The DOI matches its Crossref record; 97 of the 103 references it cites were found in a registry or an index; 1 of the 58 sources its claims cite were checked for support.
AlphaFold and Implications for Intrinsically Disordered Proteins
Open accessAbstract
Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the "structures" of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This perspective cautions against over-interpreting AlphaFold’s low-confidence and “unstructured” annotations for intrinsically disordered regions as literal structures or as failures of prediction. It argues that quantitative sequence–ensemble descriptions, not static models, are needed to understand IDR function.
Key findings
- AlphaFold annotated over 98% of human proteome sequences, but roughly 30% of residues have pLDDT scores below 50, consistent with estimates that about 30% of the human proteome is disordered.
- Low-confidence regions largely overlap intrinsically disordered regions (IDRs); the authors argue most are likely genuinely disordered rather than well-folded structures that AlphaFold missed.
- Static AlphaFold structures of IDRs often overestimate radius of gyration relative to SAXS measurements, as shown for p27 CTD and the hnRNP-A1 prion-like low-complexity domain (A1-LCD).
- Quantitative ensembles from atomistic simulations vetted by experiments reveal sequence-specific behavior: p27 CTD behaves on average like a Flory random coil, A1-LCD uses aromatic stickers/spacers, and Httex1 prefers tadpole-like conformations with polyQ radius scaling as n^1/3.
Methods
- Perspective/cautionary note, not primary research; it synthesizes the AlphaFold papers, human proteome annotations, and prior IDP literature.
- Uses illustrative examples—p27 Kip1 CTD, hnRNP-A1 A1-LCD, and huntingtin exon 1 (Httex1)—comparing AlphaFold static structures with SAXS, NMR, smFRET, and atomistic simulations based on the ABSINTH implicit solvation model and forcefield.
- Main analysis is interpretive and qualitative, focused on why static AlphaFold depictions of IDRs lack physical meaning and how sequence–ensemble relationships should instead be quantified.
Limitations
- The paper does not state formal limitations.
- Evident from the text: it is a perspective/opinion piece, not a systematic review, and its examples are drawn largely from the authors’ own work and close colleagues.
- Evident from the text: no new experimental data are reported; the quantitative examples rely on previously published simulations and experiments, and the scope is limited to interpreting AlphaFold annotations for IDRs.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
38 Not checked yet · 9 Own interpretation · 9 No basis given · 5 Own result · 1 Own method · accuracy not yet measured
None of the sources cited for this claim have been checked yet.
In the paper
“Conservative estimates indicate that roughly 30% of sequences, 30residues or longer, drawn at random from the human proteome, are likely to be IDRs. 3,5”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [3] source
- Could not verifycites [5] sourceExperimental — accuracy not yet measured
The Results describe DisProt annotations and note that the dataset is biased toward model organisms, including humans; they do not estimate the prevalence of IDRs in randomly sampled human-proteome sequences. Searches for a 30% estimate and a 30-residue threshold found no passage supporting those figures. (LLM, conf=0.70)
hedged- 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.
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.
- Load-bearing: passes — The paper’s conclusions depend on it.
- 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.
- 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.
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.
- 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 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.
- 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.
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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The C-terminal domain of p27, p27 CTD , generally shows low pLDDT scores (<70) in the AlphaFold prediction.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
plain- Asserted: passes — The authors put it forward as true.
- 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.
In the paper
“This is because, the static AlphaFold structure likely overestimates the radius of gyration of IDRs as measured using small angle X-ray scattering (SAXS) 64 (Figure 2(A)).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [64]
hedgedconditional- Asserted: passes — The authors put it forward as true.
- 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.
- 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.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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.
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.
- 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
“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.
- 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
“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.
- 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.
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.
- 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.
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”
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Comparison of multiple Amber force fields and development of improved protein backbone parameters
Horn̆ák, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A. E., & Simmerling, C. L. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21123 · Cited by 6,989
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025
Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm
Wright, P. E., & Dyson, H. J. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3110 · Cited by 2,968
Classification of Intrinsically Disordered Regions and Proteins
van der Lee, R., Buljan, M., Lang, B., Weatheritt, R. J., Daughdrill, G. W., Dunker, A. K., Fuxreiter, M., Gough, J., Gsponer, J., Jones, D. T., et al. (2014) · Chemical Reviews · doi:10.1021/cr400525m · Cited by 2,249
Self-citationPrediction and Functional Analysis of Native Disorder in Proteins from the Three Kingdoms of Life
Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F., & Jones, D. T. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.02.002 · Cited by 2,010
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434
Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families
Lockless, S. W., & Ranganathan, R. (1999) · Science · doi:10.1126/science.286.5438.295 · Cited by 1,288
Valence and patterning of aromatic residues determine the phase behavior of prion-like domains
Martin, E. W., Holehouse, A. S., Peran, I., Farag, M., Incicco, J. J., Bremer, A., Grace, C. R., Soranno, A., Pappu, R. V., & Mittag, T. (2020) · Science · doi:10.1126/science.aaw8653 · Cited by 1,147
Self-citation
Cited by
Sorted by citations
Items 101–200 of 683
Goulet, A., & Cambillau, C. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.907452 · Cited by 31
“Also, large ribbon-like structures with low pLDDT values predict intrinsically disordered regions (IDRs) and should not be interpreted as structures (Ruff and Pappu, 2021; Tunyasuvunakool et al., 2021).”
BackgroundBhopatkar, A. A., & Kayed, R. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105122 · Cited by 28
“Additionally, our knowledge regarding the interaction partners of disordered interaction modules is constantly growing (296), and with the advent of neural networks and artificial intelligence, our predictive capabilities for structural properties of disordered regions are also becoming more reliable (146, 147).”
BackgroundProtein Language Models: Is Scaling Necessary?Preprint
Fournier, Q., Vernon, R. M., van der Sloot, A. M., Schulz, B., Chandar, S., & LANGMEAD, C. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.23.614603 · Cited by 42
“As previously mentioned, AlphaFold2’s confidence has been used to predict protein disorder (Ruff and Pappu, 2021), but we show that AlphaFold2 cannot differentiate between disordered proteins and non-protein sequences, whereas AMPLIFY can.”
Raffele, D. D., & Ilie, I. M. (2023) · Chemical Communications · doi:10.1039/d3cc04630c · Cited by 32
Varadi, M., Bordin, N., Orengo, C., & Velankar, S. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102543 · Cited by 29
“While AlphaFold already demonstrated the prevalence of structurally flexibly regions in many proteomes, these models only provide single snapshots from all the possible conformations [55].”
Assembly of JAZ–JAZ and JAZ–NINJA complexes in jasmonate signaling
Zhou, X. E., Zhang, Y., Yao, J., Zheng, J., Zhou, Y., He, Q., Moreno, J. E., Lam, V. Q., Cao, X., Sugimoto, K., et al. (2023) · Plant Communications · doi:10.1016/j.xplc.2023.100639 · Cited by 33
“typical of intrinsically disordered regions (Necci et al., 2021; Ruff and Pappu, 2021; Shao et al., 2022).”
Intrinsically disordered proteins studied by NMR spectroscopy
Schiavina, M., Bracaglia, L., Bolognesi, T., Rodella, M. A., Tagliaferro, G., Tino, A. S., Pierattelli, R., & Felli, I. C. (2023) · Journal of Magnetic Resonance Open · doi:10.1016/j.jmro.2023.100143 · Cited by 30
Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation
Bárcenas, O., Kuriata, A., Zalewski, M., Iglesias, V., Pintado‐Grima, C., Firlik, G., Burdukiewicz, M. J., Kmiecik, S., & Ventura, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae382 · Cited by 28
“These disordered regions are occupying (pseudo)-random positions, generally close to the globular core (if it exists) ( 38 ).”
Machine learning meets physics: A two-way street
Levine, H., & Tu, Y. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403580121 · Cited by 33
Kravchenko, S. V., Domnin, P. A., Grishin, S. Y., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23010524 · Cited by 28
Protein Crystallography: Achievements and Challenges
Timofeev, V. I., & Самыгина, В. Р. (2023) · Crystals · doi:10.3390/cryst13010071 · Cited by 34
Dissecting the structural heterogeneity of proteins by native mass spectrometry
Reid, D. J., Thibert, S. M., & Zhou, M. (2023) · Protein Science · doi:10.1002/pro.4612 · Cited by 28
“Nevertheless, a recent estimation showed that approximately 30% of sequences in the human proteome are likely IDP/IDRs (Ruff & Pappu, 2021) that are involved in a variety of crucial biological functions.”
BackgroundNaveed, M., Ain, N., Aziz, T. A., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11041041 · Cited by 29
“3D Structure Retrieval of Protein DeepMind developed an artificial intelligence application AlphaFold for the prediction of 3D structures of proteins using the deep learning model [7].”
BackgroundIlluminating Intrinsically Disordered Proteins with Integrative Structural Biology
Evans, R., Ramisetty, S. K., Kulkarni, P., & Weninger, K. (2023) · Biomolecules · doi:10.3390/biom13010124 · Cited by 27
Decoding molecular mechanisms for loss-of-function variants in the human proteomePreprint
Cagiada, M., Jonsson, N., & Lindorff‐Larsen, K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.21.595203 · Cited by 34
“%) as affecting function, though we note that AF2 structures do not generally represent IDRs accurately ( Ruff and Pappu, 2021 ; Tesei et al., 2024 ).”
Liquid–liquid phase separation of H3K27me3 reader BP1 regulates transcriptional repression
Tang, G., Xia, H., Huang, Y., Guo, Y., Chen, Y., Ma, Z., & Liu, W. (2024) · Genome biology · doi:10.1186/s13059-024-03209-7 · Cited by 26
“Some regions below 50 pLDDT may be unstructured in isolation [30].”
BackgroundAccurate predictions of disordered protein ensembles with STARLING
Novak, B., Lotthammer, J. M., Emenecker, R. J., & Holehouse, A. S. (2026) · Nature · doi:10.1038/s41586-026-10141-2 · Cited by 66
Pater, J. A., Penney, C., O’Rielly, D. D., Griffin, A., Kamal, L., Brownstein, Z., Vona, B., Vinkler, C., Shohat, M., Barel, O., et al. (2022) · Human Genetics · doi:10.1007/s00439-022-02444-x · Cited by 28
“AlphaFold produces a per-residue confidence score (pLDDT) between 0 and 100, which reliably predicts the Cα local-distance and estimates how well the predicted structure would agree with the experimental structure (Ruff and Pappu 2021).”
BackgroundThe impact of AlphaFold2 on experimental structure solution
Edich, M., Briggs, D. C., Kippes, O., Gao, Y., & Thorn, A. (2022) · Faraday Discussions · doi:10.1039/d2fd00072e · Cited by 29
Onco-condensates: formation, multi-component organization, and biological functions
Xu, C., Kim, A., Corbin, J. M., & Wang, G. G. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.05.006 · Cited by 27
Machine learning methods to study sequence–ensemble–function relationships in disordered proteins
von Bülow, S., Tesei, G., & Lindorff‐Larsen, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103028 · Cited by 27
“…procedure, using the same color code as for the stickiness scale in (B). a small number of structures may be representative for highly populated states in folded proteins and informative for function, highly dynamic IDR ensembles are characterized by a large set of diverse conformations [11, 69].”
Rush, C., Jiang, Z., Tingey, M., Feng, F., & Yang, W. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1245939 · Cited by 26
“However, these models struggle to provide information about IDPs as a result of their disordered nature and, therefore, are not currently fully applicable to FG-Nups (Ruff and Pappu, 2021; Mosalaganti et al., 2022).”
BackgroundAubel, M., Eicholt, L. A., & Bornberg‐Bauer, E. (2023) · F1000Research · doi:10.12688/f1000research.130443.1 · Cited by 24
“This could, for example, lead to a wrong estimation of disorder levels [Ruff and Pappu, 2021].”
BackgroundRandom, de novo, and conserved proteins: How structure and disorder predictors perform differently
Middendorf, L., & Eicholt, L. A. (2024) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26652 · Cited by 24
“For this purpose, we used a dataset of de novo proteins from Drosophila , 21 length-matched conserved proteins from Drosophila proteome, and random proteins generated in silico matching the length and amino acid distribution of the de novo protein dataset.”
MethodDCTPep, the data of cancer therapy peptides
Sun, X., Liu, Y., Ma, T., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03388-9 · Cited by 25
Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs
Alhumaid, N. K., & Tawfik, E. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810139 · Cited by 25
“Despite the impressive accuracy of AF2 predictions of protein domain structure, the flexible parts of protein, including the intrinsically disordered protein and loop regions, failed to reach high accuracy and confidence [12,13].”
BackgroundHypoxia-induced phase separation of ZHX2 alters chromatin looping to drive cancer metastasis
Gao, C., Gao, A., Jiang, Y., Gao, R., Guo, Y., Peng, Z., Jiang, W., Zhang, M., Zhou, Z., Yan, C., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.03.009 · Cited by 26
AlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 25
“Additionally, an interesting perspective emphasizing the necessity for careful interpretation of AF-derived results when studying IDRs have been published [105].”
Bias in, bias out – AlphaFold-Multimer and the structural complexity of protein interfaces
Strom, J. M., & Luck, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103002 · Cited by 23
Advanced Strategies in Phage Research: Innovations, Applications, and Challenges
Wu, P., Li, W., Zhang, W., Li, S., Deng, B., Xu, S., & Li, Z. (2025) · Microorganisms · doi:10.3390/microorganisms13081960 · Cited by 25
“Experimental techniques such as small-angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryo-EM), along with simulation-Microorganisms informed hybrid modeling, remain essential to resolving the conformational ensemble of IDRs [61].”
Evolution is not Uniform Along Coding Sequences
Bricout, R., Barbieri-Weill, D., Stroebel, D., Genovesio, A., & Crollius, H. R. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad042 · Cited by 20
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
Brotzakis, Z. F., Zhang, S., & Vendruscolo, M. (2023) · doi:10.1101/2023.01.19.524720 · Cited by 22
The Influence of AI: The Revolutionary Effects of Artificial Intelligence in Healthcare Sector
Saxena, A. K., Ness, S., & Khinvasara, T. (2024) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2024/v26i31092 · Cited by 23
Exploring protein functions from structural flexibility using CABS ‐flex modeling
Nithin, C., Fornari, R. P., Pilla, S. P., Wróblewski, K., Zalewski, M., Madaj, R., Koliński, A., Macnar, J. M., & Kmiecik, S. (2024) · Protein Science · doi:10.1002/pro.5090 · Cited by 21
“Thanks to AlphaFold, a myriad of previously elusive systems (Varadi et al., 2022), including disordered regions (Ruff and Pappu, 2021), are now accessible for comprehensive dynamic simulations.”
BackgroundErdős, G., & Dosztányi, Z. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102950 · Cited by 28
Wang, L., Li, F., Ma, X., Cang, Y., & Bai, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01033 · Cited by 22
Han, B., Ren, C., Wang, W., Li, J., & Gong, X. (2023) · Genes · doi:10.3390/genes14020432 · Cited by 26
“ability to predict disordered regions [146].”
Distinguishing features of fold‐switching proteins
Chakravarty, D., Schafer, J. W., & Porter, L. L. (2023) · Protein Science · doi:10.1002/pro.4596 · Cited by 19
All Domains of SARS-CoV-2 nsp1 Determine Translational Shutoff and Cytotoxicity of the Protein
Frolov, I. V., Agback, T., Palchevska, O., et al. (2023) · Journal of Virology · doi:10.1128/jvi.01865-22 · Cited by 20
“suggested the presence of a disordered region (33).”
BackgroundIntrinsic Protein Disorder, Conditional Folding and AlphaFold2
(2022) · doi:10.1101/2022.03.03.482768 · Cited by 161
“We have shown how AlphaFold can be used both to predict IDRs as well as the binding regions inside them.”
BackgroundInfluentialTesei, G., Trolle, A. I., Jonsson, N., Betz, J., Pesce, F., Johansson, K. E., & Lindorff‐Larsen, K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.08.539815 · Cited by 21
“When methods such as AlphaFold are applied to IDRs they, however, result in a single or few configurations of low confidence that do not generally represent the conformational heterogeneity and functional states of IDRs [2].”
MethodDeep learning tools predict variants in disordered regions with lower sensitivity
Luppino, F., Lenz, S., Chow, C. F. W., & Tóth-Petróczy, Á. (2025) · BMC Genomics · doi:10.1186/s12864-025-11534-9 · Cited by 23
“In humans ~ 30% of the proteome contains disordered regions [2, 3].”
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 34
“However, it is important to recognize the atomic structure of the low-confidence region will not be accurately modelled [63].”
Žunar, B., Mosrin, C., Bénédetti, H., et al. (2022) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2022.114502 · Cited by 21
Bioinformatics tools and data resources for assay development of fluid protein biomarkers
Waury, K., Willemse, E. A. J., Vanmechelen, E., Zetterberg, H., Teunissen, C. E., & Abeln, S. (2022) · Biomarker Research · doi:10.1186/s40364-022-00425-w · Cited by 20
“Such regions have also been shown to highly overlap with the low confidence prediction regions of AlphaFold [107].”
ResultComplementing machine learning‐based structure predictions with native mass spectrometry
Allison, T. M., Degiacomi, M. T., Marklund, E. G., Jovine, L., Elofsson, A., Benesch, J. L. P., & Landreh, M. (2022) · Protein Science · doi:10.1002/pro.4333 · Cited by 20
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 23
“Although AlphaFold has made advances in this area, predicting these regions remains a challenge, emphasizing the need for further improvements in modeling flexible and disordered protein segments [270].”
AlphaFold: A Special Issue and A Special Time for Protein Science
Serpell, L. C., Radford, S. E., & Otzen, D. E. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167231 · Cited by 18
Agave REVEILLE1regulates the onset and release of seasonal dormancy inPopulus
Liu, D., Tang, D., Xie, M., Zhang, J., Zhai, L., Mao, J., Luo, C., Lipzen, A., Zhang, Y., Savage, E., et al. (2022) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiac588 · Cited by 19
“…the “confidence” scores in the AF2 predictions, whereas the residues with pLDDT larger than 70 are considered as high confidence in the prediction, the residues with low pLDDT (e.g. < 50) are often in the intrinsically disordered protein regions (IDPRs) (Ruff and Pappu, 2021; Akdel et al., 2022).”
BackgroundCharacterization of a unique polysaccharide monooxygenase from the plant pathogen Magnaporthe oryzae
Martinez-D’Alto, A., Xia, Y., Detomasi, T. C., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2215426120 · Cited by 20
From propensities to patterns to principles in protein folding
Rose, G. D. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26540 · Cited by 20
“Intrinsi-cally disordered proteins 36 or those that favor liquid – liquid phase separation 37 are excluded.”
Structural coverage of the human interactome
Kosoglu, K., Aydin, Z., Tuncbag, N., Gursoy, A., & Keskin, O. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad496 · Cited by 21
“A significant portion of AF predicted very low– and low-confidence regions are found to have overlap with predicted IDRs [50].”
BackgroundPredicting the pathogenicity of missense variants using features derived from AlphaFold2
Schmidt, A., Röner, S., Mai, K., Klinkhammer, H., Kircher, M., & Ludwig, K. U. (2022) · doi:10.1101/2022.03.05.483091 · Cited by 79
Protein Structural Phylogenetics
Puente-Lelièvre, C., Malik, A. J., & Douglas, J. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf139 · Cited by 27
Zhu, J., Li, Z., Tong, H., Lu, Z., Zhang, N.-J., Wei, T., & Chen, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad429 · Cited by 22
“This phenomenon is consistent with the low confidence often observed in disordered regions in previous protein structure prediction or generation tasks, indicating the necessity of our model for reconstructing and characterizing the diversity of disordered regions [22].”
ResultBruley, A., Bitard‐Feildel, T., Callebaut, I., & Duprat, É. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26441 · Cited by 18
Zarubin, M. P., Murugova, T. N., Ryzhykau, Y. L., Ivankov, O. I., Uversky, V. N., & Kravchenko, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-74335-2 · Cited by 20
“It should be noted that full atom models were not used to estimate the Dsup secondary structure due to the limitations of I-TASSER and Alphafold 2.0 algorithms for predicting intrinsically disordered protein structure and low abundance of Dsup homologs in databases of protein structures 38 .”
MethodInfluentialUse of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs
Sil, S., Datta, I., & Basu, S. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1542267 · Cited by 22
“…transformer architecture to capture complex sequence-structure dependencies, leveraging multi-head self-attention mechanisms to model the intricate spatial relationships among amino acid residues, thereby achieving unprecedented accuracy in predicting protein structure (Ruff and Pappu, 2021).”
InfluentialJeyaraj, G., Rajendran, A. K., Sathishkumar, K., Almutairi, B. O., Vadivelu, A., Chokkakula, S., Tu, Y., & Xie, W. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1688455 · Cited by 25
“…flexible termini (e.g., in p53) are frequently mis-modeled; multi-subunit complexes may omit relevant dynamic conformers; and protein–protein or protein-nucleic acid interfaces in disordered regions are often only partially captured (Evans et al., 2022; Jumper et al., 2021; Ruff and Pappu, 2021).”
Heterologous expression of naturally evolved putative de novo proteins with chaperones
Eicholt, L. A., Aubel, M., Berk, K., Bornberg‐Bauer, E., & Lange, A. (2022) · Protein Science · doi:10.1002/pro.4371 · Cited by 18
“While contemporary prediction methods can certainly provide a first estimate on structure, the intrinsic nature of de novo proteins, with their short length, high disorder content and lack of homology, will demand some scepticism while analyzing such predictions.(67,68) Surprisingly, the AlphaFold prediction of Goddar…”
BackgroundExploring Structures and Dynamics of Protamine Molecules through Molecular Dynamics Simulations
Shadman, H., Gallops, C. E., Ziebarth, J. D., et al. (2022) · ACS Omega · doi:10.1021/acsomega.2c04227 · Cited by 17
Zalewski, M., Iglesias, V., Bárcenas, O., Ventura, S., & Kmiecik, S. (2024) · Protein Science · doi:10.1002/pro.5180 · Cited by 18
“These regions tend to congregate around the globular core, introducing potential surface features that could mislead the interpretation of STAPs (Ruff & Pappu, 2021) and undermine the accuracy of A4D predictions (Badaczewska-Dawid et al., 2022; Badaczewska-Dawid et al., 2023).”
BackgroundInfluentialGolroudbari, A. A., & Sabour, M. H. (2023) · doi:10.22541/au.168664884.43899660/v1 · Cited by 22
Accurate predictions of conformational ensembles of disordered proteins with STARLINGPreprint
Novak, B., Lotthammer, J. M., Emenecker, R. J., & Holehouse, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.14.638373 · Cited by 17
“However, these methods are poorly suited to investigate IDRs 29 owing to a reduction in alignment-based conservation in disordered proteins, a paucity of appropriate experimental training data, and their training objective (predicting a single best structure from sequence, whereas IDRs should be…”
Recent progress and future challenges in structure-based protein-protein interaction prediction
Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 25
“210 Nevertheless, leveraging the growing body of experimental data, researchers have begun using computational methods to understand how IDRs drive LLPS. Saar et al. developed DeePhase, an integrated model that distinguished LLPS-prone sequences from structured and intrinsically disordered proteins…”
Generation of protein dynamics by machine learning
Janson, G., & Feig, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103115 · Cited by 21
Generalizable and scalable protein stability prediction with rewired protein generative models
Li, Z., & Luo, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-67609-4 · Cited by 24
“While we recognize that AlphaFold2-predicted structures used in this analysis may not modelIDRsaccurately 83 ,recentstudiesonIDRconformationmodeling mayfacilitateabetterunderstandingofstability ’ sroleinpathogenicity in IDRs 84 .”
BoMiProt 2.0: An update of the bovine milk protein database
Das, A., Giri, K., Behera, R. N., Maity, S., & Ambatipudi, K. (2022) · Journal of Proteomics · doi:10.1016/j.jprot.2022.104696 · Cited by 17
Liu, J., Yuan, R., Shao, W., Wang, J., Silman, I., & Sussman, J. L. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26496 · Cited by 18
(2023) · doi:10.1093/bioinformatics/btad739 · Cited by 18
“Computational predicted structures contain coordinates for IDRs, but these coordinates generally have low confidence predictions and are not likely representative (Ruff and Pappu 2021).”
Bianchi, G., Brocca, S., Longhi, S., & Uversky, V. N. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032151 · Cited by 17
“Although AlphaFold2 models can in no way be regarded as accurate descriptions of IDPs/IDRs, the latter being only describable as conformational ensembles [85], they are still useful because they provide a convenient cartoon representation that graphically enables conveying and capturing the conformational heterogeneit…”
BackgroundFantini, J., Azzaz, F., Scala, C. D., Aulas, A., Chahinian, H., & Yahi, N. (2025) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2025.108797 · Cited by 20
Tuly, J. A., Ma, H., Lee, H., Song, J., Parvez, A., Saqib, M. N., Yaseen, W., & Zhang, X. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.135854 · Cited by 16
Chettri, D., & Verma, A. K. (2023) · Carbohydrate Research · doi:10.1016/j.carres.2023.108853 · Cited by 19
Chitinous material bioconversion by three new chitinases from the yeast Mestchnikowia pulcherrima
Minguet-Lobato, M., Cervantes, F. V., Míguez, N., et al. (2024) · Microbial Cell Factories · doi:10.1186/s12934-024-02300-9 · Cited by 17
“These regions are thought to acquire concrete structures dependent on the context and to participate in coupled folding, binding and dynamic processes determining protein functions [31].”
BackgroundWaszkiewicz, R., Michaś, A., Białobrzewski, M. K., Klepka, B. P., Cieplak-Rotowska, M. K., Staszałek, Z., Cichocki, B., Lisicki, M., Szymczak, P., & Niedzwiecka, A. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c00312 · Cited by 16
Diversification of the Rho transcription termination factor in bacteria
Moreira, S. M., Chyou, T., Wade, J. T., & Brown, C. M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae582 · Cited by 19
Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration
Chen, J., Zia, A., Luo, A., Meng, H., Wang, F., Hou, J., Cao, R., & Si, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae118 · Cited by 19
“AlphaFold models are often less confident at the inter-domain regions due to the flexible and intrinsically disordered nature of these regions [18, 24].”
BackgroundThe pathogen-encoded signalling receptor Tir exploits host-like intrinsic disorder for infection
Vieira, M. F. M., Hernandez, G., Zhong, Q., Arbesú, M., Veloso, T., Gomes, T., Martins, M. L., Monteiro, H., Frazão, C., Frankel, G., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-05856-9 · Cited by 14
“Despite the impressive performance of AF2 algorithms in predicting the native single static lowest energy structure of ordered proteins, they fail to represent disordered regions accurately 114 .”
Dimerization Activity of a Disordered N-Terminal Domain from Drosophila CLAMP Protein
Tikhonova, E., Mariasina, S. S., Arkova, O. V., Maksimenko, O. G., Georgiev, P. G., & Bonchuk, A. N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073862 · Cited by 13
“IDRs’ protein–protein interaction properties are difficult to predict [56], and thus similar domains could be widespread within”
BackgroundDuarte-Velázquez, I., de la Mora, J., Ramírez-Prado, J. H., et al. (2022) · PeerJ · doi:10.7717/peerj.13772 · Cited by 15
Intrinsic disorder and flexibility in proteins: a challenge for structural biology and drug design
Zanotti, G. (2023) · Crystallography Reviews · doi:10.1080/0889311x.2023.2208518 · Cited by 16
“Finally, in the context of disorder in proteins we cannot neglect to mention AlphaFold [53], the AI-based software used to predict the 3D structure of proteins with a very high reliability.”
BackgroundMethodInfluentialNishide, G., Lim, K., Tamura, M., Kobayashi, A., Zhao, Q., Hazawa, M., Ando, T., Nishida, N., & Wong, R. W.-C. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c01440 · Cited by 15
Jiang, H., Sun, Y., Li, F., Yu, X., Lei, S., Du, S., Wu, T., Jiang, X., Zhu, J., Wang, J., et al. (2024) · BMC Biology · doi:10.1186/s12915-024-01835-y · Cited by 15
“The Alphafold Protein Structure Database (https:// alpha fold. ebi. ac . uk /) was used to analyze the tertiary structure of the RPSA protein [30, 31].”
MethodMurphy, R. D., Chen, T., Lin, J., He, R., Wu, L., Pearson, C. R., Sharma, S., Kooi, C. D. V., Sinai, A. P., Zhang, Z., et al. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102089 · Cited by 12
“Conversely, regions displaying the highest solvent uptake were also the low-confidence regions of the AlphaFold2 model, potentially revealing intrinsically disordered protein-like character of these inserts (62, 63).”
BackgroundZhong, G., Zhao, Y., Zhuang, D., Chung, W. K., & Shen, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-62318-4 · Cited by 15
Chennakesavalu, S., & Rotskoff, G. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c08195 · Cited by 15
Bourgeais, M., Fouladkar, F., Weber, M., Erba, E. B., & Wild, R. (2024) · Glycobiology · doi:10.1093/glycob/cwae016 · Cited by 14
“It is noteworthy that while AlphaFold may not definitively confirm protein disorderliness, it does suggest regions amenable to structural flexibility (Ruff and Pappu 2021; Guo et al. 2022).”
Local structural flexibility drives oligomorphism in computationally designed protein assemblies
Khmelinskaia, A., Bethel, N. P., Fatehi, F., Mallik, B. B., Antanasijevic, A., Borst, A. J., Lai, S.-H., Chim, H. Y., Wang, J. Y. ‘., Miranda, M. C., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01490-z · Cited by 18
“Previous work has correlated the degree of flexibility and the presence of unstructured regions with AF2 pLDDT at the residue level 67–69 .”
Soft disorder modulates the assembly path of protein complexes
Seoane, B., & Carbone, A. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010713 · Cited by 15
Rosenkranz, A. A., & Slastnikova, T. A. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15030987 · Cited by 14
Computational function prediction of bacteria and phage proteins
Grigson, S. R., Bouras, G. S., Dutilh, B. E., Olson, R., & Edwards, R. A. (2025) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00022-25 · Cited by 18
“AlphaFold might incorrectly assign a structure to an intrinsically disordered region, increasing the risk of mis-annotating a protein’s function (152, 153).”
Integrating Large-Scale Protein Structure Prediction into Human Genetics Research
Marrero, M. C., Jänes, J., Baptista, D., & Beltrão, P. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120622-020615 · Cited by 13
Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2
He, J., Turzo, S. B. A., Seffernick, J. T., Kim, S. S., & Lindert, S. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c05508 · Cited by 11
Gaalswyk, K., Haider, A., & Ghosh, K. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01140 · Cited by 12
Visualizing everything, everywhere, all at once: Cryo-EM and the new field of structureomics
Lucas, B. A. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102620 · Cited by 12
Structural insights into pink-eyed dilution protein (Oca2)
Mesdaghi, S., Murphy, D. L., Simpkin, A. J., & Rigden, D. J. (2023) · Bioscience Reports · doi:10.1042/bsr20230060 · Cited by 13
“The region has very low confidence (pLDDT 50) and as seen with other intrinsically disordered regions modelled by AlphaFold2 with the with the disordered region tending to be packed with the main domain (Ruff & Pappu, 2021).”
Background
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
