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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs).”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“However, the efficiency of T187 phosphorylation does not change monotonically with changes to R g .”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Roughly 30% of the residues across predicted structures in the human proteome tend to have pLDDT scores that are less than 50.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
hedged- Asserted: passes — The authors put it forward as true.
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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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- Load-bearing: passes — The paper’s conclusions depend on it.
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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
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“These conformational features are driven by the distributed network of contacts formed between pairs of sequenceproximal or distal aromatic residues and influenced or diluted by the spacer regions that are interspersed between aromatic residues (stickers).88”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88] source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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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- Load-bearing: passes — The paper’s conclusions depend on it.
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- It may state two separate claims as one. 10
- The quote only partly states this claim. 2
- Probably not a substantial claim. 9
In the paper
“Modulating the charge patterning changes the mean size of the conformational ensemble monotonically, consistent with SAXS experiments.64”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [64]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“A sizable fraction of the N-terminal region is predicted to adopt a helical structure by Alpha-Fold, and this is consistent with experimental characterizations based on nuclear magnetic resonance (NMR) spectroscopy.85”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Atomistic simulations reproduce the average R g value measured using SAXS.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > DECLARATION OF COMPETING INTEREST
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“This ensemble is sequence-specific 30,31 and the extent of heterogeneity 36 as well as the relative preferences of conformations within the ensemble will depend on the primary sequence, solution conditions, and functional contexts.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [30]
- Not checked yetcites [31]
- Not checked yetcites [36]
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Instead, disorder implies that a diverse conformational ensemble best describes the region of interest.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The N-terminal portion (residues 1-95) folds upon binding to Cdk2/cyclin A and this leads to kinase inhibition.85,86”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Examples of sequence-ensemble descriptions obtained from atomistic simulations vetted by comparisons to multipronged experiments
What the sources say
- Not checked yetcites [85]
- Not checked yetcites [86]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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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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.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“created quite a stir when it was deployed to predict the structures of 98.5% of proteins in the human proteome.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“leading to the postulate, tested through theory, computations, and experiments, that aromatic residues function as cohesive motifs known as stickers that drive single chain compaction as well as phase separation 88 -the latter through a network of intermolecular interactions.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > The prion-like complexity domain from hnRNPA1
What the sources say
- Not checked yetcites [88]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Under standard conditions used to study and characterize protein structures, IDPs/IDRs are defined by conformational heterogeneity, and the preference for heterogeneity is encoded in their amino acid sequences.11,[29][30][31]”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [11]
- Not checked yetcites [29]
- Not checked yetcites [30]
- Not checked yetcites [31]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The overall number of distinct conformations in the ensemble, the conformational heterogeneity, in terms of features such as the sizes and shapes sampled, and the relative importance of different conformations within the ensemble are defined by a combination of sequence-encoded short-and long-range interactions 37,38 as well as the modulation of these interactions by solution conditions.39,40”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [37]
- Not checked yetcites [38]
- Not checked yetcites [39]
- Not checked yetcites [40]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Importantly, prediction engines are also available to identify regions that undergo disorder-to-order, order-to-disorder, and disorder-to-disorder transitions upon binding.44”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Introduction
What the sources say
- Not checked yetcites [44]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The Pro-rich C-terminal region forms a semi-flexible tail.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > Conformational ensembles of the exon 1 encoded region of huntingtin (Httex1)
What the sources say
- Not checked yetcites [94]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
- Checkable: passes — It can be checked against evidence.
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It may state two separate claims as one.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Evidence in favor of this point of view is the successful prediction of natively disordered protein regions by AlphaFold and the correlation between the decrease of pLDDT and propensity to be in a disordered region [25].”
From Using AlphaFold to predict the impact of single mutations on protein stability and function (2023) · cites this paper as [25]
In the citing paper
“like peptides, IDPs and IDRs are best thought of as sampling diverse conformational ensembles rather than a single static conformation42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Ruff et al showed that radius of gyration values of IDPs / IDR containing proteins calculated using static AF2 models significantly deviates from those experimentally obtained by small-angle X-ray scattering (SAXS)42”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary studies suggest that the majority of targets with very low-confidence score (pLDDT < 50) assigned by AF2 are likely to be IDPs / IDRs rather than well-folded structures that AF2 fails to predict42–44”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Some groups have used MD simulations to suggest that pLDDT and PAE metrics provide information on dynamics and disorder42,65”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“Preliminary comparison of SAXS-derived versus AF calculated P(r) curves revealed that for many cases a static AF2 model does not adequately describe solution state structures42,72.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [42]
In the citing paper
“It is therefore not surprising that it was recently shown [214] that data-driven approaches can hardly predict the structure of so-called intrinsically disordered proteins”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [214]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. Miskei, M., Antal, C., Fuxreiter, M., (2016). FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies. Nucleic Acids Res., 45, D228- D235.”
§ AlphaFold and Implications for Intrinsically Disordered Proteins > References
“26. To ´th-Petro ´czy, A ´., Oldfield, C.J., Simon, I., Takagi, Y., Dunker, A.K., Uversky, V.N., et al., (2008). Malleable machines in transcription regulation: The mediator complex. PLoS Comput. Biol., 4, e1000243”
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Valence and patterning of aromatic residues determine the phase behavior of prion-like domains
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Protein ensemble modeling and analysis with MMMx
Jeschke, G. (2024) · Protein Science · doi:10.1002/pro.4906 · Cited by 12
“Analysis of AF2 predictions for the human proteome has shown that IDRs and IDPs play an important role that has not yet been reflected in the treatment of intrinsic disorder in molecular and structural biology research (Ruff & Pappu, 2021).”
BackgroundInfluentialMachine learning & deep learning tools in pharmaceutical sciences: A comprehensive review
Javid, S., Rahmanulla, A., Ahmed, M. G., Sultana, R., & Kumar, B. R. P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.11.003 · Cited by 18
Stagno, F., Russo, S., Murdaca, G., Mirabile, G., Alvaro, M. E., Nasso, M. E., Zemzem, M., Gangemi, S., & Allegra, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26062535 · Cited by 16
“AlphaFold is an artificial intelligence program created by DeepMind that uses a deep learning model to predict the three-dimensional structures of proteins [48].”
Capturing the Conformational Heterogeneity of HSPB1 Chaperone Oligomers at Atomic Resolution
Berkeley, R. F., Plonski, A. P., Phan, T. M., Grohe, K., Becker, L., Wegner, S., Herzik, M. A., Mittal, J., & Debelouchina, G. T. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c18668 · Cited by 12
Exhaustive Mapping of the Conformational Space of Natural Dipeptides by the DFT-D3//COSMO-RS Method
Kalvoda, T., Culka, M., Rulı́šek, L., & Andris, E. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c02861 · Cited by 12
Bekker, G., Araki, M., Oshima, K., Okuno, Y., & Kamiya, N. (2023) · Communications Biology · doi:10.1038/s42003-023-04720-6 · Cited by 15
Reciprocal Best Structure Hits: Using AlphaFold models to discover distant homologues
Monzon, V., Paysan-Lafosse, T., Wood, V., & Bateman, A. (2022) · doi:10.1101/2022.07.04.498216 · Cited by 46
From interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2
Bret, H., Andreani, J., & Guerois, R. (2023) · doi:10.1101/2023.05.25.542287 · Cited by 103
Atomic resolution ensembles of intrinsically disordered proteins with Alphafold
Schnapka, V., Morozova, T. I., Sen, S., & Bonomi, M. (2026) · Nature Communications · doi:10.1038/s41467-026-69172-y · Cited by 23
Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins
Scalvini, B., Sheikhhassani, V., van de Brug, N., Heling, L. W. H. J., Schmit, J. D., & Mashaghi, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00391 · Cited by 10
Nucleotide-induced hyper-oligomerization inactivates transcription termination factor ρ
Wang, B., Said, N., Hilal, T., Finazzo, M., Wahl, M. C., & Artsimovitch, I. (2025) · Nature Communications · doi:10.1038/s41467-025-56824-8 · Cited by 14
Remote on–off switching of protein activity by intrinsically disordered region
Ji, T., Ge, P., Zhang, S., Wan, C., Liu, H., Qu, X., Zhu, F., Gong, Q., Xu, W., Wang, C., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01585-7 · Cited by 12
“3,4 and have pivotal roles in diverse cellular processes, notably in signaling 5–7 .”
AlphaFold2: A role for disordered protein prediction?
(2021) · doi:10.1101/2021.09.27.461910 · Cited by 173
“As has already been pointed out by several authors 5–7 , dynamics of protein folding remains a formidable problem; prediction of the folding pathways, effects of mutations, the solution environment, aggregation and, as a very particular category, intrinsically disordered proteins (IDPs).”
BackgroundGarcia, M., Dixit, S. M., & Rocklin, G. J. (2026) · Protein Science · doi:10.1002/pro.70453 · Cited by 26
“In AlphaFold predictions, regions with low pLDDT scores often correspond to intrinsically disordered segments lacking a stable three-dimensional fold (Ruff and Pappu 2021).”
Guo, S., Gao, W., Zeng, M., Liu, F., Yang, Q., Chen, L., Wang, Z., Jin, Y., Xiang, P., Chen, H., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1163781 · Cited by 11
“It has achieved successes in its highly accurate structure predictions (58).”
BackgroundDisrupting Dimeric β-Amyloid by Electric Fields
Vargas-Rosales, P. A., D’Addio, A., Zhang, Y., & Caflisch, A. (2023) · ACS Physical Chemistry Au · doi:10.1021/acsphyschemau.3c00021 · Cited by 11
Mass spectrometry-based methods for characterizing transient protein–protein interactions
Veale, C. G. L., & Clarke, D. J. (2024) · Trends in Chemistry · doi:10.1016/j.trechm.2024.05.002 · Cited by 11
Pharma innovation: how evolutionary economics is shaping the future of pharma R&D
Schuhmacher, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104222 · Cited by 14
Phase separation in DNA damage response: New insights into cancer development and therapy
Li, L., Yao, L., Wang, M., Zhou, X., & Xu, Y. (2024) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2024.189206 · Cited by 11
The physics-AI dialogue in drug design
Vargas-Rosales, P. A., & Caflisch, A. (2025) · RSC Medicinal Chemistry · doi:10.1039/d4md00869c · Cited by 15
Systems biology of Haemonchus contortus – Advancing biotechnology for parasitic nematode control
Zheng, Y., Young, N. D., Wang, T., Chang, B. C. H., Song, J., & Gasser, R. B. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108567 · Cited by 14
Protein structure prediction improves the quality of amino‐acid sequence alignment
Lesk, A. M., & Konagurthu, A. S. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26392 · Cited by 8
An agnostic analysis of the human AlphaFold2 proteome using local protein conformations
de Brevern, A. G. (2022) · Biochimie · doi:10.1016/j.biochi.2022.11.009 · Cited by 12
Walter, Y. (2023) · AI and Ethics · doi:10.1007/s43681-023-00276-7 · Cited by 14
“The emergent human-AI interactions produce findings that would have taken decades for biologists to discover, meaning that the whole process becomes a lot more effective [38, 103].”
Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology
Graille, M., Sacquin‐Mora, S., & Taly, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00381 · Cited by 12
Multiscale modelling of the extracellular matrix
Wong, H., Crowet, J., Dauchez, M., Ricard‐Blum, S., Baud, S., & Belloy, N. (2021) · Matrix Biology Plus · doi:10.1016/j.mbplus.2021.100096 · Cited by 11
“Also of note, while recent, AI based techniques to solve the 3D structure of proteins have made large contributions toward providing plausible structures of previously unresolved or unresolveable structures [40,41], although one has to be aware of the limitations of such technique especially regarding disordered regio…”
BackgroundImpacts of a Zwitterionic Peptide on its Fusion Protein
McMullen, P., Fang, L., Qiao, Q., Shao, Q., & Jiang, S. (2022) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.2c00176 · Cited by 12
Towards a General Intermolecular Binding Affinity CalculatorPreprint
Li, W. (2022) · Preprints.org · doi:10.20944/preprints202208.0213.v1 · Cited by 10
Atomic resolution ensembles of intrinsically disordered proteins with Alphafold
Schnapka, V., Morozova, T. I., Sen, S., & Bonomi, M. (2025) · doi:10.1101/2025.06.18.660298 · Cited by 23
Nam, J., Lee, Y., Lee, S., Choi, H., Lee, S. Y., & Yang, D. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108782 · Cited by 11
SARS-CoV-2 Nsp8 N-terminal domain folds autonomously and binds dsRNA
Treviño, M. Á., Pantoja‐Uceda, D., Laurents, D. V., & Mompeán, M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad714 · Cited by 9
“…of the Nsp8 NTD ( 15 ) and residues 1– 15 and 50–84 are predicted with lower confidence by Al-phaFold2 ( 16 ), which is a hallmark of disorder ( 17 ) and which may correlate with the absence of large portions of NTD residues in many crystallo gra phic and cryo-EM structures (Supplementary…”
Unveiling Recent Trends in Biomedical Artificial Intelligence Research: Analysis of Top-Cited Papers
Glicksberg, B. S., & Klang, E. (2024) · Applied Sciences · doi:10.3390/app14020785 · Cited by 12
What Is life? Rethinking Biology in Light of Fundamental Parameters
Fantini, J., Matveeva, M., Lefebvre, M., & Chahinian, H. (2024) · Life · doi:10.3390/life14030280 · Cited by 15
“Yet we can consider that life, if it emerges, is the consequence of the quantum properties of atoms, which are identical everywhere in the universe and onto which fundamental parameters apply, themselves matching these quantum properties.”
BackgroundThe emerging roles of liquid–liquid phase separation in tumor immunity
Liu, Y., Cao, L.-Y., & Sun, Z. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.113212 · Cited by 8
Duran-Romaña, R., Houben, B., Migens, P. F., Zhang, Y., Rousseau, F., & Schymkowitz, J. (2025) · Nature Communications · doi:10.1038/s41467-025-57033-z · Cited by 10
Jeschke, G. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103046 · Cited by 11
One Step Closer to the Understanding of the Relationship IDR-LCR-Structure
Gonçalves-Kulik, M., Schmid, F., & Andrade-Navarro, M. A. (2023) · Genes · doi:10.3390/genes14091711 · Cited by 9
“In any case, the prediction of the structures of IDRs is still problematic and must be analyzed with caution, in a synergy between computational and experimental techniques [36].”
BackgroundRecruitment of trimeric eIF2 by phosphatase non-catalytic subunit PPP1R15B
Fatalska, A., HODGSON, G. S., Freund, S. M. V., Maslen, S., Morgan, T. E., Thorkelsson, S. R., van Slegtenhorst, M., Lorenz, S., Andreeva, A. E., Kaat, L. D., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.12.011 · Cited by 9
Depicting a cellular space occupied by condensates
Liu, D. X., Riggi, M., Lee, H. O., Currie, S. L., Goodsell, D. S., Iwasa, J., & Rog, O. (2023) · Molecular Biology of the Cell · doi:10.1091/mbc.e22-11-0519 · Cited by 8
“These threads, which loop around alpha helices and beta sheets in structured parts of proteins, capture the lack of fixed structure but fail to communicate that the actual structure is not completely random but rather falls within a statistical ensemble of conformations (Ruff and Pappu, 2021).”
BackgroundIntrinsically disordered regions (IDRs): A vague and confusing concept for protein function
Struhl, K. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.02.023 · Cited by 9
PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model
Huang, J., Li, W., Xiao, B., Zhao, C., Zheng, H., Li, Y., & Wang, J. (2024) · iScience · doi:10.1016/j.isci.2024.110850 · Cited by 10
“23 24–26 Lastly, the dynamic nature of proteins, with conformational changes under varying circumstances, can result in inaccuracies.”
Rani, P., Rajak, B. K., Mahato, G. K., Rathore, R. S., Chandra, G., & Singh, D. V. (2024) · Pest Management Science · doi:10.1002/ps.8455 · Cited by 9
Zerebinski, J., Margerie, L., Han, N. S., Moll, M., Ritvos, M., Jahnmatz, P., Ahlborg, N., Ngasala, B., Rooth, I., Sjöberg, R., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1501700 · Cited by 15
Numata, K. (2025) · Biomacromolecules · doi:10.1021/acs.biomac.4c01621 · Cited by 9
Identification of a Novel Interferon Lambda Splice Variant in Chickens
Chen, J., Li, P., Zou, W., Jiang, Y., Li, L., Hao, P., Gao, Z., Qu, Q., Pang, Z., Zhuang, X., et al. (2023) · Journal of Virology · doi:10.1128/jvi.01743-22 · Cited by 9
Ianni, A. D., Tüting, C., Kipping, M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-35437-5 · Cited by 9
Decoding Protein Dynamics in Cells Using Chemical Cross‐Linking and Hierarchical Analysis**
Zhang, B., Gong, Z., Zhao, L., An, Y., Gao, H., Chen, J., Liang, Z., Liu, M., Zhang, Y., Zhao, Q., et al. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202301345 · Cited by 11
Engagement of intrinsic disordered proteins in protein–protein interaction
Roterman, I., Stąpor, K., & Konieczny, L. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1230922 · Cited by 9
“This research is accompanied by the development of experimental techniques allowing for the identi fi cation and tracking of the process of protein synthesis and folding with the accuracy of a single amino acid (Henry Brinkerhoff et al., 2021; Ruff and Pappu, 2021; Serpell et al., 2021).”
Houston, L., Phillips, M., Torres, A., Gaalswyk, K., & Ghosh, K. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01114 · Cited by 8
Biochemical propensity mapping for structural and functional anatomy of importin α IBB domain
Jibiki, K., Liu, M., Lei, C., et al. (2021) · Genes to Cells · doi:10.1111/gtc.12917 · Cited by 9
Gao, X., Zou, R., Sun, H., et al. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.991171 · Cited by 9
“5% of the human proteome (Ruff and Pappu, 2021) as well as multimer interactions (Mirdita et al.”
BackgroundFirouzi, R., Sowlati‐Hashjin, S., Chávez-García, C., Ashouri, M., Karimi‐Jafari, M. H., & Karttunen, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24098161 · Cited by 10
“It will also be interesting to see how and if Alphafold [52] will change the situation as it has already been applied to non-IDP-related binding problems [53], but IDPs appear to be a challenge even for Alphafold [54].”
BackgroundHidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMRPreprint
Huang, Y. J., Ramelot, T. A., Spaman, L. E., Kobayashi, N., & Montelione, G. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.26.600902 · Cited by 9
Wolf, K., Kosiński, J., Gibson, T. J., Wesch, N., Dötsch, V., Genuardi, M., Cordisco, E. L., Zeuzem, S., Brieger, A., & Plotz, G. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad418 · Cited by 10
Roterman, I., Stąpor, K., Dułak, D., & Konieczny, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23169502 · Cited by 8
Molecular modeling of ARF6 dysregulation caused by mutations in IQSEC2
Shokhen, M., Walikonis, R. S., Uversky, V. N., Allbeck, A., Zezelic, C., Feldman, D., Levy, N. S., & Levy, A. P. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2199085 · Cited by 8
“This lack of validity of a structure produced by AlphaFold is in agreement with the previously described poor ability of AlphaFold to predict the 3D structure of other intrinsically disordered proteins (Azzaz et al., 2022; Ruff & Pappu, 2021).”
ResultPopelka, H., Lahiri, V., Hawkins, W. D., Leprevost, F. D. V., Nesvizhskii, A. I., & Klionsky, D. J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242015036 · Cited by 8
“Low-confidence regions annotated by AlphaFold overlap with amino acid sequences that are predicted to be intrinsically disordered protein regions (IDPRs) [23], which are protein domains lacking a well-defined 3D structure [24–27].”
BackgroundPrediction of folding patterns for intrinsic disordered protein
Yang, J., Cheng, W., Wu, G., Sheng, S., & Zhang, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-45969-5 · Cited by 6
Emerging roles of liquid-liquid phase separation in liver innate immunity
Zhang, X., Yang, Z., Fu, C., Yao, R., Li, H., Peng, F., & Li, N. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01787-4 · Cited by 9
“It is predicted that more than 30% of the human proteome tends to form LLPS condensates [160].”
BackgroundZuccarotto, A., Sollitto, M., Leclère, L., Panzella, L., Gerdol, M., Leone, S., & Castellano, I. (2024) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2024.11.037 · Cited by 8
Frontiers in integrative structural modeling of macromolecular assemblies
Majila, K., Arvindekar, S. M., Jindal, M., & Viswanath, S. (2025) · QRB Discovery · doi:10.1017/qrd.2024.15 · Cited by 9
AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy
Stuke, J. F. M., & Hummer, G. (2025) · Autophagy · doi:10.1080/15548627.2025.2493999 · Cited by 8
“Additionally, as has been discussed previously, low pLDDT scores in AlphaFold2 strongly indicate structural disorder [85] further justifying the use of a minimum residue depth value for these residues.”
Molecular Docking as a Key Driver of Biocontrol for Agri-Food Security
Íñiguez-Luna, M. I., Cadena-Zamudio, J. D., Ramírez-Mosqueda, M. A., Aguirre‐Noyola, J. L., Cadena-Zamudio, D. A., Cadena-Íñiguez, J., & Armenta-Medina, A. (2025) · BioTech · doi:10.3390/biotech14040080 · Cited by 9
“…these predictions should be treated cautiously when applied to docking, as the single static conformation generated by AlphaFold does not capture conformational ensembles, allosteric transitions, or the dynamic energy landscape of proteins, which may be critical for ligand binding [91,92].”
Chakraborty, C., Bhattacharya, M., & Lee, S.-S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1739303 · Cited by 11
Structural and dynamical insights into SilE silver binding from combined analytical probes
Monneau, Y. R., Arrault, C., Duroux, C., Martin, M., Chirot, F., MacAleese, L., Girod, M., Comby‐Zerbino, C., Hagège, A., Walker, O., et al. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp04206a · Cited by 6
AlphaCutter: Efficient removal of non‐globular regions from predicted protein structures
Tam, C., & Iwasaki, W. (2023) · PROTEOMICS · doi:10.1002/pmic.202300176 · Cited by 8
“Because a majority of non-globular regions are potential IDRs [1, 7], wecomparedintrinsicdisorderscoresofthedomainsandtheremoved regions.”
BackgroundMethodInfluentialSun, Z., Wang, M., Wang, W., et al. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.130847 · Cited by 8
Exploring LLM Embedding Potential for Dementia Detection Using Audio Transcripts
Llaca-Sánchez, B. A., García-Noguez, L. R., Aceves-Fernández, M. A., Takács, A., & Tovar‐Arriaga, S. (2025) · Eng—Advances in Engineering · doi:10.3390/eng6070163 · Cited by 8
Chávez-García, C., Hénin, J., & Karttunen, M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01354 · Cited by 8
Popelka, H., & Uversky, V. N. (2022) · Membranes · doi:10.3390/membranes12050457 · Cited by 8
“conformational ensembles of IDPRs [29], as a single static structure cannot be used to reflect the”
BackgroundLuo, S., Wohl, S., Zheng, W., & Yang, S. (2023) · Biomolecules · doi:10.3390/biom13030530 · Cited by 10
“0 [141,142] can be used to distinguish folded and disordered regions.”
BackgroundWei, T., Liu, J., Li, C., Tan, Y., Wei, R., Wang, J., Wu, H., Li, Q., Liu, H., Tang, Y., et al. (2023) · Chemical Science · doi:10.1039/d3sc01109g · Cited by 8
“62 By combining computational modelling and biochemistry characterization, we identi ed that the three-dimensional structure of HMGB1 protein indeed prevents Lys11 from being involved in thrombin-mediated degradation (ESI Notes and Fig.”
BackgroundStewart, R. J., Frandsen, P. B., Pauls, S. U., & Heckenhauer, J. (2022) · Molecules · doi:10.3390/molecules27185945 · Cited by 8
“Regions with low confidence often correspond to intrinsically disordered structures [23].”
BackgroundDirect Generation of Protein Conformational Ensembles via Machine Learning
Janson, G., Valdes-Garcia, G., Heo, L., & Feig, M. (2022) · doi:10.1101/2022.06.18.496675 · Cited by 192
Local structural flexibility drives oligomorphism in computationally designed protein assemblies
Khmelinskaia, A., Bethel, N. P., Fatehi, F., Antanasijevic, A., Borst, A. J., Lai, S.-H., Wang, J. Y. (., Mallik, B. B., Miranda, M. C., Watkins, A. M., et al. (2023) · doi:10.1101/2023.10.18.562842 · Cited by 18
“Previous work has correlated the degree of flexibility and the presence of unstructured regions with AF2 pLDDT at the residue level 66–68 .”
BackgroundAdvancing in silico drug design with Bayesian refinement of AlphaFold modelsPreprint
Sen, S., Hoff, S., Morozova, T. I., Schnapka, V., & Bonomi, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661454 · Cited by 6
“21 In many of the instances mentioned above, the poor performance of AF models in docking and VS can be traced to inaccuracies in side chain conformations, such as steric clashes or unlikely rotamer states, 22 even when the backbone is predicted with high accuracy.”
Hassan, M. M., Martin, S., Feng, K., et al. (2022) · Plant Biotechnology Reports · doi:10.1007/s11816-022-00788-4 · Cited by 7
“Disordered protein domains are often important for intracellular signaling and can transition from a disordered to an ordered state upon binding to other proteins (Ruff and Pappu 2021; David et al.”
Janssen, K., Duran‐Romaña, R., Bottu, G., Guharoy, M., Botzki, A., Rousseau, F., & Schymkowitz, J. W. H. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05407-9 · Cited by 7
“Since these regions are exposed and unstructured [22], variants outside domains are typically not predicted to destabilize or impact the protein’s function, agreeing with their actual clinical phenotype (Fig.”
BackgroundGonzález-Esparragoza, D., Carrasco‐Carballo, A., Rosas-Murrieta, N. H., Peña, L. M.-P., Luna, F., & Camacho, I. H. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb46050280 · Cited by 8
“Currently, it is known that IDRs are functional, as they are considered dynamic structures that are usually at the connection points of interactions with other proteins, mainly in cell signaling pathways [97].”
BackgroundAzizian, S., & Cui, J. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05985-2 · Cited by 7
A review on computational models for predicting protein solubility
Pimtawong, T., Ren, J., Lee, J., Lee, H., & Na, D. (2025) · The Journal of Microbiology · doi:10.71150/jm.2408001 · Cited by 9
“However, owing to recent advances in computational protein structure prediction methods, highly accurate protein structures are now easily obtained in silico (Liu et al., 2022; Pak et al., 2023; Ruff & Pappu, 2021).”
Intrinsically Disordered Regions Define Unique Protein Interaction Networks in CHD Family Remodelers
Tabar, M. S., Parsania, C., Giardina, C., Feng, Y., Wong, A. C. H., Metierre, C., Nagarajah, R., Dhungel, B. P., Rasko, J. E. J., & Bailey, C. G. (2025) · The FASEB Journal · doi:10.1096/fj.202402808rr · Cited by 7
“Cells were maintained in a humidified incubator with 5% CO 2 at 37°C. Expi293F cells were cultured to a density of 1.5 × 10 6 cells/mL in Expi293 Expression Medium (Thermo Fisher Scientific, USA) on a horizontal orbital shaker (130 rpm).”
InfluentialA comprehensive application of FiveFold for conformation ensemble-based protein structure prediction
Niazi, S. K., & Yang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-17022-0 · Cited by 9
Modeling intrinsically disordered regions from AlphaFold2 to AlphaFold3
Mehdiabadi, M., Tosatto, S. C. E., & Piovesan, D. (2025) · Protein Science · doi:10.1002/pro.70426 · Cited by 8
“However, IDRs exhibit dynamic behavior and conformational heterogeneity, and unpacked looping coils should not be interpreted as static representations of disorder, as they include too many serious outliers to have physical significance (Ruff & Pappu, 2021; Williams et al., 2025).”
Mashraqi, M. M., Alzamami, A., Alturki, N. A., Alshamrani, S., Alshahrani, M. M., Almasoudi, H. H., & Basharat, Z. (2023) · Pathogens · doi:10.3390/pathogens12070857 · Cited by 7
“of these proteins were obtained from AlphaFold, a database consisting of state-of-the-art predicted protein structures [21].”
BackgroundIn silico molecular docking validation of procalcitonin-binding aptamer and sepsis diagnosis
Park, D. Y., Shin, W., Kim, S. Y., et al. (2023) · Molecular & Cellular Toxicology · doi:10.1007/s13273-023-00384-9 · Cited by 7
“The 3D structure model of PCT protein was obtained from Alphafold database (P01258) (https:// alpha fold. ebi. ac. uk/) (Ruff and Pappu 2021).”
Byrne, A. S., Bissonnette, N., Ollier, S., & Tahlan, K. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.01716-23 · Cited by 7
AI-Assisted Methods for Protein Structure Prediction and Analysis
Goel, D., Kumar, R., & Kumar, S. (2024) · Microorganisms for sustainability · doi:10.1007/978-981-99-9621-6_22 · Cited by 7
Rhoades, R. E., Henry, B., Pritchett, D. L., Fang, Y., & Teng, S. (2022) · Genes · doi:10.3390/genes13050789 · Cited by 7
“One limitation of using AlphaFold to study proteins involved in mental disorders is that AlphaFold may not adequately predict the conformations of intrinsically disordered regions of proteins [58].”
BackgroundKrokengen, O. C., Raasakka, A., & Kursula, P. (2023) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2023.101474 · Cited by 7
“disorder and should not be interpreted as a structure; a score below 70 should be treated with caution [21,22].”
BackgroundMethod唐, Y. 一. T., 杨, Z. 中. Y., 姚, Y. 逸. Y., 周, Y. 运. Z., 谈, Y. 圆. T., 王, Z. 子. W., 潘, T. 瞳. P., 熊, R. 瑞. X., 孙, J. 俊. S., & Wei, G. (2024) · Chinese Physics B · doi:10.1088/1674-1056/ad1a92 · Cited by 8
Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1352703 · Cited by 7
Artificial Intelligence: An Introduction
Kejriwal, M. (2022) · Future of business and finance · doi:10.1007/978-3-031-19039-1_1 · Cited by 7
McCafferty, C. L., Pennington, E. L., Papoulas, O., Taylor, D. W., & Marcotte, E. M. (2022) · doi:10.1101/2022.08.25.505345 · Cited by 35
Golroudbari, A. A., & Sabour, M. H. (2023) · doi:10.22541/au.168664884.43899660/v2 · Cited by 9
ProtEx: A Retrieval-Augmented Approach for Protein Function PredictionPreprint
Shaw, P., Gurram, B., Belanger, D., Gane, A., Bileschi, M. L., Colwell, L. J., Toutanova, K., & Parikh, A. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.30.596539 · Cited by 8
“Disordered proteins, which are not well characterized by a static structure would need to be handled carefully, however [57].”
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 ›
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