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One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening

The potential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) escape mutants is a threat to the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutation landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural coronavirus disease 2019 (COVID-19) infection. Escape mutations predominantly mapped to the periphery of the angiotensin-converting enzyme 2 (ACE2) recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2.


In brief
Francino-Urdaniz et al. describe a deep mutational scanning method to identify escape mutants on SARS-CoV-2 S RBD. Escape mutants were identified for five neutralizing antibodies elicited from natural infection, revealing hotspots at positions K417, D420, Y421, F486, and Q493 at the periphery of the ACE2 recognition site.
Dozens of studies have reported the structural, epitopic, and functional landscape of non-neutralizing monoclonal antibodies and neutralizing antibodies (nAbs) targeting trimeric S (Banach et al., 2021;Rogers et al., 2020;Yuan et al., 2020a). A prophetic understanding of the mutations on S that could evade antibody recognition would enable development of better vaccine boosters and monoclonal antibody therapies. In particular, US Food and Drug Administration (FDA)-approved monoclonal antibody therapies targeting the S RBD developed by Regeneron (Baum et al., 2020) and Lilly (Jones et al., 2020) have shown significantly decreased effectiveness with Beta and Gamma variants (Chen et al., 2021;Greaney et al., 2021) Thus, we sought to develop an S RBD yeast surface display (YSD) platform (Fig-ure S1;Wrapp et al., 2020), because we hypothesized that broad identification of SARS-CoV-2 S escape mutants could be accomplished by integrating high-throughput screening platforms with deep sequencing. Although a similar platform uses the loss of nAb binding to identify escape mutants (Greaney et al., 2021;Starr et al., 2020), we rationalized that a functional screening assay that directly measures the ability of a nAb to compete with ACE2 for S RBD binding would be a comparatively strong predictor of RBD escapability, because it accounts for mutations in RBD that would disrupt S binding to ACE2.

RESULTS
We had previously developed an aglycosylated S-RBD YSD platform (S RBD(333-537)-N343Q) from the original Wuhan-Hu-1 strain (Banach et al., 2021) that can bind specifically to ACE2 ( Figure 1A). This S RBD construct has its one native Nlinked glycan removed (N343Q) because the heavy N-linked mannosylation of S. cerevisiae could hamper anti-S RBD mAb recognition (Jigami, 2008). Cell-surface titrations of CR3022 IgG and nAb HKU-910-30 IgG yielded apparent dissociation constants comparable with reported in vitro results (Banach et al., 2021;Yuan et al., 2020a; Figure S2A). We then tested a panel of 11 additional anti-S RBD mAbs for binding to aglycosylated RBD. These mAbs were isolated from convalescent donors infected in late 2019/early 2020 and thus are representative of anti-S mAbs raised during natural infection (Rogers et al., 2020). Ten of the 11 mAbs recognized aglycosylated S RBD ( Figure 1B). The one panel member that did not bind, CC6.33, selectively recognizes the S309 epitope on the RBD containing the N-linked glycan at position 343 (Pinto et al., 2020).
Next, we evaluated the ability of the mAb panel to competitively inhibit ACE2 binding to aglycosylated S RBD in an assay conceptually similar to the one previously described by Tan et al. (2020). Yeast displaying aglycosylated S RBD was first labeled with a saturating concentration of a given mAb and then co-incubated with biotinylated ACE2. Six mAbs completely ablated ACE2 binding, one mAb partially inhibited ACE2, and the remaining four did not prevent ACE2 binding ( Figure 1C). An inverse correlation was observed between the previously determined neutralization potency of the antibody (Banach et al., 2021;Yuan et al., 2020a) and the fluorescence signal increase in the competition assay ( Figure 1C). We conclude from these experiments that, excluding the S309 epitope, the aglycosylated S RBD platform faithfully recapitulates binding interactions of nAbs with S RBD (Rogers et al., 2020).
Our strategy for identifying potential S RBD escape mutants was as follows. First, we constructed a saturation mutagenesis library of aglycosylated S RBD containing all possible single missense and nonsense mutations for the 119 surface-exposed positions of the RBD (96% coverage of the 2,380 possible library members; Data S1 contains library coverage statistics) (Wrenbeck et al., 2016). For each codon, mutations were encoded using oligonucleotides containing a degenerate NNK sequence. This degenerate sequence encodes all 20 amino acids plus a stop codon, which is useful as an internal negative control for the assay. We labeled yeast displaying these RBD variants with a saturating concentration of nAb and then co-incubated with a saturating concentration of biotinylated ACE2. We then used fluorescence-activated cell sorting (FACS) to screen for mutants that could bind ACE2, indicating that the RBD mutation allows for evasion of the nAb while not disrupting the ACE2 interaction critical for cell entry ( Figure 1D; Figures S2B and S2C). Importantly, a control with no ACE2 labeling was sorted to set an empirical false discovery rate (FDR) for putative escape mutant hits ( Figure 1E; Figure S2C). Plasmid DNA from sorted cells was prepped and deep sequenced. We determined the enrichment ratio (ER)-the base-2 logarithm of the ratio of the frequency mutant in the sorted population to its frequency in the reference population-and then used the control population to set the FDR ( Figure 1E; Figure S2D). We screened five different nAbs identified earlier as having completely ablated ACE2 (C) Competitive binding between IgG and ACE2 was performed by labeled yeast displaying aglycosylated S RBD with 10 mg/mL of indicated mAbs followed by labeling with biotinylated ACE2. (D) Single-site saturation mutagenesis S RBD libraries were sorted by FACS using a competition experiment. The top cytogram shows the cell population collected for the control population without ACE2 labeling, while the bottom cytogram shows the cell population enriched in mutations able to bind ACE2 in the presence of a competing IgG. The specific cytogram shown is for nAb CC12.3 using the S RBD library corresponding to mutations at positions 437-537. (E) Per-mutation enrichment ratio (ER) distributions as a function of average depth of coverage control (left) and CC12.3 nAb competing experiment (right). See also Figure S2. binding (CC6.29,CC6.31,CC12.1,CC12.3,CC12.13). In all, we identified a total of 97 S RBD mutants that can escape recognition by at least one nAb (Data S1).
For all five nAbs, the putative escape mutant hits were localized to specific locations within the S RBD primary sequence ( Figure 2A; Data S1). CC12.1 and CC12.3 belong to the public germline class VH3-53 (Banach et al., 2021;Wu et al., 2020b;Yuan et al., 2020a) and are representative of the subset of VH3-53 public antibodies with relatively short CDRH3 regions (Wu et al., 2020a). Strikingly, these two nAbs share over 90% of the same RBD escape mutants ( Figure 2B), even though the light chain differs between the nAbs. Structural complexes of antibodies CC12.1 and CC12.3 were previously solved in complex with S RBD (Yuan et al., 2020b), affording a structural explanation for individual escape mutants. Escape mutants for both of the VH3-53 nAbs CC12.1 and CC12.3 clustered at the same location on the S RBD, mainly on the periphery of the ACE2 binding site ( Figure 2C; Figure S3A). To confirm that the mutations did not have a large effect on equilibrium binding to ACE2, we determined the dissociation constant of eight single-point mutants using YSD titrations. Binding affinities of each mutant were comparable with the S RBD N343Q dissociation constants and were in agreement with a previous deep mutational scanning study (Starr et al., 2020; Figure 2D). Thus, mutations identified that escape antibody recognition in this assay can still bind ACE2.
Having identified a number of putative escape mutants from the mutagenesis library screening, we sought to determine how this functional screening correlated with the more conventional pseudovirus neutralization assay. A panel of MLV-based SARS-CoV-2 pseudoviruses were generated that contained single mutations predicted by the mutagenesis scanning to allow escape from one of the antibodies screened, as well as several irrelevant control mutations. Antibodies CC12.1, CC12.3, and CC6.29 were screened against the original SARS-CoV-2 pseudovirus, as well as this panel of mutant pseudoviruses in duplicate ( Figure 2E), and the resulting IC 50 values were compared to calculate the effect on antibody neutralization potency (Figure 2F;Figure S3B). Consistent with the RBD mutagenesis library and structural analysis, CC6.29 failed to neutralize the F486I, E484K, and T478R variants. Additionally, K417N, K417T, and D420K hotspot mutants completely escaped neutralization for both CC12.1 and CC12.3. The only instance we tested where the mutagenesis scanning data differed from the pseudovirus results was at N501Y that was predicted to confer escape from CC12.1 and CC12.3 but had no effect on the in vitro neutralization potency. Although it is unclear why this discrepancy occurred, we note that N501Y significantly increases the affinity of the RBD for ACE2, which could result in ACE2 out-competing bound nAbs.
Finally, we performed biological replicates where the mutagenesis library corresponding to S RBD positions 437-537 was separately transformed into yeast and screened against nAbs CC6.29, CC12.1, and CC12.3. Although the ERs were lower than in the initial experiment, nearly the same set of escape mutants was identified for CC6.29, and escape mutants originally identified for all nAbs had significantly higher ERs than other variants in the replicate (p value range, 4.2eÀ4 to 1.9eÀ11, onesided Welch's t test) ( Figure S3C).
Selected per-position heatmaps and structural mapping of S RBD escape mutants are shown in Figure 3 for all five nAbs. Closer examination of these datasets reveals key features of the RBD escape mutant response. CC12.1 and CC12.3 nAbs share over 90% of the same RBD escape mutants ( Figure 2B), including notable hotspot mutations occurring at K417, D420, Y421, and Q498 ( Figure 3A). Interestingly, multiple aromatic substitutions at Q498 escape recognition for CC12.1 and CC12.3, even though the antibodies have different light chains and recognition motifs for that position. Introduction of an aromatic residue at Q498 introduces substantial van der Waals clashes that are likely unresolved without antibody loop movement. The other VH3-53 nAb tested, CC12.13, has a 15-amino acid length CDRH3 that likely has a distinct binding mode than that for CC12.1 and CC12.3 (Wu et al., 2020a). Consistent with this, the CC12.13 escape mutants identified are mostly different from those for CC12.1/CC12.3 (Data S1).
Another nAb screened, CC6.29, has a completely different escape mutant profile compared with CC12.1/CC12.3. The 15 potential RBD escape mutants for CC6.29 center around the structural ''knob'' of positions A475, S477, T478, E484, and F486 ( Figure 3B). E484K shared by the B.1.351 and B.1.526 lineages is identified as an escape mutant for this nAb, but the structurally adjacent S477N mutation newly identified in the B.1.526 lineage does not escape CC6.29 neutralization. Intriguingly, S477P is identified as an escape mutant for this nAb. F486 is a mutational hotspot even though that position is involved in the recognition of ACE2. This is consistent with a previous mutational scan of S RBD showing that mutation of F486 does not significantly impact ACE2 binding affinity (Starr et al., 2020). CC6.31 escape mutants partially overlap with CC6.29 but implicate a different set of mutants ( Figure 3B). Multiple mutations at Q493 escape CC6.31, including Q493 substitutions to the aromatic amino acids F/W.
In total, the five nAbs map a partially overlapping surface with the ACE2 binding site that is primed for antibody escape. In comparison with the binding footprint of ACE2 ( Figure 3C), the escape mutants almost completely map to the outer binding shell and periphery of the interaction surface, akin to an O-ring circumscribing the receptor binding site (RBS). Out of the identified escape mutants, residues K417, F486, Q493, N501, and Y505 are located on the ACE2 footprint ( Figure 3C). Although mutations on K417 and F486 do not significantly change the RBD affinity to ACE2, mutations on N501 can increase or decrease affinity depending on the substitution. The Y505W mutant shared by CC6.31, CC12.1, and CC12.3 also increases ACE2 affinity (Starr et al., 2020).
We were puzzled by the fact that the mutations at D420 were so deleterious to the neutralization potency of the VH3-53 nAbs given that this residue is on the outer periphery of the binding epitope. Consequently, we performed 100-ns aqueous molecular dynamics (MD) simulations of CC12.1 and CC12.3 in complex with wild-type S RBD. We also simulated S RBD incorporated with the D420E, D420K, or Y421N mutation (see Supplemental Information for details). In the control simulation with CC12.1, D420 on the RBD and CDRH2 S56 on CC12.1 form persistent hydrogen bonds, and Y421 on the RBD is tightly bound within a pocket of CC12.1 residues ( Figure 4A). With the D420E Cell Reports 36, 109627, August 31, 2021 3 Article ll mutation, the increased length of E420 disrupts its ability to hydrogen bond with S56, requiring it to adopt a bent conformation ( Figure 4B). This forces Y421 out of the antibody pocket, causing increased fluctuations in neighboring RBD loops that persist throughout the entire 100-ns production simulation (Figures S4A and S4B). With the D420K mutation, hydrogen bonding with S56 is completely disrupted. With the Y421N mutation, N421 is too short to interact with the antibody pocket (Figure S4C). Similar escape mechanisms are observed for CC12.3 with all three RBD mutations, including increased fluctuations at one of the same key sites (K458) on the RBD in response to the D420E mutation ( Figures S4D and S4E).
There have been a number of recent approaches to identify specific S escape mutants (summarized in Data S2; Baum et al., 2020;Greaney et al., 2021;Li et al., 2020;Liu et al., 2021;Weisblum et al., 2020). A survey of the existing escape mutant literature, along with escape mutants identified in the present work, allows us to identify the absolute and near-absolute escape-resistant ACE2 RBS residues in the context of the original lineage ( Figure 4C). One resistant patch is found around F456/Y473/N487/Y489, while other residues are discontinuous patches on the remainder of the RBS. We note that many of these same resistant residues are identical to those from SARS-CoV (Y449, N487, Y489, G496, T500, and G502). The lack of a contiguous surface at the RBS that is conserved makes it highly unlikely that one could identify a naive nAb targeting the RBS that is completely resistant to escape.
A major near-term concern with public health implications is identification of the set of single-nucleotide polymorphisms that encode for escape mutants on the S RBD. A summary of 1-nt escape mutants identified in the present work is shown in Figure 4D. To our knowledge, 40/54 (74%) of 1-nt escape mutants identified from this nAb panel have not previously been identified, including hotspot positions D420 and Y421 that escape recognition by the abundant VH3-53 nAbs. Other notable residues identified here include S477, Q498, and Y501, because these positions lie directly on the RBS and all have been shown to slightly increase binding affinity to ACE2 (Starr et al., 2020). Mutants K417N, E484K, and N501Y in currently circulating lineages escape some, but not all, of the nAbs on the panel. The current study has been performed in the context of the original Wuhan-Hu-1 strain. Nonetheless, new variants are emerging, and further research should be conducted to gain insight on the escape mutants in the presence of multiple mutations. To that end, we have constructed new libraries containing a constant mutation to E484K and N501Y present in the Alpha, Beta, and Iota variants of concern (88.7% and 91.8% library coverage, respectively; Data S1 contains library coverage statistics).

DISCUSSION
We have developed a yeast platform that allows for the rapid identification of SARS-CoV-2 S RBD escape mutants for a given nAb. Although other platforms to identify escape mutants have recently been described, key advantages of the approach presented here include: (1) screening by competitive binding against ACE2, which more precisely mimics how actual viral infection can still persist despite antibody binding; (2) a robust and

Limitations of study
There also exist drawbacks. First, the present method is limited to mapping escape mutants for anti-S-RBD nAbs that directly compete with ACE2 for binding. Many nAbs neutralize by targeting S epitopes across protomers (Barnes et al., 2020) or on the N-terminal domain (Chi et al., 2020), and a robust platform for S ectodomain display would enable more comprehensive studies. We attempted to develop a YSD platform for the full S ectodomain but were unsuccessful: we screened media composition, expression temperature, protein orientation (Figures S1 Figure 4. Mechanistic, structural, and sequence analysis of SARS-CoV-2 escape mutants (A and B) Snapshots from MD trajectories showing (A) key interactions in the control simulation of S RBD in complex with CC12.1, and (B) mechanism of escape of S RBD from CC12.1 as a result of the D420E mutation. Images were rendered with Visual Molecular Dynamics (VMD) (Humphrey et al., 1996), and black dotted lines indicate persistent hydrogen bonds. (C) S RBS positions are colored by the number of escape mutants identified to date. RBS residues involving the S RBD-ACE2 structural complex (PDB: 6M0J) are colored by number of escape mutants identified to date. (D) Summary of 1-nt escape mutants identified in the present study. Lineage column indicates presence of the given mutation among currently circulating SARS-CoV-2 strains, while the observed column refers to an escape mutant previously identified in literature (Baum et al., 2020;Greaney et al., 2021;Li et al., 2020;Liu et al., 2021;Weisblum et al., 2020). ACE2 binding indicates affinity to ACE2 based on the measurements by Starr et al. (2020). See also Figure S4 and Data S2. and S5), and mutations (1,909 mutants screened with only two potential hits) (Data S3; Figure S5). Second, the presented assay measures the ability of a given mutant to escape nAb blockade of ACE2. Although from all available data the assay appears to correlate well in the context of pseudo-virus, each mutation is pleiotropic with unknown fitness effects beyond escape for a given nAb; the true RBS escape mutants that do not appreciably impede viral fitness will be a subset of the mutations identified here.
Still, using this method, we were able to identify specific failure mechanisms for five different nAbs. This tool can be easily adapted and contribute to developing the next generation of broad nAbs against SARS-CoV-2, as well as suggest mutations to include for the next generation of vaccines. The two major prospective applications for this tool then are for monoclonal antibody therapy and universal vaccine design against SARS-CoV-2 (i.e., generating vaccine-elicited antibodies that are resistant to viral escape). The rationale for using this tool in the context of monoclonal antibody therapy is arguably stronger, because FDA-approved therapies such as bamlanivimab (Jones et al., 2020), among others, are not as effective against currently circulating variants. The antibodies used here are from convalescent patients and represent antibodies raised during natural infection. Although some FDA-approved antibodies were not derived from convalescent patients, in principle, any nAb that directly competes with ACE2 binding should be amenable to this technique. We have developed mutagenesis libraries in three different RBD backgrounds, and new libraries could be developed to match genotypes for future variants of concern.
In contrast, it remains to be seen whether this yeast platform could be used in the context of universal vaccine design, because individual nAbs, or combinations thereof, are often not representative of bulk sera. Thus, it would be interesting to see whether our yeast platform presented here is robust enough to identify escape mutants from bulk sera from convalescent or vaccinated individuals.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:   Article ll OPEN ACCESS the efficiency of the transformation (Data S1). Biological replicates were made on a different day by co-transforming each tile into EBY100 exactly as described. Yeast stocks for each transformation were stored in yeast storage buffer (20 w/v % glycerol, 200 mM NaCl, 20 mM HEPES pH 7.5) at À80 C. Mutagenic libraries for the N-terminal spike orientation were constructed following oligo pool mutagenesis exactly as described (Medina-Cucurella et al., 2019;Wrenbeck et al., 2016) using pUC19-S-ecto-A-Nterm-KanR, pUC19-S-ecto-B, and pUC19-S-ecto-C-Nterm as templates. For the oligo pool we computationally selected 1,909 mutations hypothesized to either destabilize the 'down' conformation, stabilize the 'up' conformation, or both (Data S3). The majority of these mutations targeted S 1 (94%, 1793/1909) at the NTD, RBD, SD1, and SD2 domains, with the remainder mapping to the boundary between the HR1 and CH domains on S 2 . After mutagenesis, the mutational libraries were digested with BsaI-HFv2, fractionated by agarose gel electrophoresis, and gel excised and purified with Monarch Gel Extraction kit (NEB). 40 fmol of pUC19-S-ecto-A-NSM-Nterm-KanR, pUC19-S-ecto-B-NSM, and pUC19-S-ecto-C-NSM-Nterm were ligated together with T4 DNA Ligase (NEB), cleaned up and concentrated each to a final volume of 6 mL with Monarch PCR & DNA Cleanup kit (NEB), and transformed into chemically competent E.coli Mach1 cells (Invitrogen cat. #C862003). The resulting library had on average 3 mutations per spike protein per plasmid. Library statistics were determined post sequencing.
To construct the surface display library in yeast, the spike plasmid library was digested with NotI-HF (NEB) and the S coding region was gel purified. The YSD vector pJS698 was digested with BsaI-HFv2 and column purified. 1.3 mg of insert (S coding region) and 1.7 mg of vector were electroporated into 400 mL EBY100 using the method of Benatuil et al. (2010) as written, except that electroporation was performed at 2 kV rather than 2.5 kV. Serial dilutions were plated on SDCAA Agar to calculate the complexity of the library. After recovery, the cells were transferred to 50 mL SDCAA (20 g/L dextrose, 6.7g/L Difco yeast nitrogen base, 5g/L Bacto casamino acids, 5.4g/L Na 2 HPO 4 , and 8.56 g/L NaH 2 PO 4 $H 2 O) and grown at 30 C for two days to saturation. The cultures were passaged twice in medium M37D (diluted to OD 600 = 0.05 in 120 ml, then to OD 600 = 0.4 in 50 ml) and stocks prepared at OD 600 = 1 as in Whitehead et al. (2012). The final composition of M37 is 20 g L -1 dextrose or galactose (for M37D, M37G respectively), 5 g L -1 casamino acids, 6.7 g L -1 yeast nitrogen base with ammonium sulfate, 50 mM citric acid, 50 mM phosphoric acid, 80 mM MES acid, neutralized with 90% sodium hydroxide / 10% potassium hydroxide to pH 7. Both media should be prepared by dissolving all reagents except yeast nitrogen base into MilliQ water, adjusting the pH to 7.0 with freshly prepared sodium hydroxide / potassium hydroxide mixture, and adjusting the volume to 9/10 th of the final desired volume. Pass the solution through a 0.22 mm filter, both for sterility and to remove particulates that would nucleate struvite. Finish the media by addition of 1/10 th volume of 10x filtered yeast nitrogen base.

Yeast Display Titrations and Competition Binding
For cell surface titrations, EBY100 harboring the RBD display plasmid was grown in 1 mL M19D (5 g/l casamino acids, 40 g/l dextrose, 80 mM MES free acid, 50 mM citric acid, 50 mM phosphoric acid, 6.7 g/l yeast nitrogen base, adjusted to pH7 with 9M NaOH, 1M KOH) overnight at 30 C. Expression was induced by resuspending the M19D culture to OD 600 = 1 in M19G (5 g/l casamino acids, 40 g/l galactose, 80 mM MES free acid, 50 mM citric acid, 50 mM phosphoric acid, 6.7 g/l yeast nitrogen base, adjusted to pH7 with 9M NaOH, 1M KOH) and growing 22 h at 22 C with shaking at 300 rpm. For CR3022 IgG, yeast surface display titrations were performed as described by Chao et al. (2006) with an incubation time of 4h at room temperature and using secondary labels anti-c-myc-FITC (Miltenyi Biotec) and Goat anti-Human IgG Fc PE conjugate . Titrations were performed in biological replicates and technical triplicates (n = 6). The levels of display and binding were assessed by fluorescence measurements for FITC and SAPE using the Sony SH800 cell sorter equipped with a 70 mm sorting chip and 488 nm laser.
To test the individual antibody panel binding to S RBD, EBY100 harboring the RBD display plasmid was grown from À80 C cell stocks in 1 mL SDCAA for 4h at 30 C. Expression was induced by resuspending the SDCAA culture to OD 600 = 1 in SGCAA and growing at 22h at 22 C with shaking at 300rpm. 1x10 5 yeast cells were labeled with 10 mg/ml antibody IgG for 30 min at room temperature in PBSF (PBS containing 1g/l BSA). The cells were centrifuged and washed with 200 mL PBSF. They were labeled with 0.6 mL FITC (Miltenyi Biotec), 0.25 mL Goat anti-Human IgG Fc PE (ThermoFisher Scientific) and 49.15 mL PBSF for 10min at 4 C. Cells were then centrifuged, washed with PBSF, and read on a flow cytometer to measure binding of the ACE2. Experiments were performed at least in biological duplicate.
Competitive binding assays on the yeast surface were performed between a free antibody and biotinylated ACE2. S. cerevisiae EBY100 harboring the RBD display plasmid was grown from À80 C cell stocks in 1 mL SDCAA for 4h at 30 C. Expression was induced by resuspending the SDCAA culture to OD 600 = 1 in SGCAA and growing at 22h at 22 C with shaking at 300rpm. 1x10 5 yeast cells were labeled with 10 mg/ml antibody IgG for 30 min at room temperature in PBSF (PBS containing 1g/l BSA). The same cells were labeled with 30nM chemically biotinylated hACE2, in the same tube without washing, for 30min at room temperature in PBSF. The cells were centrifuged and washed with 200 mL PBSF. They were labeled with 0.6 mL FITC (Miltenyi Biotec), 0.25 mL SAPE (Invitrogen) and 49.15 mL PBSF for 10min at 4 C. Cells were then centrifuged, washed with PBSF. The pellet was resuspended in 100 mL and read on a flow cytometer to measure binding of the hACE2.

Yeast Display Screening of S and S RBD libraries
For full-length S ectodomain screening, pUC19-S-ecto-Nterm and pJS698 were independently linearized via digest with restriction enzymes at 37 C for 1 hour, and gel extracted based on size using Monarch DNA Gel Extraction Kit. The linearized regions were cotransformed in a molar ratio of 3:1 insert to vector into chemically competent EBY100 following published protocols (Medina-Cucurella and Whitehead, 2018). EBY100 cells were recovered in nuclease free water for 5 minutes and then plated on two different yeast e14 Cell Reports 36, 109627, August 31, 2021 Article ll OPEN ACCESS media agar plates: SDCAA and M37D. Cells were incubated at 30 C for 3 days. After initial growth, colonies from each plate were selected and grown up at 30 C and 250 rpm overnight in the respective dextrose media: SDCAA, M37D. Cells were then induced in respective galactose media at an OD 600 = 1 at three different temperatures, 18 C, 22 C, and 30 C for 20 hours.
Induced EBY100 cells were washed with PBSF (8 g/L NaCl, 0.2 g/L KCl, 1.44 g/L Na 2 HPO 4 , 0.24 g/L KH 2 PO 4 , and 1g/L bovine serum albumin, pH to 7.4 and filter sterilized) and resuspended in PBSF at an OD 600 = 10. The cells were then incubated with either 500nM of the biotinylated ACE2-Fc or 500nM of the biotinylated CR3022 for 1 hour at room temperature. The cells were then washed with PBSF and labeled with anti-c-myc fluorescein isothiocyanate (FITC) (Miltenyi Biotec) and streptavidin-R-phycoerythrin (SAPE) (Invitrogen) and incubated on ice for 10 minutes.
The Spike mutagenic library was labeled with CR3022 and, separately, ACE2-Fc under the optimal conditions were screened. Approximately 10 8 yeast cells were sorted using fluorescence activated cell sorting (FACS), and the top 1% of cells by fluorescence were collected. The two resulting sorted libraries were expanded and sorted in a second round, again screening 10 8 cells and collecting the top 1% by fluorescence intensity. The selected populations were amplified and purified based on tile, deep sequenced, and count data compared with a reference population.
For the escape mutant screening of the S RBD, 3x10 7 induced EBY100 yeast cells displaying S RBD were labeled with 10 mg/ml antibody IgG for 30 min at room temperature with mixing by pipetting every 10 min in PBSF (PBS containing 1g/l BSA). The same cells were labeled with 75nM chemically biotinylated ACE2, in the same tube, for 30min at room temperature in PBSF with mixing by pipetting every 10 min. The cells were centrifuged and washed with 1mL PBSF. Cells were then labeled with 1.2 mL FITC, 0.5 mL SAPE and 98.3 mL PBSF for 10min at 4 C. Cells were centrifuged, washed with 1mL PBSF, resuspended to 1 mL PBSF and sorted using FACS. Multiple gates were used for sorting as shown in Figure S3, including an FSC/SSC + gate for isolation of yeast cells, FSC-H/FSC-A gate to discriminate single cells, a FSC-A/FITC+ gate selects the cells displaying the RBD on their surface and from this last gate, the top 2% by a PE + /FITC + is collected. At least 2.0x10 5 cells were collected and were recovered in SDCAA with 50 mg/mL Kanamycin and 1x PenStrep for 30h. For the biological replicates ( Figure S3) the ACE2 concentration was 30nM but all other conditions were identical.

Deep Sequencing Preparation
Libraries were prepared for deep sequencing following the ''Method B'' protocol from Kowalsky et al. (2015) exactly as described for the spike ectodomain libraries and with a few changes for the RBD libraries. A Monarch PCR & DNA Cleanup kit was used. PCR of extracted and cleaned-up yeast plasmid DNA was performed using 2xQ5 HotStart Master Mix (NEB) and the following protocol: d 1 min @ 98 C d 25 cycles of: d 10 s @ 98 C d 20 s @ 64 C d 30 s (replicate 1) or 1 min (replicate 2) @ 72 C d 2 min @ 72 C d Hold @ 4 C Primers used in library prep are given in Key resources table. Amplicons were fractionated by agarose gel electrophoresis and purified using a Monarch DNA Gel Extraction Kit (NEB). Samples were then further purified using Agencourt Ampure XP beads (Beckman Coulter), quantified using PicoGreen (ThermoFisher), pooled, and sequenced on an Illumina MiSeq using 2 3 250 bp paired-end reads at the BioFrontiers Sequencing Core (University of Colorado, Boulder, CO). All systems containing a positive charge were neutralized by the addition of Cl À ions, also modeled with the Amber99SB-ILDN force field. Each simulation consisted of approximately 192,000 atoms.

Molecular Dynamics Simulations
A steepest descent energy minimization of the initial coordinates for each system was carried out for 5,000 steps. NVT equilibration simulations were then performed for 0.5 ns at 310 K with the BussiÀDonadioÀParrinello (Bussi et al., 2007) thermostat. Subsequent NPT equilibration simulations were performed for 1 ns at 310 K and 1.0 bar, using the same thermostat and Berendsen (Berendsen et al., 1984) barostat. The time constant for coupling in both the NVT and NPT simulations was 0.1 ps. Production simulations in the NPT ensemble were then carried out at 310 K and 1.0 bar with the BussiÀDonadioÀParrinello thermostat and Parrinello-Rahman (Parrinello and Rahman, 1981) barostat. Long-range electrostatic interactions were calculated using particle mesh Ewald summations and a cutoff of 1.0 nm, and Lennard Jones interactions were calculated over 1.0 nm and shifted beyond this distance. Neighbor lists were updated every 10 steps with a cutoff of 1.0 nm. Bonds between hydrogen and heavy atoms were constrained with the LINCS (Hess et al., 1997) algorithm. Furthermore, periodic boundary conditions were used in all simulations in all directions. Production simulations were carried out for 100 ns, leading to a total of 0.8 microseconds of simulation time across the eight simulations.

Dissociation constants
The dissociation constants on Figure 2D represent the mean of the replicates (values show as open circles). There are two replicates for each single point mutant and 4 for the wild-type.

Deep Sequencing Analysis
All deep sequencing data analysis was performed by scripts written in Python, available at GitHub (https://github.com/ WhiteheadGroup/SpikeRBDStabilization).
Because all sequenced samples were PCR amplicons of known length, paired-end reads were merged by aligning at the known overlap. Mismatches in overlapping regions were resolved by selecting the base pair with the higher quality score and assigning it a quality score given by the absolute difference of the quality scores at the mismatch. Paired reads with more than 10 mismatches in the overlapping region and merged reads containing any quality score less than 10 were discarded. The total number of retained reads in each sample was recorded as n i , the number of reads in sample i.
Each read was compared to the wild-type sequence to identify all mutations. Counts for synonymous single mutations were combined to give k ij , the number of reads in sample i encoding the single amino acid mutation j. Reads including multiple mutations or mutations not encoded in the library oligos were not analyzed further. The frequency of single mutant j in sample i was calculated as f ij = k ij = n i .
Each experiment consisted of two samples: a reference sample r and a selected sample s. For each experiment, the risk ratio of variant j was calculated as r j = f sj = f rj i.e., the ratio of the variant's frequency in the selected population to its frequency in the reference population. Enrichment ratios were calculated as the binary logarithm of the risk ratio: ER j = log 2 r j . Variants with five or fewer counts in the reference population were not analyzed further. Variants with at least five counts in the reference population but no counts in the selected population were given a pseudocount of one.

Determining hits from yeast display screens
For each escape mutant screen, we collected the top 2% (PE channel) of the population of FITC + (RBD displaying) cells. This population was not labeled with biotinylated ACE2 and so serves as a null experiment where the observed enrichment ratios are due to other sources of variance and not to differential nAb binding. We fit the distribution of enrichment ratios for each of these control samples using kernel density estimation (KDE) (SciPy's scipy.stats.gaussian_kde with default parameters) (Shalloo et al., 2020). We then treated this distribution estimate as an empirical null hypothesis. Under this null hypothesis, we expect Nð1 ÀFðER t ÞÞ false positives, where N is the number of variants tested, F is the cumulative distribution function (CDF) of the control ER KDE, and ER t is a threshold. Therefore, for a target false discovery rate (FDR), we chose ER t = F À1 ð1 À FDR =NÞ, where F -1 is the inverse CDF of the KDE. In data from samples labeled with nAbs, we then tested the hypothesis that each observed ER was greater than the associated ER t using an one-sided exact Poisson rate ratio test (statsmodels.stats.rates.test_poisson_2indep from the Python library statsmodels) (Seabold and Perktold, 2010). For these tests, the null ratio was 2 ERt . The counts were given by the number of reads for the variant in the selected and reference populations, respectively, and the exposures were given by the total number of reads in the reference and selected populations, respectively. For this analysis, we identified hits for replicate 1 (tiles 1 & 2 for nAbs CC6.29, CC12.1, and CC12.3) using a target FDR of 1 and a Poisson rate ratio test significance level of 0.01. For replicate 2 (tile 2 for nAbs CC6.31, CC12.13) escape mutant hits were identified using a target FDR of 1.
For the full-length S ectodomain screen, our null experiment was the collected reference populations without selections for each of the ACE2-Fc and CR3022 experiments. These reference populations were passaged, sorted, and amplified identically to the sorted libraries except that no screen was employed. We fit the distribution of enrichment ratios for these control samples using a logistic CDF (custom MATLAB script), and the empirical FDR was calculated exactly as above.