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  • Review Article
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Protein design and optimization for synthetic cells

Nature Reviews Bioengineering volume 3, pages 645–659 (2025)Cite this article

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Abstract

Proteins are essential components in synthetic biology, providing multiple functions at the nanoscale. Newly developed protein optimization and design tools allow the generation of proteins with desired properties, offering new opportunities for the engineering of protein-based biological systems. In this Review, we explore how bottom-up synthetic biology, with its aim to construct synthetic cells, can use these tools to devise complex biological functions and functional systems from scratch. We provide an overview of current capabilities in protein optimization, de novo protein design and iterative system optimization, and discuss their potential in synthetic cell science with regard to standardization, the generation of missing functionality and integration. We conclude with the outline of an integrated pipeline that combines protein engineering, automated synthetic cell generation and active learning, which might allow the design of entirely new biological systems that do not rely on naturally evolved protein components.

Key points

  • Synthetic cell science aims to create minimal biological systems from individual protein-based modules; however, proteins often fail to show desired functions or show new functions when reconstituted in synthetic cells.

  • Protein optimization and design promise to resolve this problem, either by adapting natural proteins to the environment of the synthetic cell, or by designing entirely new proteins.

  • Zero-shot optimization can enhance stability and solubility of natural proteins, whereas de novo design enables the design of protein systems based on mechanistical theories from scratch.

  • To make complex synthetic cells, proteins must eventually be iteratively co-optimized with other components of the synthetic cell, such as lipids or buffers; this requires high-throughput generation and screening techniques.

  • International collaboration is necessary to establish a standardized workflow, integrating community-driven databases, replicable design methods and standardized functional screenings.

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Fig. 1: Synthetic cells are versatile tools to build custom microenvironments and microagents.
Fig. 2: Zero-shot protein optimization.
Fig. 3: Computational protein design.
Fig. 4: Iterative optimization.
Fig. 5: Integration of protein design, protein engineering and synthetic cell research.

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References

  1. Ashburner, M. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  Google Scholar 

  2. Baeshen, N. A. et al. Cell factories for insulin production. Microb. Cell Fact. 13, 141 (2014).

    Article  Google Scholar 

  3. Sterner, R. C. & Sterner, R. M. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 11, 69 (2021).

    Article  MathSciNet  Google Scholar 

  4. Hutchison, C. A. et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016).

    Article  Google Scholar 

  5. Hirschi, S., Ward, T. R., Meier, W. P., Müller, D. J. & Fotiadis, D. Synthetic biology: bottom-up assembly of molecular systems. Chem. Rev. 122, 16294–16328 (2022).

    Article  Google Scholar 

  6. Schwille, P. Bottom-up synthetic biology: engineering in a tinkerer’s world. Science 333, 1252–1254 (2011).

    Article  Google Scholar 

  7. Listov, D., Goverde, C. A., Correia, B. E. & Fleishman, S. J. Opportunities and challenges in design and optimization of protein function. Nat. Rev. Mol. Cell Biol. 25, 639–653 (2024).

    Article  Google Scholar 

  8. Kortemme, T. De novo protein design—from new structures to programmable functions. Cell 187, 526–544 (2024).

    Article  Google Scholar 

  9. Notin, P., Rollins, N., Gal, Y., Sander, C. & Marks, D. Machine learning for functional protein design. Nat. Biotechnol. 42, 216–228 (2024).

    Article  Google Scholar 

  10. Wittmann, B. J., Johnston, K. E., Wu, Z. & Arnold, F. H. Advances in machine learning for directed evolution. Curr. Opin. Struct. Biol. 69, 11–18 (2021).

    Article  Google Scholar 

  11. Chu, A. E., Lu, T. & Huang, P. S. Sparks of function by de novo protein design. Nat. Biotechnol. 42, 203–215 (2024).

    Article  Google Scholar 

  12. Lu, H. et al. Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662–667 (2022).

    Article  Google Scholar 

  13. Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).

    Article  Google Scholar 

  14. Gainza, P. et al. De novo design of protein interactions with learned surface fingerprints. Nature 617, 176–184 (2023).

    Article  Google Scholar 

  15. Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).

    Article  Google Scholar 

  16. Pacesa, M. et al. BindCraft: one-shot design of functional protein binders. Preprint at bioRxiv https://doi.org/10.1101/2024.09.30.615802 (2024).

  17. Huddy, T. F. et al. Blueprinting extendable nanomaterials with standardized protein blocks. Nature 627, 898–904 (2024).

    Article  Google Scholar 

  18. Pillai, A. et al. De novo design of allosterically switchable protein assemblies. Nature 632, 911–920 (2024).

    Article  Google Scholar 

  19. Huang, B. et al. Designed endocytosis-inducing proteins degrade targets and amplify signals. Nature 638, 796–804 (2025).

    Article  Google Scholar 

  20. Edman, N. I. et al. Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies. Cell 187, 3726–3740.e43 (2024).

    Article  Google Scholar 

  21. Piraner, D. I. et al. Engineered receptors for soluble cellular communication and disease sensing. Nature 638, 805–813 (2025).

    Article  Google Scholar 

  22. Rideau, E., Dimova, R., Schwille, P., Wurm, F. R. & Landfester, K. Liposomes and polymersomes: a comparative review towards cell mimicking. Chem. Soc. Rev. 47, 8572–8610 (2018).

    Article  Google Scholar 

  23. Percec, V. et al. Self-assembly of Janus dendrimers into uniform dendrimersomes and other complex architectures. Science 328, 1009–1014 (2010).

    Article  Google Scholar 

  24. Samanta, A., Baranda Pellejero, L., Masukawa, M. & Walther, A. DNA-empowered synthetic cells as minimalistic life forms. Nat. Rev. Chem. 8, 454–470 (2024).

    Article  Google Scholar 

  25. Huang, X. et al. Interfacial assembly of protein–polymer nano-conjugates into stimulus-responsive biomimetic protocells. Nat. Commun. 4, 2239 (2013).

    Article  Google Scholar 

  26. Dimova, R. & Marques, C. M. (eds) The Giant Vesicle Book (CRC, 2019).

  27. Litschel, T. & Schwille, P. Protein reconstitution inside giant unilamellar vesicles. Annu. Rev. Biophys. 50, 525–548 (2021).

    Article  Google Scholar 

  28. Czogalla, A., Franquelim, H. G. & Schwille, P. Biophysical perspective DNA nanostructures on membranes as tools for synthetic biology. Biophys J. 110, 1698–1707 (2016).

    Article  Google Scholar 

  29. Franquelim, H. G., Khmelinskaia, A., Sobczak, J. P., Dietz, H. & Schwille, P. Membrane sculpting by curved DNA origami scaffolds. Nat. Commun. 9, 811 (2018).

    Article  Google Scholar 

  30. Tran, M. P. et al. Genetic encoding and expression of RNA origami cytoskeletons in synthetic cells. Nat. Nanotechnol. https://doi.org/10.1038/s41565-025-01879-3 (2025).

    Article  Google Scholar 

  31. Yagüe Relimpio, A. et al. Bottom-up assembled synthetic SARS-CoV-2 miniviruses reveal lipid membrane affinity of omicron variant spike glycoprotein. ACS Nano 17, 23913–23923 (2023).

    Article  Google Scholar 

  32. Chen, Z. et al. Synthetic beta cells for fusion-mediated dynamic insulin secretion. Nat. Chem. Biol. 14, 86–93 (2018).

    Article  Google Scholar 

  33. Schwille, P. et al. MaxSynBio: avenues towards creating cells from the bottom up. Angew. Chem. Int. Ed. 57, 13382–13392 (2018).

    Article  Google Scholar 

  34. Ramirez-Diaz, D. A. et al. FtsZ induces membrane deformations via torsional stress upon GTP hydrolysis. Nat. Commun. 12, 3310 (2021).

    Article  Google Scholar 

  35. Ramirez-Diaz, D. A. et al. Treadmilling analysis reveals new insights into dynamic FtsZ ring architecture. PLoS Biol. 16, e2004845 (2018).

    Article  Google Scholar 

  36. Pontani, L. L. et al. Reconstitution of an actin cortex inside a liposome. Biophys. J. 96, 192–198 (2009).

    Article  Google Scholar 

  37. Miyazaki, M., Chiba, M., Eguchi, H., Ohki, T. & Ishiwata, S. Cell-sized spherical confinement induces the spontaneous formation of contractile actomyosin rings in vitro. Nat. Cell Biol. 17, 480–489 (2015).

    Article  Google Scholar 

  38. Sciortino, A. et al. Active membrane deformations of a minimal synthetic cell. Nat. Phys. https://doi.org/10.1038/s41567-025-02839-3 (2025).

    Article  Google Scholar 

  39. Hovijitra, N. T., Wuu, J. J., Peaker, B. & Swartz, J. R. Cell-free synthesis of functional aquaporin Z in synthetic liposomes. Biotechnol. Bioeng. 104, 40–49 (2009).

    Article  Google Scholar 

  40. Fujii, S., Matsuura, T., Sunami, T., Kazuta, Y. & Yomo, T. In vitro evolution of α-hemolysin using a liposome display. Proc. Natl Acad. Sci. USA 110, 16796–16801 (2013).

    Article  Google Scholar 

  41. Dannhauser, P. N. & Ungewickell, E. J. Reconstitution of clathrin-coated bud and vesicle formation with minimal components. Nat. Cell Biol. 14, 634–639 (2012).

    Article  Google Scholar 

  42. Takei, K. et al. Generation of coated intermediates of clathrin-mediated endocytosis on protein-free liposomes. Cell 94, 131–141 (1998).

    Article  Google Scholar 

  43. Kohyama, S., Yoshinaga, N., Yanagisawa, M., Fujiwara, K. & Doi, N. Cell-sized confinement controls generation and stability of a protein wave for spatiotemporal regulation in cells. eLife 8, e44591 (2019).

    Article  Google Scholar 

  44. Litschel, T., Ramm, B., Maas, R., Heymann, M. & Schwille, P. Beating vesicles: encapsulated protein oscillations cause dynamic membrane deformations. Angew. Chem. Int. Ed. 57, 16286–16290 (2018).

    Article  Google Scholar 

  45. Kohyama, S., Frohn, B. P., Babl, L. & Schwille, P. Machine learning-aided design and screening of an emergent protein function in synthetic cells. Nat. Commun. 15, 2010 (2024).

    Article  Google Scholar 

  46. Lussier, F., Staufer, O., Platzman, I. & Spatz, J. P. Can bottom-up synthetic biology generate advanced drug-delivery systems? Trends Biotechnol. 39, 445–459 (2021).

    Article  Google Scholar 

  47. Boyd, M. A. & Kamat, N. P. Designing artificial cells towards a new generation of biosensors. Trends Biotechnol. 39, 927–939 (2021).

    Article  Google Scholar 

  48. Mukwaya, V., Mann, S. & Dou, H. Chemical communication at the synthetic cell/living cell interface. Commun. Chem. 4, 161 (2021).

    Article  Google Scholar 

  49. Sela, M. et al. Brain-targeted liposomes loaded with monoclonal antibodies reduce alpha-synuclein aggregation and improve behavioral symptoms in Parkinson’s disease. Adv. Mater. 35, e2304654 (2023).

    Article  Google Scholar 

  50. Toparlak, Ö. D. et al. Artificial cells drive neural differentiation. Sci. Adv. 6, eabb4920 (2020).

    Article  Google Scholar 

  51. Lee, K. Y. et al. Photosynthetic artificial organelles sustain and control ATP-dependent reactions in a protocellular system. Nat. Biotechnol. 36, 530–535 (2018).

    Article  Google Scholar 

  52. Adamala, K. P. et al. Present and future of synthetic cell development. Nat. Rev. Mol. Cell Biol. 25, 162–167 (2023).

    Article  Google Scholar 

  53. Schwille, P. & Frohn, B. P. Hidden protein functions and what they may teach us synthesizing from the bottom-up. Trends Cell Biol. 32, 102–109 (2022).

    Article  Google Scholar 

  54. Gierasch, L. M. & Gershenson, A. Post-reductionist protein science, or putting Humpty Dumpty back together again. Nat. Chem. Biol. 5, 774 (2009).

    Article  Google Scholar 

  55. Fu, M. et al. Mechanochemical feedback loop drives persistent motion of liposomes. Nat. Phys. 19, 1211–1218 (2023).

    Article  Google Scholar 

  56. Ramm, B. et al. A diffusiophoretic mechanism for ATP-driven transport without motor proteins. Nat. Phys. 17, 850–858 (2021).

    Article  Google Scholar 

  57. Reverte-López, M. et al. Self-organized spatial targeting of contractile actomyosin rings for synthetic cell division. Nat. Commun. 15, 10415 (2024).

    Article  Google Scholar 

  58. Sternke, M., Tripp, K. W. & Barrick, D. Consensus sequence design as a general strategy to create hyperstable, biologically active proteins. Proc. Natl Acad. Sci. USA 116, 11275–11284 (2019).

    Article  Google Scholar 

  59. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

    Article  Google Scholar 

  60. Elnaggar, A. et al. ProtTrans: towards cracking the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127 (2022).

    Article  Google Scholar 

  61. Heinzinger, M. et al. Bilingual language model for protein sequence and structure. Preprint at bioRxiv https://doi.org/10.1101/2023.07.23.550085 (2024).

  62. Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025).

    Article  Google Scholar 

  63. Zhang, Z. et al. Protein language models learn evolutionary statistics of interacting sequence motifs. Proc. Natl Acad. Sci. USA 121, e2406285121 (2024).

    Article  Google Scholar 

  64. Shroff, R. et al. Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS Synth. Biol. 9, 2927–2935 (2020).

    Article  Google Scholar 

  65. Hie, B. L. et al. nature biotechnology efficient evolution of human antibodies from general protein language models. Nat. Biotechnol. 42, 275–283 (2024).

    Article  Google Scholar 

  66. Dauparas, J. et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).

    Article  Google Scholar 

  67. Sumida, K. H. et al. Improving protein expression, stability, and function with ProteinMPNN. J. Am. Chem. Soc. 146, 2054–2061 (2024).

    Article  Google Scholar 

  68. Garenne, D. et al. Cell-free gene expression. Nat. Rev. Methods Primers 1, 49 (2021).

    Article  Google Scholar 

  69. Kai, L. & Schwille, P. Cell-free protein synthesis and its perspectives for assembling cells from the bottom-up. Adv. Biosyst. 3, e1800322 (2019).

    Article  Google Scholar 

  70. Hettiaratchi, M. H. et al. Reengineering biocatalysts: computational redesign of chondroitinase ABC improves efficacy and stability. Sci. Adv. 6, eabc6378 (2020).

    Article  Google Scholar 

  71. Benegas, G., Ye, C., Albors, C., Li, J. C. & Song, Y. S. Genomic language models: opportunities and challenges. Preprint at https://doi.org/10.48550/arXiv.2407.11435 (2024).

  72. Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).

    Article  Google Scholar 

  73. Brixi, G. et al. Genome modeling and design across all domains of life with Evo 2. Preprint at bioRxiv https://doi.org/10.1101/2025.02.18.638918 (2025).

  74. Hwang, Y., Cornman, A. L., Kellogg, E. H., Ovchinnikov, S. & Girguis, P. R. Genomic language model predicts protein co-regulation and function. Nat. Commun. 15, 2880 (2024).

    Article  Google Scholar 

  75. Goverde, C. A. et al. Computational design of soluble and functional membrane protein analogues. Nature 631, 449–458 (2024).

    Article  Google Scholar 

  76. Shimizu, Y. et al. Cell-free translation reconstituted with purified components. Nat. Biotechnol. 19, 751–755 (2001).

    Article  Google Scholar 

  77. Koblan, L. W. et al. Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction. Nat. Biotechnol. 36, 843–846 (2018).

    Article  Google Scholar 

  78. Zakas, P. M. et al. Enhancing the pharmaceutical properties of protein drugs by ancestral sequence reconstruction. Nat. Biotechnol. 35, 35–37 (2017).

    Article  Google Scholar 

  79. Khersonsky, O. et al. Automated design of efficient and functionally diverse enzyme repertoires. Mol. Cell 72, 178–186.e5 (2018).

    Article  Google Scholar 

  80. Funk, J. et al. ProteusAI: an open-source and user-friendly platform for machine learning-guided protein design and engineering. Preprint at bioRxiv https://doi.org/10.1101/2024.10.01.616114 (2024).

  81. Cabré, E. J. et al. Bacterial division proteins FtsZ and ZipA induce vesicle shrinkage and cell membrane invagination. J. Biol. Chem. 288, 26625–26634 (2013).

    Article  Google Scholar 

  82. Fan, S. et al. Morphology remodelling and membrane channel formation in synthetic cells via reconfigurable DNA nanorafts. Nat. Mater. 24, 278–286 (2025).

    Article  Google Scholar 

  83. Woolfson, D. N. A brief history of de novo protein design: minimal, rational, and computational. J. Mol. Biol. 433, 167160 (2021).

    Article  Google Scholar 

  84. Korendovych, I. V. & DeGrado, W. F. De novo protein design, a retrospective. Q. Rev. Biophys. 53, e3 (2020).

    Article  Google Scholar 

  85. Ovchinnikov, S. & Huang, P. S. Structure-based protein design with deep learning. Curr. Opin. Chem. Biol. 65, 136–144 (2021).

    Article  Google Scholar 

  86. Ferruz, N. et al. From sequence to function through structure: deep learning for protein design. Comput. Struct. Biotechnol. J. 21, 238–250 (2023).

    Article  Google Scholar 

  87. Mallik, B. B., Stanislaw, J., Alawathurage, T. M. & Khmelinskaia, A. De novo design of polyhedral protein assemblies: before and after the AI revolution. ChemBioChem 24, e202300117 (2023).

    Article  Google Scholar 

  88. Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070–1078 (2023).

    Article  Google Scholar 

  89. Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).

    Article  Google Scholar 

  90. Harteveld, Z. et al. Exploring “dark matter” protein folds using deep learning. Cell Syst. 15, 898–910 (2024).

    Article  Google Scholar 

  91. Frank, C. et al. Scalable protein design using optimization in a relaxed sequence space. Science 386, 439–445 (2024).

    Article  Google Scholar 

  92. Dauparas, J. et al. Atomic context-conditioned protein sequence design using LigandMPNN. Preprint at bioRxiv https://doi.org/10.1101/2023.12.22.573103 (2023).

  93. Akpinaroglu, D., Seki, K., Zhu, E. & Kortemme, T. Frame2seq: structure-conditioned masked language modeling for protein sequence design. In Machine Learning for Structural Biology Workshop (NeurIPS, 2023).

  94. Bennett, N. R. et al. Improving de novo protein binder design with deep learning. Nat. Commun. 14, 2625 (2023).

    Article  Google Scholar 

  95. Johnson, S. R. et al. Computational scoring and experimental evaluation of enzymes generated by neural networks. Nat. Biotechnol. 43, 396–405 (2025).

    Article  Google Scholar 

  96. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  Google Scholar 

  97. Lisanza, S. L. et al. Multistate and functional protein design using RoseTTAFold sequence space diffusion. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02395-w (2024).

    Article  Google Scholar 

  98. Chu, A. E. et al. An all-atom protein generative model. Proc. Natl Acad. Sci. USA 121, e2311500121 (2024).

    Article  Google Scholar 

  99. Wang, J. et al. Scaffolding protein functional sites using deep learning. Science 377, 387–394 (2022).

    Article  Google Scholar 

  100. Yeh, A. H. W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).

    Article  Google Scholar 

  101. Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).

    Article  Google Scholar 

  102. Marchand, A. et al. Targeting protein–ligand neosurfaces with a generalizable deep learning tool. Nature 639, 522–531 (2025).

    Article  Google Scholar 

  103. Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378, 56–61 (2022).

    Article  Google Scholar 

  104. Praetorius, F. et al. Design of stimulus-responsive two-state hinge proteins. Science 381, 754 (2023).

    Article  Google Scholar 

  105. Eguchi, R. R. et al. Deep generative design of epitope-specific binding proteins by latent conformation optimization. Preprint at bioRxiv https://doi.org/10.1101/2022.12.22.521698 (2022).

  106. Bennett, N. R. et al. Atomically accurate de novo design of antibodies with RFdiffusion. Preprint at bioRxiv https://doi.org/10.1101/2024.03.14.585103 (2025).

  107. Schweke, H. et al. An atlas of protein homo-oligomerization across domains of life. Cell 187, 999–1010.e15 (2024).

    Article  Google Scholar 

  108. Iacovache, I., Bischofberger, M. & van der Goot, F. G. Structure and assembly of pore-forming proteins. Curr. Opin. Struct. Biol. 20, 241–246 (2010).

    Article  Google Scholar 

  109. Huber, F. et al. Emergent complexity of the cytoskeleton: from single filaments to tissue. Adv. Phys. 62, 1–112 (2013).

    Article  Google Scholar 

  110. Kirchhausen, T. Bending membranes. Nat. Cell Biol. 14, 906–908 (2012).

    Article  Google Scholar 

  111. Xu, C. et al. Computational design of transmembrane pores. Nature 585, 129–134 (2020).

    Article  Google Scholar 

  112. Berhanu, S. et al. Sculpting conducting nanopore size and shape through de novo protein design. Science 385, 282–288 (2024).

    Article  Google Scholar 

  113. Joh, N. H. et al. De novo design of a transmembrane Zn2+-transporting four-helix bundle. Science 346, 1520 (2014).

    Article  Google Scholar 

  114. Mahendran, K. R. et al. A monodisperse transmembrane α-helical peptide barrel. Nat. Chem. 9, 411–419 (2017).

    Article  Google Scholar 

  115. Lu, P. et al. Accurate computational design of multipass transmembrane proteins. Science 359, 1042–1046 (2018).

    Article  Google Scholar 

  116. Votteler, J. et al. Designed proteins induce the formation of nanocage-containing extracellular vesicles. Nature 540, 292–295 (2016).

    Article  Google Scholar 

  117. Svitkina, T. The actin cytoskeleton and actin-based motility. CSH Perspect. Biol. 10, a018267 (2018).

    Google Scholar 

  118. Robert-Paganin, J., Pylypenko, O., Kikuti, C., Sweeney, H. L. & Houdusse, A. Force generation by myosin motors: a structural perspective. Chem. Rev. 120, 5–35 (2020).

    Article  Google Scholar 

  119. Thomas, C. & Tampé, R. Structural and mechanistic principles of ABC transporters. Annu. Rev. Biochem. 89, 605–636 (2020).

    Article  Google Scholar 

  120. Rudden, L. S. P., Hijazi, M. & Barth, P. Deep learning approaches for conformational flexibility and switching properties in protein design. Front. Mol. Biosci. 9, 928534 (2022).

    Article  Google Scholar 

  121. Harrington, L., Fletcher, J. M., Heermann, T., Woolfson, D. N. & Schwille, P. De novo design of a reversible phosphorylation-dependent switch for membrane targeting. Nat. Commun. 12, 1472 (2021).

    Article  Google Scholar 

  122. Courbet, A. et al. Computational design of mechanically coupled axle-rotor protein assemblies. Science 376, 383–390 (2022).

    Article  Google Scholar 

  123. Ng, A. H. et al. Modular and tunable biological feedback control using a de novo protein switch. Nature 572, 265–269 (2019).

    Article  Google Scholar 

  124. Langan, R. A. et al. De novo design of bioactive protein switches. Nature 572, 205–210 (2019).

    Article  Google Scholar 

  125. Lajoie, M. J. et al. Designed protein logic to target cells with precise combinations of surface antigens. Science 369, 1637–1643 (2020).

    Article  Google Scholar 

  126. Shui, S. et al. A rational blueprint for the design of chemically-controlled protein switches. Nat. Commun. 12, 5754 (2021).

    Article  Google Scholar 

  127. Glasgow, A. A. et al. Computational design of a modular protein sense-response system. Science 366, 1024–1028 (2019).

    Article  Google Scholar 

  128. Gantz, M., Neun, S., Medcalf, E. J., van Vliet, L. D. & Hollfelder, F. Ultrahigh-throughput enzyme engineering and discovery in in vitro compartments. Chem. Rev. 123, 5571–5611 (2023).

    Article  Google Scholar 

  129. Utada, A. S. et al. Monodisperse double emulsions generated from a microcapillary device. Science 308, 537–541 (2005).

    Article  Google Scholar 

  130. Lamborg, M. R. & Zamecnik, P. C. Amino acid incorporation into protein by extracts of E. coli. Biochim. Biophys. Acta 42, 206–211 (1960).

    Article  Google Scholar 

  131. Abil, Z. et al. Darwinian evolution of self-replicating DNA in a synthetic protocell. Nat. Commun. 15, 9091 (2024).

    Article  Google Scholar 

  132. Okauchi, H. & Ichihashi, N. Continuous cell-free replication and evolution of artificial genomic DNA in a compartmentalized gene expression system. ACS Synth. Biol. 10, 3507–3517 (2021).

    Article  Google Scholar 

  133. van Nies, P. et al. Self-replication of DNA by its encoded proteins in liposome-based synthetic cells. Nat. Commun. 9, 1583 (2018).

    Article  Google Scholar 

  134. Holstein, J. M., Gylstorff, C. & Hollfelder, F. Cell-free directed evolution of a protease in microdroplets at ultrahigh throughput. ACS Synth. Biol. 10, 252–257 (2021).

    Article  Google Scholar 

  135. Fallah-Araghi, A., Baret, J. C., Ryckelynck, M. & Griffiths, A. D. A completely in vitro ultrahigh-throughput droplet-based microfluidic screening system for protein engineering and directed evolution. Lab Chip 12, 882–891 (2012).

    Article  Google Scholar 

  136. Mazutis, L. et al. Droplet-based microfluidic systems for high-throughput single DNA molecule isothermal amplification and analysis. Anal. Chem. 81, 4813–4821 (2009).

    Article  Google Scholar 

  137. Packer, M. S. & Liu, D. R. Methods for the directed evolution of proteins. Nat. Rev. Genet. 16, 379–394 (2015).

    Article  Google Scholar 

  138. Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009).

    Article  Google Scholar 

  139. Yang, G. & Withers, S. G. Ultrahigh-throughput FACS-based screening for directed enzyme evolution. ChemBioChem 10, 2704–2715 (2009).

    Article  Google Scholar 

  140. Goto, H. et al. Microfluidic screening system based on boron-doped diamond electrodes and dielectrophoretic sorting for directed evolution of NAD(P)-dependent oxidoreductases. Lab Chip 20, 852–861 (2020).

    Article  Google Scholar 

  141. Rees, P., Summers, H. D., Filby, A., Carpenter, A. E. & Doan, M. Imaging flow cytometry. Nat. Rev. Methods Primers 2, 86 (2022).

    Article  Google Scholar 

  142. Godino, E., Restrepo Sierra, A. M. & Danelon, C. Imaging flow cytometry for high-throughput phenotyping of synthetic cells. ACS Synth. Biol. 12, 2015–2028 (2023).

    Article  Google Scholar 

  143. Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).

    Article  Google Scholar 

  144. Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M. & Church, G. M. Low-N protein engineering with data-efficient deep learning. Nat. Methods 18, 389–396 (2021).

    Article  Google Scholar 

  145. Pandi, A. et al. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat. Commun. 13, 3876 (2022).

    Article  Google Scholar 

  146. Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).

    Article  Google Scholar 

  147. Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).

    Article  Google Scholar 

  148. Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).

    Article  Google Scholar 

  149. Rapp, J. T., Bremer, B. J. & Romero, P. A. Self-driving laboratories to autonomously navigate the protein fitness landscape. Nat. Chem. Eng. 1, 97–107 (2024).

    Article  Google Scholar 

  150. Munsamy, G. et al. Conditional language models enable the efficient design of proficient enzymes. Preprint at bioRxiv https://doi.org/10.1101/2024.05.03.592223 (2024).

  151. Braun, M. et al. Computational design of highly active de novo enzymes. Preprint at bioRxiv https://doi.org/10.1101/2024.08.02.606416 (2024).

  152. Kim, D. et al. Computational design of metallohydrolases. Preprint at bioRxiv https://doi.org/10.1101/2024.11.13.623507 (2024).

  153. Lauko, A. et al. Computational design of serine hydrolases. Science 388, eadu2454 (2025).

    Article  Google Scholar 

  154. Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13, 4348 (2022).

    Article  Google Scholar 

  155. Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1099–1106 (2023).

    Article  Google Scholar 

  156. Smolke, C. D. Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol. 27, 1099–1102 (2009).

    Article  Google Scholar 

  157. Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)—Round XV. Proteins 91, 1539–1549 (2023).

    Article  Google Scholar 

  158. Castelvecchi, D. Drivers gear up for world’s first nanocar race. Nature 544, 278–279 (2017).

    Article  Google Scholar 

  159. Fu, M., Li, X. & Zhao, W. The Asian synthetic cell initiative: highlights from the first SynCell Asia workshop. Natl Sci. Rev. 12, nwae377 (2024).

    Article  Google Scholar 

  160. Otim, G. et al. SynBio Africa’s story from the grassroots, the present, and the future. Biotechnol. Notes 4, 1–6 (2023).

    Article  Google Scholar 

  161. Noireaux, V. & Libchaber, A. A vesicle bioreactor as a step toward an artificial cell assembly. Proc. Natl Acad. Sci. USA 101, 17669–17674 (2004).

    Article  Google Scholar 

  162. Shi, X., Wu, T., Cole, C. M., Devaraj, N. K. & Joseph, S. Optimization of ClpXP activity and protein synthesis in an E. coli extract-based cell-free expression system. Sci. Rep. 8, 3488 (2018).

    Article  Google Scholar 

  163. Pols, T. et al. A synthetic metabolic network for physicochemical homeostasis. Nat. Commun. 10, 4239 (2019).

    Article  Google Scholar 

  164. Blanken, D., Foschepoth, D., Serrão, A. C. & Danelon, C. Genetically controlled membrane synthesis in liposomes. Nat. Commun. 11, 4317 (2020).

    Article  Google Scholar 

  165. Eto, S. et al. Phospholipid synthesis inside phospholipid membrane vesicles. Commun. Biol. 5, 1016 (2022).

    Article  Google Scholar 

  166. Kita, H. et al. Replication of genetic information with self-encoded replicase in liposomes. ChemBioChem 9, 2403–2410 (2008).

    Article  Google Scholar 

  167. Zhao, J. & Han, X. Investigation of artificial cells containing the Par system for bacterial plasmid segregation and inheritance mimicry. Nat. Commun. 15, 4956 (2024).

    Article  Google Scholar 

  168. Osawa, M., Anderson, D. E. & Erickson, H. P. Reconstitution of contractile FtsZ rings in liposomes. Science 320, 792–794 (2008).

    Article  Google Scholar 

  169. Kohyama, S., Merino-Salomón, A. & Schwille, P. In vitro assembly, positioning and contraction of a division ring in minimal cells. Nat. Commun. 13, 6098 (2022).

    Article  Google Scholar 

  170. De Franceschi, N., Barth, R., Meindlhumer, S., Fragasso, A. & Dekker, C. Dynamin A as a one-component division machinery for synthetic cells. Nat. Nanotechnol. 19, 70–76 (2024).

    Article  Google Scholar 

  171. Ichihashi, N. et al. Darwinian evolution in a translation-coupled RNA replication system within a cell-like compartment. Nat. Commun. 4, 2494 (2013).

    Article  Google Scholar 

  172. Adamala, K. P., Martin-Alarcon, D. A., Guthrie-Honea, K. R. & Boyden, E. S. Engineering genetic circuit interactions within and between synthetic minimal cells. Nat. Chem. 9, 431–439 (2016).

    Article  Google Scholar 

  173. Buddingh, B. C., Elzinga, J. & van Hest, J. C. M. Intercellular communication between artificial cells by allosteric amplification of a molecular signal. Nat. Commun. 11, 1652 (2020).

    Article  Google Scholar 

  174. Hindley, J. W. et al. Building a synthetic mechanosensitive signaling pathway in compartmentalized artificial cells. Proc. Natl Acad. Sci. USA 116, 16711–16716 (2019).

    Article  Google Scholar 

  175. Shin, J. & Noireaux, V. E. coli cell-free expression toolbox: application to synthetic gene circuits and artificial cells. ACS Synth. Biol. 1, 29–41 (2012).

    Article  Google Scholar 

  176. Nishimura, K. et al. Cell-free protein synthesis inside giant unilamellar vesicles analyzed by flow cytometry. Langmuir 28, 8426–8432 (2012).

    Article  Google Scholar 

  177. Hüttemann, M. et al. The multiple functions of cytochrome c and their regulation in life and death decisions of the mammalian cell: from respiration to apoptosis. Mitochondrion 11, 369–381 (2011).

    Article  Google Scholar 

  178. Callaway, E. Who will make AlphaFold3 open source? Scientists race to crack AI model. Nature 630, 14–15 (2024).

    Article  Google Scholar 

  179. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article  Google Scholar 

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Acknowledgements

The authors thank L. Milles for helpful discussion and critical reading, V. Belousova and M. Reverte-López for input on functional protein modules, and K. Al Nahas for discussion on high-throughput synthetic cell generation and screening.

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Authors and Affiliations

  1. Max Planck Institute of Biochemistry, Martinsried, Germany

    Béla P. Frohn, Shunshi Kohyama & Petra Schwille

  2. Department of Physics, The University of Tokyo, Tokyo, Japan

    Shunshi Kohyama

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  1. Béla P. Frohn
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  2. Shunshi Kohyama
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Contributions

B.P.F. conceptualized the manuscript, P.S. and B.P.F wrote the manuscript, and all authors reviewed and edited the manuscript.

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Correspondence to Petra Schwille.

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Nature Reviews Bioengineering thanks Xin Zhou, who co-reviewed with Zhixing Ma, and the other, anonymous, reviewers(s) for their contribution to the peer review of this work.

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Related links

BindCraft: https://github.com/martinpacesa/BindCraft

Build-A-Cell collaboration: https://www.buildacell.org/

ColabFold: https://github.com/sokrypton/ColabFold

ESM2: https://github.com/facebookresearch/esm

ESM3: https://github.com/evolutionaryscale/esm

iGEM competition: https://competition.igem.org/

List of papers about proteins design using deep learning: https://github.com/Peldom/papers_for_protein_design_using_DL

METIS: https://github.com/amirpandi/METIS

Nucleus: https://nucleus.bnext.bio/

ProteinMPNN: https://github.com/dauparas/ProteinMPNN

RFdiffusion: https://github.com/RosettaCommons/RFdiffusion

SynCellEU network: https://syntheticcell.eu/

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Frohn, B.P., Kohyama, S. & Schwille, P. Protein design and optimization for synthetic cells. Nat Rev Bioeng 3, 645–659 (2025). https://doi.org/10.1038/s44222-025-00318-1

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