De novo design of high-affinity binders of bioactive helical peptides

Susana Vázquez Torres, Philip J. Y. Leung, Preetham Venkatesh, Isaac D. Lutz, Fabian Hink, Huu-Hien Huynh, Jessica Becker, Andy Hsien-Wei Yeh, David Juergens, Nathaniel R. Bennett, Andrew N. Hoofnagle, Eric Huang, Michael J. MacCoss, Marc Expòsit, Gyu Rie Lee, Asim K. Bera, Alex Kang, Joshmyn Cruz, Paul M. Levine, Xinting Li, Mila Lamb, Stacey R. Gerben, Analisa Murray, Piper Heine, Elif Nihal Korkmaz, Jeff Nivala, Lance Stewart, Joseph L. Watson (), Joseph M. Rogers () and David Baker ()
Additional contact information
Susana Vázquez Torres: University of Washington
Philip J. Y. Leung: University of Washington
Preetham Venkatesh: University of Washington
Isaac D. Lutz: University of Washington
Fabian Hink: University of Copenhagen
Huu-Hien Huynh: University of Washington
Jessica Becker: University of Washington
Andy Hsien-Wei Yeh: University of Washington
David Juergens: University of Washington
Nathaniel R. Bennett: University of Washington
Andrew N. Hoofnagle: University of Washington
Eric Huang: University of Washington
Michael J. MacCoss: University of Washington
Marc Expòsit: University of Washington
Gyu Rie Lee: University of Washington
Asim K. Bera: University of Washington
Alex Kang: University of Washington
Joshmyn Cruz: University of Washington
Paul M. Levine: University of Washington
Xinting Li: University of Washington
Mila Lamb: University of Washington
Stacey R. Gerben: University of Washington
Analisa Murray: University of Washington
Piper Heine: University of Washington
Elif Nihal Korkmaz: University of Washington
Jeff Nivala: University of Washington
Lance Stewart: University of Washington
Joseph L. Watson: University of Washington
Joseph M. Rogers: University of Copenhagen
David Baker: University of Washington

Nature, 2024, vol. 626, issue 7998, 435-442

Abstract: Abstract Many peptide hormones form an α-helix on binding their receptors1–4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41586-023-06953-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:626:y:2024:i:7998:d:10.1038_s41586-023-06953-1

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-023-06953-1

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().