Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2020 Nov 16:2020.11.16.385278.
doi: 10.1101/2020.11.16.385278.

High resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

Affiliations

High resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

Meghan E Garrett et al. bioRxiv. .

Update in

Abstract

Defining long-term protective immunity to SARS-CoV-2 is one of the most pressing questions of our time and will require a detailed understanding of potential ways this virus can evolve to escape immune protection. Immune protection will most likely be mediated by antibodies that bind to the viral entry protein, Spike (S). Here we used Phage-DMS, an approach that comprehensively interrogates the effect of all possible mutations on binding to a protein of interest, to define the profile of antibody escape to the SARS-CoV-2 S protein using COVID-19 convalescent plasma. Antibody binding was common in two regions: the fusion peptide and linker region upstream of the heptad repeat region 2. However, escape mutations were variable within these immunodominant regions. There was also individual variation in less commonly targeted epitopes. This study provides a granular view of potential antibody escape pathways and suggests there will be individual variation in antibody-mediated virus evolution.

PubMed Disclaimer

Conflict of interest statement

DECLARATION OF INTERESTS

MEG and JO are inventors on a patent application on Phage-DMS.

Figures

Figure 1.
Figure 1.. Schematic of the design of the Spike Phage-DMS library.
(A) Structure of the S protein and location of important protein domains. Structure was made in BioRender.com (PDB: 6VXX). (B) Sequences were computationally designed to code for peptides 31 amino acids long and to tile stepwise across the Wuhan-Hu-1 SARS-CoV-2 S protein ectodomain by 1 amino acid. There are 20 peptides representing all 20 possible amino acids at the central position, containing either the wild type residue (shown in black) or a mutant residue (shown in red). Within the 31 aa region surrounding the D614G mutation, peptides were also generated with G614 in addition to the 20 amino acid variants at the central position. (C) The designed sequences were cloned into a T7 phage display vector and amplified to create the final S protein Phage-DMS library. This library was then used in downstream immunoprecipitation and deep sequencing experiments with human plasma.
Figure 2.
Figure 2.. Linear epitopes bound by COVID-19 patient plasma.
Lines represent the enrichment of wildtype peptides from the Spike Phage-DMS library from individual plasma samples. Samples from convalescent COVID-19 patient plasma taken at approximately day 30 p.s.o. (top panel) or day 60 p.s.o. (bottom panel) are shown. Lines are colored by patient, with the key to the patient IDs on the right. Grey boxes highlight immunogenic regions where enrichment was detected in at least one individual across timepoints. Peptides that were included in the design, but absent from the phage library (Figure S1), are shown as breaks in the line plots. A schematic of S protein domains is shown above, with locations defined based on numbering used in: https://cov.lanl.gov/components/sequence/COV/annt/annt.comp
Figure 3.
Figure 3.. Effect of mutations on binding by COVID-19 patient plasma within the FP region.
Heatmaps depicting the effect of all mutations, as measured by scaled differential selection, at each site within the FP epitope for representative COVID-19 patients (numbered at top). Mutations enriched above the wildtype residue are colored blue and mutations depleted as compared to the wildtype residue are colored red. The intensity of the colors reflects the amount of differential selection as indicated to the right. The wildtype residue is indicated with a black dot. Line plots showing the enrichment of wildtype peptides for each patient are shown above, with a solid line for the day 30 p.s.o. patient samples and a dashed line for the day 60 p.s.o. patient samples.
Figure 4.
Figure 4.. Effect of mutations on binding by COVID-19 patient plasma within the linker/HR2 region.
Heatmaps depicting the effect of all mutations, as measured by scaled differential selection, at each site within the linker region/HR2 epitope for representative COVID-19 patients (numbered at top). Mutations enriched above the wildtype residue are colored blue and mutations depleted as compared to the wildtype residue are colored red. The intensity of the colors reflects the amount of differential selection as indicated to the right. The wildtype residue is indicated with a black dot. Line plots showing the enrichment of wildtype peptides for each patient are shown above, with a solid line for the day 30 p.s.o. patient samples and a dashed line for the day 60 p.s.o. patient samples. Peptides missing from the library are shown as grey boxes in the heatmaps and as breaks in the line plots.
Figure 5.
Figure 5.. Predicted effects of commonly circulating S protein variants on antibody escape.
(A) Scatterplot comparing the effect of mutations on patient plasma antibody binding and the frequency of all circulating S protein variants. The mutational entropy of every circulating protein variant, as reported at the https://cov.lanl.gov website and based on GISAID global sequencing, is plotted on the x-axis. The average of the scaled differential selection values for all mutants at each site is plotted on the y-axis. Patient ID’s are indicated on the top. Each site is colored by its location, as indicated on the bottom. (B) Effect of mutant peptides representing commonly circulating S protein variants on binding to COVID-19 patient plasma. We selected sites with a mutational entropy of greater than 0.02, as this is the cutoff used by LANL to determine sites of interest. On the top are the 17 sites of high mutational entropy and on the bottom are two selected sites that were noted as sites of antibody escape within immunodominant epitopes by Phage-DMS. On the right are the mutations examined, named according to the wildtype aa, followed by the site number, followed by the mutant aa of interest. Mutations chosen at sites of high mutational entropy represent the most common variant found in nature. The scaled differential values found by Phage-DMS for each mutant peptide are shown as dots and are colored by patient as indicated to the left. Data is from samples taken day 60 p.s.o.. SS = signal sequence, S2 = N-terminal region of S2, LR = linker region.
Figure 6.
Figure 6.. Epistatic effects of D614G mutation on antibody binding.
Enrichment values for paired mutant peptides made in either the wildtype Wuhan Hu-1 strain (on the left, D614) or D614G background (on the right, G614) for each patient (numbered at top). All mutant peptides that contained site 614 were included in this analysis (spanning aa 599–629). Data is from samples taken day 60 p.s.o.. Wilcoxon paired signed-rank test was performed (n = 380 paired mutant peptides). The effect size for all patient samples was small (Wilcoxon r < 0.3) except for patient 10, whose antibodies exhibited a moderate effect (Wilcoxon r = 0.46)

References

    1. Baum A., Fulton B.O., Wloga E., Copin R., Pascal K.E., Russo V., Giordano S., Lanza K., Negron N., Ni M., et al. (2020). Antibody cocktail to SARS-CoV-2 spike protein prevents rapid mutational escape seen with individual antibodies. Science 369, 1014–1018. - PMC - PubMed
    1. Belouzard S., Chu V.C., and Whittaker G.R. (2009). Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. Proc Natl Acad Sci U S A 106, 5871–5876. - PMC - PubMed
    1. Brouwer P.J.M., Caniels T.G., van der Straten K., Snitselaar J.L., Aldon Y., Bangaru S., Torres J.L., Okba N.M.A., Claireaux M., Kerster G., et al. (2020). Potent neutralizing antibodies from COVID-19 patients define multiple targets of vulnerability. Science 369, 643–650. - PMC - PubMed
    1. Di Tommaso P., Chatzou M., Floden E.W., Barja P.P., Palumbo E., and Notredame C. (2017). Nextflow enables reproducible computational workflows. Nat Biotechnol 35, 316–319. - PubMed
    1. Dingens A.S., Arenz D., Weight H., Overbaugh J., and Bloom J.D. (2019). An Antigenic Atlas of HIV-1 Escape from Broadly Neutralizing Antibodies Distinguishes Functional and Structural Epitopes. Immunity 50, 520–532.e523. - PMC - PubMed

Publication types