Collagen as a source of bioactive peptides: a bioinformatics approach

BACKGROUND: Collagen is the most abundant protein in animals and can be obtained from residues of the food industry. Its hydrolysate has many desirable properties that make it suitable as an additive in foods and cosmetics, or as a component of scaffold materials to be used in biomedicine. RESULTS: We report here the characterization of type I collagen from five different sources, namely bovine, porcine, chicken, trout and salmon, as well as their hydrolysates by means of bioinformatics tools. As expected, the results showed that bovine and porcine collagen, as well as trout and salmon collagen, can be used interchangeably due to their high identity. This result is consistent with the evolution of proteins with highly identical sequences between related species. Also, 156 sequences were found as potential bioactive peptides, 126 from propeptide region and 30 from the central domain, according to the comparison with reported active sequences. CONCLUSIONS: Collagen analysis from a bioinformatic approach allowed us to classify collagen from 5 different animal sources, to establish its interchangeability as potential additive in diverse fields and also to determine the content of bioactive peptides from its in silico hydrolysis.

Advances in strategies for activating silent biosynthetic gene clusters in Streptomyces / 中国药科大学学报

@#Microbial secondary metabolites have always been one of the important sources of discovery and development of new drugs due to their remarkable biological activities. The explosion of genome sequences has revealed that Streptomyces harbor an immensely untapped biosynthetic potential. However, the number of active secondary metabolites with new skeletons or structural units found from Streptomyces is much lower than that of biosynthetic gene clusters(BGCs), mainly due to the fact that many BGCs are either expressed weakly or transcriptionally silent under conventional laboratory conditions. Beginning with the bioinformatics tools for BGCs prediction, this review focuses on the classical approaches to activate silent BGCs of Streptomyces in native and heterologous hosts. Moreover, several new strategies including transcriptional factors decoy, reporter-guided high-throughput selection and muliplexed CRISPR-TAR were detailed, which provide methodological references for mining new secondary metabolites from Streptomyces.