Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional neural network, and predict its health effects using information about therapeutic targets for diseases. We constructed predictive models based on 21,103 peptide-protein interactions involving 10,950 peptides and 2,533 proteins, and applied the models to food peptides (e. g., lactotripeptide, isoleucyltyrosine and sardine peptide) defined in food for specified health use. The models suggested potential effects such as blood-pressure lowering effects, blood glucose level lowering effects, and anti-cancer effects for several food peptides. The interactions of food peptides with target proteins were confirmed by docking simulations.Keywords: foods · peptides · health effects · peptide-protein interaction · graph convolutional neural network · deep learning[a] I.

Understanding animal behavior in its natural habitat is a challenging task. One of the primary step for analyzing animal behavior is feature detection. In this study, we propose the use of deep convolutional neural network (CNN) to locate monkey features from raw RGB images of monkey in its natural environment. We train the model to identify features such as the nose and shoulders of the monkey at about 0.01 model loss.

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Proton pump inhibitors (PPIs) are potent inhibitors of gastric acid secretion, used as first-line agents in treating peptic ulcers. However, we have previously reported that PPIs may diminish the therapeutic effect of anti-vascular endothelial growth factor (VEGF) drugs in patients with cancer. In this study, we explored the effects of vonoprazan, a novel gastric acid secretion inhibitor used for the treatment of peptic ulcers, on the secretion of VEGF in cancer cells and attempted to propose it as an alternative PPI for cancer chemotherapy. The effects of PPI and vonoprazan on VEGF expression in cancer cells were compared by real-time reverse transcriptionpolymerase chain reaction and ELISA. The interaction of vonoprazan and PPIs with transcriptional regulators by docking simulation analysis. In various cancer cell lines, including the human colorectal cancer cell line (LS174T), PPI increased VEGF messenger RNA expression and VEGF protein secretion, while this effect was not observed with vonoprazan. Molecular docking simulation analysis showed that vonoprazan had a lower binding affinity for estrogen receptor alpha (ER-α), one of the transcriptional regulators of VEGF, compared to PPI. Although the PPI-induced increase in VEGF expression was counteracted by pharmacological ER-α inhibition, the effect of vonoprazan on VEGF expression was unchanged. Vonoprazan does not affect VEGF expression in cancer cells, which suggests that vonoprazan might be an alternative to PPIs, with no interference with the therapeutic effects of anti-VEGF cancer chemotherapy.

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