Computationally-driven discovery and identification of small molecules in biological systems
EMSL Project ID
51780
Abstract
In this project we will build the critical tools for an eventual full standards-free (e.g. computation derived identification libraries) and library-free (i.e. de novo) small molecule identification pipeline in biological samples, initially testing and applying these methods on a model E. coli and/or P. fluorescens systems. This proposed work builds upon PNNL’s expertise and strengths, which have been demonstrated in the nascent success of standards-free metabolomics approaches that our teams has been spearheading. In order to fully characterize the metabolome of an engineered organisms, we need two complementary approaches. First, molecules can be identified using computationally-derived identification libraries that rely on predicting chemical properties from molecular structures of known chemicals (including ones not available for purchase as authentic reference material). We will advance these methods by using our new tool that rapidly generates custom in silico libraries on-the-fly directly from the genomes of the organism of interest, and then use at least 10 different adduct ion forms to significantly increase identification confidence from experimental features. The remaining challenge in analytical chemistry is to fashion a similar approach to elucidate unknown molecular structures from experimental features that fit no entry in a library. Thus, the second approach will focus entirely on the true unknowns, that is, the molecules in samples whose structures have never been determined. Here, we propose the development of new capabilities that can also be merged with existing tools as needed to solve the inverse problem of predicting a structure based on measured properties of uncharacterized molecules.
Project Details
Start Date
2020-12-08
End Date
2021-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members