Machine learning approaches for the discovery, repurposing, and optimization of natural products with therapeutic potential - Supplement to support grad training of Adrian Russ

Vanderbilt University, Nashville TN

Investigators

Linked publications, trials & patents

Abstract

Project Summary Natural products from bacteria, fungi, and plants have long been a rich source of molecules with fascinating chemical structures and therapeutically-relevant bioactivities. One major challenge in natural product discovery is rediscovery of known compounds. One way to overcome this challenge is to mine for natural products from underexplored taxa. As part of the parent award, we applied machine learning and other bioinformatics techniques to identify the taxa that are most likely to produce multiple novel and bioactive compounds and few or no known active compounds, increasing the chances of discovering novel active compounds. In the work supported by this supplement, a graduate student, Adrian Russ, will perform further bioinformatic analysis of these prioritized strains, isolate active natural products from the strains, and investigate the strains’ response to commonly used elicitors of secondary metabolism. This work is related to the first project in the parent award, which involves the development application of machine learning methods for studying structure-activity relationships of natural products and applying these machine learning methods to discover natural products with therapeutically relevant bioactivities. Adrian’s project has three aims 1) to apply bioinformatics to investigate the conservation and diversity of BGCs in the genera of interest to determine which genera are most likely to harbor unsequenced biosynthetic diversity 2) to isolate and characterize bioactive natural products from the prioritized strains and 3) to determine if the strains’ secondary metabolism is stimulated by elicitors that have been successful in Streptomyces and other well- studied secondary metabolite producers. Through this project, Adrian will learn multiple computational, microbiology, molecular biology, and analytical chemistry techniques which will likely be useful in his planned career. This award will also support Adrian’s career development by funding his travel to conferences and enabling him to dedicate more time to research.