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Expanding the molecular language of protein liquid–liquid phase separation

Nature Chemistry (2024)Cite this article

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Abstract

Understanding the relationship between a polypeptide sequence and its phase separation has important implications for analysing cellular function, treating disease and designing novel biomaterials. Several sequence features have been identified as drivers for protein liquid–liquid phase separation (LLPS), schematized as a ‘molecular grammar’ for LLPS. Here we further probe how sequence modulates phase separation and the material properties of the resulting condensates, targeting sequence features previously overlooked in the literature. We generate sequence variants of a repeat polypeptide with either no charged residues, high net charge, no glycine residues or devoid of aromatic or arginine residues. All but one of 12 variants exhibited LLPS, albeit to different extents, despite substantial differences in composition. Furthermore, we find that all the condensates formed behaved like viscous fluids, despite large differences in their viscosities. Our results support the model of multiple interactions between diverse residue pairs—not just a handful of residues—working in tandem to drive the phase separation and dynamics of condensates.

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Fig. 1: A diverse range of interactions between residue pairs contribute to phase separation of WT (GRGDSPYS)25.
Fig. 2: Atomistic simulations of the WT sequence highlight the diverse interactions encoded within the sequence.
Fig. 3: Presence of arginine promotes but is not required for phase separation.
Fig. 4: Aromatic residues promote but are not required for phase separation.
Fig. 5: Variants result in condensates with diverse material properties.
Fig. 6: A multitude of interactions work in tandem to drive LLPS.

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Data availability

Starting and ending configurations for all atomistic simulations have been deposited on Zenodo (https://doi.org/10.5281/zenodo.10523201). Materials are available upon reasonable request to the corresponding authors. Source data are provided with this paper.

Code availability

Codes to run and analyse atomistic simulations are available publicly and can be found at https://openmm.org/, https://gromacs.org/ and https://www.mdanalysis.org/. Codes to reproduce residue pairwise contacts and angle-versus-distance distributions have been deposited on Zenodo (https://doi.org/10.5281/zenodo.10523201).

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Acknowledgements

This Article is based on research supported in part by the National Science Foundation (DMR-2004796 to K.L.K. and J.M.), the National Institute of General Medical Science of the National Institute of Health (R01GM136917 to J.M. and R35GM142903 to B.S.S.) and the Welch Foundation (A-2113-202203311 to J.M.). Use of the Texas A&M High Performance Research Computing is greatly acknowledged for the computational resources utilized in this work. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Shiv Rekhi, Cristobal Garcia Garcia, Mayur Barai.

Authors and Affiliations

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

    Shiv Rekhi, Azamat Rizuan & Jeetain Mittal

  2. Department of Materials Science and Engineering, University of Delaware, Newark, DE, USA

    Cristobal Garcia Garcia & Kristi L. Kiick

  3. Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, USA

    Mayur Barai & Benjamin S. Schuster

  4. Department of Biomedical Engineering, University of Delaware, Newark, DE, USA

    Kristi L. Kiick

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

    Jeetain Mittal

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

    Jeetain Mittal

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Contributions

S.R., C.G.G., K.L.K. and J.M. conceived the research. B.S.S., K.L.K. and J.M. designed and supervised the research. S.R. and J.M. designed the sequences. C.G.G. expressed and purified all polypeptides and performed turbidity experiments. M.B. performed microscopy, microrheology and additional turbidity experiments. S.R. performed the simulations. S.R. analysed the simulations aided by A.R. S.R., C.G.G., M.B., B.S.S. and J.M. wrote the paper with help from the other authors.

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Correspondence to Benjamin S. Schuster, Kristi L. Kiick or Jeetain Mittal.

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Extended data

Extended Data Fig. 1 Salt dependence of polycation LLPS.

(a) Turbidity curves for the polycationic GRGNSPYS variant at three salt concentrations (50, 137, and 300 mM NaCl). Three independent experiments are shown at each concentration. (b) Transition temperatures estimated from the turbidity curves as shown in (a). Data are presented as mean values +/− SD, n = 3 independent experiments.

Source data

Extended Data Fig. 2 Pairwise contacts of R-to-Q and D-to-N variants.

Average residue pairwise contacts for the (a) GRGNSPYS and (b) GQGDSPYS variants with respect to WT. Residue pairs not involving mutated residues are shown as dark gray circles while residue pairs involving the mutated residues are shown as red squares (D-to-N) or orange diamonds (R-to-Q).

Source data

Extended Data Fig. 3 Turbidity and partial phase diagram of GRGASPYA.

Turbidity (a) and partial phase diagram (b) of GRGASPYA at different concentrations of protein in PBS. Data are presented as mean values +/− SD, n = 3 independent experiments.

Source data

Extended Data Fig. 4 Pairwise contacts of aromatic substitutions in the polycation.

Average residue pairwise contacts for the (a) GRGNSPFS and (b) GRGNSPWS variants with respect to GRGNSPYS. Upper plots: Residue pairs not involving mutated residues are shown as dark gray circles while residue pairs involving the mutated residues are shown as purple triangles. Lower plots: Contact ratio between residue pairs for the GRGNSPFS and GRGNSPWS variants to that of the GRGNSPYS variant.

Source data

Extended Data Fig. 5 Material properties of A-IDP condensates.

(a) FRAP of WT and GRGNSPWS with RGG-GFP-RGG as a fluorescent tracer. Data are presented as mean values +/− SD, n = 4 (WT), n = 9 (GRGNSPWS) different condensates from one experiment. (b) Effect of total protein concentration on condensate viscosity, as measured by microrheology. Measurements were conducted for GRGNSPFS and GQGNSPYS, at two concentrations each. Data are presented as mean values +/− SD, n = 4 videos from one experiment. (c) MSDs measured in polycationic GRGNSPYS condensates using PEGylated and carboxylated beads. (d) Viscosity of GRGDSPYS (WT), GQGNSPYS, and GRGNSPYS determined by particle tracking microrheology of 0.5 μm PEGylated vs. carboxylated beads. Two factor with replication ANOVA confirmed difference in viscosities between 0.5 μm PEGylated and carboxylated beads is not statistically significant, with p-value of 0.753. Data are presented as mean values +/− SD, n = 4 videos from one experiment. (e) Viscosity of GRGDSPYS (WT), GQGNSPYS, and GRGNSPYS determined by particle tracking microrheology of 0.5 μm vs. 1 μm bead diameters. Two factor with replication ANOVA confirmed difference in viscosities between 0.5 μm and 1 μm beads is not statistically significant, with p-value of 0.268. Data are presented as mean values +/− SD, n = 4 videos from one experiment.

Source data

Extended Data Fig. 6 Sequence based predictors of LLPS.

(a) Solvation free energy from Wolfenden et al.1 vs. saturation concentrations measured in this work. Each data point represents a unique variant used in this work. Variants differing by only one residue are connected by lines such that each mutation results in increasing saturation concentration. Dashed lines indicate variants that follow the trend of preferred interaction with solvent leading to lower phase separation propensity, while solid lines show mutations that result in less favorable interaction with solvent and lower phase separation propensity. (b) Ratio of phase separation propensity score for each sequence relative to the propensity score for WT, calculated using several online sequence-based predictors – DeePhase, PScore, PSpredictor, FuzDrop, LLPhysScore and catgranule. Experimental values are shown as black circles. All predictor values are normalized with the WT to account for different scales used by the predictors. In all cases, when the normalized score is above 1, the sequence is predicted to undergo LLPS more avidly than the WT, while values below 1 indicate a lower propensity to undergo LLPS when compared to WT. Experimental values are calculated from the saturation concentration values (Csat) measured at 37 °C. The experimental values are represented as Csat of WT divided by Csat of variant, such that here too, a value above 1 indicates greater phase separation propensity compared to WT, whereas a value below 1 indicates lower phase separation propensity. (c) Correlation between experimental values and predictor results. Data for all data sets are normalized from 0 to 1. Symbols are the same as shown in (b) for the predictors.

Source data

Extended Data Fig. 7 Temperature dependence of saturation concentrations for A-IDP variants.

(a) Ratio of saturation concentrations (Csat) for different sequences with respect to Csat of WT at different temperatures. Lines sloping down indicate that phase separation propensity with respect to the WT is enhanced at higher temperatures, whereas lines sloping upwards indicate reduction in phase separation propensity with respect to WT at higher temperatures. Solid lines indicate the temperatures at which saturation concentration was estimated using turbidimetry experiments, while dashed lines indicate the temperatures at which values were extrapolated from a logarithmic fit to the experimental binodal data. (b) Thermodynamic analysis performed for the different variants based on the estimated saturation concentrations at 20 °C. Higher values indicate greater reduction in phase separation propensity upon carrying out the mutation. Direct estimate refers to values that can be calculated from the experimental variants directly, whereas indirect estimate refers to values for which the mutation was not carried out in this work and the values were inferred based on data from multiple related experimental variants.

Source data

Extended Data Fig. 8 Droplet morphology and dynamics after 24 hrs.

(a) Microscopy images for different mutants after 24 hrs of phase separation, showing regular spherical droplets and no signs of fibrillization or aggregation. (Scale bar: 5 µm). Data presented is representative of multiple images acquired for each sample and validated through imaging a second independent sample for 7 out of 12 variants. (b) Microscopy images of GRGNSPYS (cationic sequence), GRGDSPYS (WT) and GQGNSPYS (neutral sequence) undergoing droplet fusion and relaxing into a single spherical droplet, showing liquid-like behavior at 24 hrs. (Scale bar: 2 μm). Data presented is representative of results from two independent trials. Representative WT snapshots from Supplementary Movie 2. (c) Ensemble mean-squared displacement versus lag time at 24 hrs for the three representative variants (GRGNSPYS, GRGDSPYS, and GQGNSPYS), showing liquid-like behavior even after 24 hrs of phase separation.

Source data

Extended Data Table 1 System specific changes to saturation concentration upon mutation
Full size table
Extended Data Table 2 Sequence dependence of saturation concentration change with mutations
Full size table

Supplementary information

Supplementary Information

Supplementary methods, Figs. 1–20, Tables 1–4, references.

Reporting Summary

Supplementary Video 1

Representative particle tracking microrheology video for WT showing Brownian motion of the 0.5-μm carboxylated fluorescent tracer beads embedded in a droplet. Scale bar, 5 μm.

Supplementary Video 2

Sample video of two WT droplets fusing. Video was recorded after 24 h of phase separation. Video shows droplets contacting, fusing and relaxing into a single spherical droplet even after 24 h of phase separation. Scale bar, 5 μm.

Supplementary Data 1

DNA and peptide sequences for all constructs in this work.

Source data

Source Data Fig. 1

Source data for turbidity plots and phase diagrams for panel b, c and g.

Source Data Fig. 2

Source data for density profile, and contacts for panels b, c and d.

Source Data Fig. 3

Source data for phase diagrams, Csat measurements, contacts of variants for panels a, c, d, e, f and h.

Source Data Fig. 4

Source data for contacts and phase diagrams of variants for panels a, b and d

Source Data Fig. 5

Source data for MSD vs.lag time, viscosity over 8 trials, saturation concentrations and viscosity at 18 °C for panels b, c and d.

Source Data Fig. 6

Source data for saturation concentration values for variants at 37 °C for panel b.

Source Data Extended Data Fig. 1

Source data for salt dependent turbidity and transition temperatures of polycationic variant.

Source Data Extended Data Fig. 2

Source data for pairwise contacts of GQGDSPYS and GRGNSPYS variants.

Source Data Extended Data Fig. 3

Source data for turbidity and partial phase diagrams for GRGASPYA variant.

Source Data Extended Data Fig. 4

Source data for pairwise contacts of GRGNSPWS and GRGNSPFS variants.

Source Data Extended Data Fig. 5

Source data for FRAP, concentration dependent viscosity, MSD and viscosities for surface modified beads and bead size dependence of viscosity measures.

Source Data Extended Data Fig. 6

Source data for A-IDP variant scores from sequence based LLPS predictors.

Source Data Extended Data Fig. 7

Source data for temperature vs. saturation concentration of all variants normalized by WT. Csat changes upon mutation calculated at 20 °C.

Source Data Extended Data Fig. 8

Source data for MSD of three representative sequences at 24-h time point.

Source Data Extended Data Table 1

Source data for saturation concentrations of all variants at 37 °C.

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Rekhi, S., Garcia, C.G., Barai, M. et al. Expanding the molecular language of protein liquid–liquid phase separation. Nat. Chem. (2024). https://doi.org/10.1038/s41557-024-01489-x

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