Fig. 3: Experimental validations of final model predictions on melanin binding and cell-penetration.
From: Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery
a Schematic showing an in vitro melanin binding assay with melanin nanoparticles (mNPs) using a biotin quantification kit. The DyLight 494-tagged avidin emitted fluorescence when the biotinylated peptides displaced the weakly interacting 4′-hydroxyazobenzene-2-carboxylic acid (HABA or H). Created with BioRender.com. b Plot of the relationship between predicted melanin binding and binding measured experimentally in vitro. The x-axis indicates melanin binding predictions from the final super learner, and the y-axis indicates the experimental melanin binding values (n = 4 for each peptide). Dots represent the mean value for peptides. The black linear trend line conveys the Pearson correlation relationship (two-tailed), and the gray area indicates the 95% confidence interval. c, d Comparison of melanin binding and cell-penetration in melanin-induced human adult retinal pigment epithelial (ARPE-19) cells. Blue triangles denote predicted non-cell-penetrating peptides (non-CPP), and magenta dots represent predicted cell-penetrating peptides (CPP). The x-axes indicate melanin binding measured in vitro (n = 4 for each peptide), and the y-axes convey intracellular peptide concentration measured from the cell uptake assay (n = 3 for each peptide). Black linear trend lines indicate Pearson correlation relationships, with 95% confidence intervals shown as shaded areas. The correlation coefficients and p-values (two-tailed) are shown. e Summary of CPP (n = 113) and non-CPP (n = 14) intracellular concentrations. Box plot conveys median (middle line), 25th and 75th percentiles (box), and the 1.5 × interquartile range (whiskers). The p value was calculated using a Mann–Whitney U test (two-tailed).
