Friday, May 1, 2026
No menu items!
HomeNatureThe past, present and future of de novo protein design

The past, present and future of de novo protein design

  • Pan, X. & Kortemme, T. Recent advances in de novo protein design: principles, methods, and applications. J. Biol. Chem. 296, 100558 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Marchand, A., Van Hall-Beauvais, A. K. & Correia, B. E. Computational design of novel protein-protein interactions—an overview on methodological approaches and applications. Curr. Opin. Struct. Biol. 74, 102370 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Khakzad, H. et al. A new age in protein design empowered by deep learning. Cell Syst. 14, 925–939 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. 57, 4143–4148 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Zhu, J. et al. Protein assembly by design. Chem. Rev. 121, 13701–13796 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223–230 (1973). Demonstration that a protein’s amino-acid sequence intrinsically encodes its native three-dimensional structure, establishing the foundational principle for all modern protein folding and design research.

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003). Kuhlman et al. achieved the first atomic-level de novo design of a fully novel protein fold, providing decisive proof that computational methods can accurately encode and realize new three-dimensional protein architectures.

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Dahiyat, B. I. & Mayo, S. L. De novo protein design: fully automated sequence selection. Science 278, 82–87 (1997).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). Jumper et al. reported AlphaFold, a deep-learning model capable of predicting protein structures at near-experimental resolution, greatly enhancing our ability to infer protein structure from protein sequence.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Norn, C. et al. Protein sequence design by conformational landscape optimization. Proc. Natl Acad. Sci. USA 118, e2017228118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pearce, R. & Zhang, Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 68, 194–207 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anishchenko, I. et al. De novo protein design by deep network hallucination. Nature 600, 547–552 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jendrusch, M. A. et al. AlphaDesign: a de novo protein design framework based on AlphaFold. Mol. Syst. Biol. 21, 1166–1189 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goverde, C. A., Wolf, B., Khakzad, H., Rosset, S. & Correia, B. E. De novo protein design by inversion of the AlphaFold structure prediction network. Protein Sci. 32, e4653 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramesh, A. et al. Zero-shot text-to-image generation. in Proc. 38th Int. Conf. Mach. Learn. Vol. 139 (eds Meila, M. & Zhang, T.) 8821–8831 (PMLR, 2021).

  • Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023). Watson et al. presented RFdiffusion, a diffusion framework that enables accurate and versatile de novo protein structure and function design.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with CLIP latents. Preprint at https://arxiv.org/abs/2204.06125 (2022).

  • Geffner, T. et al. Proteina: scaling flow-based protein structure generative models. in 13th Int. Conf. Learn. Res. https://openreview.net/forum?id=TVQLu34bdw&noteId=ypeoraSUA0 (ICLR, 2025).

  • Lin, Y., Lee, M., Zhang, Z. & AlQuraishi, M. Out of many, one: designing and scaffolding proteins at the scale of the structural universe with Genie 2. Preprint at https://arxiv.org/abs/2405.15489 (2024).

  • Yim, J. et al. SE(3) diffusion model with application to protein backbone generation. in Proc. 40th Int. Conf. Mach. Learn. Vol. 202 (eds Krause, E. et al.) 40001–40039 (PMLR, 2023).

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zambaldi, V. et al. De novo design of high-affinity protein binders with AlphaProteo. Preprint at https://arxiv.org/abs/2409.08022 (2024).

  • Yim, J. et al. Fast protein backbone generation with SE(3) flow matching. Preprint at https://arxiv.org/abs/2310.05297 (2023).

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, J., Guo, Z., You, H., Zhang, C. & Lai, L. All-atom protein sequence design based on geometric deep learning. Angew. Chem. 136, e202411461 (2024).

  • Liu, Y. et al. De novo protein design with a denoising diffusion network independent of pretrained structure prediction models. Nat. Methods 21, 2107–2116 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • O’Connell, J. et al. SPIN2: Predicting sequence profiles from protein structures using deep neural networks. Proteins 86, 629–633 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Huang, B. et al. A backbone-centred energy function of neural networks for protein design. Nature 602, 523–528 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hsu, C. et al. Learning inverse folding from millions of predicted structures. in Proc. 39th Int. Conf. Mach. Learn. Vol. 162 (eds Chaudhuri, K. et al.) 8946–8970 (PMLR, 2022).

  • Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022). Dauparas et al. introduced ProteinMPNN, a deep-learning model that enables accurate and highly robust protein sequence design across diverse structural backbones.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Geffner, T. et al. La-Proteina: atomistic protein generation via partially latent flow matching. Preprint at https://arxiv.org/abs/2507.09466 (2025).

  • Ren, M., Zhu, T. & Zhang, H. CarbonNovo: joint design of protein structure and sequence using a unified energy-based model. In Proc. 41st Int. Conf. Mach. Learn Vol. 235 (eds Salakhutdinov, R. et al.) 42462–42483 (PMLR, 2024).

  • Campbell, A., Yim, J., Barzilay, R., Rainforth, T. & Jaakkola, T. Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design. in Proc. 41st Int. Conf. Mach. Learn. Vol. 235 (eds Salakhutdinov, R.) 5453–5512 (PMLR, 2024).

  • Qu, W. et al. P(all-atom) is unlocking new path for protein design. Preprint at bioRxiv https://doi.org/10.1101/2024.08.16.608235 (2024).

  • Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. in Proc. 2019 Conf. North Am. Chapter Assoc. Comput. Linguist: Human Lang Technol. Vol. 1 (eds Burstein, J. et al.) 4171–4186 (Association for Computational Linguistics, 2018).

  • Brown, T. B. et al. Language models are few-shot learners. in Proc. 34th Conf. Neural Inf. Process. Syst. (eds Larochelle, H. et al.) (NeurIPS, 2020).

  • Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Brandes, N., Ofer, D., Peleg, Y., Rappoport, N. & Linial, M. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38, 2102–2110 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. ProGen2: exploring the boundaries of protein language models. Cell Syst. 14, 968–978 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alamdari, S. et al. Protein generation with evolutionary diffusion: sequence is all you need. Preprint at bioRxiv https://doi.org/10.1101/2023.09.11.556673 (2023).

  • Wang, X. et al. Diffusion language models are versatile protein learners. in Proc. 41st Int. Conf. Mach. Learn. Vol. 35 (eds Salakhutdinov, R.) 52309–52333 (PMLR, 2024).

  • Goel, S. et al. Token-level guided discrete diffusion for membrane protein design. Preprint at https://arxiv.org/abs/2410.16735 (2025).

  • Hayes, T. et al. Simulating 500 million years of evolution with a language model. Science 387, 850–858 (2025). Hayes et al. showed that a large language model can accurately simulate hundreds of millions of years of protein evolution, enabling unprecedented reconstruction, exploration and prediction of evolutionary trajectories.

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Regan, L. & DeGrado, W. F. Characterization of a helical protein designed from first principles. Science 241, 976–978 (1988).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Huang, P.-S. et al. De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat. Chem. Biol. 12, 29–34 (2016).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Brunette, T. J. et al. Exploring the repeat protein universe through computational protein design. Nature 528, 580–584 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dou, J. et al. De novo design of a fluorescence-activating β-barrel. Nature 561, 485–491 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Doyle, L. A. et al. De novo design of knotted tandem repeat proteins. Nat. Commun. 14, 6746 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pearce, R., Huang, X., Omenn, G. S. & Zhang, Y. De novo protein fold design through sequence-independent fragment assembly simulations. Proc. Natl Acad. Sci. USA 120, e2208275120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Koga, R. et al. Robust folding of a de novo designed ideal protein even with most of the core mutated to valine. Proc. Natl Acad. Sci. USA 117, 31149–31156 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liang, H. et al. De novo design of a βαβ motif. Angew. Chem. Int. Ed. 48, 3301–3303 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Pan, X. et al. Expanding the space of protein geometries by computational design of de novo fold families. Science 369, 1132–1136 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. et al. Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding. Nat. Mach. Intell. 7, 1104–1118 (2025).

    Article 

    Google Scholar
     

  • Roel-Touris, J., Nadal, M. & Marcos, E. Single-chain dimers from de novo immunoglobulins as robust scaffolds for multiple binding loops. Nat. Commun. 14, 5939 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Albanese, K. I. et al. Rationally seeded computational protein design of α-helical barrels. Nat. Chem. Biol. 20, 991–999 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Correia, B. DiffTopo: fold exploration using coarse grained protein topology representations. Preprint at bioRxiv https://doi.org/10.1101/2024.02.01.578456 (2024).

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, J. et al. De novo design of transmembrane fluorescence-activating proteins. Nature 640, 249–257 (2025).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Vorobieva, A. A. et al. De novo design of transmembrane β barrels. Science 371, eabc8182 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, Y. et al. Bottom-up design of Ca2+ channels from defined selectivity filter geometry. Nature 648, 468–476 (2025).

  • Zhou, C. et al. De novo designed voltage-gated anion channels suppress neuron firing. Cell 188, 7495–7511 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Joh, N. H. et al. De novo design of a transmembrane Zn2+-transporting four-helix bundle. Science 346, 1520–1524 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mills, J. H. et al. Computational design of an unnatural amino acid dependent metalloprotein with atomic level accuracy. J. Am. Chem. Soc. 135, 13393–13399 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hosseinzadeh, P. et al. Comprehensive computational design of ordered peptide macrocycles. Science 358, 1461–1466 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mulligan, V. K. et al. Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1. Proc. Natl Acad. Sci. USA 118, e2012800118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bhardwaj, G. et al. Accurate de novo design of membrane-traversing macrocycles. Cell 185, 3520–3532 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Salveson, P. J. et al. Expansive discovery of chemically diverse structured macrocyclic oligoamides. Science 384, 420–428 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Peng, X. et al. Unified modeling of 3D molecular generation via atomic interactions with PocketXMol. Cell https://doi.org/10.1016/j.cell.2026.01.003 (2026).

  • Rettie, S. A. et al. Cyclic peptide structure prediction and design using AlphaFold2. Nat. Commun. 16, 4730 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kong, X. et al. Peptide design through binding interface mimicry with PepMimic. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-025-01507-4 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • King, N. P. et al. Accurate design of co-assembling multi-component protein nanomaterials. Nature 510, 103–108 (2014). King et al. achieved the accurate de novo design of multicomponent protein nanomaterials that co-assemble with atomic precision, demonstrating a general strategy for building complex higher-order architectures.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Divine, R. et al. Designed proteins assemble antibodies into modular nanocages. Science 372, eabd9994 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bale, J. B. et al. Accurate design of megadalton-scale two-component icosahedral protein complexes. Science 353, 389–394 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lutz, I. D. et al. Top-down design of protein architectures with reinforcement learning. Science 380, 266–273 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, S. et al. Bond-centric modular design of protein assemblies. Nat. Mater. 24, 1644–1652 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Walls, A. C. et al. Elicitation of potent neutralizing antibody responses by designed protein nanoparticle vaccines for SARS-CoV-2. Cell 183, 1367–1382 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chao, C. W. et al. Protein nanoparticle vaccines induce potent neutralizing antibody responses against MERS-CoV. Cell Rep. 43, 115036 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boyoglu-Barnum, S. et al. Quadrivalent influenza nanoparticle vaccines induce broad protection. Nature 592, 623–628 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hendricks, G. G. et al. Computationally designed mRNA-launched protein nanoparticle immunogens elicit protective antibody and T cell responses in mice. Sci. Transl. Med. 17, eadu2085 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, E. C. et al. Computational design of non-porous pH-responsive antibody nanoparticles. Nat. Struct. Mol. Biol. 31, 1404–1412 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Castells-Graells, R. et al. Cryo-EM structure determination of small therapeutic protein targets at 3 Å-resolution using a rigid imaging scaffold. Proc. Natl Acad. Sci. USA 120, e2305494120 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, Z. et al. Programmable design of orthogonal protein heterodimers. Nature 565, 106–111 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sahtoe, D. D. et al. Reconfigurable asymmetric protein assemblies through implicit negative design. Science 375, eabj7662 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bermeo, S. et al. De novo design of obligate ABC-type heterotrimeric proteins. Nat. Struct. Mol. Biol. 29, 1266–1276 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kibler, R. D. et al. Design of pseudosymmetric protein hetero-oligomers. Nat. Commun. 15, 10684 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, Z. et al. De novo design of protein logic gates. Science 368, 78–84 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, Z. et al. A synthetic protein-level neural network in mammalian cells. Science 386, 1243–1250 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dowling, Q. M. et al. Hierarchical design of pseudosymmetric protein nanocages. Nature 638, 553–561 (2024).

  • Lee, S. et al. Four-component protein nanocages designed by programmed symmetry breaking. Nature 638, 546–552 (2024).

  • Shen, H. et al. De novo design of self-assembling helical protein filaments. Science 362, 705–709 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bethel, N. P. et al. Precisely patterned nanofibres made from extendable protein multiplexes. Nat. Chem. 15, 1664–1671 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shen, H. et al. De novo design of pH-responsive self-assembling helical protein filaments. Nat. Nanotechnol. 19, 1016–1021 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shen, H. et al. Nucleation limited assembly and polarized growth of a de novo-designed allosterically modulatable protein filament. Preprint at bioRxiv https://doi.org/10.1101/2024.09.20.613980 (2024).

  • Ben-Sasson, A. J. et al. Design of biologically active binary protein 2D materials. Nature 589, 468–473 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Watson, J. L. et al. Synthetic Par polarity induces cytoskeleton asymmetry in unpolarized mammalian cells. Cell 186, 4710–4727 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, Z. et al. Accurate computational design of three-dimensional protein crystals. Nat. Mater. 22, 1556–1563 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lanci, C. J. et al. Computational design of a protein crystal. Proc. Natl Acad. Sci. USA 109, 7304–7309 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, R. et al. An all-atom generative model for designing protein complexes. Preprint at https://arxiv.org/abs/2504.13075 (2025).

  • Pacesa, M. et al. One-shot design of functional protein binders with BindCraft. Nature 646, 483–492 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ren, M. et al. PXDesign: fast, modular, and accurate de novo design of protein binders. Preprint at bioRxiv https://doi.org/10.1101/2025.08.15.670450 (2025).

  • Vázquez Torres, S. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Glögl, M. et al. Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists. Science 386, 1154–1161 (2024).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, J., McFee, M., Fang, Q., Abdin, O. & Kim, P. M. Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol. Sci. 44, 175–189 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, R. et al. A generative foundation model for antibody design. Preprint at bioRxiv https://doi.org/10.1101/2025.09.12.675771 (2025).

  • Cutting, D., Dreyer, F. A., Errington, D., Schneider, C. & Deane, C. M. De novo antibody design with SE(3) diffusion. J. Comput. Biol. 32, 351–361 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dreyer, F. A. et al. Computational design of therapeutic antibodies with improved developability: efficient traversal of binder landscapes and rescue of escape mutations. MAbs 17, 2511220 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bennett, N. R. et al. Atomically accurate de novo design of antibodies with RFdiffusion. Nature 649, 183–193 (2025).

  • Sahtoe, D. D. et al. Transferrin receptor targeting by de novo sheet extension. Proc. Natl Acad. Sci. USA 118, e2021569118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sappington, I. et al. Improved protein binder design using β-pairing targeted RFdiffusion. Nat. Commun. 17, 1101 (2026).

  • Xu, X. et al. HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer. Bioinformatics 40, btae364 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rettie, S. A. et al. Accurate de novo design of high-affinity protein-binding macrocycles using deep learning. Nat. Chem. Biol. 21, 1948–1956 (2025). Rettie et al. established a deep-learning framework that achieves precise de novo design of potent protein-binding macrocycles.

  • Bhat, S. et al. De novo design of peptide binders to conformationally diverse targets with contrastive language modeling. Sci. Adv. 11, eadr8638 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, K. et al. De novo design of modular peptide-binding proteins by superhelical matching. Nature 616, 581–589 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wu, K. et al. Design of intrinsically disordered region binding proteins. Science 389, eadr8063 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, C. et al. Diffusing protein binders to intrinsically disordered proteins. Nature 644, 809–817 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sahtoe, D. D. et al. Design of amyloidogenic peptide traps. Nat. Chem. Biol. 20, 981–990 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vázquez Torres, S. et al. De novo design of high-affinity binders of bioactive helical peptides. Nature 626, 435–442 (2024).

  • Zhang, J. Z. et al. De novo design of Ras isoform selective binders. Cell Chem. Biol. 33, 396–408.e7 (2026).

  • Chevalier, A. et al. Massively parallel de novo protein design for targeted therapeutics. Nature 550, 74–79 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fleishman, S. J. et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816–821 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cao, L. et al. Design of protein-binding proteins from the target structure alone. Nature 605, 551–560 (2022). Cao et al. introduced a powerful approach for creating high-affinity binders solely from target structural information, greatly advancing de novo protein–protein interface design.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Linsky, T. W. et al. De novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2. Science 370, 1208–1214 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cao, L. et al. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science 370, 426–431 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ragotte, R. J. et al. Designed miniproteins potently inhibit and protect against MERS-CoV. Cell Rep. 44, 115760 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sesterhenn, F. et al. De novo protein design enables the precise induction of RSV-neutralizing antibodies. Science 368, eaay5051 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hunt, A. C. et al. Multivalent designed proteins neutralize SARS-CoV-2 variants of concern and confer protection against infection in mice. Sci. Transl. Med. 14, eabn1252 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, J. et al. The computationally designed TRI2-2 miniprotein inhibitor protects against multiple SARS-CoV-2 Omicron variants. Commun. Biol. 9, 224 (2026).

  • Case, J. B. et al. Ultrapotent miniproteins targeting the SARS-CoV-2 receptor-binding domain protect against infection and disease. Cell Host Microbe 29, 1151–1161 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Koday, M. T. et al. A computationally designed hemagglutinin stem-binding protein provides in vivo protection from influenza independent of a host immune response. PLoS Pathog. 12, e1005409 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ragotte, R. J. et al. De novo design of potent inhibitors of clostridial family toxins. Proc. Natl Acad. Sci. USA 122, e2509329122 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lv, X. et al. De novo design of mini-protein binders broadly neutralizing Clostridioides difficile toxin B variants. Nat. Commun. 15, 8521 (2024).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ragotte, R. J. et al. De novo designed inhibitor confers protection against lethal toxic shock. Preprint at bioRxiv https://doi.org/10.1101/2024.08.23.608890 (2024).

  • Torres, S. V. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).

  • Muratspahić, E. et al. De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptors. Preprint at bioRxiv https://doi.org/10.1101/2025.03.23.644666 (2025).

  • Du, H. et al. A general system for targeting MHC class II-antigen complex via a single adaptable loop. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02466-y (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Du, H. et al. Targeting peptide antigens using a multiallelic MHC I-binding system. Nat. Biotechnol. 43, 1683–1693 (2025). Du et al. developed a multiallelic MHC-I-binding platform that enables precise targeting of diverse peptide antigens, offering a broadly applicable strategy for antigen-specific immunotherapies.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bryan, C. M. et al. Computational design of a synthetic PD-1 agonist. Proc. Natl Acad. Sci. USA 118, e2102164118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, W. et al. Design of high-affinity binders to immune modulating receptors for cancer immunotherapy. Nat. Commun. 16, 2001 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Silva, D.-A. et al. De novo design of potent and selective mimics of IL-2 and IL-15. Nature 565, 186–191 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chun, J.-H. et al. Design of a potent interleukin-21 mimic for cancer immunotherapy. Sci. Immunol. 10, eadx1582 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Berger, S. et al. Preclinical proof of principle for orally delivered Th17 antagonist miniproteins. Cell 187, 4305–4317 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Keshri, R. et al. Direct cell reprogramming by a designed agonist inducing HER2-FGFR proximity. Preprint at bioRxiv https://doi.org/10.1101/2025.10.12.681903 (2025).

  • Abedi, M. et al. High-throughput de novo protein design yields novel immunomodulatory agonists. Preprint at bioRxiv https://doi.org/10.1101/2025.10.12.681920 (2025).

  • Expòsit, M. et al. Geometric tuning of cytokine receptor association modulates synthetic agonist signaling. Preprint at bioRxiv https://doi.org/10.1101/2025.10.12.681819 (2025).

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

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Weinberg, Z. Y. et al. De novo-designed minibinders expand the synthetic biology sensing repertoire. eLife https://doi.org/10.7554/elife.96154.2 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, Z. et al. De novo designed protein guiding targeted protein degradation. Nat. Commun. 16, 6598 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, B. et al. Designed endocytosis-inducing proteins degrade targets and amplify signals. Nature https://doi.org/10.1038/s41586-024-07948-2 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dou, J. et al. Sampling and energy evaluation challenges in ligand binding protein design. Protein Sci. 26, 2426–2437 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bick, M. J. et al. Computational design of environmental sensors for the potent opioid fentanyl. eLife 6, e28909 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Park, J. et al. De novo design of a homo-trimeric amantadine-binding protein. eLife 8, e47839 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lu, L. et al. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 384, 106–112 (2024). Lu et al. demonstrated that drug-binding proteins can be de novo designed with predictable binding energy and specificity, establishing a general strategy for small-molecule binding protein design.

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thomas, F. et al. De novo-designed α-helical barrels as receptors for small molecules. ACS Synth. Biol. 7, 1808–1816 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yang, W. & Lai, L. Computational design of ligand-binding proteins. Curr. Opin. Struct. Biol. 45, 67–73 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Zhou, L. et al. A protein engineered to bind uranyl selectively and with femtomolar affinity. Nat. Chem. 6, 236–241 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Polizzi, N. F. et al. De novo design of a hyperstable non-natural protein-ligand complex with sub-Å accuracy. Nat. Chem. 9, 1157–1164 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, G. R. et al. Small-molecule binding and sensing with a designed protein family. Preprint at bioRxiv https://doi.org/10.1101/2023.11.01.565201 (2023).

  • An, L. et al. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 385, 276–282 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Marchand, A. et al. Targeting protein-ligand neosurfaces with a generalizable deep learning tool. Nature 639, 522–531 (2025). Marchand et al. demonstrated that protein–ligand neosurfaces can be accurately targeted using a generalizable deep-learning surface-descriptor framework, enabling de novo design of small-molecule-conditioned protein interactions.

  • Dauparas, J. et al. Atomic context-conditioned protein sequence design using LigandMPNN. Nat. Methods 22, 717–723 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mann, S. I. et al. De novo design of proteins that bind naphthalenediimides, powerful photooxidants with tunable photophysical properties. J. Am. Chem. Soc. 147, 7849–7858 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cho, Y., Pacesa, M., Zhang, Z., Correia, B. E. & Ovchinnikov, S. Boltzdesign1: inverting all-atom structure prediction model for generalized biomolecular binder design. Preprin at bioRxiv https://doi.org/10.1101/2025.04.06.647261 (2025).

  • Kong, X. et al. UniMoMo: unified generative modeling of 3D molecules for de novo binder design. Preprint at https://arxiv.org/abs/2503.19300 (2025).

  • Kang, S. et al. COMBINES-CID: an efficient method for de novo engineering of highly specific chemically induced protein dimerization systems. J. Am. Chem. Soc. 141, 10948–10952 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shui, S., Buckley, S., Scheller, L. & Correia, B. E. Rational design of small-molecule responsive protein switches. Protein Sci. 32, e4774 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leonard, A. C. et al. Rationalizing diverse binding mechanisms to the same protein fold: Insights for ligand recognition and biosensor design. ACS Chem. Biol. 19, 1757–1772 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Foight, G. W. et al. Multi-input chemical control of protein dimerization for programming graded cellular responses. Nat. Biotechnol. 37, 1209–1216 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bialas, C. et al. Engineering an artificial flavoprotein magnetosensor. J. Am. Chem. Soc. 138, 16584–16587 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Klima, J. C. et al. Incorporation of sensing modalities into de novo designed fluorescence-activating proteins. Nat. Commun. 12, 856 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Beltrán, J. et al. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat. Biotechnol. 40, 1855–1861 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Braun, M. et al. Computational enzyme design by catalytic motif scaffolding. Nature 649, 237–245 (2026).

  • Jiang, L. et al. De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 (2008).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Röthlisberger, D. et al. Kemp elimination catalysts by computational enzyme design. Nature 453, 190–195 (2008).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Privett, H. K. et al. Iterative approach to computational enzyme design. Proc. Natl Acad. Sci. USA 109, 3790–3795 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Obexer, R. et al. Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase. Nat. Chem. 9, 50–56 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Giger, L. et al. Evolution of a designed retro-aldolase leads to complete active site remodeling. Nat. Chem. Biol. 9, 494–498 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Khersonsky, O. et al. Optimization of the in-silico-designed kemp eliminase KE70 by computational design and directed evolution. J. Mol. Biol. 407, 391–412 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Blomberg, R. et al. Precision is essential for efficient catalysis in an evolved Kemp eliminase. Nature 503, 418–421 (2013).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Reig, A. J. et al. Alteration of the oxygen-dependent reactivity of de novo Due Ferri proteins. Nat. Chem. 4, 900–906 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lombardi, A., Pirro, F., Maglio, O., Chino, M. & DeGrado, W. F. De novo design of four-helix bundle metalloproteins: one scaffold, diverse reactivities. Acc. Chem. Res. 52, 1148–1159 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Summa, C. M., Rosenblatt, M. M., Hong, J.-K., Lear, J. D. & DeGrado, W. F. Computational de novo design, and characterization of an A2B2 diiron protein. J. Mol. Biol. 321, 923–938 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anishchenko, I. et al. Modeling protein–small molecule conformational ensembles with PLACER. Proc. Natl Acad. Sci. USA 122, e2427161122 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lauko, A. et al. Computational design of serine hydrolases. Science 388, eadu2454 (2025). Lauko et al. demonstrated that serine hydrolases can be computationally designed de novo with high catalytic efficiency, establishing a general strategy for designing new enzymes.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Elaily, W. et al. Computational design of a thermostable de novo biocatalyst for whole cell biotransformations. Preprint at bioRxiv https://doi.org/10.1101/2024.10.07.617055 (2024).

  • Kalvet, I. et al. Design of heme enzymes with a tunable substrate binding pocket adjacent to an open metal coordination site. J. Am. Chem. Soc. 145, 14307–14315 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reedy, C. J. & Gibney, B. R. Heme protein assemblies. Chem. Rev. 104, 617–649 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mann, S. I., Nayak, A., Gassner, G. T., Therien, M. J. & DeGrado, W. F. De novo design, solution characterization, and crystallographic structure of an abiological Mn-porphyrin-binding protein capable of stabilizing a Mn(V) species. J. Am. Chem. Soc. 143, 252–259 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hou, K. et al. De novo design of porphyrin-containing proteins as efficient and stereoselective catalysts. Science 388, 665–670 (2025).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, W. et al. De Novo design, directed evolution and computational study of heme-binding helical bundle protein catalysts for biocatalytic enantioselective Ge-H insertion. J. Am. Chem. Soc. https://doi.org/10.1021/jacs.5c13909 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zou, Z. et al. De novo design and evolution of an artificial metathase for cytoplasmic olefin metathesis. Nat. Catal. 8, 1208–1219 (2025).

  • Kim, D. et al. Computational design of metallohydrolases. Nature 649, 246–253 (2026).

  • Wolfe, S. A., Nekludova, L. & Pabo, C. O. DNA recognition by Cys2,His2 zinc finger proteins. Annu. Rev. Biophys. Biomol. Struct. 29, 183–212 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Joung, J. K. & Sander, J. D. TALENs: a widely applicable technology for targeted genome editing. Nat. Rev. Mol. Cell Biol. 14, 49–55 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, J. Y. & Doudna, J. A. CRISPR technology: a decade of genome editing is only the beginning. Science 379, eadd8643 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Milk, L., Daber, R. & Lewis, M. Functional rules for lac repressor-operator associations and implications for protein-DNA interactions. Protein Sci. 19, 1162–1172 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Glasscock, C. J. et al. Computational design of sequence-specific DNA-binding proteins. Nat. Struct. Mol. Biol. 32, 2252–2261 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Quijano-Rubio, A. et al. De novo design of modular and tunable protein biosensors. Nature 591, 482–487 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J. Z. et al. Computationally designed sensors detect endogenous Ras activity and signaling effectors at subcellular resolution. Nat. Biotechnol. 42, 1888–1898 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, J. Z., Ong, S.-E., Baker, D. & Maly, D. J. Single-cell sensor analyses reveal signaling programs enabling Ras-G12C drug resistance. Nat. Chem. Biol. 21, 47–58 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rose, J. C. et al. A computationally engineered RAS rheostat reveals RAS-ERK signaling dynamics. Nat. Chem. Biol. 13, 119–126 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Giordano-Attianese, G. et al. A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy. Nat. Biotechnol. 38, 426–432 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Praetorius, F. et al. Design of stimulus-responsive two-state hinge proteins. Science 381, 754–760 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guo, A. B. et al. Deep learning-guided design of dynamic proteins. Science 388, eadr7094 (2025). Guo et al. demonstrated that deep learning can guide the rational design of dynamic proteins, enabling precise control over conformational landscapes and function.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Broerman, A. J. et al. Design of facilitated dissociation enables timing of cytokine signalling. Nature 647, 528–535 (2025).

  • Pyles, H., Zhang, S., De Yoreo, J. J. & Baker, D. Controlling protein assembly on inorganic crystals through designed protein interfaces. Nature 571, 251–256 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Davila-Hernandez, F. A. et al. Directing polymorph specific calcium carbonate formation with de novo protein templates. Nat. Commun. 14, 8191 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saragovi, A. et al. De novo design of metal-oxide templating proteins. Preprint at bioRxiv https://doi.org/10.1101/2024.06.24.600095 (2025).

  • Blankenship, R. E. et al. Comparing photosynthetic and photovoltaic efficiencies and recognizing the potential for improvement. Science 332, 805–809 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hitchcock, A. et al. Redesigning the photosynthetic light reactions to enhance photosynthesis—the PhotoRedesign consortium. Plant J. 109, 23–34 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Ennist, N. M. et al. De novo design of proteins housing excitonically coupled chlorophyll special pairs. Nat. Chem. Biol. 20, 906–915 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ennist, N. M., Stayrook, S. E., Leslie Dutton, P. & Moser, C. C. Rational design of photosynthetic reaction center protein maquettes. Fronti. Mol. Biosci. 9, 997295 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Barber, J. & Tran, P. D. From natural to artificial photosynthesis. J. R. Soc. Interface 10, 20120984 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Romero, E., Novoderezhkin, V. I. & van Grondelle, R. Quantum design of photosynthesis for bio-inspired solar-energy conversion. Nature 543, 355–365 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Bahrami, A. A., Payandeh, Z., Khalili, S., Zakeri, A. & Bandehpour, M. Immunoinformatics: in silico approaches and computational design of a multi-epitope, immunogenic protein. Int. Rev. Immunol. 38, 307–322 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tsuboyama, K. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wittmann, B. J. et al. Strengthening nucleic acid biosecurity screening against generative protein design tools. Science 390, 82–87 (2025).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, X. et al. De Novo design of integrin α5β1 modulating proteins to enhance biomaterial properties. Adv. Mater. 37, e2500872 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, X. et al. Tuning insulin receptor signaling using de novo-designed agonists. Mol. Cell https://doi.org/10.1016/j.molcel.2025.09.020 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ho, S. P. & Degrado, W. F. Design of a 4-helix bundle protein: synthesis of peptides which self-associate into a helical protein. J. Am. Chem. Soc. 109, 6751–6758 (1987).

  • DiMaio, F., Leaver-Fay, A., Bradley, P., Baker, D. & André, I. Modeling symmetric macromolecular structures in Rosetta3. PLoS ONE 6, e20450 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, P.-S. et al. High thermodynamic stability of parametrically designed helical bundles. Science 346, 481–485 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Padilla, J. E., Colovos, C. & Yeates, T. O. Nanohedra: using symmetry to design self assembling protein cages, layers, crystals, and filaments. Proc. Natl Acad. Sci. USA 98, 2217–2221 (2001).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lai, Y.-T., Cascio, D. & Yeates, T. O. Structure of a 16-nm cage designed by using protein oligomers. Science 336, 1129 (2012).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hsia, Y. et al. Design of multi-scale protein complexes by hierarchical building block fusion. Nat. Commun. 12, 2294 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • King, N. P. et al. Computational design of self-assembling protein nanomaterials with atomic level accuracy. Science 336, 1171–1174 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fallas, J. A. et al. Computational design of self-assembling cyclic protein homo-oligomers. Nat. Chem. 9, 353–360 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sheffler, W. et al. Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock. PLoS Comput. Biol. 19, e1010680 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • RELATED ARTICLES

    Most Popular

    Recent Comments