Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies

Complete mapping of RBD mutations that reduce binding by plasma collected ∼1 month post-symptom onset

To completely map RBD mutations that reduce binding by polyclonal plasma antibodies, we extended a deep-mutational scanning method previously developed to identify mutations that escape binding by monoclonal antibodies (Greaney et al., 2021). Briefly, we used libraries of yeast that each expressed a different RBD variant on their surface. The library covered nearly all possible single amino-acid mutations to the RBD (Starr et al., 2020). We incubated these yeast libraries with polyclonal human plasma and used fluorescence-activated cell sorting (FACS) with an IgG/IgA/IgM secondary antibody to enrich for yeast expressing RBD mutants that bound appreciably less plasma antibodies than unmutagenized RBD (Figures S2A–S2C). We then used deep sequencing to measure the frequency of each RBD mutation in the initial population and the antibody-escape FACS bin. We quantified the effect of each RBD mutation on plasma antibody binding as that mutation’s “escape fraction,” which is the fraction of all yeast cells expressing RBD with that mutation that fall into the FACS escape bin. These escape fractions range from 0 (no effect on plasma antibody binding) to 1 (all cells with this mutation are in the antibody-escape bin) (Figures S2A–S2C) (Greaney et al., 2021; Starr et al., 2020). All mapping experiments were performed in biological duplicate using independently constructed RBD mutant libraries; the replicates were highly correlated (Figures S2D and S2E), and we report the average measurements across the two libraries throughout.

We began by mapping mutations that reduced binding by plasma antibodies in samples collected from 11 individuals at approximately 30 days post-symptom onset (range 15–61 days). These samples had neutralizing titers against lentiviral particles pseudotyped with the G614 variant of the SARS-CoV-2 spike that ranged from 140 to 30,000, with the extent of neutralization attributable to RBD-targeting antibodies ranging from 63% to 99% (first time point for subjects shown in orange in Figure 1B). We quantified each RBD mutation’s escape fraction and visualized the results using “escape maps,” which are logo plots where the height of each letter is proportional to that mutation’s escape fraction (Figures 2A and S2A). Interactive versions of these escape maps are at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera. The total height of the letter stacks for each site represents the sum of the escape fractions for all mutations at that site, and so can range from 0 (no effect of any mutation) to 19 (all mutations at the site have an escape fraction of 1).

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Figure 2

Complete maps of RBD mutations that reduce binding by polyclonal plasma antibodies from 11 individuals

(A) The line plots at left indicate the total effect of all mutations at each site in the RBD on plasma antibody binding, with larger values indicating a greater reduction in antibody binding. The logo plots at right zoom in on individual mutations at key sites (indicated by purple highlighting on the x axis of the line plots). In these logo plots, the height of each letter is that mutation’s escape fraction, so larger letters indicate mutations that cause a greater reduction in antibody binding. Escape fractions are comparable across sites within a sample, but not necessarily between samples due to the use of sample-specific FACS gates—therefore, for each plasma, the y axis is scaled independently (see STAR methods). Sites in the logo plots are colored by RBD epitope.

(B) For coloring of the logo plots, we designated three RBD epitopes based on the structural locations where mutations had large effects on plasma antibody binding. The images show the structure of the RBD bound to ACE2 (PDB: 6M0J) (Lan et al., 2020) in several representations. The receptor-binding-ridge epitope is dark blue, the epitope containing the 443–450 loop is cyan, the core-RBD epitope is orange, the rest of the RBD is gray, and ACE2 is purple. For the cartoon rendering in the top structure, alpha carbons for sites of strong binding escape for any of the 11 plasma (i.e., all sites shown in the logo plots) are represented as spheres.

Interactive versions of these escape maps are available at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera/.

Although the effects of mutations on plasma antibody binding varied widely across individuals, the escape maps revealed several common patterns. Mutations that strongly reduced binding fell in one of three discrete regions of the RBD: the receptor-binding ridge within the receptor-binding motif (RBM), a loop in the RBM opposite the ridge (spanning sites 443–450, and the structurally adjacent sites at 494–501), or a surface patch in the core RBD (Figure 2B). The receptor-binding ridge and 443–450 loop are also targeted by many potently neutralizing antibodies, including the two antibodies in the REGN-COV2 cocktail (Greaney et al., 2021; Hansen et al., 2020; Starr et al., 2021). The core RBD epitope is targeted by monoclonal antibodies that tend to be less potently neutralizing but more broadly cross-reactive to SARS-like coronaviruses (Barnes et al., 2020a; Piccoli et al., 2020; Yuan et al., 2020b; Zost et al., 2020a). In particular, binding by all 11 samples was reduced by mutations at site F456, and binding by most samples (9 of 11) was reduced by mutations at site E484 (Figure 2A). Both of these sites are within the receptor-binding ridge epitope. Notably, E484 is a site at which mutations have recently been demonstrated to reduce neutralization by both monoclonal antibodies and human sera or plasma (Andreano et al., 2020; Greaney et al., 2021; Starr et al., 2021; Wang et al., 2021; Weisblum et al., 2020).

We grouped the samples into several classes based on which mutations most strongly reduced plasma antibody binding (Figure 3 and the interactive visualizations at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera/; sera were grouped manually based on qualitative examination of escape profiles). Binding by 6 of the 11 samples was reduced primarily by mutations in the receptor-binding ridge. For instance, binding by plasma antibodies from subject B (day 26) was most strongly affected by mutations at sites F456 and E484 (Figures 2 and ​and3A).3A). Binding by three samples was strongly reduced by mutations across a broader swath of the RBM, including the 443–450 loop (Figure 3B). An example is subject G (day 18), which was strongly affected by mutations at sites 443–450 in addition to F456 and E484. Binding by two samples was most affected by mutations in the core RBD epitope (Figure 3C). The sites where mutations reduced binding by these core-RBD targeting plasma clustered around the lipid-binding pocket in the RBD, where binding of free fatty acids may contribute to locking spike into a “closed” conformation (Carrique et al., 2020; Toelzer et al., 2020). Notably, for the sample from subject K (day 29), no single RBD mutation had more than a small effect on plasma antibody binding (Figures 2 and ​and33D).

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Figure 3

Regions of the RBD where mutations strongly reduced binding by the antibodies in plasma collected from 11 individuals

The total effect of mutations at each site (sum of escape fractions) are projected onto the structure of the RBD (PDB: 6M0J), with white indicating no effect of mutations at that site and red indicating a large reduction in antibody binding. Two views of the RBD are shown: the surface of the RBD that is buried in the “down” conformation and the surface that is always exposed and accessible (Walls et al., 2020; Wrapp et al., 2020).

(A) For some individuals (typified by subject B), antibody binding is predominantly reduced by mutations in the receptor-binding ridge, particularly at sites F456 and E484.

(B) For some individuals (typified by subject G), antibody binding is strongly reduced by mutations in the 443–450 loop of the RBM in addition to the receptor-binding ridge.

(C) For a few individuals (typified by subject J), antibody binding is affected by mutations in the core RBD epitope around site P384.

(D) Samples from the other eight individuals fall in one of the three classes detailed in panels (A–C). For panels (A–D), the white-to-red coloring scale is set to span the same range as the y axis limits for that plasma in Figure 2.

(E) Mutations in two major surface regions (the S309 epitope and the sites near E465) do not strongly affect plasma antibody binding for any of the subjects. Shown is a surface representation of the RBD, with the three polyclonal plasma epitopes colored as in Figure 2. The S309 epitope and region near E465 (“E465 patch”) are shown in pink and maroon. ACE2 is shown in a dark gray cartoon representation.

Interactive versions of these structural visualizations are available at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera/.

There are some regions of the RBD where mutations did not strongly affect plasma antibody binding for any sample in our panel. These regions include the sites near the 343 glycan that are targeted by the SARS-CoV-1 cross-reactive antibody S309 (Pinto et al., 2020), and the region near residue E465 on the “lateral” side of the RBD, which to our knowledge is not an epitope for any known neutralizing antibodies (Figure 3E) (Barnes et al., 2020a; Greaney et al., 2021; Piccoli et al., 2020; Starr et al., 2020). Antibodies targeting these two regions may be rare, have low binding avidity in the context of polyclonal plasma, or be subdominant relative to other RBD epitopes (Barnes et al., 2020b; Piccoli et al., 2020; Weisblum et al., 2020).

How mutations affect plasma antibody binding can shift over time in the same individual

Next, we examined how the RBD mutations that affect plasma antibody binding change over time as an individual’s immune response matures. We speculated that such changes might occur because other studies have shown that anti-SARS-CoV-2 antibodies become more somatically hypermutated and less clonal in the months following recovery from infection (Gaebler et al., 2020; Nielsen et al., 2020; Rodda et al., 2021). Moreover, we reasoned that mapping mutations that affect plasma antibody binding several months after infection would be relevant for determining which viral mutations might alter the effectiveness of immunity if these individuals were re-exposed to a distinct SARS-CoV-2 variant in the future.

We performed escape mapping for samples collected at later time points (76–152 days post-symptom onset) from all 11 individuals for whom we had characterized plasma antibody binding at the ∼1-month time point. For some but not all individuals, there were substantive changes over time in how binding was affected by RBD mutations (Figures 4 , S3, and S4 and interactive visualizations at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera) (Hilton et al., 2020).

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Figure 4

The RBD mutations that affect plasma antibody binding change over time for some individuals

Escape maps, colored as in Figure 2, demonstrating temporal patterns: (A) no change over time, (B) broadening over time, (C) increasing prominence of one antigenic region, the 443–450 loop, or (D) narrowing over time. This figure shows the escape maps over time for 6 of the 11 individuals to illustrate representative trends; see Figure S3 for escape maps for all individuals at all time points. Figure S4 shows the effects of mutations at each site projected onto the RBD structure. Different sets of sites are shown in the logo plots in panels (A and C), and in panels (B and D). Sites highlighted in the logo plots are indicated in purple on the x axes of the associated line plots. The y axis limits were set as in Figure 2A (see STAR methods). Interactive versions of these visualizations are available at https://jbloomlab.github.io/SARS-CoV-2-RBD_MAP_HAARVI_sera/.

Specifically, for over half of the 11 individuals, there was relatively little change in which RBD mutations affected plasma antibody binding (Figures 4A, S3, and S4). For two individuals, antibody binding became strikingly more broad and less affected by any single RBD mutation (Figures 4B, S3, and S4). For one individual, mutations in the 443–450 loop had a much larger effect on binding by plasma antibodies from the later time compared to the earlier time (Figure 4C, S3, and S4). Finally, for one individual, there was a strong narrowing of the response, with no single RBD mutation having a large effect on binding by plasma from the early time point, but mutations at F456 and to a lesser extent, E484, having large effects at the later time point (Figure 4D, S3, and S4). In summary, while the specificity of plasma antibody binding is often maintained over time, in some individuals, the specificity broadens to become relatively unaffected by any single RBD mutation, while in other individuals the specificity narrows so that single mutations have a greater impact.

However, there was no clear relationship between changes in the fine specificity of antibody binding and overall plasma neutralizing activity. For instance, subjects B and C maintained similar binding specificities over time (Figure 4A) even though the neutralization titers of both subjects’ plasma decreased (Figure 1B). Similarly, subject D showed major changes in binding specificity over time (Figure 4B), although this change in specificity was not accompanied by a substantial change in overall plasma neutralization titer.

For some plasma, RBD mutations that reduce antibody binding strongly reduce neutralization

To determine how mutations that reduced plasma antibody binding to the RBD affected viral neutralization, we characterized a subset of mutations in neutralization assays with spike-pseudotyped lentiviral particles. For these assays, we chose mutations that our mapping showed had substantial effects on plasma antibody binding by samples from multiple individuals, and prioritized mutations present in circulating isolates of SARS-CoV-2.

In many cases, single mutations that were mapped to strongly reduce plasma antibody binding also greatly reduced viral neutralization. The effect of mutations at site E484 were particularly striking (Figures 5A and 5B). For several plasmas, the neutralization titer dropped by over an order of magnitude against viruses pseudotyped with spikes with E484 mutated to K, Q, or P. For instance, these three mutations to E484 caused 35- to 115-fold decreases in the neutralization titer of the plasma collected from subject C (day 32) (Figures 5A and 5D). As another example, both E484K and E484Q reduced neutralization by the plasma from subject B (day 26) by 10-fold, the same reduction achieved by depleting the plasma of all RBD-binding antibodies (Figures 5A and 5D).

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Figure 5

Mutations mapped to reduce plasma antibody binding often reduce viral neutralization

(A–C) Violin plots at left show the distribution of how mutations at all sites in the RBD affect plasma binding in the mapping experiments. The plots at right then show the effects of tested mutations on neutralization (the fold-change in neutralization inhibitory concentration 50% [IC₅₀]). For instance, the top row in (A) shows that mutations at E484 and F456 are mapped to reduce plasma antibody binding for subject C at both days 32 and 104, and that multiple different mutations at E484 but not F456 greatly reduced plasma neutralization (e.g., a >100-fold increase in IC₅₀ for E484K for the day-32 plasma). Sites that are accessible in the down conformation of the RBD in the context of full spike are indicated by red circles (e.g., E484), and sites that are inaccessible in the RBD’s down conformation are indicated by blue triangles (e.g., F456). In the plots showing the fold-change in IC₅₀s, the dashed gray line indicates a value of one (no change in neutralization), and the dotted orange line indicates the change in inhibitory concentration if all RBD-binding antibodies are removed (see Figure 1B).

(D) Full neutralization curves for a subset of plasma and viral mutants demonstrating how E484Q, E484K, G446V, and G485R substantially reduce viral neutralization for some plasma. Error bars are the standard error for n = 2 replicates.

For all neutralization curves used to determine changes in neutralization plotted in (A–C), see Figure S5. The y axis limits in the violin plots are set as the maximum of the y axis limit for all time points of a subject in the escape maps in Figures 2A and S3. Numerical IC₅₀ values and fold-change IC₅₀ relative to wildtype are listed at https://github.com/jbloomlab/SARS-CoV-2-RBD_MAP_HAARVI_sera/blob/main/experimental_validations/results/mutant_neuts_results/mutants_foldchange_ic50.csv.

While mutations at E484 generally caused the largest drops in neutralization, other mutations mapped to decrease antibody binding for specific plasma also affected neutralization. A dramatic example was G446V, which caused a ∼30-fold decrease in the neutralization titer of subject G (day 18) (Figures 5B and 5D). Mutations G485R and S494P also caused lesser but still appreciable (∼3- to 5-fold) decreases in neutralization titer for a few plasmas (Figure 5B). However, no single mutation completely abrogated neutralization for any of the plasma samples (Figure S5C).

In general, there was good concordance between the mapping of how mutations affected plasma antibody binding and their impact on viral neutralization. This concordance can be seen in Figures 5A–5C, where the violin plots show the distributions of the effects of mutations on plasma antibody binding across all sites. The sites in the upper tails of these violin plots are ones where mutations had large effects on binding, and mutations to such sites usually reduced neutralization. The one major exception was site F456, where mutations often caused large reductions in binding to yeast-displayed RBD but never appreciably affected neutralization (Figures 2A and ​and5A–5C).5A–5C). This discrepancy is not because antibodies targeting this region are inherently non-neutralizing or unaffected by mutations at site 456, as F456A and F456K disrupt neutralization by two monoclonal antibodies with epitopes that include F456 (Figure S5B) (Greaney et al., 2021; Shi et al., 2020; Starr et al., 2021; Zost et al., 2020a). Rather, we hypothesize that the discrepancy is because we mapped how mutations affected binding using isolated RBD, but in the native viral context of full spike, RBD can be positioned in either a “down” or “up” conformation (Walls et al., 2020; Wrapp et al., 2020). All sites where mutations that reduced binding also affected neutralization are accessible in both conformations, but F456 is only accessible in the up conformation. Because the RBD is usually in the down conformation (Cai et al., 2020; Ke et al., 2020; Walls et al., 2020), we speculate that sites accessible only in the up conformation may be subdominant in the context of polyclonal plasma neutralization of full spike even if they are dominant when assaying binding to isolated yeast-displayed RBD.

The neutralization assays also validated one of the most notable findings from the mapping: that the antigenic effects of mutations varied markedly across samples from different individuals. For several samples, the maps of binding escape were relatively “flat” with no mutations having large effects, and for these samples, no tested mutations substantially affected neutralization (Figure 5C). Additionally, sometimes the effects of mutations changed over time for the same individual. Such a temporal change was especially notable for subject G: mapping of the day-18 sample showed a strong effect of mutations centered around G446, but by day 94, the escape map had flattened (Figure 4B). Concordant with the maps, G446V greatly decreased neutralization by subject G’s day-18 plasma, but had only a modest effect on the day-94 plasma, even when combined with mutations at several other key sites (Figures 5B and 5D). These facts highlight how the antigenic effects of mutations vary across people and time and suggest that some plasmas are more resistant than others to erosion by viral evolution.

Resource availability

Experimental model and subject details

Method details

Quantification and statistical analysis

Quantitative analyses were performed using custom code, available at https://github.com/jbloomlab/SARS-CoV-2-RBD_MAP_HAARVI_sera. Plasma mapping experiments were performed in biological duplicate using the independent mutant RBD libraries.

To quantify the effects of mutations on binding by convalescent plasma antibodies, (see Method details section, “Analysis of deep sequencing data to compute each mutation’s plasma escape fraction”), we computed the “escape fraction” for each barcoded variant using the deep sequencing counts for each variant in the original and plasma-escape populations and the total fraction of the library that escaped antibody. To deconvolve the variant-level escape scores into escape fraction estimates for single mutations, we used global epistasis models (Otwinowski et al., 2018) implemented in the dms_variants package, as detailed at (https://jbloomlab.github.io/dms_variants/dms_variants.globalepistasis.html). The reported scores throughout the paper are the average across the biological duplicate libraries; these scores are also in Table S3. Correlations in final single mutant escape scores are shown in Figures S2D and S2E.

We did not determine statistical significance for the escape metrics as there is no established method for doing so. Rather, for plotting and analyses that required identifying RBD sites of “strong escape,” (e.g., choosing which sites to show in logo plots in Figures 2A, 2B, and S4 or label in Figure 4B), we considered a site to mediate strong escape if the total escape (sum of mutation-level escape fractions) for that site exceeded the median across sites by > 10-fold, and was at least 10% of the maximum for any site.

We thank Adam Dingens for experimental assistance; Tulio de Oliveira for helpful advice; the Flow Cytometry and Genomics core facilities at the Fred Hutchinson Cancer Research Center for experimental support, especially Dolores Covarrubias and Andy Marty; Neil King, Alexandra Walls, David Veesler, and the UW Institute for Protein Design for purified RBD and spike proteins; and Seth Zost and James Crowe of Vanderbilt University for monoclonal antibodies. We also thank all research participants in the Hospitalized or Ambulatory Adults with Respiratory Viral Infections (HAARVI) study for their generous participation and all HAARVI study researchers and staff, especially Caitlin Wolf. This work was supported by the NIAID/NIH (R01AI141707 and R01AI127893 to J.D.B., T32AI083203 to A.J.G., and F30AI149928 to K.H.D.C.) and the Gates Foundation (INV-004949). The Scientific Computing Infrastructure at Fred Hutch is funded by ORIP grant S10OD028685. T.N.S. is a Washington Research Foundation Innovation Fellow at the University of Washington Institute for Protein Design and a Howard Hughes Medical Institute Fellow of the Damon Runyon Cancer Research Foundation (DRG-2381-19). J.D.B. is an Investigator of the Howard Hughes Medical Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US government or the other sponsors.