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Antibodies expand the scope of angiotensin receptor pharmacology

Nature Chemical Biology (2024)Cite this article

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Abstract

G-protein-coupled receptors (GPCRs) are key regulators of human physiology and are the targets of many small-molecule research compounds and therapeutic drugs. While most of these ligands bind to their target GPCR with high affinity, selectivity is often limited at the receptor, tissue and cellular levels. Antibodies have the potential to address these limitations but their properties as GPCR ligands remain poorly characterized. Here, using protein engineering, pharmacological assays and structural studies, we develop maternally selective heavy-chain-only antibody (‘nanobody’) antagonists against the angiotensin II type I receptor and uncover the unusual molecular basis of their receptor antagonism. We further show that our nanobodies can simultaneously bind to angiotensin II type I receptor with specific small-molecule antagonists and demonstrate that ligand selectivity can be readily tuned. Our work illustrates that antibody fragments can exhibit rich and evolvable pharmacology, attesting to their potential as next-generation GPCR modulators.

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Fig. 1: Evolution of AT118 family of nanobody antagonists.
Fig. 2: Structure of AT118-H bound to AT1R.
Fig. 3: AT118-H mimics peptide agonist binding to AT1R.
Fig. 4: AT118-H binding to AT1R and AT2R variants.
Fig. 5: AT118-H stabilizes a hybrid active–inactive state of AT1R.
Fig. 6: AT118 family members exhibit probe dependence with small molecules.

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Data availability

The cryo-EM model and maps were deposited under the following accession numbers: AT118-H AT1R complex: PDB 8TH3, EMDB-41248; AT118-L AT1R complex: PDB 8TH4, EMDB-41249. Sequencing data can be found on GitHub: https://github.com/kruselab/AT118-library-sequencing. Source data are provided with this paper.

Code availability

The code for analyzing NGS sequencing data can be found on GitHub: https://github.com/kruselab/AT118-library-sequencing.

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Acknowledgements

We thank R. Lefkowitz for allowing preliminary pharmacological experiments to be performed in his lab, M. Bao and X. Zhou for critical reading of the paper, A. Kossiakoff for the BAG2 anti-BRIL Fab and NabFab, and R. Walsh and M. Mayer for assistance and advice during cryo-EM data collection at the Harvard Center for Cryo-Electron Microscopy at Harvard Medical School. The SBGrid Consortium provided computation support of structural biology software. This work was funded by a Merck Fellowship from the Helen Hay Whitney Foundation (M.A.S.); a Hanna H. Gray Fellowship from HHMI (M.S.A.G.); NIH grants no. K99HD110612 (M.A.S.), no. DP5OD021345 (A.C.K.), no. R21HD101596 (A.C.K.), no. R01CA260415 (A.C.K.) and no. R01NS088566 (M.K.L.); the Vallee Foundation (A.C.K.); the Smith Family Foundation (A.C.K.); the Pew Charitable Trusts (L.M.W.); the Whitehead Foundation (L.M.W.); the New York Stem Cell Foundation (M.K.L); the William Randolph Hearst Fellowship (H.X.); the Swiss National Science Foundation (SNSF grants no. 31003A_182263 and no. 310030_208179) (P.B.); Swiss Cancer Research (grant no. KFS-4687-02-2019) (P.B.); funds from EPFL (P.B.); and the Ludwig Institute for Cancer Research (P.B.).

Author information

Author notes

  1. Sarah M. Sterling

    Present address: Cryo-EM Facility at MIT.nano, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

  2. Dean P. Staus

    Present address: Septerna, South San Francisco, CA, USA

  3. Conor McMahon

    Present address: Sanofi, Large Molecule Research, Cambridge, MA, USA

Authors and Affiliations

  1. Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Meredith A. Skiba, Sarah M. Sterling, Shaun Rawson, Genevieve R. Nemeth, Morgan S. A. Gilman, Joseph D. Hurley, Conor McMahon & Andrew C. Kruse

  2. Interfaculty Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Shuhao Zhang & Patrick Barth

  3. Department of Pathology, Boston Children’s Hospital, Boston, MA, USA

    Huixin Xu & Maria K. Lehtinen

  4. Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Haoran Jiang, Dean P. Staus, Jihee Kim & Howard A. Rockman

  5. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Pengxiang Shen

  6. Howard Hughes Medical Institute, Duke University Medical Center, Durham, NC, USA

    Dean P. Staus & Jihee Kim

  7. Department of Cell Biology, Duke University Medical Center, Durham, NC, USA

    Howard A. Rockman

  8. Ludwig Institute for Cancer Research Lausanne, Epalinges, Switzerland

    Patrick Barth

  9. Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA

    Laura M. Wingler

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Contributions

M.A.S., D.P.S., C.M., L.M.W. and A.C.K. conceived of the project. M.A.S. generated the AT118 library and performed nanobody selections. J.D.H. and M.A.S. analyzed sequencing data. M.A.S., D.P.S. and L.M.W. purified all receptor constructs. M.A.S. and G.R.N. purified all nanobodies and Fab fragments. M.A.S., J.K. and L.M.W. performed radioligand binding assays. M.A.S. and G.R.N. performed flow cytometry binding and signaling assays. M.A.S. and D.P.S. designed AT118-Fc fusion constructs, which were purified by M.A.S. H.J. performed mouse blood pressure experiments under the supervision of H.A.R. H.X. carried out mouse placental transfer assays under the supervision of M.K.L. M.A.S. performed ELISA assays. M.A.S., S.M.S. and S.R. conceived and designed the cryo-EM screening strategy. M.A.S. and S.M.S. collected cryo-EM data. M.A.S., S.R. and M.S.A.G. processed cryo-EM data. M.A.S. modeled and analyzed the cryo-EM structures. S.Z. performed molecular dynamics simulations under the supervision of P.B. P.S. performed HPLC and HR-MS validation of small-molecule ligands. A.C.K. supervised all aspects of the research. M.A.S. wrote the paper with input from all authors.

Corresponding author

Correspondence to Andrew C. Kruse.

Ethics declarations

Competing interests

A.C.K., C.M., L.M.W., D.P.S. and M.A.S. are co-inventors on a patent application for AT1R blocking nanobodies. A.C.K. is a cofounder and consultant for biotechnology companies Tectonic Therapeutic and Seismic Therapeutic, and also for the Institute for Protein Innovation, a nonprofit research institute. L.M.W. is a scientific advisor for Septerna. D.P.S. is a Septerna employee. C.M. is a Sanofi employee. P.B. holds patents and provisional patent applications in the field of engineered T-cell therapies and protein design. The other authors declare no competing interests.

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Nature Chemical Biology thanks Haitao Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Engineering of high affinity AT1R nanobody antagonist with low non-specific binding.

a) Flowchart of nanobody selection. AT1R binders were enriched through two rounds of magnetic-activated cell sorting (MACS). Fluorescence-activated cell sorting (FACS) was used to isolate clone with low polyreactivity. A final FACS step enriched high-affinity AT1R binders. b) FACS round 1 plot. 1.38% of the population containing high-affinity AT1R binders with reduced polyspecificity were collected. c) FACS round 2 plot 0.7% of the population was collected containing high affinity AT1R binders. d) Binding of yeast-display library to FLAG-AT1R throughout each selection round. e) Distribution of AT1R-binding and polyreactive nanobodies in the yeast-display library throughout the selection process.

Extended Data Fig. 2 Effects of AT118-L and AT118-L-Fc fusion proteins on AT1R binding and signaling.

a) Binding of AT118-H variants displayed on yeast to detergent solubilized AT1R. Data are presented as mean ± SEM from three experiments. b) Non-specific binding of AT118-H variants displayed on yeast to biotinylated insect cell membrane polyspecificity reagent. Data are presented as mean ± SEM from three experiments. c–e) 3H-olmesartan competition experiments in Expi293 cell membranes containing AT1R with purified AT118-H variants. Variants containing D103N and S102G fail to displace olmesartan. The addition of V101D to D103N rescues the loss in pharmacological function. Data are presented as mean ± SEM from three experiments. Error is too small to be displayed if no bar is present. f) Accumulation of AT118-L F47T Y98F Fc fusion protein, that does not bind AT1R, in fetal serum. Data are presented as mean ± SEM from nine embryos from three separate litters for the control Fc (pMAS512, Supplementary Table 1) and eight embryos from two litters for the engineered Non-FcRn binding Fc (pMAS513, Supplementary Table 1).

Source data

Extended Data Fig. 3 Cryo-EM Construct Screening.

a) Fusion of AT118-H to the N-terminus of AT1R enhances total receptor expression. Data are presented as mean ± SEM from three experiments. b–f) Constructs screened for structure determination and representative 2-D class averages. b) AT118-AT1R fusion protein, c) Anti-nanobody Fab fragment bound to free AT118, but not AT118 in complex with AT1R23. d) MBP-AT118 in complex with AT1R25. e) AT118-AT1R-kappa opioid receptor (κOR) ICL3 fusion protein in complex nanobody 6 with an engineered alpaca framework, which binds κOR ICL3, anti-nanobody Fab, and anti-Fab nanobody23,24. f) AT118-AT1R-BRIL fusion protein in complex with anti-BRIL Fab and an anti-Fab nanobody22.

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Extended Data Fig. 4 AT118-H AT1R Data Processing.

a) Size-exclusion chromatography trace b) SDS-PAGE gel under reducing conditions. c) cryo-EM data processing scheme, d) representative micrograph (n = 7,064, scale bar = 50 nm), and e) representative 2D class averages of AT118-H-AT1R-BRIL, anti-BRIL Fab, anti-Fab nanobody complex. Two independent purifications of this complex yielded similar size-exclusion and SDS-PAGE results. Fourier shell correlation (FSC) used to determine the f) global map and g) locally refined map resolutions. h) Local resolution estimate after local refinement.

Extended Data Fig. 5 Molecular Dynamics Simulations.

Distance landscape of a) AT118-H AT1R-BRIL and b) AT118-H AT1R between TM3 (residue 3.50) and TM6 (residue 6.34) and TM3 (residue 3.50) and TM7 (residue 7.53) displayed as individual runs (colored in purple, orange, blue, and yellow) and overall density. Active state (PDB ID: 6OS0) and inactive state (PDB ID: 4YAY) are provided for reference. Neither construct, AT1R or AT1R-BRIL in complex with AT118-H, visits an active like state. c) Examination of dihedral distributions of allosteric activation network within AT1R’s core for AT1R and AT1R-BRIL in complex with AT118-H plotted by Chi 1 angles. Kullback–Leibler (KL) divergence between the two constructs is zero, indicating that the BRIL fusion does not induce substantial conformational change in the activation network.

Extended Data Fig. 6 Binding of AT118-H and AT118-L with a broad panel of small molecule AT1R antagonists.

a) Molecular structures of small-molecule AT1R antagonists. b) Binding of AT118-H (orange) and AT118-L (purple) with a series of small-molecule AT1R antagonists. Error bars represent mean ± SEM from three independent experiments.

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Extended Data Fig. 7 Allosteric effects of AT118-H and AT118-L.

a) Binding of AT118-H with modeled olmesartan. D103CDR3 of AT118-H would clash with olmesartan. Weak density for W842.60 is observed in the antagonist binding site in the orthosteric pocket of the AT118-H AT1R fusion protein structure. b) Binding of AT118-L with modeled ZD7155 (pink sticks, PDB 4YAY ref. 32). c) Allosteric effect of AT118-L on small molecule inhibition of AT1R activation. AT118-L potentiates the inhibitory effects of losartan (gray), but has no effect on olmesartan (green). Log EC50 data are expressed as mean ± SEM from three independent experiments. *p = 0.026 was determined with one-way repeated-measures ANOVA with Dunnett’s correction for multiple comparison.

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Extended Data Fig. 8 AT118-L AT1R Data Processing.

a) Size exclusion trace, b) SDS-PAGE gel under reducing conditions, and c) cryo-EM data processing scheme of AT118-L AT1R-BRIL, anti-BRIL Fab, anti-Fab nanobody complex. One purification of this complex was performed and similar size-exclusion and SDS-PAGE results are in agreement with the analogous complex prepared in Extended Data Fig. 4. d) Fourier shell correlation (FSC) used to determine the global map resolution. e) FSC used to determine locally refined map resolution. f) Experimental density of losartan within the orthosteric binding pocket from locally refined map. g) Comparison of orthosteric binding pocket in AT118-L, Losartan, AT1R-BRIL complex (blue) and olmesartan bound AT1R (purple, PDB 4ZUD).

Extended Data Fig. 9 Antibody GPCR binding model.

Nanobodies and other antibody fragments can adopt modular binding modes where one region mediates GPCR binding and another influences pharmacological function. The GPCR binding moiety can be formatted into a conventional antibody increasing avidity for the target GPCR or combined with secondary antibodies that recognize a tissue specific marker in a bispecific format. Pharmacokinetics, effector function, and tissue localization and delivery, can be further tuned by the antibodies constant Fc region.

Supplementary information

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Supplementary Data 1

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Skiba, M.A., Sterling, S.M., Rawson, S. et al. Antibodies expand the scope of angiotensin receptor pharmacology. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01620-6

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