Quantitative structure-activity relationship models for bitter-tasting tripeptides based on integrated descriptors

Mastering the art of taming: Reducing bitterness in fish by-products derived peptides

Processed fish by-products are valuable sources of peptides due to their high protein content. However, the bitterness of these peptides can limit their use. This review outlines the most recent advancements and information regarding the reduction of bitterness in fish by-products derived peptides. The sources and factors influencing bitterness, the transduction mechanisms involved, and strategies for reducing bitterness are highlighted. Bitterness in peptides is mainly influenced by the source, preparation method, presence of hydrophobic amino acid groups, binding to bitter receptors, and amino acid sequence. The most widely utilized techniques for eliminating bitterness or enhancing taste include the Maillard reaction, encapsulation, seperating undesirable components, and bitter-blockers. Finally, a summary of the current challenges and future prospects in the domain of fish by-products derived peptides is given. Despite some limitations, such as residual bitterness and limited industrial application, there is a need for further research to reduce the bitterness of fish by-products derived peptides. To achieve this goal, future studies should focus on the technology of fish by-products derived peptide bitterness diminishment, with the aim of producing high-quality products that meet consumer expectations.

Characteristics of the enzyme-induced release of bitter peptides from wheat gluten hydrolysates

Bitter peptides in the enzymatic hydrolysates were prepared and purified from wheat gluten using aqueous ethanol solutions and macroporous resin, which has opened a new road for the extraction and separation of bitter peptides. This report contains the release regularity of bitter peptides and the factors affecting the change of bitter intensity during enzymatic hydrolysis, providing a scientific basis for the research on debitterizing method. In this study, the effects of different degrees of hydrolysis (DH) and enzyme active sites on the bitter peptide content and bitter taste thresholds were discussed. The relationship between amino acid composition, molecular weight distribution, surface hydrophobicity and bitter taste thresholds was extensively researched. The results showed the exposure of hydrophobic amino acids and the bitterness intensity of the hydrolysates increased as the DH increased, and the bitterness of wheat gluten hydrolysates (WGHs) hydrolyzed by Alcalase was stronger than that of Trypsin. According to correlation analysis, the proportion of total hydrophobic amino acid is the first factor that affects the sensory properties of bitter peptide, and the release content of bitter peptides and the content of total bitter amino acids are the second, following by the content of peptide in the molecular weight range of 500–1,000 Da and the surface hydrophobicity. The amino acid sequence of bitter peptides from WGHs were identified and predicted using high performance liquid chromatography-mass spectrometry (HPLC-MS/MS) and bioinformatics. It was found that the molecular weight of most of the peptides was below 1,500 Da, and the Q value was higher than 5.86 kJ/mol.

In silico exploration of hydroxylated polychlorinated biphenyls as estrogen receptor β ligands by 3D-QSAR, molecular docking and molecular dynamics simulations

Hydroxylated polychlorinated biphenyls (HO-PCBs), as the major metabolites of PCBs, have been reported to act as estrogen receptor β (ERβ) agonists. However, the chemical-biological interactions governing their activities toward ERβ have not been elucidated. Therefore, three dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations, to the best of our knowledge, for the first time were performed to explore the correlation between the structures and activities. The best 3D-QSAR model presented higher predictive ability (R²_cv=0.543, R²_pred=0.5793/R²_cv=0.543, R²_pred=0.6795) based on comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), respectively. At the same time, the derived contour maps indicated the important structural features required for improving the activity. Furthermore, molecular docking studies and MD simulations predicted the binding mode and the interactions between the ligand and the receptor. All the results would lead to a better understanding of the specific mechanism of HO-PCBs on estrogen receptor β (ERβ).Communicated by Ramaswamy H. Sarma.

Molecular-level understanding of the hTAS2R1 receptor-bitter tasting tetra-peptide binding: a structural biology study based on computational approaches

Effective computational approaches for bitter-tasting peptides have been developed and analyzed in the present work.

Activity prediction of aminoquinoline drugs based on deep learning

The results of the traditional prediction method for the activity of aminoquinoline drugs are inaccurate, so the prediction method for the activity of aminoquinoline drugs based on the deep learning is designed. The molecular holographic distance vector method was used to describe the molecular structure of 40 aminoquinoline compounds, and the principal component regression method was used for modeling and quantitative analysis. Two methods were used to predict the activity of aminoquinoline drugs. The correlation coefficients of the results obtained from the two sets of activity data and the cross test were 0.9438 and 0.9737, and 0.8305 and 0.9098, respectively. Our data suggested that method for the activity prediction of aminoquinoline drugs based on deep learning studied in this paper can better predict the activity of aminoquinoline drugs and provide a strong basis for the activity prediction of aminoquinoline drugs.