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. 2024 Dec 12;38:gzae018. doi: 10.1093/protein/gzae018

Optimized single-cell gates for yeast display screening

Xiaoli Pan 1,2,3,2, Matheus O de Souza 4,5,6,2, Francisco M Figueiras 7,8, Aric Huang 9, Bailey B Banach 10, Jacy R Wolfe 11, Azady Pirhanov 12,13, Bharat Madan 14,15,16, Brandon J DeKosky 17,18,19,20,✉
PMCID: PMC11723770  PMID: 39667035

Abstract

Yeast display is a widely used technology in antibody discovery and protein engineering. The cell size of yeast enables fluorescence-activated cell sorting (FACS) to precisely screen gene libraries, including for multi-parameter selection of protein phenotypes. However, yeast cells show a broader size distribution than mammalian cells that complicates single-cell gate determination for FACS. In this report, we analyze several yeast display gating options in detail and present an optimized strategy to select single yeast cells via flow cytometry. These data reveal optimized single-cell gating strategies to support robust and high-efficiency yeast display studies.

Keywords: Yeast surface display, flow cytometry, single cells, monoclonal antibodies

Introduction

Yeast surface display (YSD) was first reported around 30 years ago and has been widely used in protein engineering to improve the affinity, specificity, and stability of various proteins and peptides (Boder and Wittrup, 1997; Chao et al., 2006). As one major example, the discovery and optimization of monoclonal antibodies is widely performed by YSD, often using Saccharomyces cerevisiae as the host organism (Wang et al., 2018; Teymennet-Ramírez et al., 2022; Deichmann et al., 2024). For antibody engineering via YSD, an antibody fragment is often genetically fused to the yeast agglutinin protein Aga2, which forms disulfide bonds with Aga1, facilitating attachment of the displayed antibody fragment to the yeast cell wall. Expression of the antibody fragment-Aga2 fusion protein is regulated by an inducible promoter, while Aga1 is generally natively expressed or alternatively can be controlled by a separate expression cassette (Zhao et al., 2012; Zahradník et al., 2021; Lopez-Morales et al., 2023a). Single-chain variable fragments (scFv) and fragment antigen binding (Fab) formats are the most common expression formats. Other antibody expression formats, such as full IgGs and variable heavy domain of heavy-chain (VHH), have also been explored (Rakestraw et al., 2011; Rhiel et al., 2014; McMahon et al., 2018; Cross et al., 2023). Antibody libraries are often derived from immune or immunized organisms (including human, mouse, transgenic mouse, and others), and can also be synthetically generated using techniques like PCR-based mutagenesis and DNA shuffling. Due to their smaller size, scFvs offer higher levels of expression and enable easier cloning and sequencing. On the other hand, the Fab format (Fig. 1A) often provides improved stability and specificity for many antibody clones compared to scFvs (Röthlisberger et al., 2005; Sivelle et al., 2018; Bates and Power, 2019).

Figure 1.

Figure 1

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FACS screening and analysis of sorted yeast display populations via light microscopy to support sort gate design. (A) Schematic of the yeast display platform used here. The Fab expression vector contains a galactose-inducible bidirectional promoter that drives the transcription of the heavy chain variable and constant regions (VH and CH1), as well as the light chain variable and constant regions (VL and CL). The vector also contains acidic and basic leucine-zipper dimerization domains (ALZ and BLZ), along with c-Myc and FLAG protein expression tags. This study analyzed yeast expressing the VRC07 523-LS FR3-03 Fab, stained with anti-FLAG FITC and a BG505 HIV-1 SOSIP trimer antigen. (B) FACS plot based on forward scatter high vs. area (FSC-H vs. FSC-A), and (C) representative light micrographs of sorted yeast cell populations.

Yeast display offers several important advantages for antibody engineering applications compared to other commonly used display technologies such as phage display, mammalian display, ribosome display, and bacterial display (Tsuruta et al., 2018; Mustafa et al., 2024). For example, YSD utilizes a eukaryotic cell expression system, which is compatible with more complex and larger proteins compared to bacterial, phage, or ribosomal display. The simplicity of yeast homologous recombination facilitates efficient protein library generation, eliminating the need for extensive molecular cloning (Swers et al., 2004). YSD also presents minimal growth bias across rounds because the displayed protein is a small component of the overall cellular expression (Feldhaus et al., 2003; Wang et al., 2018). In addition, yeast cells are robust, fast-growing, and capable of expressing medium-sized libraries (≤ 109 variants, or ≤ 1010 with substantial effort) with high levels of protein expression (Cherf and Cochran, 2015; Teymennet-Ramírez et al., 2022). Another key advantage of yeast display is its compatibility with fluorescence-activated cell sorting (FACS), which enables high-throughput quantitative measurements of protein function during the screening process (such as binding affinity, specificity, on/off-rate kinetics, and quantitative statistics) that empower researchers to perform clonal selection (Van Antwerp and Wittrup, 2000; Feldhaus et al., 2003; Zahradník et al., 2021; Orcutt and Wittrup, 2010). Using a two-channel labeling scheme, the expression level and antigen binding affinity can be simultaneously evaluated (Reich et al., 2015; Wang et al., 2018). This dual-channel approach normalizes antigen binding based on surface expression, enabling accurate affinity discrimination between clones with different expression levels and reducing biases introduced by variations in protein display across the library.

Quantitative single-cell analysis during FACS is critical to reduce variability between cells, enhance understanding of population heterogeneity, and improve screening purity by eliminating clumps and cell aggregates (Lin et al., 2020; Pinheiro et al., 2022). Yeast cells can naturally form doublets, triplets, or clumps under certain conditions, such as during the budding process or at different growth phases (Van Deventer and Wittrup, 2014). Furthermore, vigorous cell handling, the composition of buffers and additives, and various stress conditions (e.g. nutrient limitation) can also induce aggregate formation (Goossens et al., 2011; Van Deventer and Wittrup, 2014; Cossarizza et al., 2017; Li et al., 2021; Pinheiro et al., 2022). Therefore, properly designed single-cell gates are essential to ensure accurate and reproducible results. Well-drawn single-cell gates help minimize the inclusion of cell clumps in the sorted populations, which could otherwise bring along non-selected cells (resulting in low purity sorts) or dead cell clumps that often stain positive for many antigens (false positives) (Cossarizza et al., 2017; Pinheiro et al., 2022). Appropriate single-cell gates also improve selectivity across screening rounds by decreasing non-target cells and background noise, enabling the sorting of only the most relevant cells (Li et al., 2021; Pinheiro et al., 2022).

However, individuals new to yeast display may not have a reliable, data-driven reference that supports their cell gating strategies. To support both new and experienced users, we present here a set of optimal yeast display sorting gates. These gating strategies were refined through comprehensive investigations of various potential flow cytometry gates, coupled with light microscopy analysis of the sorted yeast display populations.

We used the anti-HIV-1 monoclonal antibody VRC07 523-LS FR3-03 as an example Fab displayed on the yeast surface (Fig. 1A) (Liu et al., 2019). VRC07 523-LS FR3-03 expressing yeast were stained with anti-FLAG FITC and a PE-conjugated BG505 HIV-1 trimer antigen to analyze both Fab expression and antigen binding (see Methods for details). We evaluated several possible single-cell gates based on forward scatter height vs. area (FSC-H vs. FSC-A, Fig. 1B). Cells sorted from each gate were collected and analyzed by light microscopy, revealing distinct differences across the sorted populations (Fig. 1C). We observed that Gate A facilitated the predominant selection of single cells along with a few budding yeast. In contrast, Gate B included budding yeast (i.e. likely monoclonal parent/daughter cells), smaller (generally symmetric) doublets and triplets alongside some larger single cells. Gates C and D contained larger populations of clumped cells and/or asymmetric budding yeast in addition to doublets and triplets, with more extensive clump asymmetry in Gate D.

We next evaluated whether the populations in each of these gates exhibited phenotypic differences in antibody expression and binding affinity (Fig. S1). We found that Fab expression levels varied across the gates, with higher Fab expression detected in Gates C and D, which also contained fewer single cells and more doublets, triplets, and aggregates (Fig. S1  upper). This phenomenon is likely because, when cells are clumped, it is unclear which cells within the clump are responsible for protein expression or binding. Therefore, if even one cell in the clump emits a Fab expression signal, the entire clump will phenotypically appear as a single event expressing the Fab signal. We found that Gates C and D included a number of clumps containing mixed yeast with and without Fab expression (Fig. S2), and were also enriched in dead cells (Fig. S3). Figures S1–S3 illustrate how collecting clumps and aggregates from Gates C and D can lead to erroneous interpretation of data, and limit the clonal selection purity. We also observed that antigen binding was weakly influenced by heterogeneity of the different populations. Gates A and B showed a generally appropriate connection between Fab display and antigen binding, however, a growing disconnect between Fab display and antigen binding occurred in Gate C, and to an even greater extent in Gate D (Fig. S1  lower). We highlight that yeast display will always have non-displaying cells as a feature of the technology (Teymennet-Ramírez et al., 2022; Lopez-Morales et al., 2023b). Figures S1–S3 showed that non-expressing cells were efficiently gated out of the analysis when an appropriate display tag was paired with an appropriate single-cell gate.

Next, we assessed how staining with antigen could influence yeast cell behavior. We tested parental AWY101 yeast cells both stained with and without anti-FLAG-FITC and PE-conjugated BG505 HIV-1 trimer (Fig. S4). We observed that for all staining conditions, the AWY101 cell behavior was similar. We also validated that unstained yeast cells expressing VRC07 523-LS FR3-03 showed similar FSC-H vs. FSC-A phenotypes as shown in Fig. 1. Together, the data presented in Fig. S4 demonstrate that the antigen and antibody stains have no significant impact on cell clumping in the system evaluated here.

The collected data were used to support an optimized single-cell selection strategy for yeast surface display studies (Fig. 2). During initial round(s) of sorting, when positive binding clones are rare, we propose using a lower-stringency single-cell gate to optimize yield and achieve maximal coverage of screened yeast libraries, as presented in Fig. 2A. This Round 1 yield gate combines Gates A and B to approximately double the number of screening events (and thus doubling the chances of capturing highly rare clones) compared to screening only Gate A during early rounds. This higher-yield gate, as shown in Fig. 2A, can also be combined with other strategies to enhance sort throughput, such as increasing FACS flow rates or the number of events per droplet, to further accelerate early-round sorting of highly diverse libraries. In subsequent rounds, once the population is smaller (≤ 107) and has already been enriched for binders, we suggest adopting a high-stringency single-cell gate modeled after Gate A to enhance single-cell purity (Fig. 2B). We performed statistical analyses of cells collected from each of the single-cell sort gates A-D (Fig. S5A) and the yield gate (Fig. S5B) that validated the use of Gate A as an efficient single-cell purity gate for later sort rounds.

Figure 2.

Figure 2

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An optimized gating strategy to maximize FACS yield and specificity across multiple screening rounds. (A) An optimized single-cell gate designed to maximize yield during early screening rounds. This gate uses FSC-H vs. FSC-A to collect single cells along with dividing cells and/or small cell clumps, ensuring the recovery of desired clones even if they are aggregated with a small number of undesired clones. This gating strategy is most effective in early screening rounds to maximize initial yield, particularly when sorting libraries containing very few or very rare (< 0.1%) desired clones. (B) An optimized single-cell gate to maximize the purity of single cells, which can effectively be used from Round 2 forward. This strategy is also useful for libraries with moderate to high levels of antigen-binding clones, and in cases where sort purity is prioritized over sort yield.

Finally, we examined whether side scatter area (SSC-A) vs. forward scatter area (FSC-A) could serve as an alternative gating strategy to effectively fractionate single cells, doublets/triplets, and clumped cells (Fig. S6). We observed that approximately similar populations (e.g., Yeast A and Gate A, Yeast B and Gate B) could be obtained using either FSC-H vs. FSC-A (Fig. 1) or SSC-A vs. FSC-A (Fig. S6). However, the separation between distinct gates was less robust when SSC-A vs. FSC-A (Fig. S6) was used. Therefore, we recommend FSC-H vs. FSC-A as the most effective gating strategy in our hands to achieve precise separation of different cell populations.

Based on the above findings, we outline the following principles to group different populations on the FSC-H vs. FSC-A plots. Gate A is usually positioned on the far lower-left side of the FSC-H vs. FSC-A plot. It represents the center of a small, symmetric, and single-cell yeast group, and excludes events that are too large. Gate B is placed directly to the right of Gate A (or directly upper-right on some flow cytometers), and it encompasses the next major population that includes large singlets, doublets, and triplets. Gate C begins where there is a noticeable drop in population density, positioned to the upper-right of Gate B, and similarly includes large singlets, doublets, and triplets. Finally, Gate D is located in the lower-right area of the plot, below Gates A, B, and C, and captures larger asymmetric clumps and other asymmetric sort events.

We note that yeast populations may appear slightly different on the FSC-H vs. FSC-A plots depending on the specific FACS instrument used. Variations can occur even with the same instrument when different machine settings are applied. Despite settings or instrument differences, we consistently obtained similar population phenotypes by following the guiding principles outlined above. These principles serve as a starting point, and can be further adapted to establish the most effective single-cell gating strategy for specific FACS instruments.

In summary, we present an evidence-based, optimized gating strategy for yeast single-cell selection that can support efficient and robust functional selection from large screening libraries. We suggest beginning screening with a more permissive gate to maximize coverage in larger library screening campaigns (Fig. 2A). Then from Round 2 onward (or from Round 1 in smaller libraries), we recommend collecting yeast using a more stringent single-cell gate to maximize sort purity (Fig. 2B). The methods outlined here will support improved screening and selection campaigns for the yeast display community.

Materials and methods

Monoclonal fab display in yeast cells

A pCT-VHVL-K1 expression vector encoding the anti-HIV-1 monoclonal antibody VRC07 523-LS FR3-03 was used for Fab display. The Fab expression vector contains a galactose-inducible bidirectional promoter (Gal 1/Gal 10) for transcription of Fab heavy and light chain variable and constant regions. Expression tags c-Myc and FLAG were included on the heavy and light chains, respectively, with leucine-zipper (LZ) domains added for dimerization (Wang et al., 2018; Banach et al., 2021; de Souza et al., 2022; Huang et al., 2022). This vector was transformed into the yeast strain AWY101 (MATα AGA1::GAL1-AGA1::URA3 PDI1::GAPDH-PDI1::LEU2 ura3–52 trp1 leu2Δ1 his3Δ200 pep4::HIS3 prb1Δ1.6R can1 GAL) using a Frozen-EZ Yeast Transformation II kit (Zymo Research). Yeast cells transformed with VRC07 523-LS FR3-03 Fab were cultured in SD-CAA medium (20 g/l dextrose, 6.7 g/l yeast nitrogen base, 5 g/l casamino acids, 8.56 g/l NaH2PO4.H2O, and 10.2 g/l Na2HPO4.7H2O) to an OD600 of 2 at 30°C (Wang et al., 2018; Liu et al., 2019; Banach et al., 2021; Fahad et al., 2021; Madan et al., 2021a, b; de Souza et al., 2022; Huang et al., 2022; Pan et al., 2023).

Yeast staining, flow cytometry, and sorted cell imaging

Yeast cells transformed with the pCT-VHVL-K1 vector were incubated for 36 hrs in SGDCAA (SD-CAA media with 2 g/l dextrose and 20 g/l galactose) at an initial OD600 of 0.5 at 20°C, 225 rpm to induce Fab surface expression. For staining, 1 × 107 yeast cells were washed twice with ice-cold staining buffer (1 × PBS with 0.5% BSA and 2 mM EDTA) and incubated with anti-FLAG FITC Clone M2 (Sigma Aldrich, St. Louis, MO) and 50 nM of PE-conjugated BG505 HIV-1 SOSIP trimer for 30 min, or with an anti-FLAG PE antibody clone L5 (BioLegend, San Diego, CA) for 30 min. After incubation, yeast cells were washed three times with staining buffer and analyzed by flow cytometry.

FACS was performed using a Sony MA-900 flow cytometer using analysis channels as previously reported (Wang et al., 2018). Instruments settings were as follows. The threshold for event detection, based on the forward-scatter (FSC) trigger channel, was set at 5%. The sensor gain values for each detector were: 7 for FSC, 27% of the output signal of the back scatter (BSC) detector, 40% for the fluorescein isothiocyanate (FITC) fluorescent channel detector, 42% for the phycoerythrin (PE) channel, and 42% for the V500 channel. Sorted yeast cell populations from different gates were resuspended in staining buffer and plated in 48-well plates (Corning, Tewksbury, MA). Brightfield images of the plates were acquired immediately using a BZ-X810 all-in-one fluorescence microscope (Keyence, Itasca, IL). For statistical analyses, we counted a minimum of 50 events, collected from at least 3 representative images. A two-way ANOVA test was conducted to determine statistical significance, and a Bonferroni correction was used to correct for multiple comparisons.

Supplementary Material

Supplementary_Figure_gzae018(1)
supplementary_figure_gzae018(1).docx (13.3MB, docx)

Acknowledgements

We thank Justin Buck and Matthew Feng for support with microscope imaging and I-Ting Teng for sharing HIV-1 trimer protein. Figure 1A was generated with BioRender.

Edited by: Timothy A. Whitehead

Contributor Information

Xiaoli Pan, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA; Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA.

Matheus O de Souza, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA; Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA.

Francisco M Figueiras, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA.

Aric Huang, Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA.

Bailey B Banach, Bioengineering Graduate Program, The University of Kansas, 1530 W 15th St., Lawrence, KS 66045, USA.

Jacy R Wolfe, Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA.

Azady Pirhanov, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA.

Bharat Madan, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA; Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA.

Brandon J DeKosky, The Ragon Institute of Mass General, MIT, and Harvard, 600 Main St, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 66-350, Cambridge, MA 02139, USA; Department of Pharmaceutical Chemistry, The University of Kansas, 2093 Constant Ave, Lawrence, KS 66045, USA; Bioengineering Graduate Program, The University of Kansas, 1530 W 15th St., Lawrence, KS 66045, USA.

Author contributions

XP, MOS, AH, BBB, JRW, BM, and BJD designed the experiments. XP, MOS, FMF AP, performed the experiments. XP, MOS, and BJD analyzed the data. XP, MOS, and BJD wrote the manuscript with feedback from all authors. All authors contributed to the article and approved the submitted version.

Xiaoli Pan (Conceptualization [Equal], Data curation [Equal], Formal analysis [Equal], Investigation [Equal], Methodology [Equal], Writing - original draft [Equal], Writing - review & editing [Equal]), Matheus de Souza (Conceptualization [Equal], Data curation [Equal], Formal analysis [Equal], Investigation [Equal], Methodology [Equal], Writing - original draft [Equal], Writing - review & editing [Equal]), Francisco Figueiras (Data curation [Equal], Formal analysis [Supporting], Investigation [Supporting], Methodology [Supporting], Writing - review & editing [Equal]), Aric Huang (Conceptualization [Equal], Methodology [Supporting], Writing - review & editing [Equal]), Bailey Banach (Conceptualization [Equal], Methodology [Supporting], Writing - review & editing [Equal]), Jacy Wolfe (Conceptualization [Equal], Methodology [Supporting], Writing - review & editing [Equal]), Azady Pirhanov (Investigation [Supporting], Writing - review & editing [Equal]), Bharat Madan (Conceptualization [Supporting], Methodology [Supporting], Writing - review & editing [Equal]), Brandon DeKosky (Conceptualization [Equal], Data curation [Equal], Formal analysis [Equal], Methodology [Equal], Project administration [Equal], Supervision [Lead], Writing - original draft [Lead], Writing - review & editing [Equal]).

Conflict of interest

The authors declare no conflicts of interest.

Funding

This work was supported by the US National Institutes of Health [DP5OD023118, R21AI166396, R21AI143407, U01AI169587, and R01AI181684]; the Bill and Melinda Gates Foundation and the Mark and Lisa Schwartz AI/ML/Immunology Initiative. MOS, XP, and AH were supported by the Gretta Jean & Gerry D. Goetsch Scholarship at the University of Kansas. BBB was supported by the Madison and Lila Self Graduate Fellowship Program. MOS was supported by the Massachusetts General Hospital and Harvard T.H. Chan School of Public Health’s Fostering Diversity in HIV-1 Research Program.

References

  1. Banach  BB, Cerutti  G, Fahad  AS. et al.  Cell Rep.  2021;37:109771. 10.1016/j.celrep.2021.109771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bates  A, Ower  CA. Antibodies  2019;8:28. 10.3390/antib8020028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Boder  ET, Wittrup  KD. Nat. Biotechnol.  1997;15:553–7. 10.1038/nbt0697-553. [DOI] [PubMed] [Google Scholar]
  4. Chao  G, Lau  WL, Hackel  BJ. et al.  Nat. Protoc.  2006;1:755–68. 10.1038/nprot.2006.94. [DOI] [PubMed] [Google Scholar]
  5. Cherf  GM, Cochran  JR. In: Liu  B (ed.), Yeast Surface Display. New York, NY: Springer New York, 2015, pp. 155–75   10.1007/978-1-4939-2748-7_8. [DOI] [Google Scholar]
  6. Cossarizza  A, Chang  HD, Radbruch  A. et al.  Eur. J. Immunol.  2017;47:1584–797. 10.1002/eji.201646632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cross  FR, Fridy  PC, Ketaren  NE. et al.  J. Biol. Chem.  2023;299:102954. 10.1016/j.jbc.2023.102954. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Deichmann  M, Hansson  FG, Jensen  ED. Trends Biotechnol.  2024;42:1258–72. 10.1016/j.tibtech.2024.04.003 [DOI] [PubMed] [Google Scholar]
  9. Fahad  AS, Timm  MR, Madan  B. et al.  Front. Immunol.  2021;12:615102. 10.3389/fimmu.2021.615102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Feldhaus  MJ, Siegel  RW, Opresko  LK. et al.  Nat. Biotechnol.  2003;21:163–70. 10.1038/nbt785. [DOI] [PubMed] [Google Scholar]
  11. Goossens  KVY, Stassen  C, Stals  I. et al.  Eukaryot. Cell  2011;10:110–7. 10.1128/EC.00185-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Huang  A, Jin  W, Fahad  AS. et al.  Antibodies  2022;11:39. 10.3390/antib11020039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Li  M, Liu  H, Zhuang  S. et al.  RSC Adv.  2021;11:20944–60. 10.1039/d1ra02636d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Lin  WN, Tay  MZ, Lu  R. et al.  Cells  2020;9:1440. 10.3390/cells9061440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Liu  Q, Lai  YT, Zhang  P. et al.  Nat. Commun.  2019;10:721. 10.1038/s41467-019-08658-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Lopez-Morales  J, Vanella  R, Appelt  EA. et al.  Small Sci  2023a;3:2300095. 10.1002/smsc.202300095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Lopez-Morales  J, Vanella  R, Kovacevic  G. et al.  ACS Synth. Biol.  2023b;12:419–31. 10.1021/acssynbio.2c00351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Madan  B, Reddem  ER, Wang  P. et al.  AIChE J  2021a;67:e17440. 10.1002/aic.17440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Madan  B, Zhang  B, Xu  K. et al.  Proc. Natl. Acad. Sci. U. S. A.  2021b;118:e2011653118. 10.1073/pnas.2011653118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. McMahon  C, Baier  AS, Pascolutti  R. et al.  Nat. Struct. Mol. Biol.  2018;25:289–96. 10.1038/s41594-018-0028-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mustafa  MI, Alzebair  AA, Mohammed  A. Curr. Res. Pharmacol. Drug Discovery.  2024;6:100174. 10.1016/j.crphar.2024.100174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Orcutt  KD, Wittrup  KD. In: Kontermann  R and Dübel  S (eds.), Antibody Engineering. Heidelberg: Springer Berlin Heidelberg, Berlin, 2010, pp. 207–33   10.1007/978-3-642-01144-3_15. [DOI] [Google Scholar]
  23. Pan  X, López Acevedo  SN, Cuziol  C. et al.  Front. Immunol.  2023;14:1137069. 10.3389/fimmu.2023.1137069. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Pinheiro  S, Pandey  S, Pelet  S. Fungal Biol. Rev.  2022;39:34–45. 10.1016/j.fbr.2021.11.005. [DOI] [Google Scholar]
  25. Rakestraw  JA, Aird  D, Aha  PM. et al.  Protein Eng. Des. Sel.  2011;24:525–30. 10.1093/protein/gzr008. [DOI] [PubMed] [Google Scholar]
  26. Reich  LL, Dutta  S, Keating  AE. J. Mol. Biol.  2015;427:2135–50. 10.1016/j.jmb.2014.09.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Rhiel  L, Krah  S, Günther  R. et al.  PloS One  2014;9:e114887. 10.1371/journal.pone.0114887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Röthlisberger  D, Honegger  A, Plückthun  A. J. Mol. Biol.  2005;347:773–89. 10.1016/j.jmb.2005.01.053. [DOI] [PubMed] [Google Scholar]
  29. Sivelle  C, Sierocki  R, Ferreira-Pinto  K. et al.  MAbs  2018;10:720–9. 10.1080/19420862.2018.1468952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. De Souza  MO, Madan  B, Teng  IT. et al.  Front. Immunol.  2022;13:977064. 10.3389/fimmu.2022.977064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Swers  JS, Kellogg  BA, Wittrup  KD. Nucleic Acids Res.  2004;32:36e–36. 10.1093/nar/gnh030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Teymennet-Ramírez  KV, Martínez-Morales  F, Trejo-Hernández  MR. Front. Bioeng. Biotechnol.  2022;9:794742. 10.3389/fbioe.2021.794742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Tsuruta  LR, Dos  ML, Moro  AM. In: Böldicke  T (ed.), Antibody Engineering. London: IntechOpen United Kingdom London, UK, 2017. 10.5772/intechopen.70930. [DOI] [Google Scholar]
  34. Van Antwerp  JJ, Wittrup  KD. Biotechnol. Prog.  2000;16:31–7. 10.1021/bp990133s. [DOI] [PubMed] [Google Scholar]
  35. Van Deventer  JA, Wittrup  KD. In: Ossipow  V and Fischer  N (eds.), Monoclonal Antibodies: Methods and Protocols. Totowa, NJ: Humana Press, 2014, pp. 151–81   10.1007/978-1-62703-992-5_10. [DOI] [Google Scholar]
  36. Wang  B, DeKosky  BJ, Timm  MR. et al.  Nat. Biotechnol.  2018;36:152–5. 10.1038/nbt.4052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Zahradník  J, Dey  D, Marciano  S. et al.  ACS Synth. Biol.  2021;10:3445–60. 10.1021/acssynbio.1c00395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zhao  Q, Zhu  Z, Dimitrov  DS. In: Voynov  V and Caravella  JA (eds.), Therapeutic Proteins. NJ: Humana Press, Totowa, 2012, pp. 73–84   10.1007/978-1-61779-921-1_5. [DOI] [Google Scholar]

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