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.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- 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.
- 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. 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.
- Based: fails — The paper may give no reason why it holds.
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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- 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.
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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It may state two separate claims as one.
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.
- 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. 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.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The overall number of distinct conformations in the ensemble, the conformational heterogeneity, in terms of features such as the sizes and shapes sampled, and the relative importance of different conformations within the ensemble are defined by a combination of sequence-encoded short-and long-range interactions 37,38 as well as the modulation of these interactions by solution conditions.39,40”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [37]
- Not checked yetcites [38]
- Not checked yetcites [39]
- Not checked yetcites [40]
plaindirect- Asserted: passes — The authors put it forward as true.
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- 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.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- Based: passes — The paper gives a reason why it holds.
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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NIAS‐Server 2.0: A versatile complementary tool for structural biology studies
Feltes, B. C., Pinto, É. S. M., Mangini, A. T., & Dorn, M. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27112 · Cited by 1
Stone, J. D., Tabrizi, H. B., & Shamsuddin, R. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385640 · Cited by 1
“…with a spotlight on existing common knowledge, as well as papers describing machine-learning-based methods for research tasks, are available; attempts to perform a large-scale study of IDP that do not belong to the same cluster by relying on a distinguishing feature seem rare [22], [18], [ ?”
Background(2024) · doi:10.1002/1873-3468.14837 · Cited by 1
“…TADs are intrinsically disordered, poorly conserved and fold into multiple orientation and conformation when bound [49 – 52], (c) TADs acquired structure might vary with context such as the nature of the binding partner [53]; and (d) AlphaFold models have limited conformational variance for now.”
BackgroundThe Iconic α-Helix: From Pauling to the Present
Rose, G. D. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_1 · Cited by 1
Yang, L. Y. U., Can, H. E., Jian-ming, Z. H. A. O., & Zhang, Z. J. (2025) · Bulletin of Mineralogy Petrology and Geochemistry · doi:10.3724/j.issn.1007-2802.20240161 · Cited by 1
Khalid, M., Khalid, Z., Abbasi, S. W., et al. (2025) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2025.2477768 · Cited by 1
“However, the 3D structure predicted by AlphaFold (Ruff & Pappu, 2021) is present in AlphaFold database which uses AI based prediction method developed by DeepMind that claims to predict tertiary structure with comparable accuracy to the experimental methods.”
Malik, S., Hasnain, M. J. U., Zaib, G., Saadia, H., Malik, A., & Zahid, A. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1535482 · Cited by 1
“0 (Cheng et al., 2006), MUpro (Ashkenazy et al., 2016), and ConSurf (Ruff and Pappu, 2021).”
Structural Biology for Target Identification and Validation
Dubianok, Y., Kumar, A., & Rak, A. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4418-8_2 · Cited by 1
Li, Y., & Zhang, Y. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01560-2 · Cited by 1
Özmen, Z. A., Çaylı, F. N., Uversky, V. N., Woo, J. A., Kang, D. E., & Coskuner‐Weber, O. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110810 · Cited by 2
Intrinsic disorder and structural biology: Searching where the light isn't
Brender, J., Ramamoorthy, A., Gursky, O., & Bhunia, A. (2022) · Biophysical Chemistry · doi:10.1016/j.bpc.2022.106912 · Cited by 1
Revealing the function of HMGB1 N-terminal acetylation by a protein semi-synthesis approachPreprint
Wei, T., Liu, J., Tan, Y., Li, C., Wei, R., Wang, J., Wu, H., Li, Q., Liu, H., Tang, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.05.463167 · Cited by 1
“After 24 h, the solution was directly injected into HPLC for HMGB1(13-214) purification (10-40% CH3CN/H2O over 40 min).”
BackgroundMethodInfluentialBiochemical propensity mapping for structural and functional anatomy of importin α IBB domainPreprint
Jibiki, K., Liu, M., Lei, C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.25.465662 · Cited by 1
O’Donnell, T., & Cazals, F. (2022) · doi:10.1101/2022.06.21.497022 · Cited by 4
A Structure-Based View on ABC-Transporter Linked to Multidrug Resistance
Huang, J., & Ecker, G. F. (2022) · doi:10.20944/preprints202210.0066.v1 · Cited by 48
Identification of potential molecular mimicry in pathogen-host interactions
Rich, K. D., Srivastava, S., Muthye, V., & Wasmuth, J. D. (2023) · doi:10.1101/2023.06.14.544818 · Cited by 6
Badaoui, L. E., & Barr, A. (2023) · Preprints.org · doi:10.20944/preprints202311.1503.v1 · Cited by 1
Monté, D., Lens, Z., Dewitte, F., Villeret, V., & Verger, A. (2023) · doi:10.1101/2023.11.30.569364 · Cited by 1
“…TADs are intrinsically disordered, poorly conserved and fold into multiple orientation and conformation when bound (46–49), (iii) TADs acquired structure might vary with context such as the nature of the binding partner (50) and (iv) AlphaFold models have limited conformational variance for now.”
BackgroundMoon, J., Hu, G., & Hayashi, T. (2023) · doi:10.20944/preprints202312.1187.v1 · Cited by 7
FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph EmbeddingPreprint
Shen, H., Liu, Y., Zhang, Y., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3831396/v1 · Cited by 1
IDRWalker: A Random Walk based Modeling Tool for Disordered Regions in ProteinsPreprint
Chen, G., & Zhang, Z. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.17.585378 · Cited by 1
The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution
Lemke, O., Heineike, B. M., Viknander, S., Cohen, N., Steenwyk, J. L., Spranger, L., Li, F., Agostini, F., Lee, C. T., Aulakh, S. K., et al. (2024) · doi:10.1101/2024.05.27.596037 · Cited by 28
Computational Tools for Structural Analysis of Proteins
Brezovský, J., Sethi, A., & Surpeta, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00089-0 · Cited by 1
A one-dimensional semaglutide-GLP-1R-based mini static GIBACPreprint
Li, W. (2024) · Preprints.org · doi:10.20944/preprints202406.0953.v1 · Cited by 1
Diversification of the Rho transcription termination factor in bacteriaPreprint
Moreira, S. M., Chyou, T., Wade, J. T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.17.599428 · Cited by 1
Chapman, K. A., Ullah, F., Yahiku, Z. A., et al. (2024) · medRxiv · doi:10.1101/2024.06.27.24309417 · Cited by 1
Intrinsically Disordered Regions Define Unique Protein Interaction Networks in CHD Family Remodelers
Tabar, M. S., Parsania, C., Giardina, C., Feng, Y., Wong, A. C., Metierre, C., Nagarajah, R., Dhungel, B. P., Rasko, J. E., & Bailey, C. G. (2024) · doi:10.1101/2024.08.25.609601 · Cited by 7
Multi-purpose controllable protein generation via prompted language modelsPreprint
Wang, Z., Chen, B., Ding, K., Cao, J., Qin, M., Niu, Y., Zhuang, X., Li, X., Feng, K., Xu, T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.17.624051 · Cited by 2
Protein Structure Description with ρ, θ and ϕ : A Case Study with Caenopore-5Preprint
Li, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631847 · Cited by 1
“The recent emergence of AlphaFold has revolutionized the field, employing deep learning to achieve remarkable August 29, 2025 3/14 accuracy in structure prediction [20–22,33,46,47].”
Wang, Z., Wang, Y., Gao, H., Dai, J., Tang, N., Wang, Y., & Ji, Q. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.20.633832 · Cited by 1
A Deep Learning Method for Predicting Interactions for Intrinsically Disordered Regions of ProteinsPreprint
Majila, K., Ullanat, V., & Viswanath, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5133412 · Cited by 1
DiffPIE: Guiding Deep Generative Models to Explore Protein Conformations under External InteractionsPreprint
Wang, Y., & Chen, M. (2025) · bioRxiv · doi:10.1101/2025.04.27.650875 · Cited by 2
Protein Language Model Identifies Disordered, Conserved Motifs Driving Phase SeparationPreprint
Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309.1 · Cited by 1
Gomez-Gutierrez, S. V., Rodriguez-Diaz, C., Jaiswal, N., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.20.655180 · Cited by 1
Bridging Quantum Mechanics and Biology at the Million-Atom Scale
Garcia, M., & Wieners, L. (2025) · doi:10.21203/rs.3.rs-7327472/v1 · Cited by 1
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 · Cited by 1
Repetitive proteins that undergo large conformational changes evade structural prediction algorithms
Chang, M. P., Jin, T., Gudinas, A. P., Fernández, D., Alexander‐Katz, A., Matsui, T., & Mai, D. J. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0304777 · Cited by 2
A3D Database: Structure-based Protein Aggregation Predictions for the Human Proteome
(2021) · doi:10.1101/2021.11.17.468872 · Cited by 14
“Therefore, it is not surprising that AF-predicted structures frequently contain regions with very low (pLDDT 50) scores (Ruff and Pappu, 2021).”
BackgroundBisello, G., Kuśmierska, K., Verbeek, M. M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1395907/v1
Žunar, B., Mosrin, C., Bénédetti, H., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.04.486947
“On the other hand, the C-terminal half of the protein appeared intrinsically disordered (Ruff and Pappu, 2021), which is characteristic of activation domains (Ravarani et al., 2018).”
BackgroundDisentangling the Protein Order/Disorder Continuum Using a Sequence-Based Foldability ScorePreprint
Bruley, A., Bitard‐Feildel, T., Callebaut, I., & Duprat, É. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4116299
Chun, C., & Yoon, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.21203/rs.3.rs-1849067/v1
Liu, J., Yuan, R., Shao, W., Wang, J., Silman, I., & Sussman, J. L. (2022) · doi:10.1101/2022.08.02.502493 · Cited by 18
Rueda, A. J. V., Bulgarelli, F. L., Palópoli, N., & Parisi, G. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.02.502569
Sinz, A., Ianni, A. D., Ihling, C. H., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2271015/v1
Ruiz-Molina, N., Parsons, J., Decker, E. L., & Reski, R. (2022) · doi:10.1101/2022.11.24.517849 · Cited by 4
Structural Insights into Pink-eyed Dilution Protein (Oca2)
(2022) · doi:10.1101/2022.12.09.519718 · Cited by 13
Pervasive, conserved secondary structure in highly charged protein regions
Triandafillou, C. G., Pan, R. W., Dinner, A. R., & Drummond, D. A. (2023) · doi:10.1101/2023.02.15.528637 · 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.”
InfluentialYu, Q., Liu, J., Jiang, J., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043958
“MtSP1L, and eight GmSP1L proteins were predicted using AlphaFold2 [26], and they are”
InfluentialOh, C., Buckley, P. M., Choi, J., Hierro, A., & DiMaio, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.21.533711
“1C and S3) (Ruff & Pappu, 2021).”
Golroudbari, A. A., & Sabour, M. H. (2023) · doi:10.22541/au.168727976.63672670/v1 · Cited by 1
Prediction of Folding Patterns for Intrinsic Disordered ProteinPreprint
Yang, J., Cheng, W. X., Wu, G., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3329100/v1
iMAX FRET (Information Maximized FRET) for multipoint single-molecule structural analysisPreprint
Joshi, B. S., de Lannoy, C. V., Howarth, M. R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.27.559821
Are most human specific proteins encoded by long non-coding RNA ?Preprint
Sanejouand, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.09.566363
AlphaFold: the silver lining of the ‘protein folding problem’
Mordina, P. (2023) · Archives of Bioinformatics and Computational Biology · doi:10.61577/abcb.2023.100003
Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2023) · doi:10.1101/2023.12.08.570786 · Cited by 7
“While extrapolation by deep neural networks is theoretically plausible (Balestriero et al., 2021; Fan-njiang and Listgarten, 2023) searching for evidence of this is difficult and requires extensive classification of training data and the resulting predictions.”
BackgroundAnalyzing the implications of protein folding delay caused by translation
Houben, B., Duran-Romaña, R., Migens, P. F., Rousseau, F., & Schymkowitz, J. (2024) · doi:10.1101/2024.01.27.577370 · Cited by 10
“Disordered regions were defined using the pLDDT score provided in the AlphaFold models, as regions with low confidence scores (pLDDT 50) have been shown to overlap largely with intrinsically disorder regions [59].”
MethodWaszkiewicz, R., Michaś, A., Białobrzewski, M. K., Klepka, B. P., Cieplak-Rotowska, M. K., Staszałek, Z., Cichocki, B., Lisicki, M., Szymczak, P., & Niedzwiecka, A. (2024) · doi:10.1101/2024.02.05.578612 · Cited by 16
Miller, D., Dziulko, A., & Levy, S. (2024) · doi:10.1101/2024.02.13.580123 · Cited by 1
Karatzas, P., Brotzakis, Z. F., & Sarimveis, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.23.581804
“More evidence supports that information about conformational dynamics is contained in AlphaFold 12–14 .”
BackgroundComparison Of 3D Structures Generated by AlphaFold2 to Experimental Structures In Oncogenic Proteins
Rajpal, S., & Plymire, D. A. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i4.5532
“Their lack of a defined structure makes it difficult for AlphaFold2 to predict their structure since it does not have a clear reference point [52].”
BackgroundJanssen, K., Duran‐Romaña, R., Bottu, G., Guharoy, M., Botzki, A., Rousseau, F., & Schymkowitz, J. W. H. (2023) · Figshare · doi:10.6084/m9.figshare.23707045
Janssen, K., Duran‐Romaña, R., Bottu, G., Guharoy, M., Botzki, A., Rousseau, F., & Schymkowitz, J. W. H. (2023) · Figshare · doi:10.6084/m9.figshare.23707042
PepCA: Unveiling Protein-Peptide Interaction Sites with a Multimodal Neural Network ModelPreprint
Huang, J., Li, W., Xiao, B., Zhao, C., Zheng, H., Li, Y., & Wang, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4800950
Truncating the spliceosomal ‘rope protein’ Prp45 results in Htz1 dependent phenotypes
Abrhámová, K., Groušlová, M., Valentová, A., Hao, X., Liu, B., Převorovský, M., Gahura, O., Půta, F., Sunnerhagen, P., & Folk, P. (2024) · RNA Biology · doi:10.1080/15476286.2024.2348896 · Cited by 2
“These motifs can be also predicted in AlphaFold (bypassing direct use of structural templates; [50]), suggesting that they form independently of other structures and may bind prospective partners.”
Risti, R., Reimund, M., Seeba, N.-N., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4348425/v1
Dynamic pre-structuration of lipid nanodomain-segregating remorin proteins
Habenstein, B., Xu, Z., Schahl, A., Jolivet, M.-D., Legrand, A., Grelard, A., Berbon, M., Morvan, E., Lagardère, L., PIQUEMAL, J.-P., et al. (2024) · doi:10.21203/rs.3.rs-4364507/v1 · Cited by 6
Abraham Rebairo, J., Paul, D. S., & Stephen, A. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01891e
Protein-protein interaction techniques—a powerful tool for functional genomics
Feng, R., Liu, F., Wu, S., Li, R., & Songyang, Z. (2024) · Scientia Sinica Vitae · doi:10.1360/ssv-2024-0151
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“It is also feasible that the low scores are a consequence of the inherent disorder of the N-terminal GPIHBP1 peptide and are a predictor of the dynamic nature of the peptide, rather than a sign of low prediction confidence 28 .”
Herbert, J., & van Dijk, A. A. (2024) · Journal of General Virology · doi:10.1099/jgv.0.002029
Zerebinski, J., Margerie, L., Han, N. S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.24.614082
Hunt, H. L., Biggs, M. A., Goncavles, B. G., & Banerjee, I. A. (2024) · Soft Materials · doi:10.1080/1539445x.2024.2413063 · Cited by 1
“[57] The GADIVA and the YYGAEFIF sequences showed assemblies in both yellow and tan colors.”
AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered ProteinsPreprint
Brotzakis, Z. F., Zhang, S., Murtada, M. H., & Vendruscolo, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.09.622758 · Cited by 1
Techniques for Bioinformatic Applications in Protein Dynamics
Rackovsky, S. R. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4213-9_11
Gonçalves-Kulik, M., Baptista, L. A., Schmid, F., & Andrade-Navarro, M. A. (2024) · doi:10.1101/2024.11.16.623902
AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets
Faraggi, E., Jernigan, R. L., & Kloczkowski, A. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_8
Helsem, S. A., Alfsnes, K., Frye, S. A., Hesselberg Løvestad, A., & Ambur, O. H. (2024) · doi:10.1101/2024.11.23.625027 · Cited by 3
Capturing the Conformational Heterogeneity of HSPB1 Chaperone Oligomers at Atomic Resolution
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Advancements and Applications of Protein Structure Prediction Algorithms
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Structural Features of Pathogenic Aggregates Correlate with Cell Pathology in Differentiated CellsPreprint
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AFFIPred : AlphaFold2 structure‐based Functional Impact Prediction of missense variations
Pir, M. S., & Timucin, E. (2025) · Protein Science · doi:10.1002/pro.70030
The Influence of Artificial Intelligence in Drug Discovery and Development
Krishnan, S., Rajendran, K., Yogeshwaran, V. N., & Radhakrishnan, B. (2024) · Medical virology · doi:10.1007/978-981-97-2938-8_3
LoRA-DR-suite: adapted embeddings predict intrinsic and soft disorder from protein sequencesPreprint
Lombardi, G., Seoane, B., & Carbone, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.03.636253
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Physics-informed deep learning approach for fixing and sampling protein 3D structures
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Kharon Is Crucial for Trypanosoma cruzi Morphology but Does Not Impair In Vitro Infection
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“The Tc Kharon prediction yielded a low predicted local distance difference test (pLDDT) score, which is often associated with intrinsically disordered regions (IDRs) [35].”
Maurya, A. K., Cadena, L. R., Ehret, G., & Nowack, E. C. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.22.649992
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).
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