ECMpy2.0

Automated construction of enzyme-constrained models using ECMpy workflow.

create environment

$ conda create -n ECMpy2 python=3.7  
$ conda activate ECMpy2

install package

$ pip install cobra==0.21.0  
$ pip install openpyxl  
$ pip install requests  
$ pip install pebble  
$ pip install xlsxwriter  
$ pip install Bio   
$ pip install Require  
$ pip install quest   
$ pip install scikit-learn  
$ pip install RDKit  
$ pip install seaborn 
$ pip install pubchempy
$ pip install torch
$ pip install ipykernel 
$ pip install bioservices==1.10.4
$ pip install pyprobar
$ pip install xmltodict
$ pip install plotly
$ pip install -U kaleido
$ pip install nbformat
$ python -m ipykernel install --user --name ECMpy2 --display-name "ECMpy2"  

Preprocessing data sources

The "all--radius2--ngram3--dim20--layer_gnn3--window11--layer_cnn3--layer_output3--lr1e-3--lr_decay0.5--decay_interval10--weight_decay1e-6--iteration50","atom_dict.pickle", "bond_dict.pickle", "edge_dict.pickle", 'fingerprint_dict.pickle", and "sequence_dict.pickle" files are derived from the DLKcat method, and you can update it from GitHub(https://github.com/SysBioChalmers/DLKcat.git).
The 'bigg_models_metabolites.txt" file is downloaded from BiGG(http://bigg.ucsd.edu/static/namespace/bigg_models_metabolites.txt).
The "brenda_2023_1.txt" file is downloaded from BRENDA(https://www.brenda-enzymes.org/brenda_download/file_download.php), and "EC_kcat_max.json" is obtained from this file extraction.
The "gene_abundance.csv" file is downloaded and transformed from PaxDB(https://pax-db.org/download).

https://ecmpy.readthedocs.io/en/latest/ The "uniprot_data_accession_key.json" is compiled from the UniProt database.
The "AutoPACMEN_function.py" file is downloaded and modified from the AutoPACMEN method(https://github.com/klamt-lab/autopacmen.git).