Mining Cryptic Biosynthetic Gene Clusters for Novel Bioactive Compounds and Biocatalysts

PROJECT SUMMARY/ABSTRACT Natural products from the soil-dwelling bacteria Streptomyces have been a rich source of medicines. Additionally, the enzymes that produce natural products perform unique and challenging chemistry, providing inspiration for novel biocatalysts. One important class of natural products is cyclic nonribosomal peptides. Unfortunately, the discovery of novel cyclic peptides using traditional techniques is often unsuccessful. Also, chemical synthesis of cyclic peptides, especially small strained tetrapeptides, remains a challenge, resulting them being underexplored as potential therapeutics. Genomics data suggests a plethora of novel Streptomyces cyclic peptides and their biosynthetic enzymes remain to be discovered. However, the biosynthetic machinery responsible for producing these novel natural products is often cryptic (i.e. transcriptionally inactive). A significant gap remains in the strategies available to discover new bioactive cyclic peptide natural products and to synthesize these peptides, especially cyclic tetrapeptides, for further analysis. Our long-term goals are to 1) Discover biosynthetic enzymes that perform unique and challenging reactions and develop these enzymes as biocatalysts, and 2) Develop bioinformatics and synthetic techniques that allow us to directly access natural products from cryptic biosynthetic gene clusters. Our current research objectives are to 1) Determine activities and substrate scopes for previously identified, as well as bioinformatically predicted, tetrapeptide cyclases and develop the most promising ones as biocatalysts and 2) Utilize bioinformatics methods to identify nonribosomal peptides of interest followed by direct chemical synthesis and biological testing to identify bioactive leads. Our recent discovery of the first standalone tetrapeptide cyclase and our development of the Synthetic Natural Product Inspired Cyclic Peptide (SNaPP) methodology, make us well positioned to complete these objectives. The central hypothesis of the first project is natural product biosynthetic enzymes will be efficient biocatalysts for the generation of cyclic tetrapeptides that otherwise are very challenging to access. The objectives of the first project are to 1) better understand the mechanism of Ulm16, a known tetrapeptide cyclase 2) bioinformatically identify new tetrapeptide cyclases and 3) apply these tetrapeptide cyclases to the synthesis of bioactive cyclic tetrapeptides and the development of cyclic tetrapeptide libraries for screening. In the second project, we are directly chemically synthesizing natural products that are bioinformatically predicted from nonribosomal peptide synthetase biosynthetic gene clusters. While our previous work in this area has resulted in promising bioactive leads, challenges remain including accurate predictions of off-loading methods, unnatural amino acids, and tailoring enzymes. The objectives for the second project are to address these challenges by incorporating predictions of these three areas into our predicted peptides and then synthesizing them. This work will provide access to many novel natural product scaffolds that are currently inaccessible and that are likely to have interesting bioactivities.