Exploring the chemical space of post-translationally modified peptides in Streptomyces with machine learning
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.
- Kloosterman, A.M.
- 12 May 2021
- Thesis in Leiden Repository
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care. Genome mining has given new potential to the field of natural product discovery, as thousands of biosynthetic gene clusters (BGCs) are discovered for which the natural product is not known.Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a highly diverse class of natural products. The large number of different modifications that can be applied to a RiPP results in a large variety of chemical structures, but also stems from a large genetic variety in BGCs. As a result, no single method can effectively mine for all RiPP BGCs, making it an interesting source for new molecules.In this thesis, new methods are explored to mine genomes for the BGCs of novel RiPP variants, with a focus on discovering RiPPs that have new modifications. RRE-Finder is a new tool for the detection of RiPP Recognition Elements, domains that are often found in RiPP BGCs. DecRiPPter is another tool that employs machine learning models to discover new RiPP precursor genes encoded in the genomes. Both tools can be used to prioritize novel RiPP BGCs. Two candidate BGCs are characterized, one of which could be shown to specify a new RiPP, validating the approach.