Figure 4.

Simulations show that predictive performance of antibody binding profiles improves with increasing antibody diversity. Antibody binding profiles (<a onClick="popup('http://www.biomedcentral.com/1471-2164/13/79/mathml/M31','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/79/mathml/M31">View MathML</a>) were simulated for antibody mixtures of 1 to 16384 different antibodies. (A) Predictive performance increases with increasing number of antibody variants (nAb), (B) as does the correlation (r) between all pairs of predicted AAWS <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/79/mathml/M33','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/79/mathml/M33">View MathML</a>. In both (A) and (B), a simulated random peptide library (Xsim) of 255 14-mers and assigned AAWS (<a onClick="popup('http://www.biomedcentral.com/1471-2164/13/79/mathml/M8','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/79/mathml/M8">View MathML</a>) were generated once and were kept constant across all simulation runs. Notably, varying Xsim for every simulation run did not change either of the boxplot distributions. For every mixture of nAb-different antibodies, 100 simulations with newly generated random antibody mixtures were run. Antibody binding profiles were computed using Equation 2. Corresponding AAWS (<a onClick="popup('http://www.biomedcentral.com/1471-2164/13/79/mathml/M33','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/79/mathml/M33">View MathML</a>) were determined using Equation 1.

Greiff et al. BMC Genomics 2012 13:79   doi:10.1186/1471-2164-13-79
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