We developed the first nonempirical high-throughput computational method, CompMolNWChem_Thermo, for estimating reaction and metabolite standard Gibbs energies using NWChem(1) Computational Chemistry Calculations. We integrated our automated computational pipeline to calculate metabolic reaction free energy with the DOE Systems Biology Knowledgebase (KBase) module (2) to develop a QC based web accessible application (App). Our app integrates set of modules, each of which are selfcontained and independently validated. It uses benchmarked QC method and provide open source access as a ThermoCalculator to calculate reaction free energy (3). For this, we have used snakemake48 as an underlying package for workflow management to bind together different Python-based components ensuring scalability, portability, and fault tolerance. Within the app, ModelSEED database is used for describing the biochemical reactions that are used to construct a new genome scale model for a given organism. Our app takes ModelSEED reaction ID or reaction as an input and generates a metabolite list of ModelSEED compounds and returns QC predicted reaction free energy as well as Gibbs free energy of metabolites as an output. We have been still developing to calculate Underlying QC calculations are performed using integrated high-performance computing resources at PNNL outside the KBase app.
This is a KBase module generated by the KBase Software Development Kit (SDK).
You will need to have the SDK installed to use this module. Learn more about the SDK and how to use it.
You can also learn more about the apps implemented in this module from its catalog page or its spec file.
Add your KBase developer token to test_local/test.cfg and run the following:
$ make
$ kb-sdk testAfter making any additional changes to this repo, run kb-sdk test again to verify that everything still works.
To use this code in another SDK module, call kb-sdk install CompMolNWChem_Thermo in the other module's root directory.
(1) Apr`a, E. et al. NWChem: Past, present, and future. The Journal of Chemical Physics 2020, 152, 184102.
(2) Arkin, A. P. et al. The DOE Systems Biology Knowledgebase (KBase). bioRxiv 2016.
(3) Joshi, R., McNaughton, A.D., Thomas, D.G., Henry, C., Canon, S., McCue, L., and Kumar, N. "Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions" ACS Omega Omega. 2021 Apr 13; 6(14): 9948–9959. https://doi.org/10.1021/acsomega.1c00997
https://www.pnnl.gov/news-media/accelerating-microbial-engineering-thermodynamic-calculations
https://www.emsl.pnnl.gov/news/using-computation-to-design-better-microbes/1832
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