Regiospecific CO₂ fixation

In this example, we consider the reaction network for the regiospecific CO₂ fixation published in DOI: 10.1021/acs.organomet.9b00773 producing cyclic carbonates from a bicyclic epoxy alcohol and CO₂ , with bromide as a catalyst. More specifically, we showcase the integration of rNets with the knowledge graphs based on the OntoRXN ontology. The files required for the generation of the reaction networks can be found in examples/example_KG folder. This folder contains the following files:

example_KG
  -README
  -CycOct.owl
  -RNets_KG_Parser.py
  -run.py

Here the owl file contains a knowledge graph (non-relational database) craterd through the Python library ontorxn_tools, employing the OntoRXN ontology as the data organization scheme. From there, the file RNets_KG_Parser.py allows the translation of the owl file to the input files of rNets.

Preliminary steps

The file RNets_KG_Parser.py has dependencies to two python libraries: rdflib and owlready2 as well as numpy. The following code will allow you to install both of these libraries, but in case of doubt we recommend to follow their official documentation to install them.

$ python -m pip install rdflib owlready2 numpy

Note

The specific versions used were: * numpy 1.26.4 * owlready2 0.46 * rdflib 7.0.0

Quickstart

For convenience we created the run.py script which facilitates the generation of all the figures in one go. To execute the code the user will have to type in the following command:

python run.py

Upon execution two new folders will appear: temp and res. The temp folder contains the .dot files, which serve as input to graphviz for the actual image generation. The res folder will contain the automatically rendered .png files.

Generation of the network graph

In order to generate the graph from the owl file we will first use the RNets_KG_Parser.py.

To use the script to generate the Compounds and Reactions files needed for rNets we will execute the following command:

$ python RNets_KG_Parser.py CycOct_comps.csv CycOct_reactions.csv CycOct.owl --reference EpOr+CO2+TMABr --hidden-species CO2,TMA,TMABr

The two first arguments are the compounds (CycOct_comps.csv) and reactions (CycOct_reactions.csv) that will be read by rNets, followed by the name of the target .owl file (CycOct.owl). The optional arguments control:

• --reference: Species used to determine the energy reference, separated by "+" symbol. In this case, we select the initial reactants and catalyst for the proposed process: EpOr+CO2+TMABr.

• --hidden-species: Comma-separated list of species which will not be assigned a node in the produced DOT file, but will be considered to balance energies.

In absence of these arguments, the graph will still be generated, but with a more complex layout (as in Figure S2 in the SI), and the energies in the compounds and reaction files will be absolute.

Next we use rNets to generate the .dot file containing the reaction network:

$ python -m rnets -cf CycOct_comps.csv -rf CycOct_reactions.csv -o CycOct_network.dot

Finally we proceed to render the figure in our preferred graphical format using graphviz. In this case we will use png

$ dot -Tpng CycOct_network.dot -o CycOct_network.png

To get the horizontal layout in shown in the manuscript, the corresponding graph argument should be passed to dot, setting rankdir to LR (left-right) instead of the default TB (top-bottom):

$ dot -Grankdir=LR -Nfontsize=16 -Granksep=0.1 Gnodesep=0.5 -Tpng CycOct_network.dot -o CycOct_network_horizontal.png

For details on CRN-KG generation, check the corresponding article Garay-Ruiz and Bo, J. Cheminf. 2022, 14, 29