Interactive plot correlating the fitness estimates for spike amino-acid mutations with experimental measurements of the effects of these mutations in deep mutational scanning of the full spike ( Dadonaite et al (2023) ) or just the RBD ( Starr et al (2022) ). Each point represents an amino-acid mutation. The Pearson correlation coefficient and the number of mutations being correlated are shown in the upper left of the scatter plot.
You can mouse over points for details.
The minimum expected count slider below the plot indicates how many expected counts of an an amino acid we require before making a fitness estimate. Larger values yield more accurate estimates but for fewer amino acids. So move the slider to the left to show estimates for more amino acids at lower confidence, and move it to the right to show estimates for fewer amino acids at higher confidence.
Click yes for the only show mutations measured in all subsets box to only show the same subset of mutations in each panel; otherwise different panels may show different subsets of mutations depending on which mutations have measurements or fitness estimates for each pair of variables being correlated.
For the RBD deep mutational scanning, the effects of mutations are averaged across the different homolog backgrounds. The DMS effect RBD is quantified as the mean of the effect of the mutation on ACE2 affinity and RBD expression.
See Bloom and Neher (2023) for a paper describing the work.
See https://github.com/jbloomlab/SARS2-mut-fitness for full computer code and data.
See https://jbloomlab.github.io/SARS2-mut-fitness/ for links to all interactive plots.
This plot is for the public_2022-12-18 dataset. Here are all plots for that dataset.