Does AlphaFold2 model proteins’ intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins
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Abstract
A major goal in structural biology is to understand protein assemblies in their biologically relevant states. Here, we investigate whether AlphaFold2 structure predictions match native protein conformations. We chemically cross-linked proteins in situ within intact Tetrahymena thermophila cilia and native ciliary extracts, identifying 1,225 intramolecular cross-links within the 100 best-sampled proteins, providing a benchmark of distance restraints obeyed by proteins in their native assemblies. The corresponding structure predictions were highly concordant, positioning 86.2% of cross-linked residues within Cɑ-to-Cɑ distances of 30 Å, consistent with the cross-linker length. 43% of proteins showed no violations. Most inconsistencies occurred in low-confidence regions or between domains. Overall, AlphaFold2 predictions with lower predicted aligned error corresponded to more correct native structures. However, we observe cases where rigid body domains are oriented incorrectly, as for ciliary protein BBC118, suggesting that combining structure prediction with experimental information will better reveal biologically relevant conformations.
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Several groups reported AF2 models with low PAE (<5 Å) that still deviated from experimental data.
“Several groups have reported cases of AF2 models with low PAE values (< 5 Å) that deviated from experimental data62,63”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [62]
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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 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
scikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794 · see citing works
UCSF ChimeraX: Meeting modern challenges in visualization and analysis
Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018) · Protein Science · doi:10.1002/pro.3235 · Cited by 5,267 · see citing works
UniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436 · see citing works
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 · see citing works
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305 · see citing works
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 2× · see citing works
“While not the first attempt at proteome-wide structure prediction (9), AF2’s success stems from its high accuracy at ab initio prediction (1).”
BackgroundResultGenes and molecular pathways underpinning ciliopathies
Reiter, J. F., & Leroux, M. R. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2017.60 · Cited by 1,588 · see citing works
Deep mutational scanning: a new style of protein science
Fowler, D. M., & Fields, S. (2014) · Nature Methods · doi:10.1038/nmeth.3027 · Cited by 1,287 · see citing works
Harnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205 · see citing works
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144 · see citing works
ClinVar: improvements to accessing data
Landrum, M. J., Chitipiralla, S., Brown, G. R., Chen, C., Gu, B., Hart, J., Hoffman, D., Jang, W., Kaur, K., Liu, C., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkz972 · Cited by 994 · see citing works
A Census of Human Soluble Protein Complexes
Havugimana, P. C., Hart, T., Nepusz, T., Yang, H., Turinsky, A. L., Li, Z., Wang, P. I., Boutz, D. R., Fong, V., Phanse, S., et al. (2012) · Cell · doi:10.1016/j.cell.2012.08.011 · Cited by 843 · see citing works
Self-citationA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
AlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works
Protein structure prediction from sequence variation
Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622 · see citing works
Cross-Linking Mass Spectrometry: An Emerging Technology for Interactomics and Structural Biology
Yu, C., & Huang, L. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04431 · Cited by 354 · see citing works
Merkley, E. D., Rysavy, S. J., Kahraman, A., Hafen, R., Daggett, V., & Adkins, J. (2014) · Protein Science · doi:10.1002/pro.2458 · Cited by 320 · see citing works
High‐accuracy protein structure prediction in <scp>CASP14</scp>
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · cited 3× · see citing works
“While not the first attempt at proteome-wide structure prediction (9), AF2’s success stems from its high accuracy at ab initio prediction (1).”
BackgroundThe AlphaFold Database of Protein Structures: A Biologist’s Guide
David, A., Islam, S., Tankhilevich, E., & Sternberg, M. J. E. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167336 · Cited by 268 · see citing works
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252 · see citing works
A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies
McWhite, C. D., Papoulas, O., Drew, K., Cox, R. M., June, V. M., Dong, O. X., Kwon, T., Wan, C., Salmi, M. L., Roux, S. J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.049 · Cited by 221 · see citing works
Self-citationUnexpected features of the dark proteome
Perdigão, N., Heinrich, J., Stolte, C., Sabir, K. S., Buckley, M. J., Tabor, B., Signal, B., Gloss, B., Hammang, C. J., Rost, B., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508380112 · Cited by 210 · see citing works
Taylor, D. J., Nilsson, J., Merrill, A. R., Andersen, G. R., Nissen, P., & Frank, J. (2007) · The EMBO Journal · doi:10.1038/sj.emboj.7601677 · Cited by 193 · see citing works
Self-citationAlphaFold heralds a data-driven revolution in biology and medicine
Thornton, J. M., Laskowski, R. A., & Borkakoti, N. (2021) · Nature Medicine · doi:10.1038/s41591-021-01533-0 · Cited by 185 · see citing works
Frontera: The Evolution of Leadership Computing at the National Science Foundation
Stanzione, D., West, J. E., Evans, R. T., Minyard, T., Ghattas, O. N., & Panda, D. K. D. (2020) · Practice and Experience in Advanced Research Computing · doi:10.1145/3311790.3396656 · Cited by 167 · see citing works
Jørgensen, R., Ortiz, P. A., Carr‐Schmid, A., Nissen, P., Kinzy, T. G., & Andersen, G. R. (2003) · Nature Structural & Molecular Biology · doi:10.1038/nsb923 · Cited by 164 · see citing works
The impact of AlphaFold2 one year on
Jones, D. T., & Thornton, J. M. (2022) · Nature Methods · doi:10.1038/s41592-021-01365-3 · Cited by 152 · see citing works
Exotoxin A–eEF2 complex structure indicates ADP ribosylation by ribosome mimicry
Jørgensen, R., Merrill, A. R., Yates, S. P., Márquez, V. E., Schwan, A. L., Boesen, T. P., & Andersen, G. R. (2005) · Nature · doi:10.1038/nature03871 · Cited by 150 · see citing works
Protein complexes in cells by AI‐assisted structural proteomics
(2023) · doi:10.15252/msb.202311544 · Cited by 147 · see citing works
Improved AlphaFold modeling with implicit experimental information
Terwilliger, T. C., Poon, B. K., Afonine, P. V., Schlicksup, C. J., Croll, T. I., Millán, C., Richardson, J. S., Read, R. J., & Adams, P. D. (2022) · Nature Methods · doi:10.1038/s41592-022-01645-6 · Cited by 140 · see citing works
The structural coverage of the human proteome before and after AlphaFold
Porta-Pardo, E., Ruiz-Serra, V., Valentini, S., & Valencia, A. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009818 · Cited by 131 · see citing works
A structural biology community assessment of AlphaFold 2 applications
Akdel, M., Pires, D. E. V., Porta Pardo, E., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2021) · doi:10.1101/2021.09.26.461876 · Cited by 122 · see citing works
Membrane renewal after dibucaine deciliation of Tetrahymena
Satir, B. H., Sale, W. S., & Satir, P. (1976) · Experimental Cell Research · doi:10.1016/0014-4827(76)90657-1 · Cited by 113 · see citing works
xiView: A common platform for the downstream analysis of Crosslinking Mass Spectrometry dataPreprint
Graham, M., Combe, C., Kolbowski, L., & Rappsilber, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/561829 · Cited by 107 · see citing works
Leveraging crosslinking mass spectrometry in structural and cell biology
Graziadei, A., & Rappsilber, J. (2021) · Structure · doi:10.1016/j.str.2021.11.007 · Cited by 106 · see citing works
Integrative structure modeling with the Integrative Modeling Platform
Webb, B., Viswanath, S., Bonomi, M., Pellarin, R., Greenberg, C. H., Saltzberg, D. J., & Šali, A. (2017) · Protein Science · doi:10.1002/pro.3311 · Cited by 104 · see citing works
The accuracy of protein structures in solution determined by AlphaFold and NMR
Fowler, N. J., & Williamson, M. P. (2022) · Structure · doi:10.1016/j.str.2022.04.005 · Cited by 104 · see citing works
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
Bordin, N., Sillitoe, I., Nallapareddy, V., Rauer, C., Lam, S. D., Waman, V. P., Sen, N., Heinzinger, M., Littmann, M., Kim, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04488-9 · Cited by 103 · see citing works
Brodie, N. I., Popov, K. I., Petrotchenko, E. V., Dokholyan, N. V., & Borchers, C. H. (2017) · Science Advances · doi:10.1126/sciadv.1700479 · Cited by 100 · see citing works
Evolution of CiliaPreprint
Mitchell, D. R. (2016) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a028290 · Cited by 94 · see citing works
Structural Biology of TelomerasePreprint
Wang, Y., Sušac, L., & Feigon, J. F. (2019) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a032383 · Cited by 83 · see citing works
Can AlphaFold2 predict protein-peptide complex structures accurately?
Ko, J., & Lee, J. (2021) · doi:10.1101/2021.07.27.453972 · Cited by 83 · see citing works
Viswanath, S., Chemmama, I. E., Cimermancic, P., & Sali, A. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.10.005 · Cited by 80 · see citing works
High-density chemical cross-linking for modeling protein interactions
Mintseris, J., & Gygi, S. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902931116 · Cited by 79 · see citing works
In situ structure determination at nanometer resolution using TYGRESS
Song, K., Shang, Z., Fu, X., Lou, X., Grigorieff, N., & Nicastro, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0651-0 · Cited by 71 · see citing works
Cryo-EM and artificial intelligence visualize endogenous protein community members
Skalidis, I., Kyrilis, F. L., Tüting, C., Hamdi, F., Chojnowski, G., & Kastritis, P. L. (2022) · Structure · doi:10.1016/j.str.2022.01.001 · Cited by 61 · see citing works
Using the PRIDE Database and ProteomeXchange for Submitting and Accessing Public Proteomics Datasets
Jarnuczak, A. F., & Vizcaíno, J. A. (2017) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.30 · Cited by 52 · see citing works
DeepTracer-ID: De novo protein identification from cryo-EM maps
Chang, L., Wang, F., Connolly, K., Meng, H., Su, Z., Cvirkaite-Krupovic, V., Krupovic, M., Egelman, E. H., & Si, D. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.06.025 · Cited by 50 · see citing works
Remodeling and activation mechanisms of outer arm dyneins revealed by cryo-EM
Kubo, S., Yang, S. K., Black, C. S., Dai, D., Valente-Paterno, M., Gaertig, J., Ichikawa, M., & Bui, K. H. (2021) · EMBO Reports · doi:10.15252/embr.202152911 · Cited by 44 · see citing works
McCafferty, C. L., Papoulas, O., Jordan, M. A., Hoogerbrugge, G., Nichols, C., Pigino, G., Taylor, D. W., Wallingford, J. B., & Marcotte, E. M. (2022) · eLife · doi:10.7554/elife.81977 · Cited by 40 · see citing works
Self-citationDiscovery and Functional Evaluation of Ciliary Proteins in Tetrahymena thermophila
Gaertig, J., Włoga, D., Vasudevan, K. K., Guha, M., & Dentler, W. L. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-397944-5.00013-4 · Cited by 35 · see citing works
Tai, L., Zhu, Y., Ren, H., Huang, X., Zhang, C., & Sun, F. (2022) · Protein & Cell · doi:10.1007/s13238-021-00895-y · Cited by 32 · see citing works
Author Correction: Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2022) · Nature Communications · doi:10.1038/s41467-022-29480-5 · Cited by 24 · see citing works
Lin, Y., Liu, H., Liu, Z., Liu, Y., He, Q., Chen, P., Wang, X., & Liang, S. (2012) · Analytical Biochemistry · doi:10.1016/j.ab.2012.09.023 · Cited by 19 · see citing works
AlphaFold Protein Structure Database for Sequence-Independent Molecular Replacement
Chai, L., Zhu, P., Chai, J., Pang, C., Andi, B., McSweeney, S., Shanklin, J., & Liu, Q. (2021) · Crystals · doi:10.3390/cryst11101227 · Cited by 15 · see citing works
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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
“Several groups have reported cases of AF2 models with low PAE values ( 5 Å) that deviated from experimental data 62,63 .”
BackgroundKubo, S., Black, C. S., Joachimiak, E., Yang, S. K., Legal, T., Peri, K., Khalifa, A. A. Z., Ghanaeian, A., McCafferty, C. L., Valente-Paterno, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37868-0 · Cited by 93 · see citing works
Self-citationAlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 81 · see citing works
Terashi, G., Wang, X., Prasad, D., Nakamura, T., & Kihara, D. (2023) · Nature Methods · doi:10.1038/s41592-023-02099-0 · Cited by 69 · see citing works
Overview of AlphaFold2 and breakthroughs in overcoming its limitations
Wang, L., Wen, Z., Liu, S.-W., Zhang, L., Finley, C., Lee, H., & Fan, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108620 · Cited by 53 · see citing works
Cross-linking mass spectrometry for mapping protein complex topologies in situ
Lee, K., & O’Reilly, F. J. (2023) · Essays in Biochemistry · doi:10.1042/ebc20220168 · Cited by 53 · see citing works
McCafferty, C. L., Papoulas, O., Jordan, M. A., Hoogerbrugge, G., Nichols, C., Pigino, G., Taylor, D. W., Wallingford, J. B., & Marcotte, E. M. (2022) · eLife · doi:10.7554/elife.81977 · Cited by 40 · see citing works
Self-citationAlphaFold2 enables accurate deorphanization of ligands to single-pass receptors
Banhos Danneskiold-Samsøe, N., Kavi, D., Jude, K. M., Nissen, S. B., Wat, L. W., Coassolo, L., Zhao, M., Santana-Oikawa, G. A., Broido, B. B., Garcia, K. C., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.10.004 · Cited by 35 · see citing works
A Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 33 · see citing works
Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross‐Linking
Lu, H., Zhu, Z., Fields, L. G., Zhang, H., & Li, L. (2024) · Mass Spectrometry Reviews · doi:10.1002/mas.21908 · Cited by 26 · see citing works
Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development
Chen, X., Xu, S., Chu, B., Guo, J., Zhang, H.-K., Sun, S.-Y., Song, L., & Feng, X. (2024) · ACS Nano · doi:10.1021/acsnano.4c12599 · Cited by 16 · see citing works
AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility
Martin, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01805 · Cited by 19 · see citing works
Hevler, J. F., & Heck, A. J. R. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100657 · Cited by 11 · see citing works
McCafferty, C. L., Papoulas, O., Lee, C., Bui, K. H., Taylor, D. W., Marcotte, E. M., & Wallingford, J. B. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.11.019 · Cited by 12 · see citing works
Self-citationMcCafferty, C. L., Papoulas, O., Lee, C., Bui, K. H., Taylor, D. W., Marcotte, E. M., & Wallingford, J. B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.09.548259 · Cited by 7 · see citing works
Self-citationSawhney, A., Li, J., & Liao, L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25105247 · Cited by 6 · see citing works
Howard-Till, R. A., Li, S., Kar, U. P., Fuentes, C. N., Fabritius, A. S., & Winey, M. (2025) · Molecular Biology of the Cell · doi:10.1091/mbc.e24-12-0557 · Cited by 5 · see citing works
Opuni, K. F. M., Ruß, M., Geens, R., Vocht, L. D., Wielendaele, P. V., Debuy, C., Sterckx, Y. G. ‐., & Glocker, M. O. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.08.023 · Cited by 5 · see citing works
NMR in the Age of Modern Biomedical Research and Drug Discovery
Lu, X., Myint, W., Muli, C. S., Larion, M., Matsuo, H., & Walters, K. J. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169302 · Cited by 4 · see citing works
AlphaFold2 predicts interactions amidst confounding structural compatibilityPreprint
Martin, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.25.554771 · Cited by 4 · see citing works
Strategies for multimodal spatiotemporal profiling of phosphorylation in cilia biology
Turn, R. E., Aziz‐Zanjani, M. O., Asthana, A., & Jackson, P. K. (2025) · Journal of Cell Science · doi:10.1242/jcs.264159 · Cited by 3 · see citing works
Park, H., Youn, B., Park, D. J., Puthanveettil, S. V., & Kang, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-57975-2 · Cited by 3 · see citing works
Wu, J., Wan, C., Tian, Y., Ouyang, Y., Puscher, H., Li, S., Yin, Q., & Shen, J. (2025) · Science Advances · doi:10.1126/sciadv.adw9158 · Cited by 2 · see citing works
Recent Methods from Statistical Inference for Integrative Structural Modeling
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AFragmenter: schema-free, tuneable protein domain segmentation for AlphaFold protein structures
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Balakrishnan, S. M., Ansari, H. R., & Nedjadi, T. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.03.062 · Cited by 1 · see citing works
Haque, N., Wagenknecht, J. B., Ratnasinghe, B. D., & Zimmermann, M. T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0313308 · Cited by 1 · see citing works
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Park, H., Youn, B., Park, D. J., Puthanveettil, S. V., & Kang, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.15.571892
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Regulation of AP1 adaptor assembly by the bi-handed chaperone MEA1
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- Authors
- McCafferty, C. L., Pennington, E. L., Papoulas, O., Taylor, D. W., & Marcotte, E. M.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- McCafferty, Caitlyn L.
- Venue
- Communications Biology
- Published
- 2023-04-15
- Volume
- 6
- Issue
- 1
- Pages
- 421-421
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC10105775
- OpenAlex
- W4365816022
- Cited by
- 35
- References
- 58
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 13 · 2023: 10 · 2022: 1
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM138348, R35GM122480)
- Welch Foundation (F-1938, F-1515)
- Cancer Prevention and Research Institute of Texas (RR160088)
- U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD085901)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Keywords
- AlphaFold2, protein structure prediction, ciliary proteins, Tetrahymena thermophila, cilia, crosslinking, cross-linking mass spectrometry, distance restraints
- MeSH terms
- Protein Conformation, Proteins, Mass Spectrometry
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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