The DOI matches its Crossref record; 97 of the 103 references it cites were found in a registry or an index; 1 of the 58 sources its claims cite were checked for support.
AlphaFold and Implications for Intrinsically Disordered Proteins
Open accessAbstract
Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the "structures" of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This perspective cautions against over-interpreting AlphaFold’s low-confidence and “unstructured” annotations for intrinsically disordered regions as literal structures or as failures of prediction. It argues that quantitative sequence–ensemble descriptions, not static models, are needed to understand IDR function.
Key findings
- AlphaFold annotated over 98% of human proteome sequences, but roughly 30% of residues have pLDDT scores below 50, consistent with estimates that about 30% of the human proteome is disordered.
- Low-confidence regions largely overlap intrinsically disordered regions (IDRs); the authors argue most are likely genuinely disordered rather than well-folded structures that AlphaFold missed.
- Static AlphaFold structures of IDRs often overestimate radius of gyration relative to SAXS measurements, as shown for p27 CTD and the hnRNP-A1 prion-like low-complexity domain (A1-LCD).
- Quantitative ensembles from atomistic simulations vetted by experiments reveal sequence-specific behavior: p27 CTD behaves on average like a Flory random coil, A1-LCD uses aromatic stickers/spacers, and Httex1 prefers tadpole-like conformations with polyQ radius scaling as n^1/3.
Methods
- Perspective/cautionary note, not primary research; it synthesizes the AlphaFold papers, human proteome annotations, and prior IDP literature.
- Uses illustrative examples—p27 Kip1 CTD, hnRNP-A1 A1-LCD, and huntingtin exon 1 (Httex1)—comparing AlphaFold static structures with SAXS, NMR, smFRET, and atomistic simulations based on the ABSINTH implicit solvation model and forcefield.
- Main analysis is interpretive and qualitative, focused on why static AlphaFold depictions of IDRs lack physical meaning and how sequence–ensemble relationships should instead be quantified.
Limitations
- The paper does not state formal limitations.
- Evident from the text: it is a perspective/opinion piece, not a systematic review, and its examples are drawn largely from the authors’ own work and close colleagues.
- Evident from the text: no new experimental data are reported; the quantitative examples rely on previously published simulations and experiments, and the scope is limited to interpreting AlphaFold annotations for IDRs.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
38 Not checked yet · 9 Own interpretation · 9 No basis given · 5 Own result · 1 Own method · accuracy not yet measured
None of the sources cited for this claim have been checked yet.
In the paper
“Conservative estimates indicate that roughly 30% of sequences, 30residues or longer, drawn at random from the human proteome, are likely to be IDRs. 3,5”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [3] source
- Could not verifycites [5] sourceExperimental — accuracy not yet measured
The Results describe DisProt annotations and note that the dataset is biased toward model organisms, including humans; they do not estimate the prevalence of IDRs in randomly sampled human-proteome sequences. Searches for a 30% estimate and a 30-residue threshold found no passage supporting those figures. (LLM, conf=0.70)
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In the paper
“Integrating SAXS and NMR measurements with simulations, suggests that the ensemble of conformations fluctuate among locally compact regions, globally compact conformations, and expanded conformations.88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88] source
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In the paper
“Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“However, the efficiency of T187 phosphorylation does not change monotonically with changes to R g .”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
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In the paper
“Roughly 30% of the residues across predicted structures in the human proteome tend to have pLDDT scores that are less than 50.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“Instead, there is a cryptic motif that is proxi-mal to the primary substrate motif that regulates the efficiency of T187 phosphorylation.64”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [64]
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The R g value we compute using the very low confidence static structure excised from the AlphaFold prediction is considerably higher than values obtained from direct measurements based on SAXS (Figure 2(C)).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
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In the paper
“These conformational features are driven by the distributed network of contacts formed between pairs of sequenceproximal or distal aromatic residues and influenced or diluted by the spacer regions that are interspersed between aromatic residues (stickers).88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88] source
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The C-terminal domain of p27, p27 CTD , generally shows low pLDDT scores (<70) in the AlphaFold prediction.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This is because, the static AlphaFold structure likely overestimates the radius of gyration of IDRs as measured using small angle X-ray scattering (SAXS) 64 (Figure 2(A)).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [64]
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 10
- The quote only partly states this claim. 2
- Probably not a substantial claim. 9
In the paper
“Modulating the charge patterning changes the mean size of the conformational ensemble monotonically, consistent with SAXS experiments.64”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [64]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“A sizable fraction of the N-terminal region is predicted to adopt a helical structure by Alpha-Fold, and this is consistent with experimental characterizations based on nuclear magnetic resonance (NMR) spectroscopy.85”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
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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In the paper
“This ensemble is sequence-specific 30,31 and the extent of heterogeneity 36 as well as the relative preferences of conformations within the ensemble will depend on the primary sequence, solution conditions, and functional contexts.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [30]
- Not checked yetcites [31]
- Not checked yetcites [36]
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Instead, disorder implies that a diverse conformational ensemble best describes the region of interest.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
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In the paper
“The N-terminal portion (residues 1-95) folds upon binding to Cdk2/cyclin A and this leads to kinase inhibition.85,86”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
- Not checked yetcites [86]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In the current dataset, long regions with pLDDT < 50 adopt a readily identifiable ribbon-like appearance, and should not be interpreted as structures but rather as a prediction of disorder.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Intrinsically disordered regions often evolve more rapidly than ordered regions and thus alignments of these regions are generally poorer and involve large gaps and extensions of gaps because orthologous IDRs can span vast sequence lengths.9,23,58,60,[68][69][70]”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites 9,23,58,60,[68][69][70]
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Alleviation of steric clashes leads to a steep decrease in the potential energy, and often the local minimum will be situated closest to the region of the steepest drop in energy.72,73”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [72]
- Not checked yetcites [73]
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Over the past two decades, there has been growing recognition that the sequence-structurefunction paradigm must be generalized to account for the role of conformational heterogeneity and conformational dynamics in protein function.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“created quite a stir when it was deployed to predict the structures of 98.5% of proteins in the human proteome.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plain- Asserted: passes — The authors put it forward as true.
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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.
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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
“Under standard conditions used to study and characterize protein structures, IDPs/IDRs are defined by conformational heterogeneity, and the preference for heterogeneity is encoded in their amino acid sequences.11,[29][30][31]”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [11]
- Not checked yetcites [29]
- Not checked yetcites [30]
- Not checked yetcites [31]
plaindirect- Asserted: passes — The authors put it forward as true.
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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]
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- Not checked yetcites [40]
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Importantly, prediction engines are also available to identify regions that undergo disorder-to-order, order-to-disorder, and disorder-to-disorder transitions upon binding.44”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [44]
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The Pro-rich C-terminal region forms a semi-flexible tail.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Conformational ensembles of the exon 1 encoded region of huntingtin (Httex1)
What the sources say
- Not checked yetcites [94]
plain- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
In the paper
“IDPs/IDRs are best defined by a heterogeneous ensemble of conformations.36”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [36]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Taken together with complementary efforts focused on characterizing Httex1 phase behavior, the results obtained from multipronged studies of monomeric Httex1 predict that the growth of the “sticky head” with polyQ length mediates homotypic interactions and modulates heterotypic interactions in the cellular context.94”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Conformational ensembles of the exon 1 encoded region of huntingtin (Httex1)
What the sources say
- Not checked yetcites [94]
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Evidence in favor of this point of view is the successful prediction of natively disordered protein regions by AlphaFold and the correlation between the decrease of pLDDT and propensity to be in a disordered region [25].”
From Using AlphaFold to predict the impact of single mutations on protein stability and function (2023) · cites this paper as [25]
In the citing paper
“like peptides, IDPs and IDRs are best thought of as sampling diverse conformational ensembles rather than a single static conformation42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Ruff et al showed that radius of gyration values of IDPs / IDR containing proteins calculated using static AF2 models significantly deviates from those experimentally obtained by small-angle X-ray scattering (SAXS)42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary studies suggest that the majority of targets with very low-confidence score (pLDDT < 50) assigned by AF2 are likely to be IDPs / IDRs rather than well-folded structures that AF2 fails to predict42–44”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Some groups have used MD simulations to suggest that pLDDT and PAE metrics provide information on dynamics and disorder42,65”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary comparison of SAXS-derived versus AF calculated P(r) curves revealed that for many cases a static AF2 model does not adequately describe solution state structures42,72.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“It is therefore not surprising that it was recently shown [214] that data-driven approaches can hardly predict the structure of so-called intrinsically disordered proteins”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [214]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. Miskei, M., Antal, C., Fuxreiter, M., (2016). FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nucleic Acids Res., 45, D228- D235.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. To ´th-Petro ´czy, A ´., Oldfield, C.J., Simon, I., Takagi, Y., Dunker, A.K., Uversky, V.N., et al., (2008). Malleable machines in transcription regulation: The mediator complex. PLoS Comput. Biol., 4, e1000243”
Citations
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Highly accurate protein structure prediction for the human proteome
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Valence and patterning of aromatic residues determine the phase behavior of prion-like domains
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Self-citation
Cited by
Sorted by citations
The molecular basis for cellular function of intrinsically disordered protein regions
Holehouse, A. S., & Kragelund, B. B. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00673-0 · Cited by 741
Conformational ensembles of the human intrinsically disordered proteome
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“When methods such as AlphaFold are applied to IDRs, however, they result in a single configuration or few configurations of low confidence, which do not represent the conformational heterogeneity and functional states of IDRs 6 .”
Before and after AlphaFold2: An overview of protein structure prediction
Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 330
“Ruff and Pappu (2021) demonstrated that residues and regions predicted with low accuracy by AlphaFold2 overlaps intrinsically disordered regions, while Stevens and He (2022) showed that only short loops ( 20 amino acids) are predicted with high accuracy by AlphaFold and it has the tendency to…”
BackgroundUsing AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2023) · PLOS One · doi:10.1371/journal.pone.0282689 · Cited by 322
“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].”
BackgroundThe AlphaFold Database of Protein Structures: A Biologist’s Guide
David, A., Islam, S., Tankhilevich, E., & Sternberg, M. J. E. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167336 · Cited by 268
Liquid–liquid phase separation in tumor biology
Tong, X., Tang, R., Xu, J., Wang, W., Zhao, Y., Yu, X., & Shi, S. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01076-x · Cited by 265
Intrinsically Disordered Proteins: An Overview
Trivedi, R., & Nagarajaram, H. A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232214050 · Cited by 277
AlphaFold2 models indicate that protein sequence determines both structure and dynamics
Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14382-9 · Cited by 248
“The pLDDT scores have been considered as a predictor for the residue disorder contents: the residues with the pLDDT scores lower than 50 may be located in the IDPR 19 .”
BackgroundInfluentialSystematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240
“Thus, it is essential to critically analyze the AlphaFold2 - predicted structures of IDRs, as such regions cannot be accurately described by a single, static structure (21).”
InfluentialAlphaFold2 fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193
“6,7 These poorly predicted conformations often have low confidence scores, 5 calculated using the pLDDT, which quantifies the fraction of predicted C α distances that lie within their expected distance intervals.”
BackgroundMethodDirect generation of protein conformational ensembles via machine learning
Janson, G., Valdes-Garcia, G., Heo, L., & Feig, M. (2023) · Nature Communications · doi:10.1038/s41467-023-36443-x · Cited by 192
“This is especially true for intrinsically disordered proteins (IDPs) 13 , molecules that lack a stable structural state and exhibit high conformational variability 14,15 .”
BackgroundModeling conformational states of proteins with AlphaFold
Sala, D., Engelberger, F., Mchaourab, H. S., & Meiler, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102645 · Cited by 182
AlphaFold2: A Role for Disordered Protein/Region Prediction?
Wilson, C. J., Choy, W.-Y., & Karttunen, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094591 · Cited by 173
Advances in artificial intelligence for drug delivery and development: A comprehensive review
Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108702 · Cited by 197
Deep learning tools for advancing drug discovery and development
Nag, S., Baidya, A. T. K., Mandal, A., Mathew, A. T., Das, B., Devi, B., & Kumar, R. (2022) · 3 Biotech · doi:10.1007/s13205-022-03165-8 · Cited by 185
“Thus, AlphaFold has given the researchers a tool that has great potential in protein structure prediction (Ruff and Pappu 2021; Batool et al. 2019).”
BackgroundProtein generation with evolutionary diffusion: sequence is all you need
Alamdari, S., Thakkar, N., van den Berg, R., Tenenholtz, N., Strome, R., Moses, A. M., Lu, A. X., Fusi, N., Amini, A. P., & Yang, K. K. (2023) · doi:10.1101/2023.09.11.556673 · Cited by 154
“While pLDDT scores above 70 are often considered to be high confidence, low pLDDT can be consistent with intrinsically disordered regions (IDRs) of proteins [25].”
BackgroundIntrinsic protein disorder and conditional folding in AlphaFoldDB
Piovesan, D., Monzon, A. M., & Tosatto, S. C. E. (2022) · Protein Science · doi:10.1002/pro.4466 · Cited by 160
“10 The relative movements of residues cannot be captured with a single static structure.”
BackgroundTowards realizing nano-enabled precision delivery in plants
Lowry, G. V., Giraldo, J. P., Steinmetz, N. F., Avellan, A., Demirer, G. S., Ristroph, K. D., Wang, G., Hendren, C. O., Alabi, C. A., Caparco, A. A., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-024-01667-5 · Cited by 187
“However, the accuracy of in silico methods still needs to be validated by experimental approaches, as these predictive algorithms have limitations (for example, determining conformations of intrinsically disordered proteins) 168 and cannot yet fully replace experimental structure determination 169 .”
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155
“Some groups have used MD simulations to suggest that pLDDT and PAE metrics provide information on dynamics and disorder 42,65 .”
BackgroundMethodInfluentialThe accuracy of protein structures in solution determined by AlphaFold and NMR
Fowler, N. J., & Williamson, M. P. (2022) · Structure · doi:10.1016/j.str.2022.04.005 · Cited by 104
Sensory circuitry controls cytosolic calcium-mediated phytochrome B phototransduction
Zhao, Y. T., Shi, H., Pan, Y., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.011 · Cited by 105
Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments
Grassmann, G., Miotto, M., Desantis, F., Rienzo, L. D., Tartaglia, G. G., Pastore, A., Ruocco, G., Monti, M. M., & Milanetti, E. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00550 · Cited by 109
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103
Protein folds vs. protein folding: Differing questions, different challenges
Chen, S., Hassan, M., Jernigan, R. L., Jia, K., Kihara, D., Kloczkowski, A., Kotelnikov, S., Kozakov, D., Liang, J., Liwo, A., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214423119 · Cited by 100
“A possible objection to the preceding explanation is that AlphaFold (1) has had limited success with the class of intrinsically disordered proteins (25), which, by definition, lack persisting structure until paired with a cognate molecule, or again with allosteric proteins (26), regulatory proteins that involve popula…”
AlphaFold prediction of structural ensembles of disordered proteins
Brotzakis, Z. F., Zhang, S., Murtada, M. H., & Vendruscolo, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56572-9 · Cited by 99
AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function
Laurents, D. V. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.906437 · Cited by 95
“K.M. Ruff and R. V. Pappu (Ruff and Pappu, 2021) pointed out that it is now widely believed that almost all these low/very low con fi dence regions are intrinsically disordered, with the remaining 1 – 2 % being well folded proteins which are mispredicted by AlphaFold 2.”
BackgroundMethodInfluentialDobson, L., Szekeres, L. I., Gerdán, C., Langó, T., Zeke, A., & Tusnády, G. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac928 · Cited by 87
“AF2 correctly predicts each segment (considering the observation that low pLDDT values correlate with intrinsic disorder (17)), however, the orientation of these segments relative to each other is problematic, as the flexible linker turns back the polypeptide chain and places the domain next to the TM region.”
BackgroundDey, J., Mahapatra, S. R., Singh, P. K., et al. (2023) · Immunologic Research · doi:10.1007/s12026-023-09374-4 · Cited by 94
“uk/) [42], and SWISS-MODEL (https:// swiss model.”
Quantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93
AlphaFold illuminates half of the dark human proteins
Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102372 · Cited by 81
Kuzu, O. F., Granerud, L. J. T., & Saatcioglu, F. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02439-w · Cited by 128
Using AlphaFold to predict the impact of single mutations on protein stability and function
Pak, M. A., Markhieva, K. A., Novikova, M. S., Petrov, D. S., Vorobyev, I. S., Maksimova, E. S., Kondrashov, F. A., & Ivankov, D. N. (2021) · doi:10.1101/2021.09.19.460937 · Cited by 322
Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics
Desai, D., Kantliwala, S. V., Vybhavi, J., Ravi, R., Patel, H., & Patel, J. S. (2024) · Cureus · doi:10.7759/cureus.63646 · Cited by 99
“By providing detailed insights into how molecules interact and influence biological functions, it paves the way for groundbreaking advancements in medical science and research [17].”
BackgroundA Hairpin Motif in the Amyloid-β Peptide Is Important for Formation of Disease-Related Oligomers
Khaled, M., Rönnbäck, I., Ilag, L. L., Gräslund, A., Strodel, B., & Österlund, N. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c03980 · Cited by 87
Song, Y., Yuan, Q., Chen, S., Zeng, Y., Zhao, H., & Yang, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-52533-w · Cited by 89
Advancing microbial production through artificial intelligence-aided biology
Gong, X., Zhang, J., Gan, Q., Teng, Y., Hou, J., Lyu, Y., Liu, Z., Wu, Z., Dai, R., Zou, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108399 · Cited by 85
Fonin, A. V., Antifeeva, I. A., Kuznetsova, I. М., Константинович, Т. К., Zaslavsky, B. Y., Kulkarni, P., & Uversky, V. N. (2022) · Essays in Biochemistry · doi:10.1042/ebc20220052 · Cited by 78
Predicting the pathogenicity of missense variants using features derived from AlphaFold2
Schmidt, A., Röner, S., Mai, K., Klinkhammer, H., Kircher, M., & Ludwig, K. U. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad280 · Cited by 79
“Given that low pLDDT values may be associated with intrinsically disordered protein regions (Ruff and Pappu, 2021; Tunyasuvunakool et al., 2021), this could indicate that these regions tend to be less disease-relevant in aggregate than well-structured regions.”
BackgroundPredicting protein flexibility with AlphaFold
Ma, P., Li, D., & Brüschweiler, R. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26471 · Cited by 72
“42 When assessing whether AF2 structures are representative of experimental protein structures in solution at ambient temperatures, independent validation by NMR residual dipolar couplings showed for a small set of AF2 structures that they provide a highly accurate representation of the average…”
BackgroundNovel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
“Some models with low pLDDT point to IDRs that undergo disorder-to-order transition upon binding or are prone to fold-switching [89 – 91].”
BackgroundUnderstanding the cell: Future views of structural biology
Beck, M., Covino, R., Hänelt, I., & Müller-McNicoll, M. (2024) · Cell · doi:10.1016/j.cell.2023.12.017 · Cited by 71
“144 The generation of the NPC structure exemplifies the strength of integrative approaches to deal with very large biological systems en route toward generating digital twins of subcellular segments.”
BackgroundA paradigm shift in structural biology
Subramaniam, S., & Kleywegt, G. J. (2022) · Nature Methods · doi:10.1038/s41592-021-01361-7 · Cited by 60
Folding the unfoldable: using AlphaFold to explore spurious proteins
Monzon, V., Haft, D. H., & Bateman, A. G. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab043 · Cited by 54
Rules of Physical Mathematics Govern Intrinsically Disordered Proteins
Ghosh, K., Huihui, J., Phillips, M. W., & Haider, A. (2022) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-120221-095357 · Cited by 59
Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles
Peccati, F., Alunno-Rufini, S., & Jiménez‐Osés, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01083 · Cited by 61
“37,38 AF relies on the analysis of multiple sequence alignments to generate the three-dimensional models and evaluates the quality of the prediction using a local (residue-based) score.”
MethodUsing AlphaFold Predictions in Viral Research
Gutnik, D., Evseev, P. V., Анатольевич, М. К., & Shneider, M. M. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45040240 · Cited by 65
“When AlphaFold encounters difficulties with obtaining highly accurate predictions, the problem very often relates to intrinsically disordered proteins (IDPs) or intrinsically disordered protein regions (IDRs) [71].”
BackgroundArtificial intelligence in medical science: a review
Bindra, S., & Jain, R. (2023) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-023-03570-9 · Cited by 66
“[40].”
Karakoyun, H. K., Yüksel, Ş., Amanoglu, I., Naserikhojasteh, L., Yeşilyurt, A., Yakıcıer, C., Timuçin, E., & Akyerli, C. B. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1052383 · Cited by 66
“The power of the AF confidence scores in predicting the pathogenicity of missense variants was, to some extent, unsurprising, particularly considering the close association of structural disorder and pLDDT score (Necci et al., 2021; Ruff and Pappu, 2021).”
BackgroundMaiti, S., Singh, A., Maji, T., Saibo, N. V., & De, S. (2024) · Current Research in Structural Biology · doi:10.1016/j.crstbi.2024.100138 · Cited by 61
“Low confidence regions in AlphaFold ’ s predictions are found to align with disordered regions, indicating that over 30% of protein regions exhibit disorder (Ruff and Pappu, 2021).”
BackgroundProtein Chemistry Combined with Mass Spectrometry for Protein Structure Determination
Petrotchenko, E. V., & Borchers, C. H. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00302 · Cited by 55
Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
Yue, T., Wang, Y., Zhang, L., Gu, C., Xue, H., Wang, W., Lyu, Q., & Dun, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115858 · Cited by 192
“…revealed several limitations of the AlphaFold2 model: its performance could suffer from predicting intrinsically disordered proteins Ruff and Pappu [263]; the performance on loop prediction is only high for short loops Stevens and He [264]; and significant degradation in performance was discovered…”
BackgroundA Perspective on Protein Structure Prediction Using Quantum Computers
Doğa, H., Raubenolt, B. A., Cumbo, F., Joshi, J., DiFilippo, F. P., Qin, J., Blankenberg, D. J., & Shehab, O. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00067 · Cited by 67
“The deep learning methods often fail at properly predicting these conformations [112].”
(2024) · doi:10.1038/s42256-024-00855-1 · Cited by 57
“The transformer architecture 17 introduced a new technique using attention mechanisms for sequential data analysis across domains 18,28,29 .”
The convergence of AI and synthetic biology: the looming deluge
Groff-Vindman, C., Trump, B. D., Cummings, C. L., Smith, M., Titus, A. J., Oye, K., Prado, V., Turmus, E. K., & Linkov, I. (2025) · npj Biomedical Innovations. · doi:10.1038/s44385-025-00021-1 · Cited by 73
“Recently, DeepMind ’ s AlphaFold has solved a 50-year-old challenge that has stumped the fi eld by achieving improved accuracies at modeling protein tertiary structure for all known proteins simultaneously 24 .”
Rocafort, M., Bowen, J. K., Hassing, B., Cox, M. P., McGreal, B., de la Rosa, S., Plummer, K. M., Bradshaw, R. E., & Mesarich, C. H. (2022) · BMC Biology · doi:10.1186/s12915-022-01442-9 · Cited by 52
The biomedical potential of tardigrade proteins: A review
Kasianchuk, N., Rzymski, P., & Kaczmarek, Ł. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.114063 · Cited by 51
Biomolecular NMR spectroscopy in the era of artificial intelligence
Shukla, V. K., Heller, G. T., & Hansen, D. F. (2023) · Structure · doi:10.1016/j.str.2023.09.011 · Cited by 52
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 56
Structure-guided isoform identification for the human transcriptome
Sommer, M. J., Cha, S., Varabyou, A., Rincon, N., Park, S., Minkin, I., Pertea, M., Steinegger, M., & Salzberg, S. L. (2022) · eLife · doi:10.7554/elife.82556 · Cited by 40
“We only considered alternative 522 isoforms that had a pLDDT score ≥ 70, indicating a generally well-folded protein, to avoid 523 including any intrinsically disordered proteins (50).”
BackgroundN-glycosylation as a eukaryotic protective mechanism against protein aggregation
Duran-Romaña, R., Houben, B., De Vleeschouwer, M., Louros, N., Wilson, M. P., Matthijs, G., Schymkowitz, J., & Rousseau, F. (2024) · Science Advances · doi:10.1126/sciadv.adk8173 · Cited by 52
Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level
Shao, C., Bittrich, S., Wang, S., & Burley, S. K. (2022) · Structure · doi:10.1016/j.str.2022.08.004 · Cited by 45
Ahmad, S., Demneh, F. M., Rehman, B., Almanaa, T. N., Akhtar, N., Pazoki–Toroudi, H., Shojaeian, A., Ghatrehsamani, M., & Sanami, S. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.131517 · Cited by 52
Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models
Jussupow, A., & Kaila, V. R. I. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01027 · Cited by 42
Evolutionary selection of proteins with two folds
(2023) · doi:10.1038/s41467-023-41237-2 · Cited by 41
Reciprocal best structure hits: using AlphaFold models to discover distant homologues
Monzon, V., Paysan-Lafosse, T., Wood, V., & Bateman, A. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac072 · Cited by 46
“The disordered protein regions largely overlap with low confidence scoring regions [2, 31].”
Puente-Lelièvre, C., Malik, A. J., Douglas, J., Ascher, D. B., Baker, M. A. B., Allison, J. R., Poole, A. M., Lundin, D., Fullmer, M. S., Bouckaert, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.571181 · Cited by 36
Rational drug design targeting intrinsically disordered proteins
Wang, H., Xiong, R., & Lai, L. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1685 · Cited by 40
“8,30 In addition to conformational sampling, MD can be applied to explain the effects caused by mutations 86 or PTMs. Liu et al. 87 characterized the conformational ensembles of phosphorylated/unphosphorylated kinase-inducible domain (pKID/KID) and the free energy landscapes of their binding to…”
MethodMiles, A. J., Drew, E. D., & Wallace, B. A. (2023) · Communications Biology · doi:10.1038/s42003-023-05178-2 · Cited by 42
“IDPs (or ordered proteins with IDRs) are different types of structures than fully ordered proteins, however, David et al. 25 , Ruff and Pappu 26 and Wilson et al. 27 have asserted, that whilst the details of the AlphaFold2 predictions of the 3D structures of the IDP regions may not be exactly de fi…”
Design and discovery of metamorphic proteins
Dishman, A. F., & Volkman, B. F. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102380 · Cited by 36
Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer
Lu, Y., Yang, Y., Zhu, G., Zeng, H., Fan, Y., Guo, F., Xu, D., Wang, B., Chen, D., & Ge, G. (2023) · International Journal of Biological Sciences · doi:10.7150/ijbs.83026 · Cited by 42
“Alphafold also has some limitations, such as not being able to solve the long-standing protein folding problem; predicting a single ranked structure of a protein sequence, which cannot directly address the allosteric mechanism; and being less applicable to IDPs and IDRs, using low structural probabilities to describe…”
BackgroundMalhotra, Y., John, J., Yadav, D., Sharma, D., Vanshika, Rawal, K., Mishra, V., & Chaturvedi, N. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109842 · Cited by 42
Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond
Bruley, A., Mornon, J., Duprat, É., & Callebaut, I. (2022) · Biomolecules · doi:10.3390/biom12101467 · Cited by 37
Highly accurate isoform identification for the human transcriptome
Sommer, M. J., Cha, S., Varabyou, A., Rincon, N., Park, S., Minkin, I., Pertea, M., Steinegger, M., & Salzberg, S. L. (2022) · doi:10.1101/2022.06.08.495354 · Cited by 40
“We only considered alternative isoforms that had a pLDDT score ≥ 70, indicating a generally well-folded protein, to avoid including any intrinsically disordered proteins ( 49 ).”
BackgroundDynamics and interactions of intrinsically disordered proteins
Arai, M., Suetaka, S., & Ooka, K. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102734 · Cited by 38
Recent Advances in NMR Protein Structure Prediction with ROSETTA
Leman, J. K., & Künze, G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097835 · Cited by 38
“If structures of homologous proteins are available, they can serve as templates for modeling using the Rosetta comparative modeling (RosettaCM) [53] method, usually achieving better accuracy than de novo structure prediction.”
MethodCONSTANS, a HUB for all seasons: How photoperiod pervades plant physiology regulatory circuits
Romero, J., Serrano-Bueno, G., Camacho‐Fernández, C., Vicente, M. H., Ruiz, M. T. G., Pérez‐Castiñeira, J. R., Pérez-Hormaeche, J., Nogueira, F. T. S., & Valverde, F. (2024) · The Plant Cell · doi:10.1093/plcell/koae090 · Cited by 39
“Low-confidence regions reflect the conformational heterogeneity essential to allow the interaction of CO with many partners, which results in the formation of complexes with different functions in vivo (Ruff and Pappu 2021) (Fig.”
BackgroundOrand, T., & Jensen, M. R. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102958 · Cited by 34
Targeting protein disorder: the next hurdle in drug discovery
Lázár, T., Connor, A., DeLisle, C. F., Burger, V., & Tompa, P. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01220-6 · Cited by 45
“15,16) and AlphaFold 3 (ref.”
A Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 34
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
McCafferty, C. L., Pennington, E. L., Papoulas, O., Taylor, D. W., & Marcotte, E. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04773-7 · Cited by 35
“Moreover, it has been shown that AF2 predictions suffer for proteins that do not have available template sequences (21) and that AF2 is challenged by intrinsically disordered regions (23) and dynamics in general (24).”
BackgroundMachine Learning Generation of Dynamic Protein Conformational Ensembles
Zheng, L.-E., Barethiya, S., Nordquist, E. B., & Chen, J. (2023) · Molecules · doi:10.3390/molecules28104047 · Cited by 33
“Notwithstanding the remarkable successes of single protein structure prediction [17], the need for additional developments is well-recognized [18–21].”
Venanzi, N. A. E., Basciu, A., Vargiu, A. V., Kiparissides, A., Dalby, P. A., & Dikicioǧlu, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00999 · Cited by 32
Pajkos, M., Clerc, I., Zanon, C., Bernadó, P., & Cortés, J. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169003 · Cited by 39
Zhao, B., Ghadermarzi, S., & Kurgan, L. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.06.001 · Cited by 33
“The use of AF2 could lead to misinterpretations of predicted “structures” of IDRs in the context of the sequence-en-semble-function relationships that are characteristic for the inter-actomes of disordered proteins [88].”
BackgroundPeptide conformational sampling using the Quantum Approximate Optimization Algorithm
Boulebnane, S., Lucas, X., Meyder, A., Adaszewski, S., & Montanaro, A. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00733-5 · Cited by 32
“Nonetheless, further advances in the field could benefit de novo protein structure design, improve accurate protein structure prediction for low homology proteins, and provide better understanding of intrinsically disordered protein regions [60, 52, 56].”
BackgroundAlphaFold2 models indicate that protein sequence determines both structure and dynamics
Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · doi:10.21203/rs.3.rs-1666858/v1 · Cited by 248
Ertelt, M., Mulligan, V. K., Maguire, J. B., Lyskov, S., Moretti, R., Schiffner, T., Meiler, J., & Schoeder, C. T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011939 · Cited by 30
Reframing the Protein Folding Problem: Entropy as Organizer
Rose, G. D. (2021) · Biochemistry · doi:10.1021/acs.biochem.1c00687 · Cited by 29
Sekar, G., Ojoawo, A. M., & Moldoveanu, T. (2022) · Biochemical Society Transactions · doi:10.1042/bst20220323 · Cited by 33
Comprehending B-Cell Epitope Prediction to Develop Vaccines and Immunodiagnostics
Caoili, S. E. C. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.908459 · Cited by 32
“Accordingly, target regions may be identified for exclusion using appropriate computational tools to predict folding (vis-à-vis disorder, e.g., using AlphaFold (45, 46)) and higher-order structural organization (e.g., among supramolecular assemblies such as biomembranes (47)).”
MethodBi, B., Yuan, Y., Jia, D. T., et al. (2023) · Aquaculture Research · doi:10.1155/2023/1995494 · Cited by 31
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database
Tang, Q.-Y., Ren, W., Wang, J., & Kaneko, K. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac197 · Cited by 31
“Despite the limitations noted in previous research (Bagdonas et al. 2021; Pak et al. 2021; Ruff and Pappu 2021), AlphaFold 2 is acknowledged to provide high-accuracy predictions of protein structures, even for sequences with relatively fewer homologous sequences ( Jumper et al. 2021).”
AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles
Bowman, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-102423-011435 · Cited by 42
The roles of intrinsically disordered proteins in neurodegeneration
Utami, K. H., Morimoto, S., Mitsukura, Y., & Okano, H. (2025) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2025.130772 · Cited by 37
Flexibility or uncertainty? A critical assessment of AlphaFold 2 pLDDT
Meersche, Y. V., Diharce, J., Gelly, J., & Galochkina, T. V. (2025) · Structure · doi:10.1016/j.str.2025.09.001 · Cited by 36
Proteolytic Processing of Plant Proteins by Potyvirus NIa Proteases
Xiao, H., Lord, É., & Sanfaçon, H. (2021) · Journal of Virology · doi:10.1128/jvi.01444-21 · Cited by 30
“S2 and S5), as already noted by others (75).”
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
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