Equipment Supplement to R35GM146987: Purchase of LC-MS system for high throughput isolation of bioactive natural products

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Project Summary/Abstract Natural products from bacteria, fungi, and plants have long been a rich source of molecules with fascinating chemical structures and therapeutically-relevant bioactivities. However, due to their complex structures, it is difficult to screen many analogs of natural products to truly understand the rules governing the relationship between their structure and activity. In the parent award, we are addressing this challenge by developing machine learning methods that can functionally model the structure-activity relationships (SAR) of natural products and aid in the design of biosynthetic pathways that can synthesize natural product analogs. The first project of the parent award applies machine learning to study natural product SARs. One approach we take is a genetic approach, where we are validating a machine learning model we previously developed that predicts bioactivity based on the presence or absence of biosynthetic genes in a natural product’s biosynthetic gene cluster (BGC). One major challenge of this approach is that extractions of bacterial cultures yield a complex mixture of metabolites and it is difficult to link individual metabolites to their BGCs or observed activity. Currently, we are using standard bioactivity-guided fractionation to link natural products to observed activity. This is a low- throughput and laborious process. There are technologies that can make this process faster, which generally involve performing chromatography and collecting fractions and mass spectra in parallel, so that specific mass features can be associated with each fraction. Then activity observed in bioactivity assays can be correlated with specific mass features to identify which molecules are responsible for activity. This process is much higher throughput than our current approach but it requires an LC-MS with fraction collection, and we do not have access to an up-to-date instrument for performing this assay. This equipment supplement will allow us to purchase the required instrument and maximize the chances that this project will be successful. The second project of the parent award focuses on developing machine learning and other computational tools for designing BGCs to biosynthesize novel natural product-like molecules. In this project, we will use LC-MS to determine if our engineered biosynthetic enzymes produced the expected product. While we do have access to core facility instruments that are appropriate for this purpose, using them is associated with an hourly fee. We could also use the instrument we will purchase with this supplement to perform these experiments, which would save on core facility costs allowing for more of the parent award to go to personnel and supplies.