Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Population-based heteropolymer design to mimic protein mixtures



Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2,3,4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Population-based design to mimic the native environments of proteins.
Fig. 2: RHP/protein PCA space overlap determines their interplay.
Fig. 3: RHPs provide diverse segments with a defined range of segmental hydrophobicity to modulate transient intermolecular interactions.
Fig. 4: Designing RHP ensembles as synthetic mimics of cytosol.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the supplementary materials. For reproduction purposes, the raw data used to generate the figures are available from the Dryad Digital Repository (DOI:10.6078/D1KH8R ).

Code availability

Source code and input scripts supporting this work are available at


  1. Fulton, A. B. How crowded is the cytoplasm? Cell 30, 345–347 (1982).

    Article  CAS  PubMed  Google Scholar 

  2. Lewandowski, J. R., Halse, M. E., Blackledge, M. & Emsley, L. Direct observation of hierarchical protein dynamics. Science 348, 578–581 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Dill, K. A. Dominant forces in protein folding. Biochemistry 29, 7133–7155 (1990).

    Article  CAS  PubMed  Google Scholar 

  4. Wirth, A. J., Platkov, M. & Gruebele, M. Temporal variation of a protein folding energy landscape in the cell. J. Am. Chem. Soc. 135, 19215–19221 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Keskin, O., Gursoy, A., Ma, B. & Nussinov, R. Principles of protein–protein interactions: what are the preferred ways for proteins to interact? Chem. Rev. 108, 1225–1244 (2008).

    Article  CAS  PubMed  Google Scholar 

  6. Golumbfskie, A. J., Pande, V. S. & Chakraborty, A. K. Simulation of biomimetic recognition between polymers and surfaces. Proc. Natl Acad. Sci. USA 96, 11707–11712 (1999).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Minton, A. P. How can biochemical reactions within cells differ from those in test tubes? J. Cell Sci. 119, 2863–2869 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. DePristo, M. A., Weinreich, D. M. & Hartl, D. L. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat. Rev. Genet. 6, 678–687 (2005).

    Article  CAS  PubMed  Google Scholar 

  9. Rutherford, S. L. Between genotype and phenotype: protein chaperones and evolvability. Nat. Rev. Genet. 4, 263–274 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176, 419–434 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tamasi, M. J. et al. Machine learning on a robotic platform for the design of polymer-protein hybrids. Adv. Mater. 34, e2201809 (2022).

    Article  PubMed  Google Scholar 

  12. Webb, M. A., Jackson, N. E., Gil, P. S. & de Pablo, J. J. Targeted sequence design within the coarse-grained polymer genome. Sci. Adv. 6, eabc6216 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  13. Panganiban, B. et al. Random heteropolymers preserve protein function in foreign environments. Science 359, 1239–1243 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Jiang, T. et al. Single-chain heteropolymers transport protons selectively and rapidly. Nature 577, 216–220 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Nguyen, T. D., Qiao, B. F. & de la Cruz, M. O. Efficient encapsulation of proteins with random copolymers. Proc. Natl Acad. Sci. USA 115, 6578–6583 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  16. Bateman, A. et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).

    Article  ADS  Google Scholar 

  17. Monera, O. D., Sereda, T. J., Zhou, N. E., Kay, C. M. & Hodges, R. S. Relationship of sidechain hydrophobicity and alpha-helical propensity on the stability of the single-stranded amphipathic alpha-helix. J. Pept. Sci. 1, 319–329 (1995).

    Article  CAS  PubMed  Google Scholar 

  18. Kramer, M. A. Nonlinear principal component analysis using autoassociative neural networks. AlChE J. 37, 233–243 (1991).

    Article  CAS  Google Scholar 

  19. Vidal, R., Ma, Y. & Sastry, S. S. Generalized Principal Component Analysis 25–62 (Springer, 2016).

  20. Smith, A. A. A., Hall, A., Wu, V. & Xu, T. Practical prediction of heteropolymer composition and drift. ACS Macro Lett. 8, 36–40 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  21. Bustamante, C. J., Chemla, Y. R., Liu, S. & Wang, M. D. Optical tweezers in single-molecule biophysics. Nat. Rev. Methods Primers 1, 25 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ritchie, D. B. & Woodside, M. T. Probing the structural dynamics of proteins and nucleic acids with optical tweezers. Curr. Opin. Struct. Biol. 34, 43–51 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Suzuki, Y. & Dudko, O. K. Single molecules in an extension clamp: extracting rates and activation barriers. Phys. Rev. Lett. 110, 158105 (2013).

    Article  ADS  PubMed  Google Scholar 

  24. Zhao, Q. Nature of protein dynamics and thermodynamics. Rev. Theor. Sci. 1, 83–101 (2013).

    Article  Google Scholar 

  25. Gstraunthaler, G., Lindl, T. & van der Valk, J. A plea to reduce or replace fetal bovine serum in cell culture media. Cytotechnology 65, 791–793 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Drew, D., Lerch, M., Kunji, E., Slotboom, D. J. & de Gier, J. W. Optimization of membrane protein overexpression and purification using GFP fusions. Nat. Methods 3, 303–313 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Shastry, M. C. R. & Roder, H. Evidence for barrier-limited protein folding kinetics on the microsecond time scale. Nat. Struct. Biol. 5, 385–392 (1998).

    Article  CAS  PubMed  Google Scholar 

  28. Milo, R. & Phillips, R. Cell Biology by the Numbers (Garland Science, Taylor & Francis Group, 2016).

  29. Saio, T., Guan, X., Rossi, P., Economou, A. & Kalodimos, C. G. Structural basis for protein antiaggregation activity of the trigger factor chaperone. Science 344, 597–610 (2014).

    Article  CAS  Google Scholar 

  30. Zhou, H. X., Rivas, G. N. & Minton, A. P. Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences. Annu. Rev. Biophys. 37, 375–397 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Street, T. O., Bolen, D. W. & Rose, G. D. A molecular mechanism for osmolyte-induced protein stability. Proc. Natl Acad. Sci. USA 103, 13997–14002 (2006).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hilburg, S. L., Ruan, Z. Y., Xu, T. & Alexander-Katz, A. Behavior of protein-inspired synthetic random heteropolymers. Macromolecules 53, 9187–9199 (2020).

    Article  ADS  CAS  Google Scholar 

  33. Terashima, T., Sugita, T., Fukae, K. & Sawamoto, M. Synthesis and single-chain folding of amphiphilic random copolymers in water. Macromolecules 47, 589–600 (2014).

    Article  ADS  CAS  Google Scholar 

  34. Shu, J. Y. et al. Amphiphilic peptide-polymer conjugates based on the coiled-coil helix bundle. Biomacromolecules 11, 1443–1452 (2010).

    Article  CAS  PubMed  Google Scholar 

  35. Choi, S. H., Lodge, T. P. & Bates, F. S. Mechanism of molecular exchange in diblock copolymer micelles: hypersensitivity to core chain length. Phys. Rev. Lett. 104, 047802 (2010).

    Article  ADS  PubMed  Google Scholar 

  36. Quiroz, F. G. & Chilkoti, A. Sequence heuristics to encode phase behaviour in intrinsically disordered protein polymers. Nat. Mater. 14, 1164–1171 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hyman, A. A., Weber, C. A. & Juelicher, F. Liquid-liquid phase separation in biology. Annu. Rev. Cell Dev. Biol. 30, 39–58 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Sing, C. E. & Perry, S. L. Recent progress in the science of complex coacervation. Soft Matter 16, 2885–2914 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  39. Fournier, D., Hoogenboom, R., Thijs, H. M. L., Paulus, R. M. & Schubert, U. S. Tunable pH- and temperature-sensitive copolymer libraries by reversible addition-fragmentation chain transfer copolymerizations of methacrylates. Macromolecules 40, 915–920 (2007).

    Article  ADS  CAS  Google Scholar 

  40. Wang, X. et al. LLPSDB v2.0: an updated database of proteins undergoing liquid-liquid phase separation in vitro. Bioinformatics 38, 2010–2014 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Putnam, A., Cassani, M., Smith, J. & Seydoux, G. A gel phase promotes condensation of liquid P granules in Caenorhabditis elegans embryos. Nat. Struct. Mol. Biol. 26, 220–226 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. McSwiggen, D. T., Mir, M., Darzacq, X. & Tjian, R. Evaluating phase separation in live cells: diagnosis, caveats, and functional consequences. Gene Dev. 33, 1619–1634 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph Model 14, 33–38 (1996).

    Article  CAS  Google Scholar 

  44. AMBER 2019 (Univ. California, San Francisco, 2019).

  45. Xiao, L., Zhou, Z. J., Feng, M. L., Tong, A. J. & Xiang, Y. Cationic peptide conjugation enhances the activity of peroxidase-mimicking DNAzymes. Bioconjugate Chem. 27, 621–627 (2016).

    Article  CAS  Google Scholar 

  46. Comstock, M. J., Ha, T. & Chemla, Y. R. Ultrahigh-resolution optical trap with single-fluorophore sensitivity. Nat. Methods 8, 335–382 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Biewald, L. Machine learning experiment tracking. Weights & Biases (2020).

Download references


The work was supported by the US Department of Defense (DOD), Army Research Office, under contract no. W911NF-13-1-0232, Defense Threat Reduction Agency (DTRA) under contract no. HDTRA1-19-1-0011, the National Science Foundation under contract no. DMR-2104443, the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under contract no. DE-AC02-05-CH11231 (KC3104) and the Alfred P. Sloan Foundation (grant no. G-2021-16757). Z.R. is supported by the Kavli Energy NanoScience Institute through the Kavli ENSI Philomathia Graduate Student Fellowship Program. Scattering studies were done at Advanced Photon Source and use of the Advanced Photon Source was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH1135.

Author information

Authors and Affiliations



T.X. conceived the idea and guided the project. Z.R. and T.J. performed cell-free synthesis of membrane proteins. Z.R. and A.G. performed thermal denaturation of globular enzymes. S.L., I.J., Z.R. and H.H. performed sequence analysis. H.A. and C.B. performed optical tweezers analysis. S.H. and A.A.-K. performed all-atom simulation studies. Z.R. and Z.G. synthesized and characterized the RHPs and DHPs. H.C. performed the cell study. A.G. and H.C. performed the confocal study. All authors participated in writing the manuscript.

Corresponding author

Correspondence to Ting Xu.

Ethics declarations

Competing interests

T.X., H.H., Z.R. and S.L. have a pending PCT patent application. The rest of the authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Blocks and 50-mers along a polymer chain.

(a) Each monomer was reassigned to one of two pseudo-monomers (hydrophobic vs. hydrophilic) based on monomer’s hydrophobicity. A block comprises consecutive monomers of a single type. (b) A polymer chain was truncated into a set of 50-mers.

Extended Data Fig. 2 The FBS solution with different RHP ensembles after incubating at 52 °C for 2.5 h.

The FBS solution forms a thin film (like “milk skin”) at the air-water interface after thermal treatment (red circle). RHP4 exhibits the weakest tendency for formation of this thin film among three tested RHP ensembles. The solution is stirred to tear the thin film into suspended flakes for visualization.

Extended Data Fig. 3 Temperature-dependent 1H-NMR spectrum of RHP4 in D2O.

(a) 1H-NMR spectrum of RHP4 in D2O at 37 °C and its chemical structure. (b) The FWHM of proton peaks (a, p, and n) as a function of temperature (30–70 °C). (c) Part of 1H-NMR spectra of RHP4 as a function of temperature (30–70 °C) including proton peaks of the OEGMA side chain.

Extended Data Fig. 4 The effect of solvent polarity on 1H-NMR spectrum of RHP4 in d6-DMSO/D2O cosolvent


Extended Data Fig. 5 The distribution of DHP2 segments in PCA space.

From top to bottom, the distribution of segments from hydrophobic region, amphiphilic region, and hydrophilic region along DHP2 chains were shown.

Extended Data Fig. 6 Differential interference contrast (DIC) images of (a) RHP8 (b) RHP9 phase-separated droplets.

Each sample is 1 mg/ml in sodium phosphate buffer (50 mM, pH 7.0).

Extended Data Fig. 7 Folding status of AqpZ-eGFP in the presence of 0.2 wt% RHPs based on the eGFP fluorescence.

Error bar is 1 s.d and n = 3.

Extended Data Fig. 8 Temperature-dependent turbidimetry for RHP14 ensemble.

The polymer solution is 1mg/ml in sodium phosphate buffer (50 mM, pH 7.0).

Extended Data Fig. 9 FRAP analysis of liquid-like coacervates made from RHP10 ensemble.

The recovery trace shows the normalized recovery of a bleached region. Solid red curve fits to an exponential function (see FRAP method section).

Extended Data Table 1 The conversion from amino acids to RHP monomers (n = 4)

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1 and 2 and Figs. 1–21.

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ruan, Z., Li, S., Grigoropoulos, A. et al. Population-based heteropolymer design to mimic protein mixtures. Nature 615, 251–258 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing