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Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains


Prion-like low-complexity domains (PLCDs) have distinctive sequence grammars that determine their driving forces for phase separation. Here we uncover the physicochemical underpinnings of how evolutionarily conserved compositional biases influence the phase behaviour of PLCDs. We interpret our results in the context of the stickers-and-spacers model for the phase separation of associative polymers. We find that tyrosine is a stronger sticker than phenylalanine, whereas arginine is a context-dependent auxiliary sticker. In contrast, lysine weakens sticker–sticker interactions. Increasing the net charge per residue destabilizes phase separation while also weakening the strong coupling between single-chain contraction in dilute phases and multichain interactions that give rise to phase separation. Finally, glycine and serine residues act as non-equivalent spacers, and thus make the glycine versus serine contents an important determinant of the driving forces for phase separation. The totality of our results leads to a set of rules that enable comparative estimates of composition-specific driving forces for PLCD phase separation.

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Fig. 1: Compositional analysis and covariation of the aromatic content of hnRNPA1.
Fig. 2: The aromatic residue type and content influences the balance of entropic and enthalpic contributions to phase separation.
Fig. 3: Role of charged residues in PLCD phase separation.
Fig. 4: Role of charged residues in PLCD phase separation.
Fig. 5: NCPR modulates A1-LCD phase separation.
Fig. 6: Mean-field electrostatic effects can disrupt the strong coupling between driving forces for single-chain contraction and phase separation.
Fig. 7: Gly and Ser are spacers with different ves.

Data availability

Data supporting the findings of this study are included in the Article and Supplementary Information. NMR assignments of A1-LCD +7K+12D are available from the BMRB at accession code ID 50739. All expression plasmids are available from T.M. under a material transfer agreement with St Jude Children’s Hospital.

Code availability

Code needed to reproduce the results, SAXS data, CD data, experimental binodals, pH-dependent measurements, sequences used in the bioinformatics analysis and estimated saturation concentrations of PLCD homologues are available at


  1. Brangwynne, C. P. et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science 324, 1729–1732 (2009).

    CAS  PubMed  Google Scholar 

  2. Li, P. et al. Phase transitions in the assembly of multivalent signalling proteins. Nature 483, 336–340 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).

    PubMed  PubMed Central  Google Scholar 

  4. Riggs, C. L., Kedersha, N., Ivanov, P. & Anderson, P. Mammalian stress granules and P bodies at a glance. J. Cell Sci. 133, jcs242487 (2020).

    CAS  PubMed  Google Scholar 

  5. Yang, P. et al. G3BP1 is a tunable switch that triggers phase separation to assemble stress granules. Cell 181, 325–345.e328 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Sanders, D. W. et al. Competing protein–RNA interaction networks control multiphase intracellular organization. Cell 181, 306–324.e28 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Guillen-Boixet, J. et al. RNA-induced conformational switching and clustering of G3BP drive stress granule assembly by condensation. Cell 181, 346–361.e17 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Mitchell, S. F., Jain, S., She, M. & Parker, R. Global analysis of yeast mRNPs. Nat. Struct. Mol. Biol. 20, 127–133 (2013).

    CAS  PubMed  Google Scholar 

  9. Cascarina, S. M., Elder, M. R. & Ross, E. D. Atypical structural tendencies among low-complexity domains in the Protein Data Bank proteome. PLoS Comput. Biol. 16, e1007487 (2020).

    PubMed  PubMed Central  Google Scholar 

  10. Kim, H. J. et al. Mutations in prion-like domains in hnRNPA2B1 and hnRNPA1 cause multisystem proteinopathy and ALS. Nature 495, 467–473 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Mackenzie, I. R. et al. TIA1 mutations in amyotrophic lateral sclerosis and frontotemporal dementia promote phase separation and alter stress granule dynamics. Neuron 95, 808–816.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Martin, E. W. et al. Valence and patterning of aromatic residues determine the phase behavior of prion-like domains. Science 367, 694–699 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, W. et al. Molecular details of protein condensates probed by microsecond long atomistic simulations. J. Phys. Chem. B 124, 11671–11679 (2020).

    Google Scholar 

  15. Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Choi, J.-M., Dar, F. & Pappu, R.V. LASSI: a lattice model for simulating phase transitions of multivalent protein. PLoS Comput. Biol. 15, e1007028 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Choi, J.-M., Holehouse, A. S. & Pappu, R. V. Physical principles underlying the complex biology of intracellular phase transitions. Annu. Rev. Biophys. 49, 107–133 (2020).

    CAS  PubMed  Google Scholar 

  18. Semenov, A. N. & Rubinstein, M. Thermoreversible gelation in solutions of associative polymers. 1. Statics. Macromolecules 31, 1373–1385 (1998).

    CAS  Google Scholar 

  19. Harmon, T. S., Holehouse, A. S., Rosen, M. K. & Pappu, R. V. Intrinsically disordered linkers determine the interplay between phase separation and gelation in multivalent proteins. eLife 6, e30294 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. Harmon, T. S., Holehouse, A. S. & Pappu, R. V. Differential solvation of intrinsically disordered linkers drives the formation of spatially organized droplets in ternary systems of linear multivalent proteins. New J. Phys. 20, 045002 (2018).

    Google Scholar 

  21. Lantman, C. W., MacKnight a, W. J. & Lundberg, R. D. Structural properties of ionomers. Annu. Rev. Mater. Sci. 19, 295–317 (1989).

    CAS  Google Scholar 

  22. Dignon, G. L., Zheng, W., Best, R. B., Kim, Y. C. & Mittal, J. Relation between single-molecule properties and phase behavior of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 115, 9929–9934 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Zeng, X., Holehouse, A. S., Chilkoti, A., Mittag, T. & Pappu, R. V. Connecting coil-to-globule transitions to full phase diagrams for intrinsically disordered proteins. Biophys. J. 119, 402–418 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Zeng, X. et al. Design of intrinsically disordered proteins that undergo phase transitions with lower critical solution temperatures. APL Mater. 9, 021119 (2021).

    CAS  Google Scholar 

  25. Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).

    CAS  PubMed  Google Scholar 

  26. Milkovic, N. M. & Mittag, T. Determination of protein phase diagrams by centrifugation. Methods Mol. Biol. 2141, 685–702 (2020).

    CAS  PubMed  Google Scholar 

  27. Peran, I., Martin, E. W. & Mittag, T. Walking along a protein phase diagram to determine coexistence points by static light scattering. Methods Mol. Biol. 2141, 715–730 (2020).

    CAS  PubMed  Google Scholar 

  28. Crick, S. L., Ruff, K. M., Garai, K., Frieden, C. & Pappu, R. V. Unmasking the roles of N- and C-terminal flanking sequences from exon 1 of huntingtin as modulators of polyglutamine aggregation. Proc. Natl Acad. Sci. USA 110, 20075–20080 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Greig, J. A. et al. Arginine-enriched mixed-charge domains provide cohesion for nuclear speckle condensation. Mol. Cell 77, 1237–1250.e4 (2020).

    CAS  PubMed  Google Scholar 

  30. Fisher, R. S. & Elbaum-Garfinkle, S. Tunable multiphase dynamics of arginine and lysine liquid condensates. Nat. Commun. 11, 4628 (2020).

    PubMed  PubMed Central  Google Scholar 

  31. Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Brady, J. P. et al. Structural and hydrodynamic properties of an intrinsically disordered region of a germ cell-specific protein on phase separation. Proc. Natl Acad. Sci. USA 114, E8194–E8203 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Crabtree M. D., et al. Repulsive electrostatic interactions modulate dense and dilute phase properties of biomolecular condensates. Preprint at bioRxiv (2020).

  34. Martin, E. W. & Holehouse, A. S. Intrinsically disordered protein regions and phase separation: sequence determinants of assembly or lack thereof. Emerg. Top. Life Sci. 4, 307–329 (2020).

    CAS  PubMed  Google Scholar 

  35. Dignon, G. L., Best, R. B. & Mittal, J. Biomolecular phase separation: from molecular driving forces to macroscopic properties. Annu. Rev. Phys. Chem. 71, 53–75 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Fossat, M. J., Zeng, X. & Pappu, R. V. Uncovering differences in hydration free energies and structures for model compound mimics of charged side chains of amino acids. J. Phys. Chem. B 125, 4148–4161 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Boeynaems, S. et al. Spontaneous driving forces give rise to protein−RNA condensates with coexisting phases and complex material properties. Proc. Natl Acad. Sci. USA 116, 7889–7898 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Schuster, B. S. et al. Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior. Proc. Natl Acad. Sci. USA 117, 11421–11431 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Choi, J.-M., Hyman, A. A. & Pappu, R. V. Generalized models for bond percolation transitions of associative polymers. Phys. Rev. E 102, 042403 (2020).

    CAS  PubMed  Google Scholar 

  40. Riback, J. A. et al. Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water. Science 358, 238–241 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Mao, A. H., Crick, S. L., Vitalis, A., Chicoine, C. L. & Pappu, R. V. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 107, 8183–8188 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Müller-Späth, S. et al. Charge interactions can dominate the dimensions of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 107, 14609–14614 (2010).

    PubMed  PubMed Central  Google Scholar 

  43. Choi, J.-M. & Pappu, R. V. Improvements to the ABSINTH force field for proteins based on experimentally derived amino acid specific backbone conformational statistics. J. Chem. Theory Comput. 15, 1367–1382 (2019).

    PubMed  Google Scholar 

  44. Toombs, J. A. et al. De novo design of synthetic prion domains. Proc. Natl Acad. Sci. USA 109, 6519–6524 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Harrison, A. F. & Shorter, J. RNA-binding proteins with prion-like domains in health and disease. Biochem. J 474, 1417–1438 (2017).

    CAS  PubMed  Google Scholar 

  47. Fomicheva, A. & Ross, E. D. From prions to stress granules: defining the compositional features of prion-like domains that promote different types of assemblies. Int. J. Mol. Sci. 22, 1251 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Mahadevi, A. S. & Sastry, G. N. Cation−π Interaction: its role and relevance in chemistry, biology, and material science. Chem. Rev. 113, 2100–2138 (2013).

    CAS  PubMed  Google Scholar 

  49. Dahal, Y. R. & Schmit, J. D. Ion specificity and nonmonotonic protein solubility from salt entropy. Biophys. J. 114, 76–87 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Bremer A. et al. Deciphering how naturally occurring sequence features impact the phase behaviors of disordered prion-like domains. Preprint at (2021).

  51. Yang, Y., Jones, H. B., Dao, T. P. & Castaneda, C. A. Single amino acid substitutions in stickers, but not spacers, substantially alter UBQLN2 phase transitions and dense phase material properties. J. Phys. Chem. B 123, 3618–3629 (2019).

    CAS  PubMed  Google Scholar 

  52. Tran, H. T., Mao, A. & Pappu, R. V. Role of backbone−solvent interactions in determining conformational equilibria of intrinsically disordered proteins. J. Am. Chem. Soc. 130, 7380–7392 (2008).

    CAS  PubMed  Google Scholar 

  53. Holehouse, A. S., Garai, K., Lyle, N., Vitalis, A. & Pappu, R. V. Quantitative assessments of the distinct contributions of polypeptide backbone amides versus side chain groups to chain expansion via chemical denaturation. J. Am. Chem. Soc. 137, 2984–2995 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Mathieu, C., Pappu, R. V. & Taylor, J. P. Beyond aggregation: pathological phase transitions in neurodegenerative disease. Science 370, 56–60 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Chou, H.-Y. & Aksimentiev, A. Single-protein collapse determines phase equilibria of a biological condensate. J. Phys. Chem. Lett. 11, 4923–4929 (2020).

    CAS  PubMed  Google Scholar 

  56. Dignon, G. L., Zheng, W., Kim, Y. C., Best, R. B. & Mittal, J. Sequence determinants of protein phase behavior from a coarse-grained model. PLoS Comput. Biol. 14, e1005941 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. Dignon, G. L., Zheng, W., Kim, Y. C. & Mittal, J. Temperature-controlled liquid–liquid phase separation of disordered proteins. ACS Cent. Sci. 5, 821–830 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Dannenhoffer-Lafage, T. & Best, R. B. A data-driven hydrophobicity scale for predicting liquid–liquid phase separation of proteins. J. Phys. Chem. B 125, 4046–4056 (2021).

    CAS  PubMed  Google Scholar 

  59. Dzuricky, M., Rogers, B. A., Shahid, A., Cremer, P. S. & Chilkoti, A. De novo engineering of intracellular condensates using artificial disordered proteins. Nat. Chem. 12, 814–825 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. King, O. D., Gitler, A. D. & Shorter, J. The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 1462, 61–80 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Dasmeh, P. & Wagner, A. Natural selection on the phase-separation properties of FUS during 160 My of mammalian evolution. Mol. Biol. Evol. 38, 940–951 (2020).

    PubMed Central  Google Scholar 

  62. Gueroussov, S. et al. Regulatory expansion in mammals of multivalent hnRNP assemblies that globally control alternative splicing. Cell 170, 324–339.e323 (2017).

    CAS  PubMed  Google Scholar 

  63. Holtzer, A. & Holtzer, M. F. Use of the van’t Hoff relation in determination of the enthalpy of micelle formation. J. Phys. Chem. 78, 1442–1443 (1974).

    CAS  Google Scholar 

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We thank Y. Xia for help with the NMR experiments. We are grateful to F. Dar, A. Holehouse, M. King, R. Kriwacki, K. Lindorff-Larsen, K. Ruff, J. P. Taylor and X. Zeng for helpful discussions. Microscopy images were acquired at the Cell and Tissue Imaging Center at SJCRH, which is supported by SJCRH and NCI (grant P30 CA021765). This work was supported by the US National Institutes of Health (grant 5R01NS056114 to R.V.P. and grant R01NS121114 to R.V.P. and T.M.), the Air Force Office of Scientific Research (grant FA9550-20-1-0241 to R.V.P.), the St Jude Collaborative Research Consortium on Membraneless Organelles in Health and Disease (to T.M. and R.V.P.) and the American Lebanese Syrian Associated Charities (to T.M.). Use of the Advanced Photon Source was supported by the US Department of Energy under contract DE-AC02-06CH11357. This project was supported by grant P30 GM138395 from the National Institute of General Medical Sciences of the National Institutes of Health. Use of the Pilatus3 1M detector was provided by grant 1S10OD018090 from NIGMS.

Author information

Authors and Affiliations



A.B., M.F., W.M.B., R.V.P. and T.M. designed the study. A.B., W.M.B., I.P. and E.W.M. acquired different components of the experimental data and/or provided key reagents for the experiments. M.F. and R.V.P. designed the computational and theoretical analysis. A.B., M.F., R.V.P. and T.M. wrote and revised multiple versions of the manuscript. All the authors read and contributed revisions. R.V.P. and T.M. acquired funding.

Corresponding authors

Correspondence to Rohit V. Pappu or Tanja Mittag.

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Competing interests

R.V.P. is a member of the scientific advisory board of Dewpoint Therapeutics Inc. and T.M. is a consultant of Faze Medicines, Inc. The work reported here has not been influenced by either of these affiliations. All other authors declare no competing interests.

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Peer review information Nature Chemistry thanks David Lynn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Compositional analysis of PLCDs from homologs of hnRNPA1.

(a) 2D histogram quantifying the joint distribution of lengths of PLCDs derived from homologs of hnRNPA1 and the compositional similarities, in terms of cosine values, to A1-LCD. (b) Distribution of fractions of aromatic residues (Tyr, Phe, and Trp). (c) Distributions of fractions of Glu and Asp residues. Numerical values are mean values of the respective distribution ± the standard deviations.

Extended Data Fig. 2 1H,15N HSQC spectrum of the A1-LCD variant +7K+12D.

The plot on the right is an expansion of a crowded area of the spectrum (indicated by a box on the left). Spectra were collected in homogenous samples in the absence of phase separation. The low chemical shift dispersion indicates that the protein is primarily disordered. Despite the substantial overlap, 80% of the amide chemical shifts were assigned to their respective amino acid residues (that is, 110 of the 135 non-proline residues).

Extended Data Fig. 3 Aromatic residues are the main stickers in A1-LCD variant +7K+12D.

Panel on the left shows an overlay of 1H,15N HSQC spectra of WT A1-LCD Δhexa variant (missing residues 259-264, red) as reported in (Martin et al.13) and of A1-LCD variant +7K+12D (blue). The amino acid substitutions result in large-scale changes in resonance frequencies across the spectrum compared to the A1-LCD Δhexa. This may be expected due to the high number of charged amino acid substitutions and their widespread distribution. The panel on the right shows 15N R2 relaxation profiles for WT A1-LCD Δhexa (top) and the +7K+12D variant (bottom). The R2 relaxation rates are sensitive to differences in local dynamics due to intramolecular interactions. The solid black profile represents a pure Gaussian fit, whereas the black dashed fit represents multiple regions of enhanced relaxation centered at aromatic residues (yellow) with the blue line representing the underlying Gaussian profile from this fit with a persistence length of 7.8 amino acid residues. R2 rates for +7K+12D show clusters of enhanced rates in similar sequence positions as the WT, with an additional cluster found in the hexapeptide region that is deleted in the WT and where two aromatic residues are located. This is consistent with the aromatic residues remaining stickers. These data support our prediction that Lys and Asp residues do not act as stickers. Instead, they modulate the driving forces for phase separation through a combination of increased effective solvation volume, electrostatic repulsions, and weakening attractive interactions among primary and auxiliary stickers.

Extended Data Fig. 4 Measured pH-dependence of csat validates the prediction that the minimum value for csat is realized at positive values of NCPR.

(a) Saturation concentration of A1-LCD +7K+12D was measured at 4 °C as a function of pH. Individual data points are shown as black crosses, the mean as green symbols, vertical lines represent the standard deviation. (b) Table summarizing the theoretical net charge of A1-LCD +7K+12D and the number of Lys residues that are calculated to be protonated at each pH. (c) Measured csat values for +7K+12D were rescaled using the equation for csc,2. Here, we compare the csc,2 values when we account for all nine Lys residues (red diamonds) or only the number of protonated Lys residues (green diamonds). The latter conform to the master curve, whereas the former deviate substantially from the master curve.

Extended Data Fig. 5 Kratky plots of the SEC-SAXS data of A1-LCD variants.

Data are shown for variants testing the roles of (a) negatively charged residues, (b) positively charged residues, (c) arginines, and (d) oppositely charged residues. Data were logarithmically smoothed into 40 bins. Solid lines are fits to system-specific empirical molecular form factors (MFF). Dashed lines show the predicted behaviour at larger q values, at which the experimental data are noisy.

Extended Data Fig. 6 Compositional analysis of aromatic stickers and Gly/Ser spacers.

(a) 2D histogram quantifying the joint distribution of the fractions of Tyr and Ser across PLCDs from 770 homologs of hnRNPA1. (b) 2D histogram quantifying the joint distribution of the fractions of Phe and Ser across PLCDs from 770 homologs of hnRNPA1.

Extended Data Fig. 7 Examining the effects of other spacer residues on A1-LCD phase behavior.

(a) Diagram of variants to understand the contributions of Asn, Gln and Thr to effective solvation volumes of A1-LCD. In variant −14N-4Q+18G, the role of Asn and Gln residues in comparison to Gly spacers is assessed. In variants −14N+14Q and −23S+23T, residues with similar intrinsic free energies of solvation but different steric bulk are substituted. Vertical bars in the schematics indicate the position of residue types, namely Asn (red), Gln (yellow), Gly (green), Ser (black) and Thr (purple). (b) Measured binodals of A1-LCD variants from (a) as a function of temperature. (c) A focused view on the dilute arms (saturation concentrations) of the binodals in (b). The solution conditions for all experiments were 20 mM HEPES, 150 mM NaCl, pH 7.0.

Extended Data Fig. 8 Compositional biases in PLCDs drawn from homologs of the FUS / FET family of proteins.

(a) 2D histogram quantifying the distributions of lengths of PLCDs from FUS / FET family homologs and their compositional similarities to WT A1-LCD. (b) Histogram of the distribution of NCPR values; (c) Histogram of fraction of aromatic residues (Tyr, Phe, and Trp); (d) distribution of Tyr versus Phe asymmetries for LCDs from FUS / FET family homologs; numerical values are mean values of the respective distribution ± the standard deviation. (e) 2D histogram quantifying covariations in fractions of Tyr versus Phe residues across PLCDs.

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Bremer, A., Farag, M., Borcherds, W.M. et al. Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains. Nat. Chem. 14, 196–207 (2022).

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