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
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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
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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
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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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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.
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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.
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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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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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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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.
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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.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
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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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Learning Universal Representations of Intermolecular Interactions with ATOMICAPreprint
Fang, A., Desgagné, M., Zhang, Z., Zhou, A., Loscalzo, J., Pentelute, B. L., & Žitnik, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.02.646906 · Cited by 15
“Non-globular domains, especially intrinsically disordered regions (IDRs), are poorly captured in current structural databases due to their flexibility [115].”
Decoding molecular mechanisms for loss-of-function variants in the human proteomePreprint
Cagiada, M., Jonsson, N., & Lindorff‐Larsen, K. (2025) · eLife · doi:10.7554/elife.108160.1 · Cited by 9
Macke, A. C., Stump, J. E., Kelly, M. S., Rowley, J., Herath, V., Mullen, S., & Dima, R. I. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01511 · Cited by 4
Badaoui, L. E., & Barr, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25020820 · Cited by 7
“It should also be noted that the disordered loops, which are annotated as low confidence predictions, are not necessarily the representative conformation and are likely to exist in dynamic heterogeneous conformations in physiological conditions [45].”
BackgroundLoonen, S., van Steenis, L., Bauer, M., & Šoštarić, N. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26842 · Cited by 6
“The SARS- CoV-2 viral life cycle comprises several stages: after binding to human host cells [2], the virus replicates and assembles into new virions inside the host cell [3, 4].”
Mallon, S. D., Bos, E., Sheikhhassani, V., Shademan, M., Voortman, L. M., Mashaghi, A., Sharp, T. H., & Raz, V. (2025) · The FASEB Journal · doi:10.1096/fj.202501097r · Cited by 6
“Considering the limitations of AlphaFold for IDR prediction [32], the difference between Ala10 and Ala16 justified the investigation of structural features using puncta structure analysis.”
Tang, Y., Wen, J., Yang, Z., Yao, Y., He, S., Zeng, J., Dong, X., Qiao, Q., Zhou, Y., Perrett, S., et al. (2025) · Advanced Science · doi:10.1002/advs.202511545 · Cited by 7
Intrinsically disordered regions: a platform for regulated assembly of biomolecular condensates
Putnam, A. A., & Seydoux, G. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823967-4.00016-6 · Cited by 7
AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway
Malhotra, N., Khatri, S., Kumar, A., Arun, A., Daripa, P., Fatihi, S., Sureshkumar, V., Jain, N., & Thukral, L. (2023) · Autophagy · doi:10.1080/15548627.2023.2238578 · Cited by 6
“…context of designing proteins that bind to specified target [42], complex prediction [36], missense mutations [43–45], post-translational modifications [46], identifying disordered protein regions [47], structure-based drug designing [48,49] and relevance in the area of protein aggregation [50].”
BackgroundMethodResultInfluentialGordon, C. H., Hendrix, E., He, Y. J., & Walker, M. C. (2023) · Biomolecules · doi:10.3390/biom13081243 · Cited by 6
“Owing to its superior accuracy, AlphaFold 2 and the AlphaFold 2 multimer version have been employed in the study of intrinsically disordered proteins, complex biomolecular systems, and other structurally challenging systems [17,18].”
BackgroundOh, C., Buckley, P. M., Choi, J., Hierro, A., & DiMaio, D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2307721120 · Cited by 6
Kleizen, B., de Mattos, E., Papaioannou, O., Monti, M. M., Tartaglia, G. G., van der Sluijs, P., & Braakman, I. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115741 · Cited by 5
“…2 and 3). regions are predicted for the folded state in accordance with the experimental data (Figures Since Alphafold predictions of unstructured regions have low confidence [48,49], we also computed aggregation propensity based on sequence alone and arrived at the same conclusion (not shown).”
MethodResultRichardson, J. S., Williams, C. J., Chen, V. B., Prisant, M. G., & Richardson, D. C. (2023) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798323008847 · Cited by 7
The Enigma of Transcriptional Activation Domains
Erkine, A. M., Oliveira, M., & Class, C. A. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168766 · Cited by 6
AlphaFold with conformational sampling reveals the structural landscape of homorepeats
Bonet, D. F., Ranyai, S., Aswad, L., et al. (2024) · Structure · doi:10.1016/j.str.2024.08.016 · Cited by 6
Smirnova, E. V., Rakitina, T. V., Saratov, G. A., Kudriaeva, A. A., & Belogurov, A. A. (2022) · Crystals · doi:10.3390/cryst12020197 · Cited by 6
“It is obvious that AlphaFold2 is most in demand when working with the proteins that are most difficult for direct structural study, which include intrinsic disordered proteins (IDP) and their interaction networks [10].”
BackgroundMutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA
Chandra, S., Manjunath, K., Asok, A., & Varadarajan, R. (2023) · Protein Science · doi:10.1002/pro.4580 · Cited by 7
“…because while there have been revolutionary advancements in deep learning based structural predictions of proteins (Baek et al., 2021; Jumper et al., 2021), IDP structural predictions by these methods have been deemed unreliable (Chakravarty and Porter, 2022; Ruff and Pappu, 2021; Strodel, 2021).”
MethodComputational analysis of regulatory regions in human protein kinases
Pei, J., & Cong, Q. (2023) · Protein Science · doi:10.1002/pro.4764 · Cited by 5
“As residues in disordered regions are flexible and could adopt a variety of conformations, the conformation of these residues in the AlphaFold model and their interactions with the KDs should be interpreted with caution (Ruff & Pappu, 2021).”
A structural foundation for studying chlamydial polymorphic membrane proteins
Debrine, A. M., Karplus, P. A., & Rockey, D. D. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.03242-23 · Cited by 6
“It has been proposed that quick and significant drops in pLDDT value, such as these, signify an intrinsically disordered region of the protein (27) that could adopt a more folded structure with either host or bacterial ligands at the chlamydial cell surface.”
BackgroundMoreland, R. T., Zhang, S., Barreira, S. N., Ryan, J. F., & Baxevanis, A. D. (2024) · PROTEOMICS · doi:10.1002/pmic.202300397 · Cited by 4
“On this point, Ruff et al. [14] previously reported that roughly 30% of the residues across predicted human protein structures tend to have pLDDT scores below 50, consistent with disorder estimates for the entire human proteome [15].”
(2024) · doi:10.3390/biomimetics9030162 · Cited by 7
“When the pLDDT is lower than 50, the predicted region is expected to be intrinsically disordered [22] When the pLDDT is lower than 50, the predicted region is expected to be intrinsically disordered [22].”
BackgroundIntrinsic Disorder of the Neuronal SNARE Protein SNAP25a in its Pre-fusion Conformation
Stief, T., Gremer, L., Pribicevic, S., Espinueva, D. F., Vormann, K., Biehl, R., Jahn, R., Pérez-Lara, Á., & Lakomek, N.-A. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168069 · Cited by 6
Assessing PDB Macromolecular Crystal Structure Confidence at the Individual Amino Acid Residue Level
(2022) · doi:10.1101/2022.05.17.492280 · Cited by 6
“Among them, lower pLDDT scores have been shown to be a good predictors of protein disorder (Ruff and Pappu, 2021).”
BackgroundResultStructural basis of interdomain communication in PPARγPreprint
Mosure, S. A., Munoz‐Tello, P., Kuo, K.-T., MacTavish, B. S., Yu, X., Scholl, D., Williams, C. C., Strutzenberg, T. S., Li, J., Bass, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.13.499031 · Cited by 6
“Obtaining atomic resolution structural data on the PPAR γ AF-1 would be important for the fi eld—new regulatory mechanisms are likely to emerge, and the data may explain published observations of interdomain functional communication between the PPAR γ AF-1 and AF-2/LBD.”
BackgroundMcDonnell, R. T., Patel, N., Wehrspan, Z. J., & Elcock, A. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.28.538765 · Cited by 7
“It seems quite possible that this compaction is attributable in part to a known artifact of AlphaFold2 that can drive proteins whose structures should be largely extended into roughly spherical arrangements (Ruff & Pappu 2021; Bruley et al., 2022).”
BackgroundSingh, K., Gupta, J. K., Narayan, S., Rani, K., Jain, D., Porwal, P., Sharma, M. C., & Kumar, S. (2025) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010298545241108062449 · Cited by 6
LoRA-DR-suite: adapted embeddings predict intrinsic and soft disorder from protein sequences
Lombardi, G., Seoane, B., & Carbone, A. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf185 · Cited by 4
“However, the correlation is stronger for soft disorder, highlighting key distinctions in how these two disorder types relate to structural confidence (Ruff and Pappu 2021).”
Large protein databases reveal structural complementarity and functional locality
Szczerbiak, P., Szydlowski, L. M., Wydmański, W., Renfrew, P. D., Leman, J. K., & Kościółek, T. (2025) · Nature Communications · doi:10.1038/s41467-025-63250-3 · Cited by 9
ricePSP: a database of rice phase separation-associated proteins
Gao, R., Guo, M., Luan, S., Ouyang, W., & Li, X. (2025) · Genome biology · doi:10.1186/s13059-025-03842-w · Cited by 5
Majila, K., Ullanat, V., & Viswanath, S. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101486 · Cited by 8
Conformational Rearrangements Regulating the DNA Repair Protein APE1
Komaniecka, N., Porras, M., Cairn, L., Martín, J. A. S., Ferreiro, N., Penedo, J. C., & Bañuelos, S. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23148015 · Cited by 6
“69 for residues 1–40) (Figure S6), as expected for intrinsically disordered regions [29].”
BackgroundCasting Rubik’s Group into a Unitary Representation for Reinforcement Learning
Corli, S., Moro, L. D., Galli, D. E., et al. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2533/1/012006 · Cited by 5
Ruiz-Molina, N., Parsons, J., Decker, E. L., & Reski, R. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.002 · Cited by 4
“Very low confidence regions predicted by AF2 have been correlated with intrinsically disordered regions (IDRs) [33], which can acquire several conformational ensembles and evolve faster than ordered”
BackgroundIdentification of potential molecular mimicry in pathogen-host interactions
(2023) · doi:10.7717/peerj.16339 · Cited by 6
“The structural fl exibility of intrinsically disordered regions allows them to interact with a large array of protein partners ( Tompa, Szász & Buday, 2005 ), but also makes prediction of their tertiary structures particularly dif fi cult ( Ruff & Pappu, 2021 ).”
BackgroundIntrinsic Disorder and Other Malleable Arsenals of Evolved Protein Multifunctionality
Aftab, A., Sil, S., Nath, S., Basu, A., & Basu, S. (2024) · Journal of Molecular Evolution · doi:10.1007/s00239-024-10196-7 · Cited by 7
“Alpha-Fold (Ruff and Pappu 2021), when used to model disordered proteins, highlights the importance of IDRs (in quantitative sequence–ensemble relationships) and leaves room for misprediction and improvement in functional annotations from the predicted structures.”
Dynamic pre-structuration of lipid nanodomain-segregating remorin proteins
(2024) · doi:10.1038/s42003-024-07330-y · Cited by 6
“It is worth noting that Alphafold (2021) had a tendency to overestimate the radius of gyration of the conformational space as assessed by SAXS measurements 43 .”
Wang, Y.-X., Huang, C.-Y., Chiu, H.-J., Huang, P.-H., Chien, H.-T., Jwo, S.-H., & Liao, Y. (2022) · Journal of Physiology and Biochemistry · doi:10.1007/s13105-022-00932-2 · Cited by 5
The impact of genetically controlled splicing on exon inclusion and protein structure
Einson, J. E., Minaeva, M., Rafi, F., & Lappalainen, T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0291960 · Cited by 4
AlphaFold2 fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · doi:10.1101/2022.03.08.483439 · Cited by 193
Random, de novo and conserved proteins: How structure and disorder predictors perform differentlyPreprint
Middendorf, L., & Eicholt, L. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.18.549582 · Cited by 5
Zhong, G., Zhao, Y., Zhuang, D., Chung, W. K., & Shen, Y. (2024) · doi:10.1101/2024.02.20.581321 · Cited by 15
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202404.0576.v1 · Cited by 5
Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2
Subramaniam, T. R., Mualif, S. A., Chan, W. H., & Halim, K. B. A. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1533983 · Cited by 4
“AlphaFold2 (Cheung et al., 2023; Ruff and Pappu, 2021; Skolnick et al., 2021) produces remarkably accurate 3D structure predictions using a neural network architecture that has been trained on a large database of structural and protein sequence data.”
Expose flexible conformations for intrinsically disordered protein
Yang, J., Ji, W., Cheng, W. X., Wu, G., Sheng, S. T., Zhang, P., Lin, J., Chen, X., & Shi, Q. (2025) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2025.100170 · Cited by 4
“…pLDDT (predicted Local Distance Difference Test) (Carugo, 2023) score for IDR while it achieved the protein structure prediction competitive accuracy with experimental determination (Jumper et al., 2021; Tunyasuvunakool et al., 2021; Ruff and Pappu, 2021; Wilson et al., 2022; Guo et al., 2022).”
And… cut! – how conformational regulation of CRISPR-Cas effectors directs nuclease activity
Calvert, R. W., & Knott, G. J. (2025) · Biochemical Journal · doi:10.1042/bcj20240481 · Cited by 6
Limitations of the refolding pipeline for de novo protein design
Korbeld, K. T., Viliuga, V., & Fürst, M. J. L. J. (2026) · Protein Science · doi:10.1002/pro.70613 · Cited by 5
“Indeed, low pLDDT intrinsically disordered regions constitute a substantial proportion of AFDB models (Akdel et al., 2022; Alderson et al., 2023; Ruff & Pappu, 2021), and differences in architecture and training across folding models may cause different models to not converge on the same…”
Hidden in protein sequences: Predicting cryptic binding sites
Škrhák, V., Gamouh, H., & Hoksza, D. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3765612.3767221 · Cited by 4
“Previous studies have shown that pLDDT values are a promising proxy for residue flexibility [11, 18, 24, 25].”
Xu, S., Yang, S., Jiao, X., Cai, J., Wu, J., & Qiao, J. (2026) · Archives of Microbiology · doi:10.1007/s00203-026-04830-9 · Cited by 4
“IDRs typically yield low pLDDT (predicted local distance difference test) scores, making them difficult to model accurately (Ruff and Pappu 2021).”
The severity of MUSK pathogenic variants is predicted by the protein domain they disrupt
Cocanougher, B., Liu, S. W., Francescatto, L., et al. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100288 · Cited by 4
“37 This juxtamembrane domain that connects the transmembrane domain with the kinase domain has been shown to be involved in Dok-7 binding and is in a region of the AlphaFold structure that has ‘‘low’’ to ‘‘confi-dent’’ ratings (between 50 and 90 per-residue confidence score predicted local distance…”
Amino Acid Residue-Specific Ramachandran Distributions Derived from a Simple Mean Field Potential
Andrews, B. (2024) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.4c00064 · Cited by 4
Gollapalli, S., Banesh, S., Sugandh, H., & Saudagar, P. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.137290 · Cited by 4
Application of computational methods in the drug discovery and development of Alzheimer's disease
Zhang, Q., Gong, X., Liu, H., & Yao, X. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.038 · Cited by 5
“Notably, the AlphaFold algorithm, developed by DeepMind, has demonstrated the capability to predict protein three-dimensional structures with remarkable accuracy by integrating deep neural networks with physical modeling techniques 127,128 .”
FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding
Liu, Y., Zhang, Y., Zhou, Z. P., & Shen, H. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169412 · Cited by 4
Mummadisetti, M. P., Su, X., & Liu, H. (2022) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.08.004 · Cited by 4
Michaelis, M., Cupellini, L., Mensch, C., Perry, C. C., Delle Piane, M., & Colombi Ciacchi, L. (2023) · Chemical Science · doi:10.1039/d3sc02202a · Cited by 6
Predicted structural proteome of Sphagnum divinum and proteome-scale annotation
Davidson, R. B., Coletti, M. A., Gao, M., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad511 · Cited by 4
Sim, P. F., Chek, M. F., Nguyen, N., Nishimura, T., Inaba, T., Hakoshima, T., & Suetsugu, S. (2023) · The Journal of Biochemistry · doi:10.1093/jb/mvad065 · Cited by 3
Rebhi, S., Basharat, Z., Wei, C. R., et al. (2024) · PeerJ · doi:10.7717/peerj.17292 · Cited by 4
“The 3D protein structural prediction analysis was predicted via the AlphaFold ( Ruff & Pappu, 2021 ) v2.”
MethodCoskuner‐Weber, O., Gentili, P. L., & Uversky, V. N. (2025) · Biophysical Reviews · doi:10.1007/s12551-025-01286-x · Cited by 4
“Therefore, AlphaFold2 and AlphaFold3 are used currently in academic and commercial settings as well as databases of IDPs (Ruff and Pappu 2021; Orand and Jensen 2025; Gutnik et al. 2023).”
Zheng, S. (2025) · PLOS ONE · doi:10.1371/journal.pone.0313812 · Cited by 4
Sozańska, N., Klepka, B. P., Niedźwiecka-Kornaś, A., Zhukova, L., Dadlez, M., Greb‐Markiewicz, B., Ożyhar, A., & Tarczewska, A. (2025) · Cell Communication and Signaling · doi:10.1186/s12964-025-02154-7 · Cited by 4
Gregory, F. D., & Trump, B. D. (2025) · NATO science for peace and security series. A, Chemistry and biology · doi:10.1007/978-94-024-2296-2_2 · Cited by 5
Helsem, S. A., Alfsnes, K., Frye, S. A., Hesselberg Løvestad, A., & Ambur, O. H. (2025) · PLOS One · doi:10.1371/journal.pone.0315160 · Cited by 3
“Issues with confidences of predicted disordered protein regions, dynamic interactions, multi-chain complexes such as protein-DNA, training-set representativeness and lack of benchmarking studies are different recently discussed challenges [44–46].”
Che, K., Kress, T., Górka, M., Żerko, S., Koźmiński, W., & Kurzbach, D. (2022) · Journal of Magnetic Resonance Open · doi:10.1016/j.jmro.2022.100069 · Cited by 4
Montepietra, D., Cecconi, C., & Brancolini, G. (2022) · RSC Advances · doi:10.1039/d2ra04913a · Cited by 4
On the Unknown Proteins of Eukaryotic Proteomes
Sanejouand, Y. (2023) · Journal of Molecular Evolution · doi:10.1007/s00239-023-10116-1 · Cited by 2
“This may also mean that there are more disordered, hard-topredict proteins [49, 50], among the unknown and singleton proteins of these two viridiplantae.”
BackgroundPervasive, conserved secondary structure in highly charged protein regions
(2023) · doi:10.1371/journal.pcbi.1011565 · Cited by 4
“The first is to impute disorder to residues with a low AlphaFold confidence score [37]; the second is to ascribe disorder to regions with high-confidence coil (e.”
InfluentialYusof, N. Y., Quay, D. H. X., Kamaruddin, S., Jonet, M. A., Illias, R. M., Mahadi, N. M., Firdaus‐Raih, M., Bakar, F. D. A., & Murad, A. M. A. (2024) · Extremophiles · doi:10.1007/s00792-024-01333-7 · Cited by 4
“6A). pLDDT of < 50 indicates a very low confidence score/intrinsically disordered regions, while 50 < pLDDT < 70, 70 < pLDDT < 90 and pLDDT > 90 indicates low confidence, high and very high confidence, respectively (Ruff, and Pappu 2021).”
Ryan‐Phillips, F., Henehan, L., Ramdas, S., Palace, J. A., Beeson, D., & Dong, Y. Y. (2024) · Biomedicines · doi:10.3390/biomedicines12112549 · Cited by 5
Soft disorder modulates the assembly path of protein complexes
Seoane, B., & Carbone, A. (2022) · doi:10.1101/2022.03.05.483089 · Cited by 13
“In [3, 9], it has been observed that the regions where intrinsic disorder is present correspond to those with high uncertainty for AF2.”
BackgroundThe Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
Tang, Q.-Y., Ren, W., Wang, J., & Kaneko, K. (2022) · doi:10.1101/2022.04.07.487447 · Cited by 31
The impact of AlphaFold on experimental structure solutionPreprint
Edich, M., Briggs, D. C., Kippes, O., Gao, Y., & Thorn, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.07.487522 · Cited by 4
Dubourg-Felonneau, G., Abbasi, A., Akiva, E., & Lee, L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.29.518403 · Cited by 4
“It has been observed that regions of the protein structure 68 that have a higher pLDDT are generally associated to "less rigid" parts of the structure [13].”
BackgroundEnhancing Challenging Target Screening via Multimodal Protein-Ligand Contrastive LearningPreprint
Wang, Z., Wang, Z., Yang, M., Pang, L., Nie, F., Liu, S., Gao, Z., Zhao, G., Ji, X., Huang, D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.22.609123 · Cited by 6
“While artificial intelligence technologies such as AlphaFold can predict protein struc-53 tures, their confidence levels are relatively low for complex undruggable proteins [10, 11].”
BackgroundProtein language model identifies disordered, conserved motifs implicated in phase separation
Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309 · Cited by 4
“Following previous studies ( Ruff and Pappu, 2021 ; Alderson et al., 2023 ), we used a threshold of pLDDT = 70 to differentiate ordered from disordered residues.”
AlphaFlex: Ensembles of the human proteome representing disordered regionsPreprint
Liu, Z. H., Zhang, O., Castro, S. D., Sun, K., Ghafouri, H., Attafi, O. A., Fawzi, N. L., Tosatto, S. C. E., Monzón, A. M., Moses, A. M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.24.690279 · Cited by 6
“…for interactions between confidently predicted folded domains, the per-residue pLDDT is not the best predictor of intrinsic protein disorder 63 , which can lead to biologically inconsistent IDR assignments that obscure functional states, particularly in the case of conditionally folding IDRs 63 .”
Roy, A. S., Tsay, K., Borbat, P. P., DeStefano, A. J., Han, S., Srivastava, M., & Freed, J. H. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.5c16298 · Cited by 5
A biochemical screening platform to target chromatin states using condensates as a tool
Hsieh, L. J., Lou, T., Gourdet, M. A., Wong, E., & Narlikar, G. J. (2025) · SLAS DISCOVERY · doi:10.1016/j.slasd.2025.100236 · Cited by 3
Maurya, A. K., Cadena, L. R., Ehret, G., & Nowack, E. C. M. (2025) · mBio · doi:10.1128/mbio.02247-25 · Cited by 4
“Collectively, these findings point to an origin of ETP2 via either de novo innovation from non-coding DNA or extensive sequence divergence beyond recognition within the Strigomonadinae (35), mechanisms that may underline its intrinsically disordered nature (36).”
Deep Generative Modeling of Protein Conformations: A Comprehensive Review
Dao, T. M., & Rahman, T. (2025) · BioChem · doi:10.3390/biochem5030032 · Cited by 5
“Additionally, this approach bypasses the need for Multiple Sequence Alignments (MSAs) or experimental data, supporting the idea that IDPs do not require or benefit much from MSAs [128,129].”
ChronoSort: Revealing Hidden Dynamics in AlphaFold3 Structure Predictions
Argyle, M. E., Heaps, W. P., Kubalek, C., Gardiner, S., Bundy, B. C., & Corte, D. D. (2025) · SynBio · doi:10.3390/synbio3040018 · Cited by 3
“Additionally, investigations into AlphaFold2’s ability to predict structural ensembles of disordered proteins have shown promise, particularly for regions with low confidence scores [22,23].”
Santos-Costa, A. H., Gaieta, E. M., Albuquerque, A. O., Souza, J. S., Almeida, D. S., Sampaio, J. V., England, P., Sartori, G., & da Silva, J. H. M. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111550 · Cited by 2
Kwansa, A. L., Singh, A., Williams, J. T., et al. (2024) · Cellulose · doi:10.1007/s10570-023-05691-x · Cited by 3
“This is a general problem for intrinsically disordered proteins with AlphaFold structural predictions (Ruff and Pappu 2021), which highlights the need for continuing computational contributions to fully understand less structured proteins including CESAs.”
Kombo, D. C., LaMarche, M. J., Konkankit, C. C., & Rackovsky, S. R. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26704 · Cited by 3
Are Most Human-Specific Proteins Encoded by Long Noncoding RNAs?
Sanejouand, Y. (2024) · Journal of Molecular Evolution · doi:10.1007/s00239-024-10174-z · Cited by 3
“Note that AlphaFold2 can also be used for predicting if a protein has disordered segments [27, 30].”
Chen, G., & Zhang, Z. (2024) · ACS Omega · doi:10.1021/acsomega.4c04161 · Cited by 4
Conformational analysis of the IQSEC2 protein by statistical thermodynamics
Shokhen, M., Albeck, A., Borisov, V., Israel, Y., Levy, N. S., & Levy, A. P. (2024) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2024.100158 · Cited by 3
“…AlphaFold (Jumper et al., 2021) failed to generate a 3D structure of IQSEC2 consistent with the biology in experimental analysis (Shokhen et al., 2023) in line with the previously described failure of AlphaFold to predict the 3D structure of IDPs (Azzaz et al., 2022; Ruff and Pappu, 2021).”
ResultSun, J., Song, J., Kim, J., Kang, S., Park, E., Seo, S.-W., & Min, K. (2024) · RSC Advances · doi:10.1039/d4ra06285j · Cited by 3
“51,52 Consequently, the inclusion of IDPs in the hAF 2.0 database used in this study could impact the GCN model's accuracy in predicting protein aggregation propensity.”
InfluentialKDSAXS: A Tool for Analyzing Binding Equilibria With SAXS Data Using Explicit Models
Gomes, T., Ruíz, L., Martín-Malpartida, P., et al. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169103 · Cited by 3
Mondal, J., Biswas, S., Roy, S., et al. (2025) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2025.109098 · Cited by 3
Matthes, M. S., Yun, N., Luichtl, M., Büschges, U., Fiesselmann, B. S., Strickland, B., Lehnardt, M. S., Schneitz, K., Michel, K., & Torres‐Ruiz, R. A. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011217 · Cited by 3
“Low pLDDT scores, as those given in ENPs aa471 to aa571, likely describe intrinsically disordered regions as opposed to well-defined autonomously foldable three-dimensional structures [30,58].”
NMR in the Age of Modern Biomedical Research and Drug Discovery
Lu, X., Myint, W., Muli, C. S., Larion, M., Matsuo, H., & Walters, K. J. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169302 · Cited by 4
(2023) · doi:10.1002/jcc.27067 · Cited by 4
“However, such methods generally face difficulties for unstructured and/or highly flexible regions [43].”
BackgroundYazıcı, Y. Y., Belkaya, S., & Timuçin, E. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.06.021 · Cited by 3
“Given the established strong correlation between the AF2 confidence scores and structural disorder [44,48], high mobility of the C-termini of the computational models would be understandable.”
BackgroundZraly, C. B., Schultz, R. M., Dı́az, M. O., & Dingwall, A. K. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad698 · Cited by 3
“Howe v er, it is important to consider that the AlphaFold models did not take into account how the PHD fingers may be orientated when they are bound to chromatin or that alternati v e conformational states were e v en likely in certain contexts ( 52 ).”
Ionic liquids for stability of ker6, a potential keratinase for human hair keratin extraction
Rai, P., & Hasija, Y. (2024) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2024.126365 · Cited by 3
Survey of the Aβ-peptide structural diversity: molecular dynamics approaches
Tolstova, A. P., Adzhubei, A. A., Strelkova, M. A., Makarov, A. А., & Mitkevich, V. A. (2024) · Biophysical Reviews · doi:10.1007/s12551-024-01253-y · Cited by 4
“Recently, a bench of frameworks emerged using AlphaFold2 and its local distance difference test as a disorder predictor (Aspromonte et al. 2024; Ruff and Pappu 2021; Wilson et al. 2022; Zhao et al. 2023).”
Dagher, L. B., Madern, D., Malbos, P., & Brochier‐Armanet, C. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msae271 · Cited by 3
“This may be due to the fact that C α corresponding to indels mainly correspond to disordered regions, which on average have weaker CIs (Wrabl and Grishin 2004; Jumper et al. 2021; Ruff and Pappu 2021).”
Karatzas, P., Brotzakis, Z. F., & Sarimveis, H. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00171 · Cited by 3
“12 − 14 Despite the increase in efficiency of all-atom and coarse-grained MD force fields of IDPs and PDPs in reproducing experimental data, still the efficiency and accuracy of MD-generated statistical ensembles rely on the accuracy of the force fields, which to the present day remain imperfect.”
Mohanty, D., & Sharma, G. S. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.143672 · Cited by 3
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.
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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