei, C. R., Basharat, Z., & Adhikari, P. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110882
Protein Language Model Identifies Disordered, Conserved Motifs Implicated in Phase SeparationPreprint
Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309.2 · Cited by 1
Nartey, C. M., & Deweese, J. E. (2025) · DNA · doi:10.3390/dna5040046
“It should be noted that these regions are expected to be flexible and may take several conformations, and we should interpret this information with caution [53].”
Asim, M. N., Ahmed, S., & Dengel, A. R. (2025) · Springer eBooks · doi:10.1007/978-3-031-90454-7_2
Sarron, A., Sowers, S. B., Tsybovsky, Y., Colley, H., Crooke, S. N., Stewart-Jones, G. B. E., & Overton, I. M. (2025) · Research Square · doi:10.21203/rs.3.rs-7642777/v1
“…a step change in protein structure prediction and is having signi�cant impact across biomedical sciences [11, 12]; although further improvements are warranted for example some Alphafold2 predictions include low con�dence regions that are represented by �laments in the 3D protein structure [13].”
Matsubara, D. K., Park, P., Cuccovia, I. M., & Chaimovich, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.06.680738
“AlphaFold predictions have proven effective in identifying disordered regions [54] and context-specific conformations in protein complexes [55], [56].”
Palmitoylation of PSD95 flips the bilayer juxtaposed domain and drives cluster formation on membranePreprint
Das, P. K., Nair, D. K., & Srivastava, A. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.09.679381
“37 This approach of combining multiple prediction tools, structural alignment with experimental data, and literature-based validation confirmed the reliability of our model for use in simulations.”
Schäfer, Y., Zheng, Y., Karunarathne, P., Parida, A. C., Pietzenuk, B., Kumar, V., Rose, L. E., Kraemer, U., & Wiehe, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.29.685298
“It is possible that the HMA4 proteins have an intrinsically disordered C-terminal domain (Ruff and Pappu, 2021) so that the di-Cys residues are accessible and the protein conformation can change upon binding (Porter and Looger, 2018).”
Durán-Armenta, L. F., Mészáros, A., Pueyo, J. M., et al. (2025) · Protein Expression and Purification · doi:10.1016/j.pep.2025.106861
Correlated Evolution Drives Structural Convergence of Interacting Proteins
Calix, K. M., Piya, A. A., & Assis, R. (2025) · Journal of Molecular Evolution · doi:10.1007/s00239-025-10291-3
“Further, our assessment of protein disorder was based on the AlphaFold pLDDT metric (Jumper et al. 2021) (see Methods ), which does not directly represent disorder, but rather the level of agreement between predicted and experimental structures (Jumper et al. 2021 ; Ruff and Pappu 2021).”
InfluentialWang, H., Wang, F., Xiong, R., Sun, Q., & Lai, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.26.690418
“Comprising over 30% of the human proteome ( 5, 6 ), IDPs play crucial roles in processes such as signal transduction ( 7, 8 ) and transcription regulation ( 9, 10 ).”
Poles, C., Stefano, M. D., Piazza, L., Bononi, G., Poli, G., Macchia, M., Tuccinardi, T., & Giordano, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02437
Iglesias, V., Avalos‐Padilla, Y., Bárcenas, O., & Fernàndez‐Busquets, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.10.693351
Ghafouri, H., Janson, G., Tosatto, S. C. E., & Monzón, A. M. (2025) · Protein Science · doi:10.1002/pro.70427 · Cited by 1
Candidate orphan genes: Reassessing uniqueness
Maan, S. K., Butzin, X. Y., Ge, X., & Butzin, N. C. (2025) · PLoS ONE · doi:10.1371/journal.pone.0338891
“Large-scale structural annotation of the human proteome using AlphaFold demonstrated that regions labeled as IDPs overlap with low-confidence predictions [55].”
Hryb, M., Davis, L., Lao, S., Paradis, N. J., Staehle, M. M., Mou, X., & Wu, C. (2026) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.12.017
“…[6], I-TASSER [7,8], and RosettaMP [9] offer viable alternatives for modeling full-length receptors when high-resolution solution NMR, X-ray and cryo-EM structure models are either incomplete or missing, but predicted models of disordered regions are often unstable and unreliable [10 – 12].”
Pires, V. S., Gravina-Oliveira, M. P., Júnior, N. S., Velozo, B. S., de Sousa, S. F. M., & Mesquita, R. D. (2026) · Acta Tropica · doi:10.1016/j.actatropica.2026.107969
Howard, C. J., Weers, P. M. M., Ryan, R. O., et al. (2026) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2026.184498
Hazarika, S., & Okafor, C. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.18.619098
Bridging structure and function: artificial intelligence-based modelling of kidney proteins
Wu, S., Wang, W., Zhou, Z. H., Scalzo, F., & Kurtz, I. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01060-6 · Cited by 1
“…per-residue local confidence in the predicted structure (on a 0–100 scale, where higher values indicate greater confidence), should not be misconstrued as a measure of local inaccuracy; in many cases, low-confidence regions correctly represent intrinsically disordered parts of the protein 72,109 .”
Angel, G., & Sujatha, P. L. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412406
“This approach often overlooks intrinsic disordered protein, especially in targets like α -synuclein, leading to unreliable binding-site geometries and misleading docking outcomes [11,27,29].”
InfluentialBasharat, Z., Raza, A., Ogaly, H. A., Al‐Zahrani, F. A. M., Saleem, U., Wei, C. R., Ahmed, I., Islam, M., Jadoon, S., & Kim, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-41159-1
“The 3D structure of selected target (Riboflavin synthase) was downloaded from AlphaFold database (https://alphafold.ebi.ac.uk/entry/Q9REI0) 41 42 .”
Sun, J., Pan, W., Zhang, Y., et al. (2026) · Journal of Integrative Agriculture · doi:10.1016/j.jia.2026.03.021
Jeanne, N., Paronetto, O., Dimeglio, C., et al. (2026) · Viruses · doi:10.3390/v18030341
“However, a pLDDT score below 50 may also suggest the presence of an intrinsically disordered region (IDR), as reported by Ruff et al. [26].”
Ho, N., & Schöck, F. (2026) · eLife · doi:10.7554/elife.111101.3
Ugurlu, S. Y. (2026) · International Journal of Advances in Engineering and Pure Sciences · doi:10.7240/jeps.1834505
ProtCross: Bridging the PDB-AlphaFold Gap for Binding Site Prediction with Protein Point Clouds
Zhong, S., & Jiang, Y. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03224 · Cited by 1
Uversky, V. N. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2648612
“…to predict a single, high confidence, static 3D structure (the most stable conformation), often folding disordered regions into compact globules, which fundamentally misrepresents their heterogeneous and dynamical nature and true biological behavior as flexible, unstructured ensembles [142].”
Levels of Analysis in Evolutionary Genetics: Novel Genes and the Misuse of Homology
Johnson, B. R. (2026) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jezb.70013
Mohanty, S. S., Namdeo, M., & Ampasala, D. R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44121-9.00014-7
VAP-A, un gymnaste moléculaire engagé dans les sites de contact membranaire
Subra, M. (2023) · theses.fr (ABES) · doi:10.70675/653ac35az5fb2z4fcbz8183zb46a6a33c38d
Structural characterisation of the interaction between the nsP3 protein of CHIKV and FHL1
Ferrino-Iriarte, A. (2022) · theses.fr (ABES) · doi:10.70675/75c91464z8478z4f4dz8161z101645cc36fd
Structural prediction of protein assemblies with deep learning
Bret, H. (2023) · theses.fr (ABES) · doi:10.70675/b3849091zce01z4ab0z91efz2b71d9cc754d
UBL3 UBL domain exhibits distinct helix-centered dynamic control among ubiquitin-like proteinsPreprint
Matsuda, K., Moriya, Y., Xu, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.06.716645
Structural and interaction study of MEG family proteins and their role in liver fibrosis onset
Nedvědová, Š. (2023) · theses.fr (ABES) · doi:10.70675/8633d2c6zc9eaz4b4ezb86ez0bb8d3371901
Analyse biogéométrique de l'évolution des protéines et des traits d'histoire de vie
Dagher, L. B. (2024) · theses.fr (ABES) · doi:10.70675/3a39f4f2zb57dz4fb3z8990zbd5d3fe1a6bf
Building centaur responders: is emergency management ready for artificial intelligence?
Whyte, C., & Haupt, B. “. (2026) · Disasters · doi:10.1111/disa.70054
“In particular, the success of AlphaFold (an offshoot of AlphaGo) in rapidly solving the mystery of protein folding that had eluded scientific explanation for decades invited a kind of confidence in AI's relevance that arguably did not exist previously (Ruff and Pappu, 2021).”
Sahu, U., Sharma, S., Pandey, S. C., & Khare, P. (2026) · Archives of Microbiology · doi:10.1007/s00203-026-04896-5
“For this reason, structure-guided redesign should presently be regarded as a supportive tool that helps guide engineering decisions and prioritize promising targets for experimental validation rather than as a fully predictive solution (Ruff and Pappu 2021).”
Ho, N., & Schöck, F. (2026) · eLife · doi:10.7554/elife.111101
Zhang, J., Qi, C., Wu, Z., Zhang, Y., Shi, J., Li, Z., Guo, J., Zhou, X., Tang, S., Yang, J., et al. (2026) · Stress Biology · doi:10.1007/s44154-026-00309-1
“…significant attention to structure-based PPI prediction methods with the emergence of tools such as AlphaFold and RoseTTAFold, which have accelerated methodological progress in the discovery of protein interactions (Bryant et al. 2021; Jumper et al. 2021; Liu et al. 2022a, b; Ruff and Pappu 2021).”
Ho, N., & Schöck, F. (2026) · eLife · doi:10.7554/elife.111101.1
Liao, K.-J., & Sun, Y.-J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag233
“…Engineered descriptors quantifying intrinsic disorder from DSSP, pLDDT thresholds ( ≥ 40, ≥ 50, ≥ 60, ≥ 70, ≥ 80), and PAE cutoffs (5, 10, 15 Å) [45–47], including proportion of disordered residues, number and lengths of disordered segments, and number of disordered segments of various lengths…”
Unravelling the Complexity: GPCR Signalling Networks
Lodhi, R., Chourasiya, R., & Sulakhiya, K. (2026) · Springer eBooks · doi:10.1007/978-981-95-9081-0_3
93Quantitative assessment of chirality of supersecondary protein structures
Lutsenko, A., & Sidorova, A. (2026) · De Gruyter eBooks · doi:10.1515/9783112216620-008
Li, D., & Minkara, M. S. (2026) · Protein Science · doi:10.1002/pro.70634
“The per-residue predicted local distance difference test confidence scores for the N-terminal region are shown in Figure S11, consistent with AlphaFold2 ’ s typical performance on intrinsically flexible or disordered regions (Akdel et al., 2022; Ruff & Pappu, 2021).”
Liu, W., Tian, L., Xie, J., Wu, L., Hu, L., Ge, R., Wang, Y., & Cao, M. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108931 · Cited by 1
Kırboğa, K. K., & Keleş, E. (2026) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2026.2680471
“Despite AF2’s reported near-crystallographic accuracy, structure prediction models have intrinsically low-confidence regions, particularly in loop and linker segments, with limitations in representing conformational heterogeneity (Ruff & Pappu, 2021).”
The cost of convenient truths: artificial intelligence in matrix biology
Izzi, V. (2026) · Matrix Biology · doi:10.1016/j.matbio.2026.102028
Méndez-Zamora, A., SERNA, E. C. D. L., Diaz‐Vidal, T., et al. (2026) · Molecular Catalysis · doi:10.1016/j.mcat.2026.116074
From prediction to discovery: AI as an observatory of physical organization in protein space
Zheng, Y., Zhang, Z., Wang, Y., Kang, W., Ren, W., & Tang, Q.-Y. (2026) · AI for Science · doi:10.1088/3050-287x/ae78ea
“As summarized in table 1, low predicted local distance difference test (pLDDT) can conflate intrinsic disorder, MSA sparsity, and model uncertainty [39, 40], whereas PAE reflects positional uncertainty rather than conformational free energy.”
Intrinsically disordered N-terminal regions suppress cotranslational protein degradationPreprint
Ju, D., Xie, D., Wang, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.13.732044
Adilović, M., Akçeşme, B., Hromić-Jahjefendić, A., & Uversky, V. N. (2026) · Journal of Cellular Biochemistry · doi:10.1002/jcb.70098
Awad, M. M., Osman, Y. A., & Abdelmoteleb, M. (2026) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-026-13906-x
“8) (Ruff and Pappu 2021) and visualized with PyMOL (DeLano 2002; Yuan et al. 2016).”
Beyond the structure-function paradigm: A comprehensive review of intrinsically disordered proteins
Harake, S. N. A., Btadini, S., Qadri, A. H., Harb, F., & Faheem, I. (2026) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2026.102706
“…proxy for disorder, albeit with important caveats: low confidence may reflect either genuine disorder or, alternatively, a merely difficult-to-predict fold, and AlphaFold returns a single static structure rather than the conformational ensemble that fundamentally defines a disordered region [212].”
Sonpal, A., & Pfaendtner, J. (2026) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6c03261
Wang, H., Xiang, F., Zhang, J., Wu, T., Wang, D., Wang, Q., Lu, B., & Fu, F. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1880976
Fruitwala, A., Tiszler, J. X., Watson, D., et al. (2026) · Protein Science · doi:10.1002/pro.70719
“This discrepancy may reflect differences between the static, computationally predicted PA1216 structure and its dynamic conformation in solution (Chakravarty & Porter, 2022; Ruff & Pappu, 2021; Terwilliger et al., 2024).”
Advancing In Silico Drug Design with Bayesian Refinement of AlphaFold Models
Sen, S., Hoff, S., Morozova, T. I., Schnapka, V., & Bonomi, M. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00868 · Cited by 6
Park, Y. H., Kim, R., Song, K.-H., & Jung, H. S. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146250
“This low-confidence segment coincides with the MobiDB-lite consensus disorder region, supporting its interpretation as a likely intrinsically disordered region, although local coordinates should be treated with caution [16,17].”
InfluentialDaghino, S., Forgia, M., Serio, F. D., de la Peña, M., Ayllón, M. A., Navarro, B., & Turina, M. (2026) · Virus Research · doi:10.1016/j.virusres.2026.199780
“Such structural congruence, even in the presence of low-confidence regions that might represent intrinsically disordered domains (Ruff and Pappu, 2021), provides crucial evidence for the homology of these viral proteins, also under-scoring how structural bioinformatics can bridge the gap in viral…”
Wang, H., Zheng, T., Liu, C., et al. (2026) · Biomaterials Advances · doi:10.1016/j.bioadv.2026.215080
Exploring the Evolutionary Landscape of AI-Generated Viral Sequences: a Case Study on HIV-1
Donelli, P. A., Rancati, S., Nicora, G., Bellazzi, R., Parimbelli, E., & Portinale, L. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3819436
“To assess biophysical stability, the 69 intact Gag proteins were partitioned into structural subregions to prevent flexible linkers from confounding global structural accuracy [15]: Matrix (MA), Capsid N-terminal (CA_NTD) and C-terminal (CA_CTD) domains, Nucleocapsid (NC), and p6 Spacer.”
Matyszczak, G. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27156839 · Cited by 1
“…proved its advantage over such methods by successfully predicting the structures of c.a. 98.5% of proteins present in the human proteome, including intrinsically disordered proteins and regions, in the 14th Critical Assessment of protein Structure Prediction (CASP14) (see Figure 25a) [151,152,154].”
InfluentialGoing high-throughput: Dynamic conformations and interactions in vivo
Gong, Z., Zhao, Q., Sun, M., Zhang, B., Zhang, X., Zhang, L., Zhang, Y., & Liu, M. (2026) · Magnetic Resonance Letters · doi:10.1016/j.mrl.2026.200306
“In addition, AlphaFold and related prediction algorithms remain limited in describing the conformational heterogeneity of IDPs [41].”
Tetteh-Tsifoanya, M., Sekine, T., Kosako, H., et al. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1900304
“…these diverse environmental parameters, the precise prediction of conditional structural dynamics within IDRs, including charge-driven changes in phase separation behavior, will likely become more ef fi cient and achievable (Ruff and Pappu, 2021; Brotzakis et al., 2025; Abramson et al., 2024).”
Acid–Base Properties of Model Copolypeptides from Molecular Simulations and Experiments
Padhee, I., Keprta, V., Pineda, S. P., Štěpánek, M., & Košovan, P. (2026) · ACS Applied Polymer Materials · doi:10.1021/acsapm.6c01842
Crow, R. S., Grubb, C., Meisel, J. W., & Smith, L. C. (2026) · The Journal of Immunology · doi:10.1093/jimmun/vkag213
Niazi, S. K. (2026) · Cells · doi:10.3390/cells15171560
“Consequently, low model confidence should not be treated as primary evidence of disorder or used as a proxy; it may also result from shallow alignments, ambiguous domain orientations, and other factors [50].”
Padon, S. D. M., & Lozano, A. J. (2026) · Frontiers in Tropical Diseases · doi:10.3389/fitd.2026.1923721
“Lastly, QMEANDiSco evaluations generated global structural scores ranging from 0.38 to 0.44, penalizing structures lacking a buried hydrophobic core, anticipated for highly solvent-exposed IDPs (82, 83).”
InfluentialTamerler, C., & Snead, M. L. (2026) · Biomimetics · doi:10.3390/biomimetics11090680
“AlphaFold-Multimer was recently shown to identify the IDRs residues as interaction-prone and to predict their binding modes with their specific binding partner [142,143].”
Fan, T., Wei, C., Chen, W., Li, S., Wang, J., Lu, Y., & Renshi, X. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1915475
“It enables large-scale structural bioinformatics and facilitates drug target screening and mechanistic studies (Ruff and Pappu, 2021).”
Sullivan, J. V., Currie, M. J., Prendergast, P. J., et al. (2026) · Australasian Plant Pathology · doi:10.1007/s13313-026-01197-9
“This characteristic of AlphaFold models has been reported within the human proteome, where regions predicted to be intrinsically disordered were structured with low or very low confidence (Ruff and Pappu 2021).”
Metadata
- Authors
- Ruff, K. M., & Pappu, R. V.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Pappu, Rohit V.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 433
- Issue
- 20
- Pages
- 167208
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W3195375135
- Semantic Scholar
- 960aaaa85d1de9f8e2490d872165b720e3d4b565
- Cited by
- 635
- References
- 103
- Citations by year
- 2026: 97 · 2025: 154 · 2024: 139 · 2023: 144 · 2022: 91 · 2021: 10
- FWCI
- 10.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Molecular Biology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- AlphaFold, intrinsically disordered regions, protein structure prediction, sequence-function relationships, conformational heterogeneity, protein dynamics, protein-protein interaction network
- MeSH terms
- Deep Learning, Animals, Humans, Models, Molecular, Protein Conformation, Protein Folding, Computational Biology, Proteome, 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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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