Virtual Screening and Prediction of Binding of Caprine CSN1S2 Protein Tryptic Peptides to Glucokinase

Milk-derived bioactive peptides in insulin resistance and type 2 diabetes

Diabetes is a global health issue affecting over 6% of the world and 11% of the US population. It is closely linked to insulin resistance, a pivotal factor in Type 2 diabetes development. This review explores a promising avenue for addressing insulin resistance through the lens of Milk-Derived Bioactive Peptides (MBAPs). Taken from casein or whey fractions of various milks, MBAPs exhibit diverse health-promoting properties. Specific interactions between these peptides and enzymes involved in glucose digestion and metabolism have been examined, leading to the identification of some key peptides exerting the effects. This review emphasizes the positive impact of MBAPs on glycemic control through various mechanisms. Different cell lines have been used to investigate MBAPs' effects on insulin signaling, inflammation, and oxidative stress. Preclinical in vivo studies have also shown that MBAPs lower glucose, stimulate insulin, and reduce inflammation. Human trials further substantiate these findings and suggest the potential utility of milk protein hydrolysates containing MBAPs in individuals with insulin resistance or T2D to improve insulin action and glucose homeostasis.

Gene-environment interaction identification via penalized robust divergence

In high-throughput cancer studies, gene-environment interactions associated with outcomes have important implications. Some commonly adopted identification methods do not respect the "main effect, interaction" hierarchical structure. In addition, they can be challenged by data contamination and/or long-tailed distributions, which are not uncommon. In this article, robust methods based on γ $\gamma$ -divergence and density power divergence are proposed to accommodate contaminated data/long-tailed distributions. A hierarchical sparse group penalty is adopted for regularized estimation and selection and can identify important gene-environment interactions and respect the "main effect, interaction" hierarchical structure. The proposed methods are implemented using an effective group coordinate descent algorithm. Simulation shows that when contamination occurs, the proposed methods can significantly outperform the existing alternatives with more accurate identification. The proposed approach is applied to the analysis of The Cancer Genome Atlas (TCGA) triple-negative breast cancer data and Gene Environment Association Studies (GENEVA) Type 2 Diabetes data.

Virtual Prediction of the Delphinidin-3-O-glucoside and Peonidin-3-O-glucoside as Anti-inflammatory of TNF-α Signaling

INTRODUCTION

Anthocyanin is the bioactive compound in black rice, which promotes some health benefits for human body. Present study revealed that black rice anthocyanins improve the biomarker of the metabolic syndrome, such as tumor necrosis factor alpha (TNF-α). However, the mechanism of anthocyanin in preventing metabolic syndrome has not been elucidated.

AIM

This study was performed to identify the interaction of six types of black rice anthocyanin towards TNF-α protein and TNF-α receptor through in silico studies, to assess the molecular properties and bioactivity of black rice anthocyanin.

METHODS

We retrieved the black rice anthocyanin compounds from the PubChem database and the proteins (TNF-α protein and TNF-α receptor) from Protein Data Bank (PDB) database. Protein and ligands were docked using Hex 8.0 software and visualized by Discovery Studio 4.1 program.

RESULTS

This study found the possibility that black rice anthocyanins interacted with TNF-α have no influence into TNF-α and TNF-α receptor interaction. The binding of delphinidin-3-O-glucoside & peonidin-3-O-glucoside to TNF-α receptor inhibited the TNF-α and TNF-α receptor signaling. The black rice anthocyanins had low activity as a drug. Interestingly, black rice anthocyanins had a potency as an antioxidant due to the hydrogen donor or acceptor in their structure, as protein kinase inhibitor, nuclear receptor ligand, and enzyme kinase inhibitor.

CONCLUSION

This study suggests that delphinidin-3-O-glucoside and peonidin-3-O-glucoside might have function as anti-inflammatory factor related with TNF-α signaling.