Bile acid micelle disruption activity of short-chain peptides from tryptic hydrolyzate of edible proteins

Self-assembled condensed tannins supramolecular system can adsorb cholesterol micelles to promote cholesterol excretion

In this study, the cholesterol (CH)-lowering behavioral mechanisms and drivers of condensed tannins (CTs) were revealed using a molecular aggregation theoretical model combined with in vitro experiments, as well as the CH-lowering effects of CTs validated based on animal experiments. Theoretical model results indicated that CTs can spontaneously aggregate to form supramolecular systems, can break CH micelles and form larger aggregates, a behavior driven by van der Waals forces and hydrogen bonds; DLS and TEM results confirmed that the presence of CH leads to a larger particle size of CTs and the formation of large aggregates; thermodynamic analysis and ITC revealed that the adsorption of CH by CTs is a spontaneous reaction driven by hydrogen bonds and hydrophobic forces; Animal experiments and fecal biochemical parameters further confirmed that the intake of CTs can reduce CH absorption and promotes CH excretion. Overall, this study reveals the CH-lowering behavioral mechanism of CTs from the perspective of molecular aggregation behavior.

In Silico Screening of a Bile Acid Micelle Disruption Peptide for Oral Consumptions from Edible Peptide Database

Recently, many bioactive peptides have been identified using bioinformatics tools. Previously, our group developed a method to screen dual-functional peptides that have direct intestinal delivery with porous silica gel and bile acid micelle disruption. However, newly designed peptides were not found in any storage protein. Therefore, in this study, in silico screening was performed using a 350,000 edible peptide library consisting of 4- to 7-mer independent peptides. As an initial screening, all edible peptides were applied to the random forest model to select predicted positive peptides. For a second screening, the peptides were assessed for the possibility of intestinal delivery using a 3D color map. From this approach, three novel dual-functional peptides, VYVFDE, WEFIDF, and VEEFYC were identified, and all of them were derived from storage proteins (legumin, myosin, and 11S globulin). In particular, VEEFYCS, in which a serine residue (S) is added to VEEFYC, was assumed to be released by thermolysin from the 11S-globulin derived from Ginkgo biloba by LC-MS/MS analysis. VEEFYCS was found to have suitable direct intestinal delivery and bile acid micelle disruption activity.

Machine learning screening of bile acid-binding peptides in a peptide database derived from food proteins

Bioactive peptides (BPs) are protein fragments that exhibit a wide variety of physicochemical properties, such as basic, acidic, hydrophobic, and hydrophilic properties; thus, they have the potential to interact with a variety of biomolecules, whereas neither carbohydrates nor fatty acids have such diverse properties. Therefore, BP is considered to be a new generation of biologically active regulators. Recently, some BPs that have shown positive benefits in humans have been screened from edible proteins. In the present study, a new BP screening method was developed using BIOPEP-UWM and machine learning. Training data were initially obtained using high-throughput techniques, and positive and negative datasets were generated. The predictive model was generated by calculating the explanatory variables of the peptides. To understand both site-specific and global characteristics, amino acid features (for site-specific characteristics) and peptide global features (for global characteristics) were generated. The constructed models were applied to the peptide database generated using BIOPEP-UWM, and bioactivity was predicted to explore candidate bile acid-binding peptides. Using this strategy, seven novel bile acid-binding peptides (VFWM, QRIFW, RVWVQ, LIRYTK, NGDEPL, PTFTRKL, and KISQRYQ) were identified. Our novel screening method can be easily applied to industrial applications using whole edible proteins. The proposed approach would be useful for identifying bile acid-binding peptides, as well as other BPs, as long as a large amount of training data can be obtained.

Screening of a novel free fatty acid receptor 1 (FFAR1) agonist peptide by phage display and machine learning based-amino acid substitution

Free fatty acid receptor 1 (FFAR1 or GPR40) has attracted attention for the treatment of type 2 diabetes mellitus, and various small-molecule agonists have been developed. However, most FFAR1 agonists as well as endogenous ligands, such as linoleic acids, have high lipophilicity, and their high lipophilicity is related to off-target toxicity. Therefore, we need to focus on new ligand candidates with less toxicity. In this study, we screened peptides with FFAR1 agonist activity as new ligand candidates. First, we used phage display to identify peptides with high affinity to FFAR1. Next, the agonist activities of peptides determined by the phage display were evaluated by the TGF-α shedding assay. Finally, to improve the FFAR1 agonist activity of the peptide, we performed an inclusive single amino acid substitution and sequence analysis. Logistic regression (LR) analysis using 120 physiochemical properties was performed to predict peptides with high FFAR1 agonist activity. STTGTQY determined by phage display promoted glucose-stimulated insulin secretion in pancreatic MIN6 cells. Furthermore, STKGTF predicted by the LR analysis showed high insulin secretion at low concentrations compared to STTGTQY. The results of this study suggest that peptides could be new candidates as FFAR1 agonists.