De novo design of high-affinity binders of bioactive helical peptides
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De novo design of high-affinity binders of bioactive helical peptides. / Vázquez Torres, Susana; Leung, Philip J.Y.; Venkatesh, Preetham; Lutz, Isaac D.; Hink, Fabian; Huynh, Huu Hien; Becker, Jessica; Yeh, Andy Hsien Wei; Juergens, David; Bennett, Nathaniel R.; Hoofnagle, Andrew N.; Huang, Eric; MacCoss, Michael J.; Expòsit, Marc; Lee, Gyu Rie; Bera, Asim K.; Kang, Alex; De La Cruz, Joshmyn; Levine, Paul M.; Li, Xinting; Lamb, Mila; Gerben, Stacey R.; Murray, Analisa; Heine, Piper; Korkmaz, Elif Nihal; Nivala, Jeff; Stewart, Lance; Watson, Joseph L.; Rogers, Joseph M.; Baker, David.
In: Nature, Vol. 626, No. 7998, 2024, p. 435-442.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - De novo design of high-affinity binders of bioactive helical peptides
AU - Vázquez Torres, Susana
AU - Leung, Philip J.Y.
AU - Venkatesh, Preetham
AU - Lutz, Isaac D.
AU - Hink, Fabian
AU - Huynh, Huu Hien
AU - Becker, Jessica
AU - Yeh, Andy Hsien Wei
AU - Juergens, David
AU - Bennett, Nathaniel R.
AU - Hoofnagle, Andrew N.
AU - Huang, Eric
AU - MacCoss, Michael J.
AU - Expòsit, Marc
AU - Lee, Gyu Rie
AU - Bera, Asim K.
AU - Kang, Alex
AU - De La Cruz, Joshmyn
AU - Levine, Paul M.
AU - Li, Xinting
AU - Lamb, Mila
AU - Gerben, Stacey R.
AU - Murray, Analisa
AU - Heine, Piper
AU - Korkmaz, Elif Nihal
AU - Nivala, Jeff
AU - Stewart, Lance
AU - Watson, Joseph L.
AU - Rogers, Joseph M.
AU - Baker, David
N1 - Publisher Copyright: © The Author(s) 2023.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1038/s41586-023-06953-1
DO - 10.1038/s41586-023-06953-1
M3 - Journal article
C2 - 38109936
AN - SCOPUS:85183900079
VL - 626
SP - 435
EP - 442
JO - Nature Genetics
JF - Nature Genetics
SN - 1061-4036
IS - 7998
ER -
ID: 382848291