Characterization and relationship analysis of antioxidant and anti-inflammatory peptides in pomelo fruitlet albumin

Several macromolecules from the pomelo fruitlet (PF) have demonstrated functional potential in previous research. In this study, pomelo fruitlet albumin (PFA) was extracted from PF, its anti-inflammatory and antioxidant properties were assessed using enzyme-linked immunosorbent assays, and its capacity to clear free radicals was measured. Meanwhile, we hypothesize that the amino acid sequence may affect the anti-inflammatory and antioxidant properties, and the two may rely on common significant sites within the amino acid sequence. Therefore, we analyzed the amino acid sequence using a quantitative structure-activity relationship model to explore the connection between the antioxidant and anti-inflammatory capacities of PFA. Both capacities were closely associated with six sites within the amino acid sequence. Collectively, this study illustrates that PFA exhibits both anti-inflammatory and antioxidant capacities, with six specific sites identified as significantly affecting both activities.

Novel and efficient techniques in the discovery of antioxidant peptides

As a research hotspot in food science and nutrition, antioxidant peptides can function by scavenging free radicals, inhibiting peroxides, and chelating metal ions. Therefore, how to efficiently discover and screen antioxidant peptides has become a key issue in research and production. Traditional discovery methods are time-consuming and costly, but also challenging to resolve the quantitative structure-activity relationship of antioxidant peptides. Several novel techniques, including artificial intelligence, molecular docking, bioinformatics, quantum chemistry, phage display, switchSENSE, surface plasmon resonance, and fluorescence polarization, are emerging rapidly as solutions. These techniques possess efficient capability for the discovery of antioxidant peptides, even with the potential for high-throughput screening. In addition, the quantitative structure-activity relationship can be resolved. Notably, combining these novel techniques can overcome the drawbacks of a single one, thus improving efficiency and expanding the discovery horizon. This review has summarized eight novel and efficient techniques for discovering antioxidant peptides and the combination of techniques. This review aims to provide scientific evidence and perspectives for antioxidant peptide research.

Identification of three novel antioxidative peptides from Auxenochlorella pyrenoidosa protein hydrolysates based on a peptidomics strategy

Auxenochlorella pyrenoidosa is recognized as a potential sustainable protein material in food industry, however, its application remains still very limited. Herein, this study aimed to investigate the antioxidative properties of Auxenochlorella pyrenoidosa protein hydrolysates and identify novel antioxidative peptides from protein hydrolysates through a workflow mainly including enzymatic hydrolysis, peptidome quantification, quantitative structure-activity relationship (QSAR) modeling, in silico screening, and validation. Three novel antioxidative peptides including AGWACLVG, IDLAY and YPLDL were identified from protein hydrolysates by papain with the hydrolysis time of 4 h, in which, AGWACLVG showed strong 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity with the IC₅₀ value of 68.88 µM and Trolox equivalent antioxidative capacity of 6.20 ± 0.23 mmol TE/g. This study suggested that Auxenochlorella pyrenoidosa protein hydrolysates could be used as potential antioxidative ingredients in food industry, and the identification of novel antioxidative peptides would contribute to the construction of more robust QSAR models in the future.

Computational study on the origin of the cancer immunotherapeutic potential of B and T cell epitope peptides

Immune therapy is generally seen as the future of cancer treatment. The discovery of tumor-associated antigens and cytotoxic T lymphocyte epitope peptides spurned intensive research into effective peptide-based cancer vaccines. One of the major obstacles hindering the development of peptide-based cancer vaccines is the lack of humoral response induction. As of now, very limited work has been performed to identify epitope peptides capable of inducing both cellular and humoral anticancer responses. In addition, no research has been carried out to analyze the structure and properties of peptides responsible for such immunological activities. This study utilizes a machine learning method together with interpretable descriptors in an attempt to identify parameters determining the immunotherapeutic activity of cancer epitope peptides.