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.

1. 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.
2. Bloom lab - 10 antibodies: A selection of ten monoclonal antibodies, described in Greaney et al, Cell Host Microbe 2020.
3. Bloom lab - 4 treatment antibodies: Four monoclonal antibodies licensed for treatment. The results were described in Starr et al, Biorxiv 2021.
4. 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.
5. 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.
6. 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;.

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