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Rapid generation of potent antibodies by autonomous hypermutation in yeast

Nature Chemical Biology volume 17, pages 1057–1064 (2021)Cite this article

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Abstract

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, not always accessible and poorly compatible with many antigens. Here, we describe ‘autonomous hypermutation yeast surface display’ (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. By encoding antibody fragments on an error-prone orthogonal DNA replication system, surface-displayed antibody repertoires continuously mutate through simple cycles of yeast culturing and enrichment for antigen binding to produce high-affinity clones in as little as two weeks. We applied AHEAD to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets, offering a template for streamlined antibody generation at large.

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Fig. 1: Autonomous hypermutation yeast surface display (AHEAD).
Fig. 2: Evolution of anti-AT1R nanobodies.
Fig. 3: Evolution of anti-SARS-CoV-2 nanobodies and activities of potent anti-SARS-CoV-2 nanobodies.
Fig. 4: Epitope mapping using deep mutational scanning libraries of RBD.

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Data availability

All data generated for the present study are available upon request to the corresponding authors. pAW240 and its sequence are available at Addgene (plasmid 170791). NGS data are available at NCBI’s SRA website https://www.ncbi.nlm.nih.gov/sra?term=SRP320370 (identifier biosample accession numbers SAMN19242322, SAMN19242323, SAMN19242324, SAMN19242325, SAMN19242326, SAMN19242327 and SAMN19242328).

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Acknowledgements

We thank W. Capel for assistance with nanobody purifications, Z. Zhong, C. Carlson, T. Loveless, A. Banks and other members of the Liu and Kruse groups for experimental assistance, materials and thoughtful discussions, and G. Arzumanyan for the pGA promoter mutations discovered in his OrthoRep continuous protein evolution experiments (unrelated to this study). We thank D. Trono (EPFL), F. Zhang (Broad Institute), H. Mou (Scripps Research) and M. Farzan (Scripps Research) for the gift of plasmids and cells used in our study. We also acknowledge the support of the Center for Macromolecular Interactions at Harvard Medical School. This work was funded by NIH 1DP2GM119163 (C.C.L.), NIH NIGMS 1R35GM136297 (C.C.L.), the Moore Inventor Fellowship (C.C.L.), the UCI COVID-19 Basic, Translational and Clinical Research Fund (C.C.L.), NIH DP5OD021345 (A.C.K.), a Vallee Scholars Award (A.C.K.), NIH NIAID R01AI146779 (A.G.S.), a Massachusetts Consortium on Pathogenesis Readiness (MassCPR; A.G.S.), training grants NIGMS T32GM007753 (B.M.H. and T.M.C.) and T32AI007245 (J.F.) and NIH NCI 1R01CA260415 (C.C.L., A.C.K. and D.S.M.).

Author information

Author notes

  1. Conor McMahon

    Present address: Vertex Pharmaceuticals, Boston, MA, USA

  2. Laura M. Wingler

    Present address: Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA

  3. These authors contributed equally: Alon Wellner, Conor McMahon.

Authors and Affiliations

  1. Department of Biomedical Engineering, University of California, Irvine, CA, USA

    Alon Wellner, Jonathan R. Clements, Kianna M. Nguyen, Ming H. Ho, Vincent J. Hu & Chang C. Liu

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

    Conor McMahon, Morgan S. A. Gilman & Andrew C. Kruse

  3. Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Sarah Clark, Aaron G. Schmidt & Jonathan Abraham

  4. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Jung-Eun Shin & Debora S. Marks

  5. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

    Jared Feldman, Blake M. Hauser, Timothy M. Caradonna & Aaron G. Schmidt

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

    Laura M. Wingler

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

    Laura M. Wingler

  8. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Debora S. Marks & Jonathan Abraham

  9. Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA

    Jonathan Abraham

  10. Department of Chemistry, University of California, Irvine, CA, USA

    Chang C. Liu

  11. Department of Molecular Biology & Biochemistry, University of California, Irvine, CA, USA

    Chang C. Liu

Authors
  1. Alon Wellner
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  2. Conor McMahon
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  3. Morgan S. A. Gilman
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Contributions

All authors contributed to experimental design and data analysis. A.W., C.M., A.C.K. and C.C.L. were responsible for the conception of AHEAD. A.W., M.H.H., V.J.H. and K.M.N. carried out experiments establishing the first generation of AHEAD and made improvements to reach the second-generation AHEAD system. C.M. carried out AHEAD experiments for the evolution of anti-AT1R nanobodies and selected parent anti-SARS-CoV-2 for evolution using AHEAD. A.W., J.R.C. and M.H.H. carried out AHEAD experiments for the evolution of anti-GFP, anti-HSA and anti-SARS-CoV-2 nanobodies. A.W., C.M., M.S.A.G., S.C. and L.M.W. characterized the activities of evolved nanobodies in binding assays (A.W., C.M. and L.M.W.), SPR measurements (C.M. and M.S.A.G.), neutralization assays (S.C.) and ACE2 competition assays (S.C.). J.F., B.M.H., T.M.C. and A.W. were responsible for the expression of RBD used throughout this study. A.W. and V.J.H. were responsible for the RBD mutational scanning experiments and NGS data analysis that mapped target epitopes and RBD escape mutations for anti-RBD nanobodies. J.-E.S. and D.S.M. were responsible for computational design aspects for the naïve ~200,000-member nanobody library and A.W. inserted that library into AHEAD. A.C.K. and C.C.L. oversaw all aspects of the project, D.S.M. supervised computational nanobody library design, J.A. supervised neutralization and ACE2 competition assays, and A.G.S. supervised the preparation of RBD. A.W. carried out the deep mutational scanning analysis. A.W., C.M., A.C.K. and C.C.L. wrote the manuscript, with input and contributions from all authors.

Corresponding authors

Correspondence to Andrew C. Kruse or Chang C. Liu.

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Competing interests

Provisional patents (US Patent Application No. 63/123,558 and US Patent Application No. 63/111,860) have been filed on this work. A.C.K. is a co-founder and advisor of Tectonic Therapeutic, Inc., and of the Institute for Protein Innovation. C.C.L. is a co-founder of K2 Biotechnologies, Inc., which focuses on the use of continuous evolution technologies applied to antibody engineering.

Additional information

Peer review information Nature Chemical Biology thanks Theam Soon Lim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Antibody fragments.

Single-chain variable fragments and nanobodies are displayed on the surface of yeast in this study. Their relationships to conventional antibodies are depicted.

Extended Data Fig. 2 Evolution of anti-AT1R nanobodies by AHEAD.

a, Contributions of individual mutations fixed during the evolution of AT110 by AHEAD. Affinity (EC50) of each nanobody for AT1R was determined by measuring binding of yeast-displayed nanobodies to each concentration of AT1R-angiotensin II complex (X-axis) in a single replicate and fitting the resulting binding curve. b, Amino acid sequence of AT110 and evolved variants. Mutations that were discovered using AHEAD are underlined in bold. Mutations that were discovered in a previous AT110 evolution experiment using a standard error prone PCR library approach19 are highlighted in yellow.

Extended Data Fig. 3 Optimization of antibody display in AHEAD.

a, Maps of orthogonal p1 plasmids containing OrthoRep parts driving expression of nanobodies in the first-generation AHEAD 1.0 and improved second-generation AHEAD 2.0 systems. Nb = nanobody, tAHD1 = ADH1 terminator, polyA = polyadenosine tail. b, Increased functional expression of nanobody AT110 using all AHEAD 2.0 parts as determined by FACS. The induced population in AHEAD 2.0 shows an ~25-fold increase in nanobody display levels (determined by mean fluorescence intensity of the cell population) compared to AHEAD 1.0.

Extended Data Fig. 4 Optimization of antibody display in AHEAD and evolution of anti-GFP and anti-HSA antibodies using the optimized second-generation AHEAD 2.0 system.

a, Architectures for nanobody display in the first-generation AHEAD 1.0 and improved second-generation AHEAD 2.0 systems. b, Selection of a new leader sequence for higher nanobody display. FACS plots showing the progressive enrichment of higher efficiency leader sequences across 3 rounds of selection (left panel). Nanobody display level using app8 compared to the selected app8i1 variant (right panel). n = 6, error bars represent ± s.d. c, Selected FACS plots showing affinity maturation of Nb.b201 through AHEAD cycles. d, Selected FACS plots showing affinity maturation of Lag42 through AHEAD cycles. e, (left) Affinities (EC50) of improved high-affinity anti-HSA nanobodies evolved using AHEAD. Binding of yeast-displayed nanobodies by each concentration of HSA was measured in replicate (n = 3, error bars represent ± s.d.) and EC50s were determined by fitting each binding curve. (right) Affinities (EC50) of improved high-affinity anti-GFP nanobodies evolved using AHEAD. Binding of yeast-displayed nanobodies by each concentration of GFP was measured in replicate (n = 3, error bars represent ± s.d.) and EC50s were determined by fitting each binding curve.

Extended Data Fig. 5 Evolution of anti-RBD nanobodies.

a, Isolation of parent anti-RBD nanobodies. (left) FACS plot showing enrichment of initial anti-RBD nanobody clones from a naïve nanobody library32. The green polygon corresponds to the gate used for sorting. (right) Schematic showing the separation of parent clones into different AHEAD experiments in order to minimize competition among parents and their lineages, avoiding early loss of weak parents that have the potential to yield superior descendants later during affinity maturation. b, Selected FACS plots showing anti-RBD affinity maturation by cycles of AHEAD in 8 independent experiments, each starting from one of the 8 parent clones identified from the naïve nanobody library (see Extended Data Fig. 5a). Red polygons correspond to the gates used for sorting.

Extended Data Fig. 6 Affinities of anti-RBD nanobodies determined by surface plasmon resonance (SPR) or EC50 measurements.

SPR or EC50 binding curves are shown for each anti-RBD nanobody characterized in this study. For SPR measurements (Y-axis = Response), kinetic fits are shown where available and steady-state affinity fits are shown for nanobodies for which the on and off rates could not be determined. For EC50 affinities (Y-axis = Normalized Fluorescence), binding of yeast-displayed nanobodies by each concentration of RBD was determined in biological triplicate (n = 3, error bars represent ± s.d.) and EC50s were determined by fitting each binding curve.

Extended Data Fig. 7 Neutralization assays and ACE2 competition assays for anti-RBD nanobodies evolved with AHEAD.

a, Neutralization plots for all anti-RBD nanobodies characterized in this study. Each nanobody concentration (X-axis) was tested in replicate. n = 6, error bars represent ± s.d. b, Biolayer interferometry (BLI) traces measuring ACE2 competition for anti-RBD nanobodies. CR3022 is an anti-RBD antibody that does not compete with ACE2 binding (no competition control) whereas SC1A-B12 is an anti-RBD antibody that competes strongly with RBD binding.

Extended Data Fig. 8 Evolution of an anti-GFP nanobody from a computationally-designed 200,000-member naïve nanobody library encoded on AHEAD.

a, Representative FACS plots showing enrichment of a GFP-binding clone from the nanobody library and subsequent emergence and fixation of a mutation that increases GFP binding across AHEAD cycles. b, Affinity (EC50) of the AHEAD-evolved anti-GFP nanobody, NbG1i1, isolated from AHEAD cycle 6 as compared to its parent, NbG1, that fixed in AHEAD cycle 3. Binding of yeast-displayed nanobodies by each concentration of GFP was determined in relicate (n = 3, error bars represent ± s.d.) and EC50s were determined by fitting each binding curve.

Extended Data Fig. 9 Gating strategy for singlets in all FACS experiments.

(left) Forward scatter (horizontal axes) versus side scatter (vertical axes) of a representative population of yeast cells. Red circle represents cells passing the gate. (right) Forward scatter area (horizontal axes) vs. forward scatter height (vertical axes) gating of cells that passed through the previous gate. Green boundary represents cells passing the gate. For all FACS experiments, only cells sorted through both gates were used in nanobody expression and binding gates and.

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Reporting Summary

Supplementary Data 1

Information and activities for all anti-SARS-CoV-2 nanobodies characterized in this study.

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Wellner, A., McMahon, C., Gilman, M.S.A. et al. Rapid generation of potent antibodies by autonomous hypermutation in yeast. Nat Chem Biol 17, 1057–1064 (2021). https://doi.org/10.1038/s41589-021-00832-4

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