De novo design of potent and selective mimics of IL-2 and IL-15
Open accessAbstract
We describe a de novo computational approach for designing proteins that recapitulate the binding sites of natural cytokines, but are otherwise unrelated in topology or amino acid sequence. We use this strategy to design mimics of the central immune cytokine interleukin-2 (IL-2) that bind to the IL-2 receptor βγ c heterodimer (IL-2Rβγ c) but have no binding site for IL-2Rα (also called CD25) or IL-15Rα (also known as CD215). The designs are hyper-stable, bind human and mouse IL-2Rβγ c with higher affinity than the natural cytokines, and elicit downstream cell signalling independently of IL-2Rα and IL-15Rα. Crystal structures of the optimized design neoleukin-2/15 (Neo-2/15), both alone and in complex with IL-2Rβγ c, are very similar to the designed model. Neo-2/15 has superior therapeutic activity to IL-2 in mouse models of melanoma and colon cancer, with reduced toxicity and undetectable immunogenicity. Our strategy for building hyper-stable de novo mimetics could be applied generally to signalling proteins, enabling the creation of superior therapeutic candidates.
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The backbones generated by previous functional-site scaffolding methods are not guaranteed to be designable.
“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [8]
Previous methods can scaffold functional sites made up of one or two contiguous chain segments.
“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [8]
With the exception of helical bundles, previous methods do not extend readily to more complex functional sites composed of three or more chain segments.
“Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be designable (encodable by some amino acid sequence).”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [8]
Designing 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 challenge.
“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 [3]
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 binding.
“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 [3]
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- Authors
- Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., Biary, T., Crowley, S. J., Marcos, E., Walkey, C. D., Weitzner, B. D., Pardo-Avila, F., Castellanos, J., Carter, L., Stewart, L., Riddell, S. R., Pepper, M., Bernardes, G. J. L., Dougan, M., Garcia, K. C., & Baker, D.
- Affiliations
- University of Washington (US)
- Johns Hopkins University (US)
- Stanford Medicine (US)
- Harvard University (US)
- Fred Hutch Cancer Center (US)
- University of Lisbon (PT)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Silva, Daniel-Adriano
- Baker, David
- ORCID
- Silva, Daniel-Adriano
- Yu, Shawn
- Ulge, Umut Y.
- Spangler, Jamie B.
- Jude, Kevin M.
- Ali, Lestat R.
- Ruterbusch, Mikel
- Leung, Isabel
- Crowley, Stephanie J.
- Marcos, Enrique
- Walkey, Carl D.
- Weitzner, Brian D.
- Carter, Lauren
- Stewart, Lance
- Riddell, Stanley R.
- Pepper, Marion
- Bernardes, Gonçalo J. L.
- Dougan, Michael
- Baker, David
- Venue
- Nature
- Published
- 2019-01-09
- Volume
- 565
- Issue
- 7738
- Pages
- 186-191
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6521699
- OpenAlex
- W2908391177
- Cited by
- 531
- References
- 97
- Citations by year
- 2026: 56 · 2025: 75 · 2024: 69 · 2023: 81 · 2022: 75 · 2021: 76 · 2020: 52 · 2019: 47
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (acceptedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Howard Hughes Medical Institute
- Royal Society (RGF/EA/180007)
- National Institutes of Health (1s10od012289-01a1, 1s10od021832-01)
- National Institute of General Medical Sciences (T32GM007266, R01GM059224, P41GM103393, R35GM122543)
- National Institute of Allergy and Infectious Diseases (R37AI051321, R01AI108626, R01AI051321)
- National Institute of Diabetes and Digestive and Kidney Diseases (K08DK114563)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- de novo protein design, interleukin-2, IL-15, melanoma, colon cancer, immunogenicity
- MeSH terms
- Amino Acid Sequence, Animals, Binding Sites, Colonic Neoplasms, Computer Simulation, Disease Models, Animal, Humans, Interleukin-2, Melanoma, Models, Molecular, Drug Design, Receptors, Interleukin-2, Signal Transduction, Crystallography, X-Ray, Molecular Mimicry, Interleukin-15, Mice, Interleukin-2 Receptor alpha Subunit, Protein Stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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