Description
The subtracks of this track show mutations that lead to escape from patient serum antibodies or monoclonal
antibodies. Most of the mutations assayed were in the receptor binding domain (RBD) of the S protein.
The data shown here were imported from different studies, listed below. The
Bloom lab papers used deep mutational scanning data to measure the effect of all
possible mutations in the Spike RBD using a yeast surface display system.
- Bloom lab - patients A-K: antibodies in sera from the Hospitalized or Ambulatory Adults with Respiratory Viral
Infections (HAARVI) cohort, described in
Greaney et al., Biorxiv 2021.
- Bloom lab - 10 antibodies: A selection of ten monoclonal antibodies, described
in Greaney et al, Cell Host Microbe 2020.
- Bloom lab - 4 treatment antibodies: Four monoclonal antibodies licensed for treatment.
The results were described in Starr et al,
Biorxiv 2021.
- Whelan lab - 21 antibodies: a selection screen of 21 neutralizing monoclonal
antibodies (mAbs) against the receptor binding domain (RBD) generated 48 escape
mutants. The results were described in
Liu et al, Biorxiv 2020.
- Rappuoli lab - serum from one patient: three mutations obtained by passaging of
cells in neutralizing serum from a single patient, described in Andreano et al, Biorxiv 2021.
- McCoy lab - mutations tested on monoclonal antibodies and patient sera, described in
Rees-Spear et al, Biorxiv 2021.
For the Bloom lab data, we show just a summary of the data. Better and detailed structural
visualizations are available from the authors via dms-view using the following links:
patient sera,
10 monoclonal antibodies,
4 treatment antibodies.
Display Conventions and Configuration
Bloom lab data
Scores represent the "escape fraction" (discussed at length in the Methods
of the paper) which "represent the fraction of a given variant that escape antibody
binding, and should in principle range from 0 to 1.".
"Note that the magnitude of the measured effects of mutations on antibody escape depends on
the antibody concentration and the flow cytometry gates applied, meaning that the
escape fractions are comparable across sites for any given antibody, but are not precisely
comparable among antibodies without external calibration."
A higher score indicates a greater level of escape.
The data summarized to protein positions are shown as 36 subtracks, one per sample, that indicate
the maximum score per amino acid position that was assayed as shades of color
or, in full mode, as a x-y barplot. Blue subtracks show data from monoclonal
antibodies, red ones from patient sera. By configuring the current track (click
on "Antibody escape" under the image), one can display the total sum of all
scores per amino acid.
The data is summarized as two x-y barplots, as the average values per amino acid,
again in red (sera) and blue (MABs). Finally, another summary track has one feature
per position where the score exceeds 0.18. These features are clickable and the details page
show the exact amino acid changes and their scores.
Whelan lab data
Features are labeled with the nucleotide and protein coordinates and the name of the antibody.
Click a feature or mouse-over a feature to show these annotations.
Rappuoli lab data
The three mutations are labeled with the protein coordinates.
McCoy lab data
Features are labeled with the amino acid mutation coordinates.
Click a feature or mouse-over a feature to show a description on the specific mutation.
Methods
Patient sera: data was downloaded from the jbloomlab Github file and parsed into bedGraph format.
10 Antibodies: Table S1 from Starr et al, was downloaded and parsed into bedGraph format.
4 treatment antibodies: Data was downloaded from the jbloomlab Github file and parsed into bedGraph format using the total and maximum values.
21 Antibodies: Table 2 from Liu et al 2020, was copied manually and converted to bedGraph format.
For the Rappuoli lab, the mutations were manually copied from the text.
Data Access
The raw data can be explored interactively with the
Table Browser, or combined with other datasets in the
Data Integrator tool.
Please refer to our
mailing list archives
for questions, or our
Data Access FAQ
for more information.
References
Greaney AJ, Loes AN, Crawford K, Starr T, Malone K, Chu H, Bloom JD.
Comprehensive mapping of mutations to the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human serum antibodies
.
Biorxiv. 2021 Jan 04;.
Greaney AJ, Starr TN, Gilchuk P, Zost SJ, Binshtein E, Loes AN, Hilton SK, Huddleston J, Eguia R,
Crawford KHD et al.
Complete Mapping of Mutations to the SARS-CoV-2 Spike Receptor-Binding Domain that Escape Antibody
Recognition.
Cell Host Microbe. 2020 Nov 19;.
PMID: 33259788; PMC: PMC7676316
Zhuoming Liu, Laura A. VanBlargan, Paul W. Rothlauf, Louis-Marie Bloyet, Rita E. Chen, Spencer Stumpf, Haiyan Zhao, John M. Errico, Elitza S. Theel, Ali H. Ellebedy, Daved H. Fremont, Michael S. Diamond, Sean P. J. Whelan
Landscape analysis of escape variants identifies SARS-CoV-2 spike mutations that attenuate monoclonal and serum antibody neutralization.
Biorxiv. 2020
Starr TN, Greaney AJ, Addetia A, Hannon WW, Choudhary MC, Dingens AS, Li JZ, Bloom JD.
Prospective mapping of viral mutations that escape antibodies used to treat COVID-19.
bioRxiv. 2020 Dec 1;.
PMID: 33299993; PMC: PMC7724661
Andreano E, Piccini G, Licastro D, Casalino L, Johnson NV, Paciello I, Monego SD, Pantano E,
Manganaro N, Manenti A et al.
SARS-CoV-2 escape <i>in vitro</i> from a highly neutralizing COVID-19 convalescent
plasma.
bioRxiv. 2020 Dec 28;.
PMID: 33398278; PMC: PMC7781313
|
|