The DOI matches its Crossref record; 97 of the 103 references it cites were found in a registry or an index; 4 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
36 Not checked yet · 9 Own interpretation · 9 No basis given · 5 Own result · 2 Supported · 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)
hedged- Asserted: passes — The authors put it forward as true.
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Every source the paper cites for this claim was checked and supports it.
In the paper
“AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Abstract
What the sources say
- Supportedcites [1] source
The Abstract describes AlphaFold as a neural network-based model and reports that it greatly outperformed other methods in CASP14. The Main section identifies CASP14 as a blind, gold-standard assessment of structure-prediction accuracy, supporting the reference to an independent assessment. (LLM, conf=0.94)
- Supportedcites [2] source
The Main section describes AlphaFold as a machine-learning structure-prediction method and reports that its CASP14 entry achieved routinely high accuracy compared with previous methods. The ‘Model confidence and added coverage’ section also says its accuracy was validated in CASP14 and reports substantial accuracy gains on CAMEO benchmark targets. (LLM, conf=0.93)
boosted- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Importantly, the detailed atomistic descriptions of conformational ensembles enabled the development of a coarse-grained stickers-and-spacers model for simulations of A1-LCD phase behavior.88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Supportedcites [88] source
Passage [1] states that single-chain observations guided the development of a coarse-grained stickers-and-spacers model for A1-LCD, parameterized using conformational distributions from all-atom simulations. Passage [2] says the model was then used to simulate A1-LCD phase behavior. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Supported
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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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
“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
plain- 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
“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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- Based: unclear — It is unclear why the paper says it holds.
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.
- 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.
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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- 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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether 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.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
- 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
“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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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.
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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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- 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
“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.
- Based: passes — The paper gives a reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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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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PEGASUS : Prediction of MD ‐derived protein flexibility from sequence
Meersche, Y. V., Duval, G., Cretin, G., Gheeraert, A., Gelly, J., & Galochkina, T. V. (2025) · Protein Science · doi:10.1002/pro.70221 · Cited by 5
“The API supports the following pLMs: ProtT5-XL-UniRef50 (Liu et al., 2024), ESM2 (Hern (cid:1) andez Borrero & El-Deiry, 2021), and Ankh models (Ruff & Pappu, 2021).”
Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA
Chandra, S., Manjunath, K., Asok, A., & Varadarajan, R. (2022) · doi:10.1101/2022.04.08.487678 · Cited by 7
RosettaDDGPrediction for high-throughput mutational scans: from stability to binding
Sora, V., Laspiur, A. O., Degn, K., Arnaudi, M., Utichi, M., Beltrame, L., De Menezes, D., Orlandi, M., Rigina, O., Sackett, P. W., et al. (2022) · doi:10.1101/2022.09.02.506350 · Cited by 90
Effective Molecular Dynamics from Neural-Network Based Structure Prediction Models
Jussupow, A., & Kaila, V. R. I. (2022) · doi:10.1101/2022.10.17.512476 · Cited by 42
Assessing Fairness of AlphaFold2 Prediction of Protein 3D StructuresPreprint
Abbas, U. L., Chen, J., & Shao, Q. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.23.542006 · Cited by 3
Liu, Z. H., Teixeira, J. M. C., Zhang, O., Tsangaris, T. E., Li, J., Gradinaru, C. C., Head-Gordon, T., & Forman-Kay, J. D. (2023) · doi:10.1101/2023.07.25.550520 · Cited by 18
N-glycosylation as a eukaryotic protective mechanism against protein aggregation
(2023) · doi:10.1101/2023.08.11.552904 · Cited by 52
“Intrinsically disordered regions (IDRs) were identified using the pLDDT score 479 provided in the AlphaFold models, as regions with low confidence scores have been shown to 480 overlap largely with IDRs [83].”
Large protein databases reveal structural complementarity and functional localityPreprint
Szczerbiak, P., Szydlowski, L. M., Wydmański, W., Renfrew, P. D., Leman, J. K., & Kościółek, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.14.607935 · Cited by 3
Fernandes, N. P., Gomes, T., & Cordeiro, T. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.26.672300 · Cited by 3
“However, reliably modelling IDPs remains a major challenge (Ruff and Pappu 2021, Lane 2023).”
Wang, M., Wu, L., Zhu, S., et al. (2022) · Genes · doi:10.3390/genes13122313 · Cited by 2
Najafi, N. N., Karbassian, R., Hajihassani, H., & Irani, M. A. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06392-x · Cited by 4
“It enables the identification of protein structures and potential drug targets, paving the way for creating drugs that can modulate these targets and offer new avenues for disease treatment [29].”
Bhoi, S., Kiran, K. S. S., Dash, B. P., Sundaray, K., Subudhi, U., & Narasimha, N. K. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.144887 · Cited by 2
The promise of AlphaFold for gene structure annotation
Davison, H. R., Böhme, U., Mesdaghi, S., Wilkinson, P. A., Roos, D. S., Jones, A. R., & Rigden, D. J. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag369 · Cited by 2
“Regions with low pLDDT scores can result not only from unreliable prediction, but can also authentically reflect intrinsically disordered regions of the target protein that lack stable structure, at least in the absence of binding partners [ 23 ].”
Habib, M. B., Aftab, M., Amir, A., et al. (2025) · Microbial Pathogenesis · doi:10.1016/j.micpath.2025.108242 · Cited by 2
Mokhtari, O., Grudinin, S., Karami, Y., & Khakzad, H. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101454 · Cited by 4
Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction
Shi, Z., Vo, T. H., Le, N. Q. K., & Chua, M. C. H. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02419 · Cited by 2
jeon, J., Yang, W., Park, S., Kim, J. H., Lee, Y., & Yu, W. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2518125123 · Cited by 2
An Unfolding Story: AlphaFold's View on Protein Flexibility
Jeschke, G. (2026) · Helvetica Chimica Acta · doi:10.1002/hlca.202500235 · Cited by 2
PMF proteins mediate mitochondrial fusion in Arabidopsis
Kenneally, R. P., Tang, Y., Bobst, W., Khor, R. T., Garibyan, L., Jag, N. R., & Gu, Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2601242123 · Cited by 4
Fernandes, N. P., Gomes, T., & Cordeiro, T. N. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169862 · Cited by 2
Pradeep, B. K., Chen, S., Dima, R., Hassan, M., Joachimiak, A., Kihara, D., Kloczkowski, A., Law, J., Liwo, A., Meller, J., et al. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169949 · Cited by 2
Prediction of protein structure and intrinsic disorder in the era of deep learning
Erdős, G., & Dosztányi, Z. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99533-7.00007-8 · Cited by 2
Importin alpha family NAAT/IBB domain: Functions of a pleiotropic long chameleon sequence
Jibiki, K., Kodama, T. S., & Yasuhara, N. (2023) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2022.11.005 · Cited by 2
Phase Separation in Kidney Diseases: Autosomal Dominant Polycystic Kidney Disease and Beyond
Zhang, L., Liu, Z., Lu, Y., Nie, J., & Chen, Y. (2023) · Kidney Diseases · doi:10.1159/000530250 · Cited by 2
“LLPS participates in various cellular processes, and it has been predicted that more than 30% of the human proteome has the propensity to form LLPS condensates [57].”
BackgroundSingle-molecule-binding studies of antivirals targeting the hepatitis C virus core protein
Nepal, S., & Holmstrom, E. D. (2023) · Journal of Virology · doi:10.1128/jvi.00892-23 · Cited by 2
“Furthermore, AlphaFold (10) predictions yield structures of HCVcp with low to very low confidence, which others have noted can be an effective predictor of protein disorder (13, 14).”
BackgroundRavichandran, A., Araque, J. C., & Lawson, J. W. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26641 · Cited by 2
“31,32 The dataset consisted of a total of 2816 structures and the resulting GNN model built using this data-set is referred to as Model-I herein.”
MethodBu, R., Yu, W., Lai, M., & Liu, J. (2023) · Chemical Physics Letters · doi:10.1016/j.cplett.2023.141059 · Cited by 2
Hussain, N., & Muccee, F. (2024) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2024.103939 · Cited by 2
“To predict the three dimensional (3D) configuration of GABAT proteins, AlphaFold Protein Structure Database (https://alphafold.ebi.ac. uk, 28 January 2023) was consulted (Ruff and Pappu, 2021).”
MethodÖzkan, S., Padilla, N., & de la Cruz, X. (2024) · Human Genetics · doi:10.1007/s00439-024-02692-z · Cited by 5
“The values of this parameter are related to the disordered state of the protein region around the residue (Ruff and Pappu 2021).”
Coskuner‐Weber, O. (2024) · Journal of the Turkish Chemical Society Section A Chemistry · doi:10.18596/jotcsa.1457169 · Cited by 3
Systematic discovery of DNA-binding tandem repeat proteins
Hu, X., Zhang, X. J., Sun, W. C., Liu, C., Deng, P., Cao, Y., Zhang, C., Xu, N., Zhang, T., Zhang, Y. E., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae710 · Cited by 3
“…Tc NORD1 and Aa MIRAGE1 both displayed low confidence ( Supplementary Figure S6 C, S7 C), likely due to the high proportion of disordered regions within the repeat sequences, consistent with the previously reported limitations of AlphaFold2 in predicting intrinsically disordered proteins ( 124 ).”
ResultLi, Z., Hu, Y., Song, Y., Li, D., Yang, X., Zhang, L., Li, T., & Wang, H. (2024) · Genes · doi:10.3390/genes15101250 · Cited by 1
Artificial intelligence in early stages of structure-based drug discovery
Ravichandran, R., Park, S., Skorupan, S., Bessette, K., & Gentile, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23932-8.00001-7 · Cited by 2
Zhang, T., Celiker, B., Shao, Y., Gai, J., Hill, C. M., Wang, C., & Zheng, L. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-2251 · Cited by 3
“AlphaFold (29, 40) in the Google’s CoLab was used to predict the structure with high confidence (Supplementary Figs.”
Structural and antigenic variation in Hepatitis B virus oncogene, HBx
Fasih, F., Jabeen, N., Hussain, M., Habib, A., Sharafat, S., & Ul‐Haq, Z. (2025) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2025.2517826 · Cited by 2
“Moreover, prediction of membrane proteins by AlphaFold is not reliable, mainly because it presents inconsistencies in the location of the trans-membrane domains (Azzaz et al., 2022; Perrakis & Sixma, 2021; Ruff & Pappu, 2021).”
Liquid-liquid phase separation: A dynamic driver of hepatocellular carcinoma
Zhou, T., Li, L., & Chen, Y. (2025) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2025.189418 · Cited by 3
Dey, S., Manna, T., Guchhait, K. C., Karmakar, M., Jana, D., Raul, P., Ballav, S., Manna, M., Hazra, S., Panda, A. K., et al. (2025) · BMC Microbiology · doi:10.1186/s12866-025-04257-0 · Cited by 2
“[55].”
Mathiesen, S. B., Støle, T. P., Romaine, A., et al. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1569185 · Cited by 2
“However, the N-terminal domain is located in an area of low model confidence, which often overlaps with intrinsically disordered regions (Ruff and Pappu, 2021), suggesting that the N-terminal region might fold differently in vivo .”
Decoding Protein Dynamics in Cells Using Chemical Cross‐Linking and Hierarchical Analysis**
Zhang, B., Gong, Z., Zhao, L., An, Y., Gao, H., Chen, J., Liang, Z., Liu, M., Zhang, Y., Zhao, Q., et al. (2023) · Angewandte Chemie · doi:10.1002/ange.202301345 · Cited by 2
Geminivirus replication enhancer (REn/C3): In silico structure and characterization
Wijaya, N. F., Nova, B., & Jamsari, J. (2023) · AIP conference proceedings · doi:10.1063/5.0127753 · Cited by 1
Tabefam, M., Smith, M. D., & Jelokhani‐Niaraki, M. (2024) · Frontiers in Biophysics · doi:10.3389/frbis.2023.1334804 · Cited by 2
AlphaFold Models Illuminate Half of Dark Human ProteinsPreprint
Binder, J., Berendzen, J., Stevens, A. O., He, Y. J., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.04.467322 · Cited by 1
“Thus, IDRs are potential targets in drug discovery [40–42].”
BackgroundFirouzi, R., Sowlati‐Hashjin, S., Chávez-García, C., Ashouri, M., Karimi‐Jafari, M. H., & Karttunen, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.09.479729 · Cited by 1
Chaperones facilitate heterologous expression of naturally evolved putative de novo proteins
Eicholt, L. A., Aubel, M., Berk, K., Bornberg-Bauer, E., & Lange, A. (2022) · doi:10.1101/2022.03.02.482622 · Cited by 18
Krokengen, O. C., Raasakka, A., & Kursula, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.13.507838 · Cited by 2
“Encouraged by work from others on understanding IDPs and their conditionally folded segments 115 [45-47], we set out to analyse earlier experimental SAXS data [13, 33] as well as literature in light of 116 AlphaFold2 models of MBP and P0ct.”
MethodThe impact of genetically controlled splicing on exon inclusion and protein structurePreprint
Einson, J. E., Minaeva, M., Rafi, F., & Lappalainen, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.05.518915 · Cited by 1
Evolutionary selection of proteins with two folds
Schafer, J. W., & Porter, L. L. (2023) · doi:10.1101/2023.01.18.524637 · Cited by 2
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
Brotzakis, Z. F., Zhang, S., & Vendruscolo, M. (2023) · doi:10.21203/rs.3.rs-2659892/v1 · Cited by 2
Abdelhamid, I., Muscoloni, A., Rotscher, D. M., Lieber, M., Markwardt, U., & Cannistraci, C. V. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.10.552825 · Cited by 2
“…that AF has issues to predict the 3D structure of proteins with unstructured or intrinsically disordered regions is discussed in previous studies 1,49 , but our study is the first to propose a method to evaluate the extent to which this issue impacts also the prediction of protein interaction…”
MethodProtein Structure Refinement via DeepTracer and AlphaFold2Preprint
Chen, J., Zia, A., Wang, F., Hou, J., Cao, R., & Si, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.16.553616 · Cited by 2
“In structural biology, a protein domain is a spatially distinct unit that is folded in a compact conformation [23].”
BackgroundWei, G.-W., Chen, D., & Liu, J. (2024) · doi:10.21203/rs.3.rs-3640878/v1 · Cited by 57
“Transformer [13] architecture offered a groundbreaking technique that leverages attention mechanisms to understand sequential data in various domains [14, 22, 23].”
BackgroundThe importance of stereochemistry in the disorder-order continuum of protein-protein interactionsPreprint
Newcombe, E. A., Due, A. D., Sottini, A., Fernandes, C. B., Staby, L., Delaforge, E., Bartling, C. R. O., Brakti, I., Bugge, K., Schuler, B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.23.581681 · Cited by 2
“This discrepancy is likely caused by the difficulty in predicting the interactions and dynamics of disordered proteins 32,33 and from our internal bias towards searching for structure.”
BackgroundMatthes, M. S., Yun, N., Luichtl, M., Büschges, U., Fiesselmann, B. S., Strickland, B., Lehnardt, M. S., & Torres‐Ruiz, R. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.11.584374 · Cited by 1
“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, 56].”
BackgroundProtein Language Model Identifies Disordered, Conserved Motifs Implicated in Phase Separation
Zhang, Y., Zheng, J., & Zhang, B. (2024) · doi:10.1101/2024.12.12.628175 · Cited by 10
“82,83 We first analyzed the relationship between ESM2 and pLDDT scores for human Hete-rochromatin Protein 1 α (HP1 α , residues 1–191).”
(2025) · doi:10.1101/2025.03.04.641377 · Cited by 2
“To further evaluate the impact of IDRs on model performance, we computed per-residue disorder probabilities using MetaPredict [45].”
InfluentialJeschke, G. (2025) · Helvetica Chimica Acta · doi:10.1002/hlca.202500032 · Cited by 2
Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score DistributionsPreprint
Passi, G., Amittai, S., & Schneidman‐Duhovny, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.24.690189 · Cited by 2
“Although the suggested threshold is 0.5, we found that a 0.7 cutoff better aligned with previous disorder estimates 44,114 , predicting approximately 28% of residues as disordered compared to 32% using the 0.5 threshold.”
InfluentialUttarkar, A., Niranjan, V., Saxena, A., & Kumar, V. (2026) · PLoS ONE · doi:10.1371/journal.pone.0342012 · Cited by 3
“Proteins must fold into specific three-dimensional structures to perform their biological functions, and misfolding can lead to diseases ranging from neurodegeneration to cancer [1,2].”
Computational Methods for the Study of Peroxisomes in Health and Disease
van Wijk, N., & Linial, M. (2022) · Physiology · doi:10.5772/intechopen.103178 · Cited by 1
Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities
Shadman, H., Ziebarth, J. D., Gallops, C. E., Luo, R., Li, Z., Chen, H., & Wang, Y. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.04.010 · Cited by 1
Characterization of DmToll and DmToll7 homologue in Litopenaeus vannamei based on structure analysis
Shi, C., Tang, Z., Jin, Z., et al. (2024) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2024.105209 · Cited by 1
Proteins as Functional Foods and Nutraceuticals
Dhiman, N., Habib, M., Jan, K., et al. (2024) · Springer eBooks · doi:10.1007/978-3-031-59365-9_2 · Cited by 1
Experimental Considerations for the Evaluation of Viral Biomolecular Condensates
Roden, C., & Gladfelter, A. S. (2024) · Annual Review of Virology · doi:10.1146/annurev-virology-093022-010014 · Cited by 1
Sun, S., Lin, J., Zhuang, Y., et al. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102626 · Cited by 1
The emerging role of artificial intelligence in heart failure
Bernstein, B. S., Streather, S., & O’Gallagher, K. (2025) · Future Cardiology · doi:10.1080/14796678.2025.2523155 · Cited by 2
“AlphaFold is a deep learning-based model which predicts protein structures [37].”
Advances in Atom Probe Tomography Analysis of Silica Embedded Proteins
Eriksson, G., Mayweg, D., Hulander, M., et al. (2025) · Microscopy and Microanalysis · doi:10.1093/mam/ozaf048.131 · Cited by 1
In silico expression analysis of germin-like protein genes from rice cultivar Nipponbare
Durrani, I. S., Asim, N., Jan, A., et al. (2025) · Molecular Biology Reports · doi:10.1007/s11033-025-10936-y · Cited by 1
“…from the SWISS-MODEL server [17], accessible at h t t p s : / / s w i s s m o d e l . e x p a s y . o r g / . and further verified through A l p h a f o l d [18], available at (https://usegalaxy.eu/) and I-Tasser server [19], available at ( h t t p s : / / z h a n g g r o u p . o r g / I -”
Tahzima, R., Charon, J., Díaz, A., Jonghe, K. D., Massart, S., Michon, T., & Vranken, W. (2025) · Frontiers in Virology · doi:10.3389/fviro.2025.1501616 · Cited by 1
Chapman, K. A., Ullah, F., Yahiku, Z. A., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.08.004 · Cited by 1
de Oliveira, N. R., Siqueira, A. S., Bueno, P. S. A., Gonçalves, E. C., & Zanette, J. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70054 · Cited by 1
Basharat, Z., Alghamdi, Y. S., Mashraqi, M. M., Ogaly, H. A., Al‐Zahrani, F. A. M., Wei, C. R., Ahmed, I., & Kim, S. (2025) · BioData Mining · doi:10.1186/s13040-025-00482-5 · Cited by 3
“AlphaFold (RRID: SCR_023662) is a state-of-the-art deep learning-based system for predicting the 3D structures of proteins [34].”
Ma, X., Dey, S., Vaishnavey, S. R., Zelinski, C., Li, Q., & Chowdhury, R. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf154 · Cited by 3
“Additionally, pipelines involving AlphaFold structure prediction followed by docking introduce compounded uncertainty and may affect accuracy due to prediction errors, especially in disordered regions [ 67 ].”
Sun, P., Zhao, L., Tang, X., et al. (2025) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2025.111026 · Cited by 1
Mining the gaps: Deciphering Alzheimer’s biology through AI-driven reconciliation
Funk, C. C., Paterson, T., Bangs, A., Cannon, D. M., Savage, G., Ringger, E. K., & Hood, L. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100402 · Cited by 4
“The wicked aspects include intrinsically disordered regions, which make up approximately 40 percent of the human proteome and never resolve into a single stable structure [62].”
Calvario, E., Segovia, L., & Zurita, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-28838-1 · Cited by 1
“…AlphaFold3 has been extensively validated for high-confidence predictions of globular protein structures, it has also proven to be a valuable tool for identifying intrinsically disordered regions, providing insights into their potential structural organization and conformational tendencies 31,32 .”
Shadman, H., Ziebarth, J. D., Cheng, Q., Laradji, M., & Wang, Y. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c02154 · Cited by 2
Disordered N‐termini enhance the thermostability of SGNH ‐hydrolase family polyesterases
Ahmed, F. H., Esquirol, L., Royan, S., Birgan, M. M., French, N. G., Newton, S., Caputo, A. T., & Scott, C. S. (2025) · Protein Science · doi:10.1002/pro.70402 · Cited by 1
“While this may be a result of a lack of empirical structures of SGNH-hydrolases with similarly extended N-termini in the databases used for model training, these features are also typical for intrinsically disordered regions (IDRs) (Ruff & Pappu, 2021).”
Wang, Y., & Chen, M. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c02567 · Cited by 2
Transformation of QSAR by AI and ML
Gomatam, A., Khan, A. A., Coutinho, K., Maheshwari, S., Adani, V., Martis, E. A. F., & Coutinho, E. C. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_25 · Cited by 1
A Survey of Predicted Protein–Protein Interactions Involving Disordered Regions in Humans
Pei, J., Zhang, J. J., & Cong, Q. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169636 · Cited by 1
Decoding peptide structures and structural stability via conformational ensemble searching
Li, Z., Ru, X., & Lin, Z. K. (2026) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2026.115666 · Cited by 1
Predicting protein interfaces in the age of AlphaFold: Why dynamics and disorder remain a challenge
Omidi, A., Bui, J. M., & Gsponer, J. A. (2026) · Cell Systems · doi:10.1016/j.cels.2025.101508 · Cited by 2
Kırboğa, K. K., & Küçüksille, E. U. (2026) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70114 · Cited by 1
“It was noted that the observed low pLDDT scores (in the 40–55 range) for the Loop-1 region are an expected and well-documented outcome for surface-exposed, structurally flexible loops [46].”
Cuéllar‐Cruz, M., Maqueda-Cabrera, E. E., Siliqi, D., & Moreno, A. (2026) · ACS Omega · doi:10.1021/acsomega.5c11751 · Cited by 1
Nakamura, K. M., & Siltberg-Liberles, J. (2026) · Journal of Molecular Evolution · doi:10.1007/s00239-025-10293-1 · Cited by 1
“It has been shown that the regions with low model confi - dence (pLDDT) often overlap with IDRs, and proposed that pLDDT can be a competitive disorder predictor compared to other currently available methods (Tunyasuvunakool et al. 2021 ; Ruff and Pappu 2021 ; Wilson et al. 2022 ).”
Computational insights into the natural phage endolysin linker landscape
Cremelie, E., Boulay, A., Vázquez, R., et al. (2026) · BioData Mining · doi:10.1186/s13040-026-00526-4 · Cited by 1
“Hence, while AlphaFold2 has been demonstrated to deliver reliable structures for globular proteins, predictions of modular proteins have their limitations and should hence be interpreted with caution [62, 63].”
Linnik, D., Sultanji, S., Stevens, J. A., Schuurman‐Wolters, G. K., de Boer, R., Punter, C. M., Marrink, . J., Maslov, I., & Poolman, B. (2026) · Nature Communications · doi:10.1038/s41467-026-69951-7 · Cited by 3
Kim, H., Lee, J., Lee, S., et al. (2026) · Journal of Neurology · doi:10.1007/s00415-026-13731-2 · Cited by 1
“As highlighted by recent studies, a substantial fraction of regions with low or very low confidence scores in AlphaFold predictions correspond to intrinsically disordered regions, which reflects conformational heterogeneity rather than model failure [6, 21].”
Beyond a Passive Tether: Structural Insights into the Disordered Tail of Hsp90
Edinach, E., Giannouli, A., Dalaloyan, A., Oranges, M., Banik, D., Yardeni, E., Elimelech, A., Sattler, M., Ntermanakis, E., Su, X.-C., et al. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c04189 · Cited by 1
“37,38 Nevertheless, because the distance distributions for a label at position 687, which is close to the folded CTD, are relatively narrow, these data may provide a meaningful benchmark for evaluating AlphaFold predictions.”
Abdelfattah, N., Broncales, B., Albert, I., Kohan, J., & Yu, L. (2026) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2026.102612 · Cited by 1
“In addition, it has been noted that multimer confidence scores such as ipTM and pTM scores might not sufficiently distinguish between alternative models of interaction, especially where proteins contain intrinsically disordered regions [6].”
Marcos-Villar, L., Perdiguero, B., Sin, L., Álvarez, E., Flores, S., Casasnovas, J. M., Pons, T., Sorzano, C. Ó. S., del Hoyo, D., Lapuhs, P., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1787877 · Cited by 1
AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases
Deng, H. X., Cao, J. L., Yao, W., Jiang, S. J., Fang, Q. Q., Zhu, B. Y., & Jiang, Y. J. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1814357 · Cited by 2
“Therefore, AlphaFold can be regarded as a generative prior, providing a structural starting point, rather than a static endpoint, for exploring the non-native energy landscapes that govern transitions from native to misfolded states (145).”
An auto-parallel training method for deep learning models with extracting model structural features
Zeng, Y., Xu, L., Lu, X., Yin, Y., Li, X., Zhang, J., & Gao, H. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115426 · Cited by 1
Duncan, M., Sylvester, T. P., Walden, E., Lozano, J. R., Turner, E., Duncan, S., Mitchell, R. F., McKenna, D. D., & Adams, R. (2026) · Insects · doi:10.3390/insects17060587 · Cited by 1
“…biology and evolution [12] and have been applied across a wide array of biological subdisciplines—including biochemistry [13], molecular biology [14], and human health [15]—as well as in specific investigations such as chromatin architecture [16] and the evolution of SARS-CoV-2 variants [17,18].”
Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins
Orr, A., & Bateman, A. G. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag160 · Cited by 2
“While both classes may exhibit low structural prediction confi-dences, IDPs are genuine functional proteins (Ruff and Pappu 2021).”
Singh, S., Singh, R., & Sharma, S. (2026) · BioData Mining · doi:10.1186/s13040-026-00577-7 · Cited by 2
“These approaches can also be used to predict difficult sequences with limited homology [136].”
Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics
Bangert, P. D., Balasubramaniam, N. K., Parker, C. E., & Borchers, C. H. (2022) · Biomedical engineering · doi:10.5772/intechopen.108422 · Cited by 2
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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