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Design of high-affinity binders to immune modulating receptors for cancer immunotherapy
Abstract
Immune receptors have emerged as critical therapeutic targets for cancer immunotherapy. Designed protein binders can have high affinity, modularity, and stability and hence could be attractive components of protein therapeutics directed against these receptors, but traditional Rosetta based protein binder methods using small globular scaffolds have difficulty achieving high affinity on convex targets. Here we describe the development of helical concave scaffolds tailored to the convex target sites typically involved in immune receptor interactions. We employed these scaffolds to design proteins that bind to TGFβRII, CTLA-4, and PD-L1, achieving low nanomolar to picomolar affinities and potent biological activity following experimental optimization. Co-crystal structures of the TGFβRII and CTLA-4 binders in complex with their respective receptors closely match the design models. These designs should have considerable utility for downstream therapeutic applications.
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“Strategies such as reducing intrinsic affinity, lowering ligand copy number, or optimizing spatial presentation allow carriers to bypass perivascular sequestration and achieve deeper interstitial penetration [93].”
From Overcoming tumor microenvironment barriers: transformable and bioinspired nanomedicine strategies for deep tumor penetration (2026) · cites this paper as [93]
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Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325
Self-citationRobust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,210
Self-citationRobust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107
Self-citationThe Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
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Self-citationTGFβ biology in cancer progression and immunotherapy
Derynck, R., Turley, S. J., & Akhurst, R. J. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0403-1 · Cited by 944
De novo design of potent and selective mimics of IL-2 and IL-15
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., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531
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Cited by
Sorted by citations
Sheng, J., Yuan, W., Zhang, M., Chen, Z., Qi, Y., Zhao, Y., & Zhang, S. (2026) · Journal of Nanobiotechnology · doi:10.1186/s12951-026-04028-7 · Cited by 35
Chen, Y., Nguyen, L. B. T., Zhong, Q.-Z., Tan, E. X., Wang, L., Phang, I. Y., Lv, Y., & Ling, X. Y. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c01066
Metadata
- Authors
- Yang, W., Hicks, D. R., Ghosh, A., Schwartze, T. A., Conventry, B., Goreshnik, I., Allen, A., Halabiya, S. F., Kim, C. J., Hinck, C. S., Lee, D. S., Bera, A. K., Li, Z., Wang, Y., Schlichthaerle, T., Cao, L., Huang, B., Garrett, S., Gerben, S. R., Rettie, S., Heine, P., Murray, A., Edman, N., Carter, L., Stewart, L., Almo, S. C., Hinck, A. P., & Baker, D.
- Venue
- Nature Communications
- Published
- 2025-02-26
- Volume
- 16
- Issue
- 1
- ISSN
- 2041-1723
- PMC
- PMC11865580
- Cited by
- 40
- References
- 11
- Language
- en
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (R01AG063845, U19AG065156)
- United States Department of Defense | Defense Advanced Research Projects Agency (HR001120S0052, HR001117S0003)
- Gates Ventures;Audacious Project;Open Philanthropy Project Improving Protein Design Fund;Howard Hughes Medical Institute
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