An antibody-escape estimator for mutations to the SARS-CoV-2 receptor-binding domain
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Abstract
A key goal of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surveillance is to rapidly identify viral variants with mutations that reduce neutralization by polyclonal antibodies elicited by vaccination or infection. Unfortunately, direct experimental characterization of new viral variants lags their sequence-based identification. Here we help address this challenge by aggregating deep mutational scanning data into an ‘escape estimator’ that estimates the antigenic effects of arbitrary combinations of mutations to the virus’s spike receptor-binding domain. The estimator can be used to intuitively visualize how mutations impact polyclonal antibody recognition and score the expected antigenic effect of combinations of mutations. These scores correlate with neutralization assays performed on SARS-CoV-2 variants and emphasize the ominous antigenic properties of the recently described Omicron variant. An interactive version of the estimator is at https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ (last accessed 11 March 2022), and we provide a Python module for batch processing. Currently the calculator uses primarily data for antibodies elicited by Wuhan-Hu-1-like vaccination or infection and so is expected to work best for calculating escape from such immunity for mutations relative to early SARS-CoV-2 strains.
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SciScore for 10.1101/2021.12.04.471236: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Three of these antibodies (CR3022, S304, and S309) were elicited by infection with SARS-CoV-1 and so are excluded from the datasets used for the calculations in this paper, although the calculator has an option (eliciting_virus) that allows optional inclusion of these antibodies. CR3022suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080)S304suggested: (Abcam Cat# ab63554, RRID:AB_1141794)Software and Algorithms Sentences Resources Python module with batch-mode calculator: A Python module that implements the calculations is at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/, and has all … SciScore for 10.1101/2021.12.04.471236: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Three of these antibodies (CR3022, S304, and S309) were elicited by infection with SARS-CoV-1 and so are excluded from the datasets used for the calculations in this paper, although the calculator has an option (eliciting_virus) that allows optional inclusion of these antibodies. CR3022suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080)S304suggested: (Abcam Cat# ab63554, RRID:AB_1141794)Software and Algorithms Sentences Resources Python module with batch-mode calculator: A Python module that implements the calculations is at https://github.com/jbloomlab/SARS2_RBD_Ab_escape_maps/, and has all the same options as the interactive calculator. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are caveats that should be kept in mind when using the escape calculator. First, the calculator only considers sites in the RBD, and ignores mutations to other regions of spike. Second, the calculator assumes the neutralizing activity of human polyclonal serum is represented by an equipotent mix of the monoclonal antibodies that happen to have been previously characterized by deep mutational scanning. Third, the calculator simply averages site-level escape measurements across antibodies, and does not yet implement a real biophysical model of the combined activity of multiple antibodies (Einav and Bloom 2020). Finally, and in our minds most significantly, the calculator estimates the impact of mutations in reference to antibodies targeted to the early Wuhan-Hu-1 RBD—an approach that is currently reasonable, but will become problematic as human exposure and vaccination histories diversify in the years to come (see last paragraph). Despite all these caveats, the escape calculator yields binding scores that correlate with experimentally measured neutralization titers. In addition, the actual antigenic evolution of SARS-CoV-2 seems to follow the principles captured by the escape calculator: variants of concern generally have combinations of mutations calculated to have additive effects on antibody escape (e.g., 417 and 484) rather than combinations calculated to have redundant effects (e.g., 484 and 490). We suspect the calculator works well because the RBD is the dominant t...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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