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Determinants that enable disordered protein assembly into discrete condensed phases

Nature Chemistry (2024)Cite this article

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Abstract

Cells harbour numerous mesoscale membraneless compartments that house specific biochemical processes and perform distinct cellular functions. These protein- and RNA-rich bodies are thought to form through multivalent interactions among proteins and nucleic acids, resulting in demixing via liquid–liquid phase separation. Proteins harbouring intrinsically disordered regions (IDRs) predominate in membraneless organelles. However, it is not known whether IDR sequence alone can dictate the formation of distinct condensed phases. We identified a pair of IDRs capable of forming spatially distinct condensates when expressed in cells. When reconstituted in vitro, these model proteins do not co-partition, suggesting condensation specificity is encoded directly in the polypeptide sequences. Through computational modelling and mutagenesis, we identified the amino acids and chain properties governing homotypic and heterotypic interactions that direct selective condensation. These results form the basis of physicochemical principles that may direct subcellular organization of IDRs into specific condensates and reveal an IDR code that can guide construction of orthogonal membraneless compartments.

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Fig. 1: Identification of disordered proteins that form discrete condensed phases in living cells and when biochemically reconstituted in vitro.
Fig. 2: Amino-acid sequence determinants of FUS LC coacervation specificity.
Fig. 3: Minimalistic polymer model uncovers co-phase separation rules.
Fig. 4: Polypeptide chain properties governing FUS LC partitioning and LLPS.
Fig. 5: Rules for LLPS specificity are applicable broadly, in multiple biochemical and biological contexts.

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Data availability

The data supporting the findings of this study are available within the Article and Supplementary Information. Raw images are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

The codes to generate initial structures and perform simulations are available at Bitbucket (https://bitbucket.org/kandarpsojitra/simulation-codes/src/master/). Scripts used to generate the macros used for analysis in ImageJ are available upon request. For data analysis and plotting, NumPy, SciPy, Seaborn and Matplotlib Python packages were used.

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Acknowledgements

We thank the E. Bi (University of Pennsylvania, Department of Cell and Developmental Biology) and J. Shorter (University of Pennsylvania, Department of Biochemistry and Biophysics) laboratories for sharing yeast strains and plasmids, A. Stout and the Penn CDB Microscopy Core for imaging and support. This study was supported by National Institute of Health grants, including National Institute of Biomedical Imaging and Bioengineering grant EB028320 (M.C.G.) and NIGMS R01GM136917 (J.M.). Additionally, the work was partly funded by a National Science Foundation (NSF) MRSEC Seed grant no. DMR1720530 (M.C.G.) and Welch Foundation grant A-2113-20220331 (J.M.). Work by M.C.G., D.A.H. and M.G. supported in part by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award no. DE-SC0007063 (M.C.G.). We gratefully acknowledge the computational resources provided by the Texas A&M High Performance Research Computing (HPRC).

Author information

Author notes

  1. These authors contributed equally: Rachel M. Welles, Kandarp A. Sojitra.

Authors and Affiliations

  1. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Rachel M. Welles, Mikael V. Garabedian & Matthew C. Good

  2. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA

    Kandarp A. Sojitra, Roshan M. Regy & Jeetain Mittal

  3. Bioengineering Graduate Program, Rice University, Houston, TX, USA

    Boao Xia

  4. Bioengineering Graduate Program, University of Pennsylvania, Philadelphia, PA, USA

    Wentao Wang, Daniel A. Hammer & Matthew C. Good

  5. Chemical and Biomolecular Engineering Graduate Program, University of Pennsylvania, Philadelphia, PA, USA

    Muyang Guan & Daniel A. Hammer

  6. Cell and Molecular Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA

    Elizabeth R. Gallagher & Matthew C. Good

  7. Chemical and Biomolecular Engineering Department, University of Pennsylvania, Philadelphia, PA, USA

    Daniel A. Hammer

  8. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Daniel A. Hammer & Matthew C. Good

  9. Department of Chemistry, Texas A&M University, College Station, TX, USA

    Jeetain Mittal

  10. Interdisciplinary Graduate Program in Genetics and Genomics, Texas A&M University, College Station, TX, USA

    Jeetain Mittal

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Contributions

M.C.G. and J.M. conceptualized the project. M.C.G., R.M.W. and M.V.G. designed the experiments. E.G. performed initial IDP cloning and strain generation for yeast co-expression screening. B.X. generated clones and strains for the initial characterization of FUS LC multimers. R.M.W. performed cloning, strain generation and imaging for all yeast experiments following initial screenings, including FUS LC mutagenesis. K.A.S. designed predicted FUS LC mutants. M.V.G. purified recombinant FUS LC and mutants and performed in vitro characterization of LLPS. K.A.S. generated the minimalistic polymer model for two-component phase separation, aided by R.M.R. R.M.W. and M.V.G. analysed the experimental data. W.W. transduced HEK293T cells and imaged the constructs. M.G. and M.V.G. purified recombinant (FUS LC)2-BFP. M.C.G., R.M.W., K.A.S., M.V.G. and J.M. wrote the manuscript.

Corresponding authors

Correspondence to Jeetain Mittal or Matthew C. Good.

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Nature Chemistry thanks Allie Obermeyer, Jeremy Schmidt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Specificity of mixing among screened IDRs and orthogonality of LAF-1 RGG and FUS LC.

a. Quantitation of IDP enrichment in LAF-1 RGG condensates from IDP co-expression screening in yeast; images from Fig. 1b. EI values for 3 IDPs in (LAF-1 RGG)2-mScarlet condensates. N of condensates: α-syn, 35; TDP-43, 39; FUS FL, 31. P values: α-syn versus TDP-43, 1.7 × 10−2; α-syn versus FUS FL, 1.9 × 10−12; TDP-43 versus FUS FL, 2.9 × 10−7). b. Plot showing possible range of enrichment indices in LAF-1 RGG condensates. Minimum value of 1. Maximum value based on LAF-1 RGG mScarlet (client) co-partitioning to (LAF-1 RGG)2-GFP condensates. N of condensates: RGG-mS, 91; FUS LC wt, 148; (FUS LC wt)3, 37. 10 RGG-mS points >10 not shown on plot. c. Schematic of CG slab at 300 K consisting of FUS LC (green) and LAF-1 RGG (magenta). Distinct phases are observed with FUS LC forming a condensed phase and LAF-1 RGG sticking at its interface. Data are presented as mean +/− 95% CI. Significance was calculated by one-way analysis of variance (ANOVA); ns P > 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, and **** P ≤ 0.0001. Data relevant to Fig. 1.

Source data

Extended Data Fig. 2 Additional images, controls, and biochemical reconstitution for FUS LC mutants.

a. Non-mixing behavior of FUS LC wildtype persists in mutants Charge+16 and Charge+32 when adding balanced charge. b. Average numbers of (LAF-1 RGG)2-GFP condensates in co-expressing FUS LC mutant strains are within twofold of (LAF-1 RGG)2 and FUS LC wildtype strain. Light blue box indicates range within twofold of wildtype. n ≥ 30 cells per column noted below column mean. c. Expression of FUS LC mutant constructs within co-expressed strains, normalized to FUS LC wildtype expression on day of imaging. Light magenta box indicates range within twofold of wildtype. n = 30 cells per column. Data are presented as mean +/− 95% CI. Significance was calculated by one-way analysis of variance (ANOVA); ns P > 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, and **** P ≤ 0.0001. Scale bar: 5 μm. Data relevant to Fig. 2. d. Partitioning of GFP-tagged (LAF-1 RGG)2 to FUS LC wildtype and condensates in vitro. e. Quantitation of enrichment of (LAF-1 RGG)2-GFP in condensed phase versus continuous phase from images in (D) 10 minutes after addition of (LAF-1 RGG)2-GFP mutants to pre-formed FUS LC condensates (50 μM of each FUS LC construct). n = 20 condensates per column. P values: wt versus Charge+32.seg, 1.1 × 10−11; wt versus Charge+42.seg, 1.1 × 10−11; Charge+32.seg versus Charge+42.seg, 1.1 × 10−11. f. Brightfield images of wells containing various concentrations of FUS LC mutants, showing their phase boundaries. Estimated Csat (right). g. Turbidity assays of FUS LC mutants (mean values from n = 4 independent trials) showing increased transition temperatures compared to wildtype. Data are presented as mean +/− 95% CI. Significance was calculated by one-way analysis of variance (ANOVA); ns P > 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001, and **** P ≤ 0.0001. Scale bar: 10 μm. Data relevant to Fig. 2.

Source data

Extended Data Fig. 3 Additional information from polymer model simulations.

a. Density profiles of polymer B and EI plot for λAB = λBB case. b. Colormap of EI from CG scanning simulation with varying λAB and λBB. c. Plot showing variation of EI as a function of λAB at λBB = 0 highlights the complex coacervate case. d. Plot showing variation of EI as a function of λBB/λAB at different constant λAB values. Maximum enrichment is observed when ratio of λBB/λAB is close to 1. e. Density profiles of polymer A and B corresponding to the simulation representations in Fig. 3e. f. Density profile from single component CG slab for varying interaction strength. For interaction strength for 0.75 or above, chains start to condense. g. Simultaneous changes in homotypic and heterotypic interaction parameters can yield experimentally observed plateauing in EI. Plot shows variation of EI (right) along an arbitrarily defined change in λAB and λBB values (blue line shown in heat map (left)). Right plot shows plateauing of EI with increasing λBB (bottom x-axis) and λAB (upper x-axis). For +20Tyr and +30Tyr, increasing tyrosine will increase both homotypic and heterotypic interactions and scanning simulation shows one can obtain similar EI values as the FUS LC wildtype. For clarity, we only show one representative combination of λAB and λBB, but there are several other possible combinations that can yield similar EI values as the FUS LC wildtype. Data relevant to Fig. 3.

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Extended Data Fig. 4 Results from simulations varying polymer lengths.

Variation of EI with B’s homotypic interaction at 0.80 heterotypic interaction. At low heterotypic interaction (≤0.80) longer chain-length do not enhance co-partitioning. Data relevant to Fig. 4.

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Extended Data Fig. 5 Additional images relevant to LLPS specificity and formation of orthogonal condensates in vitro and in mammalian cells.

a. Biochemical reconstitution in vitro using purified proteins at concentrations above their Csat, to generate droplets. (LAF-1 RGG)2-GFP strongly co-partitions with FUS LC Charge+32.seg condensates; (FUS LC)2-BFP tracer; ‘merge’ in white shows overlay. Scale bar: 5 μm. b. Loss of LLPS specificity for FUS LC mutant Charge+32.seg in HEK293T cells co-transfected with (LAF-1 RGG)2-GFP; (FUS LC Charge+32.seg)2-mCherry strongly co-partitions with (LAF-1 RGG)2-GFP condensates. Scale bar: 10 μm. c. FRAP plots from condensates of FUS LC constructs when expressed as multimers. Left: Triple version of FUS LC wildtype. Middle: Tandem version of Charge+32.seg. Right: Tandem version of Charge+42.seg. Plots are presented as mean +/− 95% CI. Samples sizes are shown on each plot. Data relevant to Fig. 5.

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Extended Data Table 1 Enrichment indices of FUS LC clients using mean client values
Full size table

Supplementary information

Supplementary Information

Supplementary Text 1, Fig. 1 and Tables 1–3.

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Source Data Fig. 1

Source data for panels c and f.

Source Data Fig. 2

Source data for column plots in panels b, d, f, g, h, i, j, k and l.

Source Data Fig. 3

Source data for simulations in all panels.

Source Data Fig. 4

Source data for column plots and simulations in panels b, d, f, and g.

Source Data Fig. 5

Source data for column plots in panels c and e.

Source Data Extended Data Fig. 1

Source data for all panels.

Source Data Extended Data Fig. 2

Source data for column plots in panels b, c, and e and turbidity assays in panel g.

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Source data for panels a, c, e, f, and g.

Source Data Extended Data Fig. 4

Source data for plot.

Source Data Extended Data Fig. 5

Source data for FRAP plots in panel c.

Source Data Extended Data Table 1

Source data for table 1.

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Welles, R.M., Sojitra, K.A., Garabedian, M.V. et al. Determinants that enable disordered protein assembly into discrete condensed phases. Nat. Chem. (2024). https://doi.org/10.1038/s41557-023-01423-7

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