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Plant polyphenols have a natural binding affinity for proteins, and their interaction can be exploited to form diverse aggregate particles. Protein-polyphenol particles utilized as food ingredients allow consumers to incorporate more health-benefiting plant bioactives into their diets. The functional properties of the protein-polyphenol particles can be influenced by many factors, including complexation conditions and starting material properties. Here, cranberry polyphenols extracted from pomace were complexed with nine pea protein isolate starting materials with different physical (particle size and protein content) and chemical (hydrolyzed and oxidized) properties to investigate the impact of protein characteristics on particle functionality. Chemical differences between proteins affected polyphenol binding; oxidized protein isolate (specifically, VegOtein N) bound 12%-27% more polyphenols than other isolates. Polyphenol binding to proteins decreased digestion rates in vitro, averaging 25% slower gastric (pepsin) digestion and a 35% slower intestinal (pancreatin) digestion. Physical differences in protein starting materials affected digestibility; isolate with the largest particle size (specifically, Nutralys F85G) produced particles with the lowest digestion rate. Solubility was impacted by both the process of forming particles and by polyphenol binding; control particles were 56% less soluble, and protein-polyphenol particles up to 75% less soluble, than unmodified proteins. The solubility of unmodified protein isolate starting materials varied widely according to the manufacturing process, but, after complexation, protein-polyphenol particles produced from all protein sources exhibited a similar depressed level of solubility. The desired functional properties of the protein-polyphenol particle food ingredients will be considerably influenced by the properties of the protein isolate starting material.
Pubmed ID: 34262733 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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