Discovering novel small molecule drugs through tandem mass spectral database search

Chemia Biosciences, Inc., Pittsburgh PA

Investigators

Linked publications, trials & patents

Abstract

Project Summary. Natural products are the major source of drug molecules. Currently, 64% of small molecule drugs approved by the Food and Drug Administration are natural products or their derivatives. Currently, the dominant technique for discovering novel natural products is bioactivity-guided isolation. However, this technique is limited to the high abundance molecular products of microbial / plant species. Since most of the widely expressed natural products (NPs) have already been picked, bioactivity-guided techniques now lead to high rediscovery rates of known molecules. This proposal focuses on developing a novel platform for discovering natural product small molecules by integrating genome mining with computational metabolomics. With the advent of high throughput tandem mass spectrometry, metabolomics datasets collected on the supernatant of microbial / plant species have become available. However, computational techniques for identifying novel small molecules from these large and complex datasets are in their early stages. Recently, we developed Dereplicator, Dereplicator+, and molDiscovery to identify known small molecules by searching their tandem mass spectra against public chemical databases, like PubChem. Currently, there is no computational approach for the systematic discovery of novel variants of natural product small molecules. Further, manual analysis of such large metabolomic datasets is infeasible. The existing dereplication-by-molecular-networking strategy is limited to identifying molecules present in spectral libraries (e.g., NIST library, hundreds of thousands of molecules) but cannot search against chemical structure databases (e.g., PubChem, hundreds of millions of molecules). To fill this gap, the overarching goal of this proposal is to develop efficient and accurate methods for identifying novel natural products from complex mass spectral datasets. We will develop accurate and efficient techniques for variable identification of small molecules from mass spectra. We will then integrate this method with our existing genome mining approaches to discover novel natural product small molecules. We will further work with the UM-Natural Products Discovery Core to collect LC-MS/MS data on culture extracts of 100 Actinobacteria from the U.S. Department of Agriculture. One NRP with novel chemistries/enzymes will be isolated and characterized at the University of Michigan, and we will screen them for antimicrobial, antitumor, and antifungal activities.