Fig. 2: In vitro evolution of spike protein RBD.
From: SARS-CoV-2 variant prediction and antiviral drug design are enabled by RBD in vitro evolution
a, Binding titration curves for the highest affinity binding variant in each successive yeast library. The mutations in each of the variants are shown in Table 2. b, Binding titration curves for selected single mutations that occur frequently in both yeast display selection and in evolving SARS-CoV-2 variants. c, Comparison of binding affinities for selected mutants and RBD variants. Data are shown as individual data points and mean values ± s.e.m. d, The effect of the isogenic mutations identified in RBD-62 on binding to ACE2 and expression level on the yeast surface (n ≥ 3; data are mean ± s.e.m.). e, Apparent binding affinities for ACE2 of single-amino-acid mutations as calculated from deep-mutational scanning of the RBD domain7 (KD,App, black dots) or from yeast titration done in this study (KD,YD, blue squares) plotted against their frequency in the GISAID database22. Mutations with fewer than ten sequences were not included in the analysis. f, The effect of the most prevalent mutations in and variants of SARS-CoV-2 RBD on ACE2 binding and yeast surface expression combined with the frequency of variants. The K417N/T mutations occur in the B.1.351 and P.1 variants in combination with N501Y/E484K. The K417N/T occur independently only infrequently. Red circles show the affinity and expression of the single Q498R mutation on top of the B.1.351, P.1 and P.3 variants. g, Double-mutant cycle calculations showing interaction energies (ΔΔGint) between selected residues, suggesting independent or cooperative behaviour. Standard errors were calculated from 3–6 repeats of individual measurements (details in Methods). h, Surface plasmon resonance binding sensorgrams for RBD-WT (top) and RBD-62 (bottom) at six concentrations of analyte (5–160 nM). The black line is the global fit using the instrument’s built-in function.
