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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Open accessAbstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
Verified 2026-10-07 · 1 reference not checked yet
Overview
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Written from the paper’s full text.
TL;DR
This review examines AlphaFold2’s strengths and failure modes for proteins beyond rigid globular folds, stressing that confidence scores do not guarantee agreement with experimental structures. It argues AF2 is best used as a hypothesis-generating complement to experimental data rather than as definitive structural truth.
Key findings
- AF2 has broad coverage—modeling 98.5% of the human proteome, with over 200 million precomputed predictions in the AlphaFold Protein Structure Database—but accuracy varies by target class.
- pLDDT and PAE are useful but imperfect: low pLDDT (<70) or high PAE (>5 Å) signal caution, yet high-confidence models can still deviate from experiments. On average, AF2 predicts about 50% of residues with high confidence, and one cited estimate suggests ~10% of high-confidence residues differ from native backbone by more than 2 Å.
- Non-globular and dynamic systems are especially challenging: peptides were excluded from training, IDPs/IDRs are often conformational ensembles, membrane proteins are underrepresented in the PDB (<3%), and AF2 struggles with alternative conformations, ligand/cofactor effects, and domain placement. Heterodimeric complex success rates were reported at 51% and 63% in two benchmarks, with antibody complexes particularly difficult.
- Integrating AF2 with SAXS, NMR, X-ray, and cryo-EM can improve models; AF-guided molecular replacement solved 187 of 215 structures in one benchmark, though cryo-EM maps worse than 6 Å are not recommended for fitting.
Methods
- Narrative review/perspective, not a primary research study; no new experimental data or systematic meta-analysis are presented.
- Synthesizes published benchmarks, case examples, and prior literature on AF2 workflow, pLDDT/PAE interpretation, protein classes, experimental integration, and conformational ensembles.
- Uses selected examples—such as OSBP1, insulin, ChRmine, SecA, and beta-endorphin amyloid—to illustrate strengths and failure modes.
Limitations
- Stated AF2 limitations include no training or validation for mutation effects, training bias toward soluble/PDB folds, peptide exclusion, membrane-protein underrepresentation, poor handling of IDPs/IDRs and alternative conformations, and cases where high pLDDT/PAE still mislead; inter-domain PAE accuracy is less benchmarked.
- Evident from the text, not stated by the authors: as a narrative review, the selection of examples and benchmarks is not systematic, and the conclusions depend on the cited literature rather than new quantitative assessment.
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Claims the paper makes
71 Not checked yet · 15 Own interpretation · 8 No basis given · 4 Supported · 4 Partly supported · 4 Own result · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Moffat et al showed the AF2 performs well on the de novo designed proteins Top7, Peak6, Foldit1, and Ferredog-Diesel52”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Supportedcites [52] Moffat, L., Greener, J. G. & Jones, D. T. Using AlphaFold for rapid and accurate fixed backbone protein design. Preprint at bioRxiv https://doi.org/10.1101/2021.08.24.457549 (2021). source
Sections 3.1 and 3.2.1–3.2.3 report high-confidence AlphaFold predictions that closely fit the target backbones for new sequences designed around Top7, Peak6, Foldit1, and Ferredog-Diesel. The paper evaluates those newly designed sequences, rather than directly testing the original protein sequences. (LLM, conf=0.92)
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- Supported
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In the paper
“Several groups have reported cases of AF2 models with low PAE values (< 5 Å) that deviated from experimental data62,63”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Partly supportedcites [62] McCafferty, C. L., Pennington, E. L., Papoulas, O., Taylor, D. W. & Marcotte, E. M. Does AlphaFold2 model proteins’ intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins. Commun. Biol. 6, 421 (2023). sourceExperimental — accuracy not yet measured
The Results and discussion report AF2 models that violated experimental cross-link constraints despite high pLDDT scores, supporting the point that confident predictions can disagree with experimental data. But pLDDT is not PAE: the paper does not identify a violating model with PAE <5 Å, and it reports no violations in the PAE range 0–3.5. Its findings therefore do not establish the cited threshold. (LLM, conf=0.84)
- Partly supportedcites [63] Motmaen, A. et al. Peptide-binding specificity prediction using fine-tuned protein structure prediction networks. Proc. Natl Acad. Sci. USA 120, e2216697120 (2023). sourceExperimental — accuracy not yet measured
The Results (page unavailable) report that PAE only partly distinguished binding from non-binding peptides and that AlphaFold tended to dock non-binding peptides in the MHC binding groove. This supports the broader point that AlphaFold models can conflict with experimental binding evidence, but the paper does not identify models with PAE <5 Å or report those models deviating from experimental structures. (LLM, conf=0.76)
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In the paper
“For the majority of globular proteins, AF2 provides accurate, reliable models with high pLDDT (> 70) or low PAE values (< 5 Å) highlighting confidence in the prediction of the position of the atomic coordinates, which match experimentally determined native structures5,17,23,59”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Partly supportedcites [5] Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021). sourceExperimental — accuracy not yet measured
In “Model confidence and added coverage” (page unavailable), the paper says pLDDT >70 indicates a generally correct backbone prediction and reports experimental-structure comparisons. It does not establish that most globular proteins have accurate models matching native structures, and the passages found do not give a PAE <5 Å threshold. The paper reserves its “high accuracy” designation for pLDDT >90. (LLM, conf=0.86)
- Partly supportedcites [17] Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022). sourceExperimental — accuracy not yet measured
The Results section on experimental model building reports overall excellent agreement between AF2 models and the experimental chains in one complex, and the structural-coverage Results section treats pLDDT >70 as a confident prediction. Neither establishes that most globular proteins match experimental structures or gives PAE <5 Å as a reliability threshold. The paper also cautions that high-confidence predictions can contain errors (page numbers unavailable in our copy). (LLM, conf=0.87)
- Partly supportedcites [23] Oeffner, R. D. et al. Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE. Acta Crystallogr. D 78, 1303–1314 (2022). sourceExperimental — accuracy not yet measured
The Introduction says AlphaFold models are usually good enough for molecular replacement, and the ‘Conversion of pLDDT values to error estimates’ section treats 0.7 (70) as a threshold for discarding low-confidence regions. Worked example 4 shows why local accuracy does not guarantee a match to an experimental structure: one domain is offset by about 15 Å. The paper does not establish a PAE cutoff of <5 Å or the combined claim for most globular proteins. Page numbers were not available in our copy. (LLM, conf=0.86)
- Partly supportedcites [59] Binder, J. L. et al. AlphaFold Illuminates half of the dark human proteins. Curr. Opin. Struct. Biol. 74, 102372 (2022). sourceExperimental — accuracy not yet measured
The Introduction reports high-quality AF2 folds in two-thirds of cases when compared with experimental structures, supporting the broad accuracy claim. But the paper does not establish this specifically for most globular proteins or use PAE <5 Å; its section on disorder uses pLDDT >80, not >70, when assessing ordered-model accuracy. No page numbers are available in our copy. (LLM, conf=0.88)
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In the paper
“Moreover, in the absence of NMR resonance assignments, AF2 models can be used as structural templates towards automated assignment90.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Supportedcites [90] Klukowski, P., Riek, R. & Güntert, P. Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction. Sci. Adv. 9, eadi9323 (2023). source
The Results section reports automated NMR assignment using AlphaFold structures as input, including structure-based backbone amide assignment. It recommends predicted structures for automated chemical shift assignment with ARTINA; prior resonance assignments are not required as input. (LLM, conf=0.93)
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In the paper
“modeling of 98.5% of the human proteome5”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Not checked yetcites [5] Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021). source
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In the paper
“are the best and most accurate answer to the structure prediction problem to date”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [17] Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022). source
- Not checked yetcites [18] Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021). source
- Not checked yetcites [19] Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). source
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In the paper
“whether the modeled structure resembles the apo or holo form of the protein is not immediately clear from analysis of pLDDT scores or PAE graphs32”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [32] Saldaño, T. et al. Impact of protein conformational diversity on AlphaFold predictions. Bioinformatics 38, 2742–2748 (2022). source
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In the paper
“Benchmarking AF2 against membrane proteins represents a challenge since the membrane environment, which includes lipids and other proteins, is not directly considered by current versions of AlphaFold38”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [38] Hegedűs, T., Geisler, M., Lukács, G. L. & Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 79, 73 (2022). source
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In the paper
“NMR structural ensembles offer a unique validation metric to assess the accuracy of predicted AlphaFold2 models since AlphaFold2 was trained on a subset of the PDB that excluded NMR data1,27–30”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [27] Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M. & Forman-Kay, J. D. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc. Natl Acad. Sci. USA 120, e2304302120 (2023). source
- Not checked yetcites [28] Fowler, N. J. & Williamson, M. P. The accuracy of protein structures in solution determined by AlphaFold and NMR. Structure 30, 925–933 (2022). source
- Not checked yetcites [29] Zweckstetter, M. NMR hawk‐eyed view of AlphaFold2 structures. Protein Sci. 30, 2333–2337 (2021). source
- Not checked yetcites [30] Tejero, R., Huang, Y. J., Ramelot, T. A. & Montelione, G. T. AlphaFold models of small proteins rival the accuracy of solution NMR Structures. Front. Mol. Biosci. 9, 877000 (2022). source
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In the paper
“Azzaz et al have shown the difficulty in AF2 in modeling membrane proteins due to “epigenetic” factors (i.e., lipid environment, co-receptor induced structural changes, post-translational modifications) that control protein structure beyond the amino acid sequence40”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [40] Azzaz, F., Yahi, N., Chahinian, H. & Fantini, J. The epigenetic dimension of protein structure is an intrinsic weakness of the AlphaFold program. Biomolecules 12, 1527 (2022). source
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In the paper
“deviations of AF2 models from experimental structures also occur when co-factors, prosthetic groups, or ligands induce structural changes, either locally or allosterically”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“Fowler et al revealed that NMR ensembles can be more accurate than static AF2 models for dynamic proteins28”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [28] Fowler, N. J. & Williamson, M. P. The accuracy of protein structures in solution determined by AlphaFold and NMR. Structure 30, 925–933 (2022). source
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In the paper
“AF2 models of many globular proteins, especially enzymes and metalloproteins, lack functionally relevant co-factors, prosthetic groups, or ligands”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“for many targets, AF2 models with predicted disorder may not be relevant for structure and function analysis other than for assigning the likelihood for conformational heterogeneity27”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [27] Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M. & Forman-Kay, J. D. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc. Natl Acad. Sci. USA 120, e2304302120 (2023). source
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In the paper
“AF2 may also exhibit difficulties in structure prediction for extended proteins or proteins with repeat elements35”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [35] Diwan, G. D., Gonzalez-Sanchez, J. C., Apic, G. & Russell, R. B. Next generation protein structure predictions and genetic variant interpretation. J. Mol. Biol. 433, 167180 (2021). source
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In the 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”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [42] Ruff, K. M. & Pappu, R. V. AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433, 167208 (2021). source
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In the paper
“AF2 was challenged by mixed secondary structure membrane and soluble peptides, such as the prion protein PRNP (Fig. 2a)25”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [25] McDonald, E. F., Jones, T., Plate, L., Meiler, J. & Gulsevin, A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 31, 111–119 (2023). source
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In the paper
“While AF2 performs exceptionally well on these types of folds on average”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [5] Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021). source
- Not checked yetcites [17] Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022). source
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In the paper
“IDPs represents a challenge for AF2 since it is difficult to identify evolutionary constraints from MSAs of IDPs and IDRs due to sequence hypervariability”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“like peptides, IDPs and IDRs are best thought of as sampling diverse conformational ensembles rather than a single static conformation42”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [42] Ruff, K. M. & Pappu, R. V. AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433, 167208 (2021). source
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In the paper
“the AF2 model of the zinc protease PqqL deviates from the open, highly extended conformation determined by X-ray crystallography (Fig. 2d)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the 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”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [42] Ruff, K. M. & Pappu, R. V. AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433, 167208 (2021). source
- Not checked yetcites [43] Wilson, C. J., Choy, W.-Y. & Karttunen, M. AlphaFold2: a role for disordered protein/region prediction? Int. J. Mol. Sci. 23, 4591 (2022). source
- Not checked yetcites [44] Piovesan, D., Monzon, A. M. & Tosatto, S. C. E. Intrinsic protein disorder and conditional folding in AlphaFoldDB. Protein Sci. 31, e4466 (2022). source
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In the paper
“McDonald et al performed a benchmark of 588 peptides revealing that AF2 predicts many α-helical and β-hairpin peptide structures with surprising accuracy25”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [25] McDonald, E. F., Jones, T., Plate, L., Meiler, J. & Gulsevin, A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 31, 111–119 (2023). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“since it is trained on both apo and holo structures from the PDB, models may still be consistent with the expected structure in the presence of ligands or cofactors despite their absence in the AF2 workflow1,5”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“the NMR structure of IDR containing protein At2g23090 deviates from the AF2 model despite the confident pLDDT score (Fig. 2g)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“AF2 has not been fully trained on novel topologies, which are not commonly found in the PDB”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“the incorrect AF2 model of the beta-endorphin amyloid fiber relative to its experimental solid-state NMR structure58”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [58] Pinheiro, F., Santos, J. & Ventura, S. AlphaFold and the amyloid landscape. J. Mol. Biol. 433, 167059 (2021). source
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In the paper
“Terwilliger et al estimate that ~10% of residues predicted by AF2 with high confidence deviate from the backbone by more than 2 Å from native conformations observed in experimental structures61”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Not checked yetcites [61] Terwilliger, T. C. et al. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat. Methods 21, 110–116 (2024). source
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In the paper
“For targets without known homologs, such as computationally designed proteins, increasing the number of recycling iterations can improve the quality of the prediction8”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [8] Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022). source
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In the paper
“AF2 models can be integrated with experimental data, typically in the form of SAXS, NMR, X-ray crystallography, and cryo-EM”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
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In the paper
“AF2 models have also been increasingly used during molecular replacement and phasing of X-ray diffraction data obtained from protein crystals76–79”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [76] Kryshtafovych, A. et al. Computational models in the service of X-ray and cryo-EM structure determination. Proteins 89, 1633–1646 (2021). source
- Not checked yetcites [77] Chai, L. et al. AlphaFold protein structure database for sequence-independent molecular replacement. Crystals 11, 1227 (2021). source
- Not checked yetcites [78] McCoy, A. J., Sammito, M. D. & Read, R. J. Implications of AlphaFold2 for crystallographic phasing by molecular replacement. Acta Crystallogr. D 78, 1–13 (2022). source
- Not checked yetcites [79] Barbarin-Bocahu, I. & Graille, M. The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report. Acta Crystallogr. D 78, 517–531 (2022). source
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In the paper
“The integration of AF2 models with experiments is especially useful for cases where template structures or homologous models are lacking”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
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In the paper
“Recently, an AF2 integrated iterative procedure for molecular replacement has been developed where AF models are utilized during the initial structure-solution cycle, followed by data guided cycles of AF structure prediction and model rebuilding60,69.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [60] Terwilliger, T. C. et al. Improved AlphaFold modeling with implicit experimental information. Nat. Methods 19, 1376–1382 (2022). source
- Not checked yetcites [69] Terwilliger, T. C. et al. Accelerating crystal structure determination with iterative AlphaFold prediction. Acta Crystallogr. D 79, 234–244 (2023). source
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In the paper
“Previous analysis suggests that AF2 predicts on average ~50% of residues across all proteins with high confidence17,59”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Not checked yetcites [17] Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022). source
- Not checked yetcites [59] Binder, J. L. et al. AlphaFold Illuminates half of the dark human proteins. Curr. Opin. Struct. Biol. 74, 102372 (2022). source
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In the paper
“AF2 had difficulty modeling antibody/protein complexes, such as the A12 nanobody/HIV gp120 complex (Fig. 2k)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“structural perturbations induced by the K59A/C65A double mutation in Prostaglandin D Synthase are not accurately captured by AF2 (Fig. 2i)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“The use of AF models in structure determination protocols has shown to reduce the time and effort required relative to ab initio model building68–70”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [68] Fontana, P. et al. Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold. Science 376, eabm9326 (2022). source
- Not checked yetcites [69] Terwilliger, T. C. et al. Accelerating crystal structure determination with iterative AlphaFold prediction. Acta Crystallogr. D 79, 234–244 (2023). source
- Not checked yetcites [70] Blanc, M. et al. Designed ankyrin repeat proteins provide insights into the structure and function of CagI and are potent inhibitors of CagA translocation by the Helicobacter pylori type IV secretion system. PLoS Pathog. 19, e1011368 (2023). source
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In the paper
“Wayment-Steele et al found that clustering MSAs by sequence similarity enables AF2 to sample known alternative states of KaiB, RfaH, and Mad247”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [47] Wayment-Steele, H. K. et al. Predicting multiple conformations via sequence clustering and AlphaFold2. Nature 625, 832–839 (2024). source
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In the 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.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [42] Ruff, K. M. & Pappu, R. V. AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433, 167208 (2021). source
- Not checked yetcites [72] Brookes, E. & Rocco, M. A database of calculated solution parameters for the AlphaFold predicted protein structures. Sci. Rep. 12, 7349 (2022).
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In the paper
“extraction of coevolutionary information from MSAs using de novo designed targets may be difficult since the amino acid sequences of de novo designed proteins deviate from naturally observed sequences”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“AF2 fails to predict the “open” activated conformation of the ATPase SecA (Fig. 2h)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“Recent methods have shown that fitting of SAXS data significantly improves when an ensemble of AF predicted structures is used rather than a static AF model71, highlighting the importance of integrating AF models with experimental data.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [71] Brookes, E., Rocco, M., Vachette, P. & Trewhella, J. AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank. J. Appl. Crystallogr. 56, 910–926 (2023). source
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In the paper
“studies have reported an inability of AF2 to predict the effects of mutations on protein structure and stability14,49,50”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [14] Roney, J. P. & Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101 (2022). source
- Not checked yetcites [49] Buel, G. R. & Walters, K. J. Can AlphaFold2 predict the impact of missense mutations on structure? Nat. Struct. Mol. Biol. 29, 1–2 (2022). source
- Not checked yetcites [50] Pak, M. A. et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS ONE 18, e0282689 (2023). source
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In the paper
“del Alamo et al modified the AF2 pipeline by reducing the number of recycles and restricting the depth of randomly subsampled MSAs to sample functionally relevant alternative conformations of transporters and G-protein-coupled receptors46”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [46] Del Alamo, D., Sala, D., Mchaourab, H. S. & Meiler, J. Sampling alternative conformational states of transporters and receptors with AlphaFold2. eLife 11, e75751 (2022). source
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In the paper
“An inverted version of AlphaFold, called AlphaDesign, has been used for de novo protein design with some success53”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [53] Goverde, C. A., Wolf, B., Khakzad, H., Rosset, S. & Correia, B. E. De novo protein design by inversion of the AlphaFold structure prediction network. Protein Sci. 32, e4653 (2023). source
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In the paper
“high pLDDT at a specific residue does not always indicate the correct rotameter has been modeled23”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Not checked yetcites [23] Oeffner, R. D. et al. Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE. Acta Crystallogr. D 78, 1303–1314 (2022). source
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In the paper
“cases in which where the AF2 evaluation metrics are good but the model doesn’t match experimental structure (Fig. 2a,d,e,k) suggest that care must be taken in blind faith in pLDDT and PAE metrics”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
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In the paper
“Both studies suggest that a robust MSA coevolutionary signal is required for accurate complex modeling”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“AlphaFold has not been trained or validated for predicting the effect of mutations.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
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In the paper
“Yin et al benchmarked 152 heterodimeric protein complexes revealing that AF2 and AF2-Multimer had a 51% success rate54,55”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [54] Yin, R., Feng, B. Y., Varshney, A. & Pierce, B. G. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Sci. 31, e4379 (2022). source
- Not checked yetcites [55] Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy. Protein Sci. 33, e4865 (2024). source
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In the paper
“This iterative procedure works extremely well as demonstrated in a benchmark where 187 of 215 structures were solved by AF-guided molecular replacement; the success was shown to be dependent on high confidence scores associated with the AF prediction69.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [69] Terwilliger, T. C. et al. Accelerating crystal structure determination with iterative AlphaFold prediction. Acta Crystallogr. D 79, 234–244 (2023). source
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In the paper
“Thus, several groups have worked to extend AlphaFold to include predictions of structural ensembles and excited state structures102,104.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [102] Sala, D., Hildebrand, P. W. & Meiler, J. Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties. Front. Mol. Biosci. 10, 1121962 (2023). source
- Not checked yetcites [104] Sala, D., Engelberger, F., Mchaourab, H. S. & Meiler, J. Modeling conformational states of proteins with AlphaFold. Curr. Opin. Struct. Biol. 81, 102645 (2023). source
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In the paper
“Apart from IDPs, well-folded globular proteins, such as G-protein coupled receptors and kinases, also sample a wide range of conformations to carry out their biological function102,103.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [102] Sala, D., Hildebrand, P. W. & Meiler, J. Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties. Front. Mol. Biosci. 10, 1121962 (2023). source
- Not checked yetcites [103] Heo, L. & Feig, M. Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins 90, 1873–1885 (2022). source
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In the paper
“AF models have also been used extensively together with single particle cryo-EM data60,76,95,96”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [60] Terwilliger, T. C. et al. Improved AlphaFold modeling with implicit experimental information. Nat. Methods 19, 1376–1382 (2022). source
- Not checked yetcites [76] Kryshtafovych, A. et al. Computational models in the service of X-ray and cryo-EM structure determination. Proteins 89, 1633–1646 (2021). source
- Not checked yetcites [95] Giri, N., Roy, R. S. & Cheng, J. Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions. Curr. Opin. Struct. Biol. 79, 102536 (2023). source
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In the paper
“Another approach is to enable dropout layers in the neural network, which are usually commonly used only during neural network training8,106,107.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [8] Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022). source
- Not checked yetcites [106] Wallner, B. AFsample: improving multimer prediction with AlphaFold using massive sampling. Bioinformatics 39, btad573 (2023). source
- Not checked yetcites [107] Johansson-Åkhe, I. & Wallner, B. Improving peptide–protein docking with AlphaFold-Multimer using forced sampling. Front. Bioinform. 2, 959160 (2022).
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Native conformations of proteins are often described as time averaged ensembles of conformations with Boltzmann-type distributions, especially IDPs and proteins with IDRs100,101.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [100] Lindorff-Larsen, K. & Kragelund, B. B. On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins. J. Mol. Biol. 433, 167196 (2021). source
- Not checked yetcites [101] Wei, G., Xi, W., Nussinov, R. & Ma, B. Protein ensembles: how does nature harness thermodynamic fluctuations for life? The diverse functional roles of conformational ensembles in the cell. Chem. Rev. 116, 6516–6551 (2016). source
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In the paper
“This iterative procedure works extremely well as demonstrated in a benchmark where 187 of 215 structures were solved by AF-guided molecular replacement; the success was shown to be dependent on high confidence scores associated with the AF prediction69.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [69] Terwilliger, T. C. et al. Accelerating crystal structure determination with iterative AlphaFold prediction. Acta Crystallogr. D 79, 234–244 (2023). source
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In the paper
“Here, NMR assignments for methyl side-chain groups can be obtained using only methyl-methyl NOEs obtained from 3D NMR experiments and the atomic coordinates of a structure (or AF2 predicted structure) as input with software such as MAUS, MAGIC, and methylFLYA91,93,94.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [91] McShan, A. C. Utility of methyl side chain probes for solution NMR studies of large proteins. J. Magn. Reson. Open 14–15, 100087 (2023).
- Not checked yetcites [93] Pritišanac, I., Würz, J. M., Alderson, T. R. & Güntert, P. Automatic structure-based NMR methyl resonance assignment in large proteins. Nat. Commun. 10, 4922 (2019). source
- Not checked yetcites [94] Clay, M. C., Saleh, T., Kamatham, S., Rossi, P. & Kalodimos, C. G. Progress toward automated methyl assignments for methyl-TROSY applications. Structure 30, 69–79 (2022). source
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In the paper
“Another burgeoning area where AF models are integrated with experimental data is solution-state NMR15,27,28,30,81–85.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [15] Laurents, D. V. AlphaFold 2 and NMR spectroscopy: partners to understand protein structure, dynamics and function. Front. Mol. Biosci. 9, 906437 (2022). source
- Not checked yetcites [27] Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M. & Forman-Kay, J. D. Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2. Proc. Natl Acad. Sci. USA 120, e2304302120 (2023). source
- Not checked yetcites [28] Fowler, N. J. & Williamson, M. P. The accuracy of protein structures in solution determined by AlphaFold and NMR. Structure 30, 925–933 (2022). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Several of the studies mentioned here also show that simple modifications in the AF2 workflow can further extend its accuracy and applications into new horizons.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Conclusions and outlook
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In the paper
“Like for X-ray diffraction studies, implicit incorporation of AF2 models, which are iteratively rebuilt on the basis of cryo-EM data, enables swift and robust structure modeling relative to ab initio model building60,98.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [60] Terwilliger, T. C. et al. Improved AlphaFold modeling with implicit experimental information. Nat. Methods 19, 1376–1382 (2022). source
- Not checked yetcites [98] Dai, X., Wu, L., Yoo, S. & Liu, Q. Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps. Brief. Bioinform. 24, bbad405 (2023).
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In the paper
“Initial efforts to enhance sampling of different conformations have involved altering the number of sequences used to generate shallower MSA representations, masking coevolutionary information provided by MSAs, and splitting conflicting coevolutionary signals by clustering MSAs46,47,105.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [46] Del Alamo, D., Sala, D., Mchaourab, H. S. & Meiler, J. Sampling alternative conformational states of transporters and receptors with AlphaFold2. eLife 11, e75751 (2022). source
- Not checked yetcites [47] Wayment-Steele, H. K. et al. Predicting multiple conformations via sequence clustering and AlphaFold2. Nature 625, 832–839 (2024). source
- Not checked yetcites [105] Stein, R. A. & Mchaourab, H. S. SPEACH_AF: sampling protein ensembles and conformational heterogeneity with AlphaFold2. PLoS Comput. Biol. 18, e1010483 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These approaches have shown great promise in increasing the ability of AF2 to predict alternative conformations, although benchmarking has been limited by the small number of structures in the PDB solved with multiple conformations.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — 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 case studies highlighted here reveal why caution must be taken in naive interpretation of AF2 models, even for cases with reasonable pLDDT and PAE confidence metrics (Fig. 2a–l).”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Conclusions and outlook
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The use of AF models in molecular replacement can be further enhanced by downweighting or removing low confidence regions23.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [23] Oeffner, R. D. et al. Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE. Acta Crystallogr. D 78, 1303–1314 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, they are not yet “one-sized fits all” solutions to the protein structure prediction problem.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Conclusions and outlook
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 15
- It may state two separate claims as one. 28
- The quote only partly states this claim. 1
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“allow users to run a modified AF2 protocol on open access servers in minutes”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Partly supportedcites [8] Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022). sourceExperimental — accuracy not yet measured
The Main section describes ColabFold as a modified, faster AlphaFold2 workflow available through Google Colaboratory, which it says is free to logged-in users. The Abstract reports a throughput of nearly 1,000 structures per day on a one-GPU server, but does not establish that users can complete a prediction on the public platform within minutes. The paper does not address OpenFold. (LLM, conf=0.86)
- Partly supportedcites [9] Ahdritz, G. et al. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat. Methods https://doi.org/10.1038/s41592-024-02272-z (2024). sourceExperimental — accuracy not yet measured
The Results section says OpenFold is an open-source implementation of AlphaFold2 that runs up to four times faster during inference. But the paper does not say that OpenFold and ColabFold let users run a protocol on open-access servers within minutes; it also says OpenFold reproduces the AlphaFold2 model architecture without modifications that alter its computations. (LLM, conf=0.85)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“AF2 models with trans-membrane region lengths corresponding to non-physiological membrane thickness values can exhibit very high pLDDT scores (high model confidence), suggesting pLDDT scores alone are not sufficient to select native membrane protein conformations”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Supportedcites [38] Hegedűs, T., Geisler, M., Lukács, G. L. & Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 79, 73 (2022). source
The Results section (‘Transmembrane topology assignments in AlphaFold2 structures’) states that AF2 structures with non-physiological hydrophobic thickness can have very high pLDDT scores, which alone may be insufficient to select correct TM structures. The Discussion reaches the same conclusion for selecting the best AF2-predicted TM model. Page numbers were not provided in our copy. (LLM, conf=0.94)
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Hegedűs et al benchmarked several membrane proteins not included in the original AF2 training set and concluded that on average AF2 predicts transmembrane proteins as well as soluble proteins38”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Supportedcites [38] Hegedűs, T., Geisler, M., Lukács, G. L. & Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 79, 73 (2022). source
The Results section identifies three transmembrane structures suitable for blind AF2 predictions because they or their homologues were absent from the training set. The Discussion concludes that AF2 likely predicts transmembrane proteins as well as soluble proteins, based on the similar pLDDT distributions; it presents that comparison cautiously rather than as a definitive accuracy measurement. (LLM, conf=0.89)
plain- 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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Preliminary reports also suggest that AF2 may also be able to predict oligomeric states of proteins and amyloids17,57”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Partly supportedcites [17] Akdel, M. et al. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 29, 1056–1067 (2022). sourceExperimental — accuracy not yet measured
The Results section on protein complex structures (page not provided) reports that AF2’s top-scoring model matched the known oligomeric state for 71 of 87 proteins, supporting the cautious claim about proteins. The passages from our copy do not establish the same ability for amyloids. (LLM, conf=0.86)
- Partly supportedcites [57] Jeppesen, M. & André, I. Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking. Nat. Commun. 14, 8283 (2023). sourceExperimental — accuracy not yet measured
The Discussion reports that AlphaFold and AlphaFold-Multimer can model protein subunits and small oligomers, supporting the protein-oligomer portion of the claim. The paper does not address amyloids, and its prediction of larger assemblies requires symmetric docking rather than AF2 alone. (LLM, conf=0.86)
hedgedconditional- 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
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 PH, CC, and ORD domain structure are assigned very high to high predicted confidence, while the FFAT domain is predicted with very low confidence”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > AlphaFold2’s AI-driven revolution
plainSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one. The quote only partly states this claim.
In the paper
“current versions of AF2 do not use energy functions that seek to identify native-like protein conformations, unlike its competitor Rosetta13”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Not checked yetcites [13] Alford, R. F. et al. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 3031–3048 (2017). source
plain- 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.
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
“requires significant computational resources to run the software locally (up to 3 terabytes of disk space and a modern NVIDIA GPUs with gigabytes of memory)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
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. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“houses over 200 million pre-run AF2 predictions”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Not checked yetcites [6] Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022). source
- Not checked yetcites [7] Varadi, M. et al. AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences. Nucleic Acids Res. 52, D368–D375 (2024). source
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.
None of the sources cited for this claim have been checked yet.
In the paper
“enables accurate prediction of three-dimensional protein structures from only the target amino acid sequence”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Main
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [2] Bertoline, L. M. F., Lima, A. N., Krieger, J. E. & Teixeira, S. K. Before and after AlphaFold2: an overview of protein structure prediction. Front. Bioinform. 3, 1120370 (2023).
- Not checked yetcites [3] Perrakis, A. & Sixma, T. K. AI revolutions in biology. EMBO Rep. 22, e54046 (2021). source
- Not checked yetcites [4] Bouatta, N., Sorger, P. & AlQuraishi, M. Protein structure prediction by AlphaFold2: are attention and symmetries all you need? Acta Crystallogr. D 77, 982–991 (2021). source
plainconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Tsaban et al showed that AF2 can be adapted to accurately model peptide/protein complexes irrespective of peptide length, although the results seemed biased towards helical structures and peptides that do not undergo large structural rearrangements upon binding24”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [24] Tsaban, T. et al. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun. 13, 176 (2022). source
hedgedcorrelational- 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.
- 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
“the predicted Local Distance Difference Test (pLDDT) score, which ranges from 0 to 100 with higher values assigned higher confidence in the model”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > AlphaFold2’s AI-driven revolution
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [22] Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For cases i and iv, low confidence in pLDDT scores and PAE graphs alerts users to interpret structures with caution, which is not immediately clear in cases ii and iii.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“membrane proteins represent less than 3% of total structures in the PDB15, meaning that the training set used by AF2 was highly biased towards soluble proteins39”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [15] Laurents, D. V. AlphaFold 2 and NMR spectroscopy: partners to understand protein structure, dynamics and function. Front. Mol. Biosci. 9, 906437 (2022). source
- Not checked yetcites [39] Topitsch, A., Schwede, T. & Pereira, J. Outer membrane β-barrel structure prediction through the lens of AlphaFold2. Proteins 92, 3–14 (2024). source
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the best ranked AF2 models (selected on the basis of high pLDDT score) often did not exhibit the lowest Cα root-mean-square deviation (RMSD) relative to the experimental structure, suggesting the pLDDT metric used by AF2 to assess protein models is not optimal for classification of peptide conformations25”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [25] McDonald, E. F., Jones, T., Plate, L., Meiler, J. & Gulsevin, A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 31, 111–119 (2023). source
hedged- Asserted: passes — The authors put it forward as true.
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- 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.
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
“AF2 predicts the structure of individual domains well but deviates in placement of domains relative to each other (Fig. 2e)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
plain- Asserted: passes — The authors put it forward as true.
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- 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
“Peptide structure prediction poses additional challenges given that the benchmark set used to train AF2 excluded peptides, the difficulty in generating robust MSAs for short sequences, and observations that many peptides exist in solution as conformational ensembles rather than a single static conformation1,24,25”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [1] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). source
- Not checked yetcites [24] Tsaban, T. et al. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun. 13, 176 (2022). source
- Not checked yetcites [25] McDonald, E. F., Jones, T., Plate, L., Meiler, J. & Gulsevin, A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 31, 111–119 (2023). 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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Several groups have suggested that the default AF2 pipeline has difficulty in modeling alternative conformations45”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [45] Lane, T. J. Protein structure prediction has reached the single-structure frontier. Nat. Methods 20, 170–173 (2023). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“AF2 also generates a Predicted Aligned Error (PAE) matrix, which evaluates the relative orientation and position of different parts (i.e., domains) of the model6”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > AlphaFold2’s AI-driven revolution
What the sources say
- Not checked yetcites [6] Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AF2 performs poorly for targets embedded in membrane thickness outside the 15–35 Å range as well as targets with novel features not commonly present in the PDB”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [38] Hegedűs, T., Geisler, M., Lukács, G. L. & Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 79, 73 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“while the AF2 model of the channelrhodopsin ChRmine captures its overall fold, the modeled N-terminal region and extracellular loops deviate from its experimental high-resolution cryogenic electron microscopy (cryo-EM) structure (Fig. 2f), likely due to ChRmine’s unique covalent Schiff base feature41”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [41] Kishi, K. E. et al. Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell 185, 672–689 (2022). source
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“the NMR structure of Ca2+ bound polycystin 2 deviates from the AF2 model, potentially due to conformational changes upon calcium binding (Fig. 2c)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“it is important to remember that the PAE values are only confidence estimates. Furthermore, the accuracy of PAE graphs for inter-domain prediction has not been as extensively benchmarked as for intra-domain contacts36”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [36] David, A., Islam, S., Tankhilevich, E. & Sternberg, M. J. E. The AlphaFold database of protein structures: a biologist’s guide. J. Mol. Biol. 434, 167336 (2022). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“the AF2 model of insulin deviates significantly from its experimental NMR structure (Fig. 2b), potentially due to the inability of AF2 to orient cysteine pairs for disulfide bond formation31”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [31] Thornton, J. M., Laskowski, R. A. & Borkakoti, N. AlphaFold heralds a data-driven revolution in biology and medicine. Nat. Med. 27, 1666–1669 (2021). source
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“high pLDDT or low PAE metrics, indicating high confidence in the prediction, do not promise agreement with native protein conformations, but instead estimate a likelihood for local and global coordinate positions and/or orientations”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > AlphaFold2’s AI-driven revolution
plain- Asserted: passes — The authors put it forward as true.
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- 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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These deviations can be broadly categorized into cases with i) inaccurate secondary, tertiary, and/or quaternary structure in regions where AF2 predicts low to moderate confidence, ii) inaccurate structure in regions where AF2 predicts high model confidence, iii) correct backbone structure but incorrect fine details (i.e., side chain rotamer placements), and iv) correct backbone”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Bryant et al reported a 63% success rate for heterodimeric complexes56”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [56] Bryant, P., Pozzati, G. & Elofsson, A. Improved prediction of protein–protein interactions using AlphaFold2. Nat. Commun. 13, 1265 (2022).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“several groups have shown that modifications of AF2 have the potential to generate models that significantly deviate from each other, allowing for sampling of conformational landscapes”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- 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.
In the paper
“other reports have compared pLDDT scores with crystallographic B-factors to suggest AF2 confidence metrics are unable to provide direct information on local flexibility66”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Not checked yetcites [66] Carugo, O. pLDDT values in AlphaFold2 protein models are unrelated to globular protein local flexibility. Crystals 13, 1560 (2023). source
hedged- 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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“if the “best” AF2 model exhibits many residues with low pLDDT (< 70) or high PAE values (> 5 Å), the likelihood that the backbone structure matches the native conformation is very low and the model cannot be reliably interpreted”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“additional strategies such as the combination of Vc, Qr, X2free, and Rsas metrics have been proposed as more robust evaluation metrics75.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [75] Rambo, R. P. & Tainer, J. A. Accurate assessment of mass, models and resolution by small-angle scattering. Nature 496, 477–481 (2013). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AlphaMissense, does not explicitly determine the structural effects of a mutation on a protein but provides the probability of a missense variant being pathogenic51”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
What the sources say
- Not checked yetcites [51] Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Standard molecular replacement strategies require 3D coordinates of a template/homologous structure and work best when the template is < 2 Å Cα RMSD from the target structure80.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [80] Abergel, C. Molecular replacement: tricks and treats. Acta Crystallogr. D 69, 2167–2173 (2013). source
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 conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which may be due to a training bias on stable structures or an inability to extract signal from small mutations through MSAs”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“Some groups have used MD simulations to suggest that pLDDT and PAE metrics provide information on dynamics and disorder42,65”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Evaluation metrics and model reliability
What the sources say
- Not checked yetcites [42] Ruff, K. M. & Pappu, R. V. AlphaFold and implications for intrinsically disordered proteins. J. Mol. Biol. 433, 167208 (2021). source
- Not checked yetcites [65] Guo, H.-B. et al. AlphaFold2 models indicate that protein sequence determines both structure and dynamics. Sci. Rep. 12, 10696 (2022).
hedgedcorrelational- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Slight deviations in tertiary structure are noted, such as for the nuclear transport factor 2 derived de novo designed protein NT-9, but the overall structure is well described (Fig. 2j)”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > A critical evaluation of AlphaFold2’s applications
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The standard implementation of AlphaFold performs well only in detecting a single structural snapshot (the “ground state” structure), likely due to the lack of a large set of redundant protein conformers in the training set16,32.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [16] Chakravarty, D. & Porter, L. L. AlphaFold2 fails to predict protein fold switching. Protein Sci. 31, e4353 (2022). source
- Not checked yetcites [32] Saldaño, T. et al. Impact of protein conformational diversity on AlphaFold predictions. Bioinformatics 38, 2742–2748 (2022). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This is especially helpful for large biological assemblies where methyl side-chain labeling affords an increase in signal and resolution91,92.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [91] McShan, A. C. Utility of methyl side chain probes for solution NMR studies of large proteins. J. Magn. Reson. Open 14–15, 100087 (2023).
- Not checked yetcites [92] Ruschak, A. M. & Kay, L. E. Methyl groups as probes of supra-molecular structure, dynamics and function. J. Biomol. NMR 46, 75–87 (2009).
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Current implementations of AF2 can provide highly accurate working models for most rigid, well-folded globular proteins, but may have issues predicting other classes of proteins.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Conclusions and outlook
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Reports suggest AF2 models should not be fit into cryo-EM maps with resolution greater than 6Å98,99.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Integration of AF2 models with experimental data
What the sources say
- Not checked yetcites [98] Dai, X., Wu, L., Yoo, S. & Liu, Q. Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps. Brief. Bioinform. 24, bbad405 (2023).
- Not checked yetcites [99] Alshammari, M., He, J. & Wriggers, W. Appraisal of AlphaFold2-predicted models in cryo-EM map interpretation. Microsc. Microanal. 29, 977–978 (2023). source
hedged- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“The use of experimental data, especially SAXS, NMR, and cryo-EM, has also been described to guide modeling of ensembles and alternative conformations71,82,108.”
§ The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins > Beyond static snapshots: ensembles and conformational landscapes
What the sources say
- Not checked yetcites [71] Brookes, E., Rocco, M., Vachette, P. & Trewhella, J. AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank. J. Appl. Crystallogr. 56, 910–926 (2023). source
- Not checked yetcites [82] Abdollahi, H., Prestegard, J. H. & Valafar, H. Computational modeling multiple conformational states of proteins with residual dipolar coupling data. Curr. Opin. Struct. Biol. 82, 102655 (2023). source
- Not checked yetcites [108] Ramelot, T. A., Tejero, R. & Montelione, G. T. Representing structures of the multiple conformational states of proteins. Curr. Opin. Struct. Biol. 83, 102703 (2023). source
plaincorrelational- 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.
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?
In the citing paper
“Investigators built an AlphaFold2 model of WSB1, refined it with molecular simulation, and conducted virtual screening across millions of compounds.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [104]
In the citing paper
“Approximately 20 candidates were selected for synthesis and testing, yielding four lead molecules.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [104]
In the citing paper
“A second example concerns WSB1, a protein linked to metastasis through hypoxia-response pathways in lung and pancreatic cancers but historically underexplored as a drug target [104].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [104]
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · cited 7×
“…with its deep learning algorithm that enables accurate prediction of three-dimensional (3D) protein structures from only the target amino acid sequence, potentially solving the half-a-century-old protein structure-prediction problem: how to predict 3D structures from only sequence information 1–4 .”
BackgroundResultInfluentialColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · cited 2×
“For targets without known homologs, such as computationally designed proteins, increasing the Perspective https://doi.org/10.1038/s41589-024-01638-w number of recycling iterations can improve the quality of the prediction 8 .”
BackgroundInfluentialVaradi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · cited 1×
“Several efforts have alleviated these limitations, including the Alpha-Fold Protein Structure Database 6,7 , which houses over 200 million pre-run AF2 predictions, as well as ColabFold 8 and OpenFold 9 , which allow users to run a modified AF2 protocol on open-access servers in minutes.”
BackgroundEvolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · cited 1×
“When more than one input sequence is provided, AlphaFold-Multimer or AF2Complex is used 20,21 .”
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 6×
“The authors of AF2 note that, because it is trained on both apo and holo structures from the PDB, models may still be consistent with the expected structure in the presence of ligands or cofactors despite their absence in the AF2 workflow 1,5 .”
BackgroundInfluentialAccurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · cited 2×
“A recent adaption of AlphaFold, AlphaMissense, does not explicitly determine the structural effects of a mutation on a protein but provides the probability of a missense variant being pathogenic 51 .”
BackgroundMethodVaradi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1×
“Several efforts have alleviated these limitations, including the Alpha-Fold Protein Structure Database 6,7 , which houses over 200 million pre-run AF2 predictions, as well as ColabFold 8 and OpenFold 9 , which allow users to run a modified AF2 protocol on open-access servers in minutes.”
BackgroundThe Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532
Research works printed but not linked: 6
- Bertoline, L. M. F., Lima, A. N., Krieger, J. E. & Teixeira, S. K. Before and after AlphaFold2: an overview of protein structure prediction. Front. Bioinform. 3, 1120370 (2023).
- Zhao, B., Ghadermarzi, S. & Kurgan, L. Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins. Comput. Struct. Biotechnol. J. 21, 3248–3258 (2023).
- McShan, A. C. Utility of methyl side chain probes for solution NMR studies of large proteins. J. Magn. Reson. Open 14–15, 100087 (2023).
- Ruschak, A. M. & Kay, L. E. Methyl groups as probes of supra-molecular structure, dynamics and function. J. Biomol. NMR 46, 75–87 (2009).
- Dai, X., Wu, L., Yoo, S. & Liu, Q. Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps. Brief. Bioinform. 24, bbad405 (2023).
- Johansson-Åkhe, I. & Wallner, B. Improving peptide–protein docking with AlphaFold-Multimer using forced sampling. Front. Bioinform. 2, 959160 (2022).
Cited by
Sorted by citations
Protein structure prediction via deep learning: an in-depth review
Meng, Y., Zhang, Z., Zhou, C., Tang, X., Hu, X., Tian, G., Yang, J., & Yao, Y. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1498662 · Cited by 52
Lee, M., Schafer, J. W., Prabakaran, J., Chakravarty, D., Clore, M. F., & Porter, L. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60759-5 · Cited by 38
“I believe the revised manuscript is now well-positioned for publication.”
Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations
Wróblewski, K., & Kmiecik, S. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.047 · Cited by 30
“By using pLDDT scores to identify regions of low confidence, these regions can be targeted in MD simulations to explore potential alternative conformations or to better understand the dynamic behavior of flexible regions [2,8].”
AlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 25
“…regions display incorrect secondary, tertiary, or quaternary structures; (2) examples of backbone structure side chain rotamer or individual domain structures are accurate but misaligned relative to each other; and (3) instances where even high-confidence regions are still inaccurate [37].”
Structural basis for the dynamic regulation of mTORC1 by amino acids
Valenstein, M. L., Wranik, M., Lalgudi, P. V., Linde-Garelli, K. Y., Choi, Y., Chivukula, R. R., Sabatini, D. M., & Rogala, K. B. (2025) · Nature · doi:10.1038/s41586-025-09428-7 · Cited by 24
Metadata
- Authors
- Agarwal, V., & McShan, A. C.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Agarwal, Vinayak
- McShan, Andrew C.
- Venue
- Nature Chemical Biology
- Published
- 2024-06-21
- Volume
- 20
- Issue
- 8
- Pages
- 950-959
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC11956457
- OpenAlex
- W4399893066
- Cited by
- 155
- References
- 112
- Citations by year
- 2026: 79 · 2025: 63 · 2024: 13
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Chemical Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM142882)
- National Science Foundation (CHE-2238650)
- 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 › Machine Learning in Bioinformatics
- Keywords
- AlphaFold2, protein structure prediction, intrinsically disordered proteins, conformational ensembles, model reliability, pLDDT, small-angle X-ray scattering, solution NMR, cryo-electron microscopy, X-ray diffraction
- MeSH terms
- Artificial Intelligence, Humans, Models, Molecular, Protein Conformation, Proteins, X-Ray Diffraction, Protein Folding, Cryoelectron Microscopy, Scattering, Small Angle
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.
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