De novo design of high-affinity binders of bioactive helical peptides
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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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Nature |
| Vol/bind | 626 |
| Udgave nummer | 7998 |
| Sider (fra-til) | 435-442 |
| ISSN | 0028-0836 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
This work was supported with funds provided by a grant (U19 AG065156) from the National Institute for Aging (S.V.T., M.J.M., E.H., J.B., A.N.H., H.-H.H., I.D.L. and D.B.), a gift from Amgen (J.L.W.), the Audacious Project at the Institute for Protein Design (A.H.-W.Y. and D.B.), a gift from Microsoft Gift supporting Computational Protein Structure Prediction and Design at the Institute for Protein Design (D.J. and D.B.), the Washington State General Operating Fund supporting the Institute for Protein Design (P.V. and L.S.), a grant (INV-010680) from the Bill and Melinda Gates Foundation (D.J., J.L.W. and D.B.), an NIH NIBIB Pathway to Independence Award (A.H.-W.Y.; K99EB031913), a National Science Foundation Training Grant (EF-2021552; P.J.Y.L.), NERSC award BER-ERCAP0022018 (P.J.Y.L), the Open Philanthropy Project Improving Protein Design Fund (P.J.Y.L., G.R.L. and D.B.), The Donald and Jo Anne Petersen Endowment for Accelerating Advancements in Alzheimer’s Disease Research (N.R.B.), an EMBO Postdoctoral Fellowship (grant number ALTF 292-2022; J.L.W.) and the Howard Hughes Medical Institute (D.B.). J.M.R. and F.H. were supported by the Novo Nordisk Foundation (NNF19OC0054441 to J.M.R.). H.-H.H. is supported by a postdoctoral fellowship provided by the Partnership for Clean Competition. This work was additionally supported with funds provided by the grant T1D U01 DK121289 (J.B. and A.N.H.) and NSF Award 2021552 (J.N.). We thank Microsoft and AWS for gifts of cloud computing resources. Crystallographic diffraction data were collected at the Northeastern Collaborative Access Team beamlines at the Advanced Photon Source, which are funded by the National Institute of General Medical Sciences from the National Institutes of Health (P30 GM124165). This research used resources of the Advanced Photon Source, a US Department of Energy (DOE) Office of Science User Facility operated for the Department of Energy Office of Science by Argonne National Laboratory under contract number DE-AC02-06CH11357.
Funding Information:
This work was supported with funds provided by a grant (U19 AG065156) from the National Institute for Aging (S.V.T., M.J.M., E.H., J.B., A.N.H., H.-H.H., I.D.L. and D.B.), a gift from Amgen (J.L.W.), the Audacious Project at the Institute for Protein Design (A.H.-W.Y. and D.B.), a gift from Microsoft Gift supporting Computational Protein Structure Prediction and Design at the Institute for Protein Design (D.J. and D.B.), the Washington State General Operating Fund supporting the Institute for Protein Design (P.V. and L.S.), a grant (INV-010680) from the Bill and Melinda Gates Foundation (D.J., J.L.W. and D.B.), an NIH NIBIB Pathway to Independence Award (A.H.-W.Y.; K99EB031913), a National Science Foundation Training Grant (EF-2021552; P.J.Y.L.), NERSC award BER-ERCAP0022018 (P.J.Y.L), the Open Philanthropy Project Improving Protein Design Fund (P.J.Y.L., G.R.L. and D.B.), The Donald and Jo Anne Petersen Endowment for Accelerating Advancements in Alzheimer’s Disease Research (N.R.B.), an EMBO Postdoctoral Fellowship (grant number ALTF 292-2022; J.L.W.) and the Howard Hughes Medical Institute (D.B.). J.M.R. and F.H. were supported by the Novo Nordisk Foundation (NNF19OC0054441 to J.M.R.). H.-H.H. is supported by a postdoctoral fellowship provided by the Partnership for Clean Competition. This work was additionally supported with funds provided by the grant T1D U01 DK121289 (J.B. and A.N.H.) and NSF Award 2021552 (J.N.). We thank Microsoft and AWS for gifts of cloud computing resources. Crystallographic diffraction data were collected at the Northeastern Collaborative Access Team beamlines at the Advanced Photon Source, which are funded by the National Institute of General Medical Sciences from the National Institutes of Health (P30 GM124165). This research used resources of the Advanced Photon Source, a US Department of Energy (DOE) Office of Science User Facility operated for the Department of Energy Office of Science by Argonne National Laboratory under contract number DE-AC02-06CH11357.
Publisher Copyright:
© The Author(s) 2023.
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