The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Molecular Grammar Governing the Driving Forces for Phase Separation of Prion-like RNA Binding Proteins
Open accessAbstract
SUMMARY Proteins such as FUS phase separate to form liquid-like condensates that can harden into less dynamic structures. However, how these properties emerge from the collective interactions of many amino acids remains largely unknown. Here, we use extensive mutagenesis to identify a sequence-encoded molecular grammar underlying the driving forces for phase separation of proteins of the FUS family, and test aspects of this grammar in cells. Phase separation is governed primarily by multivalent interactions amongst tyrosine residues from prion-like domains and arginine residues from RNA binding domains, which are modulated by negatively charged residues. Glycine residues enhance the fluidity, whereas glutamine and serine residues promote hardening. We develop a model to show that the measured saturation concentrations of phase separation are inversely proportional to the product of the numbers of arginine and tyrosine residues. These results suggest it is possible to predict phase separation properties based on amino acid sequence.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kato, M., Han, T. W., Xie, S., Shi, K., Du, X., Wu, L. C., Mirzaei, H., Goldsmith, E. J., Longgood, J., Pei, J., et al. (2012) · Cell · doi:10.1016/j.cell.2012.04.017 · Cited by 2,012
A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances
Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.053 · Cited by 1,270
Self-citationLocal Nucleation of Microtubule Bundles through Tubulin Concentration into a Condensed Tau Phase
Hernández-Vega, A., Braun, M., Scharrel, L., Jahnel, M., Wegmann, S., Hyman, B. T., Alberti, S., Diez, S., & Hyman, A. A. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.08.042 · Cited by 395 · cited 1×
“If true, this would be another example of a cell using phase separation to raise protein concentration and uncover new chemistry, as has been proposed for polymer dynamics and enzymatic reactions (Banani et al., 2017; Hernández-Vega et al., 2017; Shin and Brangwynne, 2017; Woodruff et al., 2017).”
Self-citation
Cited by
Alberti, S., Gladfelter, A., & Mittag, T. (2019) · Cell · doi:10.1016/j.cell.2018.12.035 · Cited by 2,856
Self-citationValence and patterning of aromatic residues determine the phase behavior of prion-like domains
Martin, E. W., Holehouse, A. S., Peran, I., Farag, M., Incicco, J. J., Bremer, A., Grace, C. R., Soranno, A., Pappu, R. V., & Mittag, T. (2020) · Science · doi:10.1126/science.aaw8653 · Cited by 1,147
Self-citationA framework for understanding the functions of biomolecular condensates across scales
Lyon, A. S., Peeples, W. B., & Rosen, M. K. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-00303-z · Cited by 895
Stress granules and neurodegeneration
Wolozin, B., & Ivanov, P. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0222-5 · Cited by 810
Phase separation and neurodegenerative diseases: a disturbance in the force
Zbinden, A., Pérez-Berlanga, M., De Rossi, P., & Polymenidou, M. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.09.014 · Cited by 500
Mapping Local and Global Liquid Phase Behavior in Living Cells Using Photo-Oligomerizable Seeds
Bracha, D., Walls, M. T., Wei, M.-T., Zhu, L., Kurian, M., Avalos, J. L., Toettcher, J. E., & Brangwynne, C. P. (2018) · Cell · doi:10.1016/j.cell.2018.10.048 · Cited by 462
Phase separation at the synapse
Chen, X., Wu, X., Wu, H., & Zhang, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-019-0579-9 · Cited by 261
Abyzov, A., Blackledge, M., & Zweckstetter, M. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00774 · Cited by 215
Nucleation landscape of biomolecular condensates
Shimobayashi, S. F., Ronceray, P., Sanders, D. W., Haataja, M. P., & Brangwynne, C. P. (2021) · Nature · doi:10.1038/s41586-021-03905-5 · Cited by 211
Interactions by disorder – A matter of context
Bugge, K., Brakti, I., Fernandes, C. B., Dreier, J. E., Lundsgaard, J. E., Olsen, J. G., Skriver, K., & Kragelund, B. B. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00110 · Cited by 155
“In phase separation, the context of the disordered chain also has implications for interactions as explained by the stickers-and-spacers model of phase separation developed based on studies of RNA-binding proteins with prion-like domains (Wang et al., 2018; Martin et al., 2020).”
BackgroundLiquid–liquid phase separation underpins the formation of replication factories in rotaviruses
Geiger, F., Acker, J., Papa, G., Wang, X., Arter, W. E., Saar, K. L., Erkamp, N. A., Qi, R., Bravo, J. P., Strauss, S., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2021107711 · Cited by 130
Making the Case for Disordered Proteins and Biomolecular Condensates in Bacteria
Cohan, M. C., & Pappu, R. V. (2020) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2020.04.011 · Cited by 104
Self-citationIntrinsically disordered proteins and biomolecular condensates as drug targets
Biesaga, M., Frigolé-Vivas, M., & Salvatella, X. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.02.009 · Cited by 115
Garcia Morato, J., Hans, F., von Zweydorf, F., Feederle, R., Elsässer, S. J., Skodras, A. A., Gloeckner, C. J., Buratti, E., Neumann, M., & Kahle, P. J. (2022) · Nature Communications · doi:10.1038/s41467-022-28822-7 · Cited by 94
Lindorff-Larsen, K., & Kragelund, B. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167196 · Cited by 85
(2019) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.08.002 · Cited by 78
How multisite phosphorylation impacts the conformations of intrinsically disordered proteins
Jin, F., & Gräter, F. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1008939 · Cited by 77
Nuclear Protein Condensates and Their Properties in Regulation of Gene Expression
Li, W., & Jiang, H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167151 · Cited by 64
Frazer, C., Staples, M. I., Kim, Y., Hirakawa, M., Dowell, M. A., Johnson, N. V., Hernday, A. D., Ryan, V. H., Fawzi, N. L., Finkelstein, I. J., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0760-7 · Cited by 56
Ainani, H., Bouchmaa, N., Ben Mrid, R., & El Fatimy, R. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106011 · Cited by 54
Farahi, N., Lazar, T., Wodak, S. J., Tompa, P., & Pancsa, R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22063017 · Cited by 46
Phase separation and ageing of glycine-rich protein from tick adhesive
Ganar, K. A., Nandy, M., Turbina, P., Chen, C., Suylen, D., Nihoul, E., Pascoe, E. L., van der Beelen, S., Plaum, M., van den Bos, L., et al. (2025) · Nature Chemistry · doi:10.1038/s41557-024-01686-8 · Cited by 42
Liquid-liquid phase separation in Alzheimer’s disease
Fu, Q., Zhang, B., Chen, X., & Chu, L. (2024) · Journal of Molecular Medicine · doi:10.1007/s00109-023-02407-3 · Cited by 33
The Chromatin Regulator HMGA1a Undergoes Phase Separation in the Nucleus**
Zhu, H., Narita, M., Joseph, J. A., Krainer, G., Arter, W. E., Olan, I., Saar, K. L., Ermann, N., Espinosa, J. R., Shen, Y., et al. (2023) · ChemBioChem · doi:10.1002/cbic.202200450 · Cited by 23
Intrinsic protein disorder and protein modifications in the processing of biological signals
Phillips, A. H., & Kriwacki, R. W. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.09.003 · Cited by 21
Predicting Heteropolymer Interactions: Demixing and Hypermixing of Disordered Protein Sequences
Adachi, K., & Kawaguchi, K. (2024) · Physical Review X · doi:10.1103/physrevx.14.031011 · Cited by 19
Protein intrinsically disordered regions have a non-random, modular architecture
McConnell, B. S., & Parker, M. W. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad732 · Cited by 17
Decoding Missense Variants by Incorporating Phase Separation via Machine Learning
Feng, M., Wei, X., Zheng, X., Liu, L., Lin, L., Xia, M., He, G., Shi, Y., & Lu, Q. (2024) · Nature Communications · doi:10.1038/s41467-024-52580-3 · Cited by 15
Morató, A., Elena-Real, C. A., Popovic, M., Fournet, A., Zhang, K., Allemand, F., Sibille, N., Urbanek, A., & Bernadó, P. (2020) · Biomolecules · doi:10.3390/biom10101458 · Cited by 12
Fused in sarcoma undergoes cold denaturation: Implications for phase separation
Félix, S. S., Laurents, D. V., Oroz, J., & Cabrita, E. J. (2023) · Protein Science · doi:10.1002/pro.4521 · Cited by 12
Direct ionic stress sensing and mitigation by the transcription factor NFAT5
Khandwala, C. B., Sarkar, P., Schmidt, H. B., Ma, M., Pusapati, G. V., Lamoliatte, F., Kinnebrew, M., Patel, B. B., Tillo, D., Lebensohn, A. M., et al. (2025) · Science Advances · doi:10.1126/sciadv.adu3194 · Cited by 11
Ho, W., Huang, H., & Huang, J. (2023) · Protein Science · doi:10.1002/pro.4739 · Cited by 11
Baratam, K., & Srivastava, A. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00579 · Cited by 9
Kaddis Maldonado, R., Lambert, G. S., Rice, B. L., Sudol, M., Flanagan, J. M., & Parent, L. J. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168182 · Cited by 9
Multi-scale simulations of MUT-16 scaffold protein phase separation and client recognition
Gaurav, K., Busetto, V., Páez-Moscoso, D. J., Changiarath, A., Hanson, S. M., Falk, S., Ketting, R. F., & Stelzl, L. S. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.08.001 · Cited by 7
Protein language model identifies disordered, conserved motifs implicated in phase separation
Zhang, Y., Zheng, J., & Zhang, B. (2025) · eLife · doi:10.7554/elife.105309 · Cited by 4
“These studies support the ‘stickers- and-spacers’ framework ( Choi et al., 2020 ; Pappu et al., 2023 ; Wang et al., 2018 ; Prusty et al., 2018 ; Choi et al., 2019 ; Zhang et al., 2021 ), in which specific residues, termed ‘stickers’, drive strong, specific interactions, while ‘spacer’ regions act…”
Tandem-repeat proteins introduce tuneable properties to engineered biomolecular condensates
Chris Ng, T. L., Hoare, M. P., Maristany, M. J., Wilde, E. J., Sneideris, T., Huertas, J., Agbetiameh, B. K., Furukawa, M., Joseph, J. A., Knowles, T. P. J., et al. (2024) · doi:10.1101/2024.04.16.589709 · Cited by 1
Metadata
- Authors
- Wang, J., Choi, J.-M., Holehouse, A. S., Lee, H. O., Zhang, X., Jahnel, M., Maharana, S., Lemaitre, R., Pozniakovsky, A., Drechsel, D., Poser, I., Pappu, R. V., Alberti, S., & Hyman, A. A.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 174
- Issue
- 3
- Pages
- 688-699.e16
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2810866092
- Cited by
- 2,076
- References
- 3
- FWCI
- 52.89
- Type
- journal article
- Language
- en
- Open access
- bronze
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.