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Figure 2. Physicochemical properties of rice NPN peptide profile as determined by LC-MS/MS. Histogram representation of rice NPN peptide distribution according to ( a ) length of peptide sequence, ( b ) charge of constituent peptides, and ( c ) percentage of hydrophobic residues (Hydrophobicity; peptide counts are displayed on the y axis; dashed line represents the median. 3.2. Effects of Constituent Peptides Predicted with Anti-Inflammatory Effects on TNF- α Secretion To further characterize the rice NPN, a set of eight AI-predicted peptides were synthesized for further investigation based on the AI algorithms described in Rein et al., (2019). Sequence information and physicochemical properties of positively predicted peptides are displayed in Table 1. The presence of our selected in vitro predicted peptides in rice NPN was verified by liquid To further characterize the rice NPN, a set of eight AI-predicted peptides were synthesized for further investigation based on the AI algorithms described in Rein et al., (2019). Sequence information and physicochemical properties of positively predicted peptides are displayed in Table 1. The presence Figure 2. Physicochemical properties of rice NPN peptide profile as determined by LC-MS / MS. Histogram representation of rice NPN peptide distribution according to ( a ) length of peptide sequence, ( b ) charge of constituent peptides, and ( c ) percentage of hydrophobic residues (Hydrophobicity; peptide counts are displayed on the y axis; dashed line represents the median. 3.2. E ff ects of Constituent Peptides Predicted with Anti-Inflammatory E ff ects on TNF- α Secretion
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