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Massively parallel de novo protein design for targeted therapeutics
Open accessAbstract
De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.
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“The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1–5.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
In the citing paper
“Most current computational methods used to design proteins that bind to a target surface utilize information derived from structures of the native complex on specific side-chain interactions or protein backbone placements optimal for binding1–3.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
In the citing paper
“Protein1,5, peptide36 and small-molecule inhibitors37 have been designed to bind to the stem region of group 1 HA and neutralize influenza A viruses, but none recognize the group 2 HA.”
From Design of protein-binding proteins from the target structure alone (2022) · cites this paper as [1]
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MethodDe novo design of high-affinity binders of bioactive helical peptides
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“Several designed proteins have generated very low or undetectable levels of anti-mini-binder IgG after repeated systemic dosing in mice 14–16 and repeated oral dosing in humans.”
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“This has been achieved for protein–protein binding due to the simplicity of small globular proteins (10).”
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Self-citationCo‐Evolutionary Fitness Landscapes for Sequence Design
Tian, P., Louis, J. M., Baber, J. L., Aniana, A., & Best, R. B. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201713220 · Cited by 76
De novo designed protein inhibitors of amyloid aggregation and seeding
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“incorporated into the designs, as has been done in previous studies, because of the reducing conditions of the cytoplasm (44).”
MethodHigh-throughput directed evolution: a golden era for protein science
McLure, R. J., Radford, S. E., & Brockwell, D. J. (2022) · Trends in Chemistry · doi:10.1016/j.trechm.2022.02.004 · Cited by 76
The role and future prospects of artificial intelligence algorithms in peptide drug development
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AlphaDesign: A de novo protein design framework based on AlphaFold
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“Recent examples include protein logic gates [38], self-assembling systems [39] and targeted therapeutics [40].”
BackgroundDe Novo -Designed α-Helical Barrels as Receptors for Small Molecules
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Reconfigurable asymmetric protein assemblies through implicit negative design
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“Themini- mizeddockedstrandswerethengeometri-callymatchedtothescaffoldlibraryusingthe MotifGraftMovertocreateadockedprotein-proteincomplex( 36 ).”
Self-citationRotamer-free protein sequence design based on deep learning and self-consistency
Liu, Y., Zhang, L., Wang, W., Zhu, M., Wang, C., Li, F., Zhang, J., Li, H., Chen, Q., & Liu, H. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00273-6 · Cited by 72
“12–15) suggest that all of these proteins are monomeric and fold into well-defined three-dimensional structures.”
BackgroundSelf-citationBiomolecular Modeling and Simulation: A Prospering Multidisciplinary Field
Tamar Schlick, A., Portillo‐Ledesma, S., Myers, C. G., Beljak, L., Chen, J. B., Dakhel, S., Darling, D., Ghosh, S., Hall, J. W., Jan, M., et al. (2021) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-091720-102019 · Cited by 69
“Various high-throughput miniprotein design techniques,such as the massively parallel de novo protein design platform of the Baker group (29), can design proteins with customized shapes to bind therapeutic targets.”
BackgroundDe novo design of drug-binding proteins with predictable binding energy and specificity
Lü, L., Gou, X., Tan, S. K., Mann, S. I., Yang, H., Zhong, X., Gazgalis, D., Valdiviezo, J., Jo, H., Wu, Y., et al. (2024) · Science · doi:10.1126/science.adl5364 · Cited by 78
Protein design via deep learning
Ding, W., Nakai, K., & Gong, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac102 · Cited by 66
“Advances in immune signaling [2, 3], targeted therapeutics [4, 5], sense-response systems [6], protein switches [7, 8], self-assembly materials [9, 10] and other fields not mentioned here have shown the exciting potential of utilizing proteins as functional and reproducible materials.”
Zhu, X., Turner, H. L., Lang, S., McBride, R., Bangaru, S., Gilchuk, I. M., Yu, W., Paulson, J. C., Crowe, J. E., Ward, A. B., et al. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.10.002 · Cited by 68
“Our results reveal sites of vulnerability on NA that may inform design of new drugs, such as therapeutic antibodies, small proteins, peptides or small molecules [as for the HA (Chevalier et al., 2017; Fleishman et al., 2011; Kadam et al., 2017; Laursen et al., 2018; Strauch et al., 2017; van Dongen et al., 2019)], to…”
BackgroundSelf-citationSYNBIP: synthetic binding proteins for research, diagnosis and therapy
Wang, X., Li, F., Qiu, W., Xu, B., Li, Y., Lian, X., Yu, H., Zhang, Z., Wang, J., Li, Z., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab926 · Cited by 70
Different genetic barriers for resistance to HA stem antibodies in influenza H3 and H1 viruses
Wu, N. C., Thompson, A. J., Lee, J. M., Su, W., Arlian, B. M., Xie, J., Lerner, R. A., Yen, H., Bloom, J. D., & Wilson, I. A. (2020) · Science · doi:10.1126/science.aaz5143 · Cited by 68
Self-citationPeng, J., Wang, W., Yu, Y., Gu, H., & Huang, X. (2018) · Chinese Journal of Chemical Physics · doi:10.1063/1674-0068/31/cjcp1806147 · Cited by 67
Self-citationComputational Design of Potent D-Peptide Inhibitors of SARS-CoV-2
Valiente, P. A., Wen, H., Nim, S., Lee, J., Kim, H. J., Kim, J., Perez‐Riba, A., Paudel, Y. P., Hwang, I., Kim, K., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00655 · Cited by 65
DNA-based construction at the nanoscale: emerging trends and applications
Xavier, P. L., & Chandrasekaran, A. R. (2017) · Nanotechnology · doi:10.1088/1361-6528/aaa120 · Cited by 69
“nificant roles in the development of bionanomaterials and biophysical applications [276, 277].”
BackgroundMarchand, A., Hall‐Beauvais, A. V., & Correia, B. E. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102370 · Cited by 69
Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists
Glögl, M., Krishnakumar, A., Ragotte, R. J., Goreshnik, I., Coventry, B., Bera, A. K., Kang, A., Joyce, E., Ahn, G., Huang, B., et al. (2024) · Science · doi:10.1126/science.adp1779 · Cited by 67
Self-citationStructural Biology of Influenza Hemagglutinin: An Amaranthine Adventure
Wu, N. C., & Wilson, I. A. (2020) · Viruses · doi:10.3390/v12091053 · Cited by 70
Self-citationIntegrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies
Xiang, Y., Sang, Z., Bitton, L., Xu, J., Liu, Y., Schneidman‐Duhovny, D., & Shi, Y. (2021) · Cell Systems · doi:10.1016/j.cels.2021.01.003 · Cited by 63
Engineering synthetic regulatory circuits in plants
Kassaw, T. K., Donayre-Torres, A. J., Antunes, M. S., Morey, K. J., & Medford, J. I. (2018) · Plant Science · doi:10.1016/j.plantsci.2018.04.005 · Cited by 60
Computational design of a synthetic PD-1 agonist
Bryan, C. M., Rocklin, G. J., Bick, M. J., Ford, A., Majri-Morrison, S. S., Kroll, A. V., Miller, C. J., Carter, L. P., Goreshnik, I., Kang, A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102164118 · Cited by 61
“Oligo pools were amplified and transformed into yeast as previously described (25).”
Self-citationBeyond genomics in Patescibacteria: A trove of unexplored biology packed into ultrasmall bacteria
Srinivas, P., Peterson, S. B., Gallagher, L. A., Wang, Y.-X., & Mougous, J. D. (2024) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2419369121 · Cited by 56
“The development of nanobodies or synthetic peptide binders designed to target proteins of interest could also be a useful tool for linking the numerous essential genes with no characterized homologs to phenotypes, as a first step toward uncovering their functions (65).”
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59
Comparing proteins and nucleic acids for next-generation biomolecular engineering
Pugh, G., Burns, J. R., & Howorka, S. (2018) · Nature Reviews Chemistry · doi:10.1038/s41570-018-0015-9 · Cited by 59
Programming Structured DNA Assemblies to Probe Biophysical Processes
Wamhoff, E., Banal, J. L., Bricker, W. P., Shepherd, T. R., Parsons, M. F., Veneziano, R., Stone, M. B., Jun, H., Wang, X., & Bathe, M. (2019) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-052118-115259 · Cited by 59
Next generation of multispecific antibody engineering
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Yao, Y., Kadam, R. U., Lee, C.-C. D., Woehl, J. L., Wu, N. C., Zhu, X., Kitamura, S., Wilson, I. A., & Wolan, D. W. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006893117 · Cited by 57
“4 (25) from the group 1 HA stem epitope.”
BackgroundSelf-citationRevolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era
Chang, L., Mondal, A., Singh, B., Martínez‐Noa, Y., & Perez, A. (2024) · WIREs Computational Molecular Science · doi:10.1002/wcms.1693 · Cited by 56
“53,54 Rosetta-based designs typically draw information from developer expertise and complementary experiments such as directed evolution.”
BackgroundMethodDesign and structural validation of peptide–drug conjugate ligands of the kappa-opioid receptor
Muratspahić, E., Deibler, K., Han, J., Tomašević, N., Jadhav, K. B., Olivé-Marti, A.-L., Hochrainer, N., Hellinger, R., Koehbach, J., Fay, J. F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43718-w · Cited by 55
“2) 20 .”
Self-citationCode to complex: AI-driven de novo binder design
Fox, D., Taveneau, C., Clement, J., Grinter, R., & Knott, G. J. (2025) · Structure · doi:10.1016/j.str.2025.08.007 · Cited by 62
Essentials of de novo protein design: Methods and applications
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“37 Finally, Chevalier et al. introduced disulfide bonds into the massive design of miniprotein binders, 46 and the subsequent analysis of the massive dataset allowed to determine that designed disulfide bridges can effectively stabilize de novo binders as long as its introduction results in a…”
BackgroundInfluentialSelf-citationXenoprotein engineering via synthetic libraries
Gates, Z. P., Vinogradov, A. A., Quartararo, A. J., Bandyopadhyay, A., Choo, Z.-N., Evans, E. D., Halloran, K. H., Mijalis, A. J., Mong, S. K., Simon, M. D., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1722633115 · Cited by 54
“In the future, synthetic libraries may be used to empirically evaluate the success of de novo xenoprotein designs, as has recently been achieved with yeast surface display and de novo designs based on proteogenic amino acids (91, 95).”
BackgroundDisulfide-Directed Multicyclic Peptide Libraries for the Discovery of Peptide Ligands and Drugs
Lu, S., Fan, S., Xiao, S., Li, J., Zhang, S., Wu, Y., Kong, C., Zhuang, J., Liu, H., Zhao, Y., et al. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c12462 · Cited by 53
Fragment-based computational design of antibodies targeting structured epitopes
Aguilar Rangel, M., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., Ricagno, S., Frydman, J., Vendruscolo, M., & Sormanni, P. (2022) · Science Advances · doi:10.1126/sciadv.abp9540 · Cited by 55
Addressing epistasis in the design of protein function
Lipsh‐Sokolik, R., & Fleishman, S. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314999121 · Cited by 55
“…accurate enough to propose stable combinatorial mutants in an active site, and successful engi - neering of functional variants demanded cycles of computa - tional and experimental screening often aided by protein crystallography and high - throughput functional screening methods (72, 127, 128).”
BackgroundMethodMolecular evolution of peptides by yeast surface display technology
Linciano, S., Pluda, S., Bacchin, A., & Angelini, A. (2019) · MedChemComm · doi:10.1039/c9md00252a · Cited by 49
Identifying the origin of local flexibility in a carbohydrate polymer
Anggara, K., Zhu, Y., Fittolani, G., Yu, Y., Tyrikos‐Ergas, T., Delbianco, M., Rauschenbach, S., Abb, S., Seeberger, P. H., & Kern, K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102168118 · Cited by 49
“The correlation between the sequence, the higher hierarchy structures, and the resulting properties is relatively well established for polynucleotides (13, 14) and polypeptides (15, 16), while comparatively little is known for polysaccharides (17).”
Background
Metadata
- Authors
- Chevalier, A., Silva, D.-A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K.-H., Yao, G., Bahl, C. D., Miyashita, S.-I., Goreshnik, I., Fuller, J. T., Koday, M. T., Jenkins, C. M., Colvin, T., Carter, L., Bohn, A., Bryan, C. M., Fernández-Velasco, D. A., Stewart, L., Dong, M., Huang, X., Jin, R., Wilson, I. A., Fuller, D. H., & Baker, D.
- Affiliations
- University of Washington (US)
- Scripps Research Institute (US)
- Chinese Academy of Sciences (CN)
- University of California, Irvine (US)
- Boston Children's Hospital (US)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Baker, David
- ORCID
- Chevalier, Aaron
- Silva, Daniel-Adriano
- Hicks, Derrick R.
- Vergara, Renan
- Murapa, Patience
- Bernard, Steffen M.
- Zhang, Lu
- Lam, Kwok-Ho
- Yao, Guorui
- Bahl, Christopher D.
- Miyashita, Shin-Ichiro
- Goreshnik, Inna
- Fuller, James T.
- Jenkins, Cody M.
- Carter, Lauren
- Bryan, Cassie M.
- Stewart, Lance
- Dong, Min
- Huang, Xuhui
- Jin, Rongsheng
- Wilson, Ian A.
- Fuller, Deborah H.
- Baker, David
- Venue
- Nature
- Published
- 2017-09-27
- Volume
- 550
- Issue
- 7674
- Pages
- 74-79
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5802399
- OpenAlex
- W2759860792
- Cited by
- 453
- References
- 48
- Citations by year
- 2026: 28 · 2025: 57 · 2024: 48 · 2023: 42 · 2022: 59 · 2021: 55 · 2020: 57 · 2019: 53 · 2018: 48 · 2017: 6
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- National Institutes of Health (1s10od021527-01, 5t32ai007354-30)
- National Institute of General Medical Sciences (P41GM103403)
- National Institute of Allergy and Infectious Diseases (R41AI122431, R01AI091823, R56AI127371, R56AI117675, R21AI119258, R33AI119258, R21AI123920, T32-AI007354., R01AI125704)
- National Institute of Neurological Disorders and Stroke (R01NS080833)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Keywords
- de novo protein design, miniproteins, protein binders, influenza haemagglutinin, botulinum neurotoxin B, oligonucleotide synthesis, protein stability, antiviral agents, prophylactic protection, antibody alternatives, immune response
- MeSH terms
- Botulinum Toxins, Computer Simulation, Hot Temperature, Humans, Influenza, Human, Protein Binding, Proteins, Temperature, Drug Design, Protein Engineering, Hemagglutinin Glycoproteins, Influenza Virus, Protein Stability, Molecular Dynamics Simulation, Molecular Targeted Therapy
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