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

Nature Chemistry volume 14, pages 196–207 (2022)Cite this article

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Abstract

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.

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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 https://github.com/Pappulab/PLCD-Data-Repository-and-Analysis-Routines.

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Acknowledgements

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.

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  1. These authors contributed equally: Anne Bremer, Mina Farag, Wade M. Borcherds.

Authors and Affiliations

  1. Department of Structural Biology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Anne Bremer, Wade M. Borcherds, Ivan Peran, Erik W. Martin & Tanja Mittag

  2. Department of Biomedical Engineering and Center for Science & Engineering of Living Systems (CSELS), Washington University in St Louis, St Louis, MO, USA

    Mina Farag & Rohit V. Pappu

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Contributions

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.

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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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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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Supplementary Methods, Tables 1 and 2, and Figs. 1–7.

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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). https://doi.org/10.1038/s41557-021-00840-w

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