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In silico Chemical Library Engine (ISICLE) for Expo/Metabo-lomics and Modeling


EMSL Project ID
49661

Abstract

Currently, there are no nuclear magnetic resonance or IMS databases specifically built for identifying and quantifying metabolites of relevance to environmental samples in general, or to microbial communities in specific. We propose to develop a cross-cutting computational capability to generate custom nuclear magnetic resonance metabolomics libraries, called the In silico Chemical Library Engine (ISICLE). This approach would build metabolite and (secondary metabolite) lists specific to (meta)genomes generated or used in the MinT Environmental, Exposure, and Metabolomics core thrust areas, and couple the lists of metabolites to predicted NMR chemical shifts and IMS collision cross sectional areas from quantum chemical calculations using NWChem. The overall goal is to expand identifiable compound libraries from ~1000 metabolites to a projected >100,000, with the novel ability to generate NMR chemical shift lists and IMS CCS for custom solvent conditions.

Project Details

Start Date
2016-10-03
End Date
2017-09-30
Status
Closed

Team

Principal Investigator

Ryan Renslow
Institution
Pacific Northwest National Laboratory

Team Members

Jamie Nunez
Institution
Pacific Northwest National Laboratory

Sean Colby
Institution
Pacific Northwest National Laboratory

Dennis Thomas
Institution
Pacific Northwest National Laboratory

Neeraj Kumar
Institution
Pacific Northwest National Laboratory