dGPredictor

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Requirements:

1. Python 2.7
2. RDkit (http://www.rdkit.org/)
3. pandas (https://pandas.pydata.org/)
4. matplotlib (https://matplotlib.org/stable/users/installing.html)
5. Scikit-learn (https://scikit-learn.org/stable/)
6. Streamlit==0.55.2 (https://streamlit.io/)
7. Component-contribution (https://pennstateoffice365-my.sharepoint.com/:f:/g/personal/vuu10_psu_edu/EvF9kttgsvlJnhAs1FDRWFcBu0obprfFFjrZKJbq-Yw5sw?e=dPHo2w)
8. Openbabel (https://anaconda.org/openbabel/openbabel)
9. ChemAxon's Marvin >= 5.11

Installing on windows

1. Python 2.7 (https://www.python.org/downloads/release/python-2718/) Recommended-

• Create anaconda environment using command "conda create -n dGPredictor python=2.7"
• activate the env using command "conda activate dGPredictor" or "source activate dGPredictor"

2. RDkit

• type command "conda install -c conda-forge rdkit" in your dGPredictor env to install rdkit

3. Pandas

• "conda install pandas"

4. matplotlib

• "conda install -c conda-forge matplotlib"

5. Scikit-learn

• use command "pip install -U scikit-learn"

6. Streamlit

• use command "pip install -U streamlit"

7. Component-contribution

• download the package folder using the link provided
• save the folder inside dGPredictor (delete the empty component-contribution folder)

8. Openbabel

• run "conda install -c conda-forge openbabel"

9. ChemAxon's Marvin (Component-contribution use this to estimate PkA values)

• Marvin is only required for adding structures of novel metabolites/compounds that are not in the KEGG database
• instructions (https://chemaxon.com/products/marvin/download)
• add cxcalc.bat to PATH
• you will need to get a license to use ChemAxon (it is free for academic use)

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Running web-interface loacally using streamlit

• Generate model file by "running model_gen.py" using "python model_gen.py"
• run "streamlit run ./streamlit/main.py" from dGPredictor folder
• running KEGG reaction (doesn't require ChemAxon's Marvin) : copy paste the reaction equation into reaction section and click search

Gibbs free energy prediction use automated group decomposition method

• Step 1: decompose the metabolites based on smiles files (see function decompse_ac in decompose_groups.py or notebook )
• Step 2: create group changes vectors (i.e. reaction rules) based on group changes in metabolites of reactions (see get_rxn_rule om decompose_groups.py)
• Step 3: cross validation to check model accuracy (note that for experimental replicates, we take the median value of measurements of the same chemical reaction in different conditions or by different researchers)
• Step 4: linear regression, Ridge Regression and Bayesian Ridge Regression in "predict.py"
• Step 5: Multiple regression models in notebook "analysis_dGPredictor.ipynb"

Pathway design using novoStoic

• See "mini_novoStoic.py"

demo