Figure 4

Simulations show that predictive performance of antibody binding profiles improves with increasing antibody diversity. Antibody binding profiles (${\overrightarrow{s}}_{\mathsf{\text{sim}}}^i$) were simulated for antibody mixtures of 1 to 16384 different antibodies. (A) Predictive performance increases with increasing number of antibody variants (n_Ab), (B) as does the correlation (r) between all pairs of predicted AAWS ${\overrightarrow{w}}_{\mathsf{\text{sim}}}^i$. In both (A) and (B), a simulated random peptide library (X_sim) of 255 14-mers and assigned AAWS ($\overrightarrow{h}$) were generated once and were kept constant across all simulation runs. Notably, varying X_sim for every simulation run did not change either of the boxplot distributions. For every mixture of n_Ab-different antibodies, 100 simulations with newly generated random antibody mixtures were run. Antibody binding profiles were computed using Equation 2. Corresponding AAWS (${\overrightarrow{w}}_{\mathsf{\text{sim}}}^i$) were determined using Equation 1.