2.10 |. Data analysis

A modified version of Enrich 0.2 software as described in Kowalsky, Klesmith, et al. (2015) was used to compute enrichment ratios from the raw sequencing files. Custom python scripts available at Github (user: JKlesmith) were used to normalize the enrichment ratios (ER_;) defined as:

where f_i,_sel is the frequency of variant i in the selected population, and f_i, _ref is the frequency of variant i in the reference population. Libraries statistics results are listed in Supplementary Table S5. For the pro region sorting experiments, we define an enrichment score (ES_i) for each mutant i as the enrichment ratio of the selected mutant minus the wild-type enrichment ratio:

For conformational epitope mapping experiments we define a relative binding term for each mutant as the log transform of the mean fluorescence for variant i, Fi¯normalized to the relative mean fluorescence of the wild-type construct, F¯Wt:

This equation can be written in terms of experimental observables according to:

where σ´ is the log normal fluorescence standard deviation of the clonal population, and Φ is the percentage of cells collected by the sorting gate on the flow cytometer (Supplementary Table S3) (Kowalsky, Klesmith, et al., 2015).