Peptide biosynthesis off the beaten path: Machine learning-guided discovery of non-canonical peptide natural products

• Applicant: Professor Dr. Eric Jan Nikolaus Helfrich
• Subject Area: Biological and Biomimetic Chemistry ; Analytical Chemistry ; Organic Molecular Chemistry - Synthesis and Characterisation
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 504947087

More than 50% of all drugs are natural products or have at least been inspired by natural products. Insights into the biosynthesis of natural products and the ever-increasing number of available genome sequence information in the post genomics era have resulted in the introduction of genome mining as a new discipline for the targeted identification of natural products. Genome Mining is an in-silico natural product discovery strategy that uses genome sequence information to assess the natural product biosynthetic potential of an organism. Several highly sophisticated genome mining platforms have been developed for the identification and annotation of textbook natural product biosynthetic gene clusters (BGCs) in microbial genome sequences. The discovery of natural products for which the corresponding BGCs cannot be identified by current genome mining pipelines and whose biosynthesis does not strictly follow the seemingly universal biosynthetic principles established for every natural product class, suggests that a proportion of non-canonical BGCs escapes unrecognized by currently available bioinformatic algorithms. These non-canonical BGCs display an almost untapped treasure trove for the identification of truly novel natural product scaffolds and unprecedented biochemical transformations. My group will develop machine learning-based genome mining algorithms for the targeted identification of these non-canonical BGCs. We will screen all publicly available genome sequences for the presence of (1) so far overlooked families of ribosomally synthesized and posttranslationally modified peptide (RiPP) BGCs, (2) unprecedented non-ribosomal peptide synthetase (NRPS) and polyketide synthase BGCs that harbor cryptic enzymatic domains or unprecedented module architectures and (3) BGCs which encode enzymes that biosynthesize peptides in a RiPP and NRPS-independent manner. A selection of the identified BGCs will be refactored and each gene placed under the control of a different, small molecule inducible promoter. The refactored BGCs will subsequently be expressed in highly optimized heterologous host organisms. Natural products will be purified and the structures of the purified metabolites elucidated. Biosynthetic studies will be conducted by repressing the transcription of one gene at the time and the effect on product formation will be studied. This gene knockout-independent approach allows us to propose biosynthetic models and yields small libraries of natural product intermediates, analogs and shunt products. These small natural product libraries will be subjected to a broad panel of bioactivity assays to identify natural products of potential pharmaceutical relevance and to conduct initial SAR studies. This research helps chart natural product biosynthetic dark matter, expands the chemical space of peptide natural products and leads to the identification and characterization of unprecedented biochemical transformations.

• DFG Programme: Independent Junior Research Groups
• Major Instrumentation: HPLC-MS