Figure 4.

Comparisons of in silicoScanFoldZ_avg values against three different reactivity-informed secondary structural models of SARS-CoV-2. (A) In silicoScanFold Z_avg values were binned based on their magnitude from < -2 to > +2 at intervals of 1 and are labelled across the x axis along with the number of values that are present in each bin. The positions corresponding to each Z_avg value were cross referenced between the in silicoScanFold predicted secondary structure of SARS-CoV-2 and the three model conditions proposed by Manfredonia et al. (DMS in vitro, black shading; SHAPE in vitro, dark gray shading; SHAPE in vivo, light gray shading) to calculate a percent similarity which is plotted on the y axis. Across all three model conditions, the lowest Z_avg bins consistently have the highest similarity to the reactivity informed global models. (B) The < -1 and < -2 binned Z_avg values for in silicoScanFold models of SARS-CoV-2, at both a 120 and 600 nt analysis window, were compared to the three separate models from Manfredonia et al. and a false positive rate (FPR) was calculated. The Z_avg bins are labelled across the x axis along with the number of nt positions associated with each bin and the FPR is plotted along the y axis. For the 120nt scanning analysis window, the most negative Z_avg bin (i.e. < -2) had the lowest FPR compared to the < -1 Z_avg bin and the All Z_avg bin (which had the highest FPR). The distribution of Z_avg values for the ScanFold model utilizing a 600 nt analysis window were significantly shifted to be more negative, resulting in almost all (>99%) of the Z_avg values to be in the < -2 bin, therefore there is little variation in the FPR for these Z_avg bins and the FPR in all bins are higher compared to the < -2 bin utilizing the 120 nt analysis window.