Developing KBase-NWChem workflow for integrating molecular properties with Genome-Scale Metabolic Modeling
EMSL Project ID
51160
Abstract
One of the challenges to investigate natural and engineered biological systems at molecular-level is to accurately predict chemical properties of thousands of metabolites involved in cell metabolism. The overarching objective is to develop an integration between KBase and NWChem computational chemistry code by computing thermodynamic properties of KBase (ModelSEED) compounds that are used to annotate genome-scale model. The NWChem predicted chemical properties such as free energy of the formation of metabolites and reaction free energies will be used for accurate metabolic modeling to predict the metabolic fluxes and metabolites levels. KBase Narrative interface is built on the python notebook platform and so the focus is to generate set of modules to automate the NWChem data output and integrate with KBase’s ModelSEED compounds. This integration would enable KBase users to access advanced molecular modeling EMSL capabilities for the metabolic modeling as well as to annotate genome scale model with more accurately predicted biochemistry data.
Project Details
Start Date
2020-03-06
End Date
2020-10-31
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members
Related Publications
Shane R. Canon, Christopher S. Henry, Rajendra P. Joshi, Neeraj Kumar, Lee Ann McCue, Andrew McNaughton, Dennis G. Thomas. 2021. "Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions." ACS Omega https://doi.org/10.1021/acsomega.1c00997