BioCatalyzer is a python tool that predicts enzymatic metabolism products using a rule-based approach.
BioCatalyzer is implemented as a Command Line Interface that takes as input a set of compounds represented as SMILES strings and outputs a set of predicted metabolic products and associated enzymes.
This metabolic products can then be matched with experimental MS data using this same tool.
Installing from Pypi package repository:
pip install biocatalyzer
Installing from GitHub:
-
clone the repository:
git clone https://github.com/jcorreia11/BioCatalyzer.git
-
run:
python setup.py install
biocatalyzer_cli <PATH_TO_COMPOUNDS> <OUTPUT_DIRECTORY> [--neutralize=<BOOL>] [--reaction_rules=<FILE_PATH>] [--organisms=<FILE_PATH>] [--patterns_to_remove=<FILE_PATH>] [--molecules_to_remove=<FILE_PATH>] [--min_atom_count=<INT>] [--match_ms_data=<BOOL>] [--ms_data_path=<FILE_PATH>] [--tolerance=<FLOAT>] [--n_jobs=<INT>]
Argument | Example | Description | Default |
---|---|---|---|
compounds <PATH_TO_COMPOUNDS> | file.tsv or "smile1;smiles2;smile3;etc" |
The path to the file containing the compounds to use as reactants. Or ;-separated SMILES strings.1 | |
output_directory <OUTPUT_DIRECTORY> | output/directory/ |
The path directory to save the results to. | |
neutralize | True or False |
Whether to neutralize the compounds before predicting the products. In this case the new products will also be neutralized. | False |
reaction_rules | file.tsv or None |
The path to the file containing the reaction rules to use.2 | all_reaction_rules_forward_no_smarts_duplicates_sample.tsv |
organisms | file.tsv or "org_id1;org_id2;org_id3;etc" or None |
The path to the file containing the organisms to use. Or ;-separated organisms identifiers. Reaction Rules will be selected accordingly (select only rules associated with enzymes encoded by genes from these organisms).3 | All reaction rules are used. |
patterns_to_remove | patterns.tsv or None |
The path to the file containing the patterns to remove from the products. 4 | patterns.tsv |
molecules_to_remove | molecules.tsv or None |
The path to the file containing the molecules to remove from the products. 5 | byproducts.tsv |
min_atom_count | 4 |
The minimum number of heavy atoms a product must have. | 5 |
match_ms_data | True or False |
Whether to match the predicted products to the MS data. | False |
ms_data_path | ms_data.tsv |
The path to the file containing the MS data. 6 | None |
tolerance | 0.02 |
The mass tolerance to use when matching masses. | 0.02 |
n_jobs | 6 |
The number of jobs to run in parallel (-1 uses all). | 1 |
See drugs.csv1 for an example.
The file must be tab-separated and contain the following columns:
smiles
- the SMILES representation of the compounds;compound_id
- the compounds identifiers.
Alternatively, the compounds can be passed as ;-separated string with the SMILES representations.
The output path must be a directory. The results will be saved in the following files:
new_compouds.tsv
- the predicted products;matches.tsv
(ifmatch_ms_data
is set toTrue
) - the matches between the predicted products and the MS data;
If set to True
, the compounds will be neutralized before predicting the products. In this case the new products will
also be neutralized.
See all_reaction_rules_forward_no_smarts_duplicates_sample.tsv2 for an example.
The file must be tab-separated and contain the following columns:
InternalID
- The ID of the Reaction Rule. # TODO: change the name of this columnReactants
- The Reactants of the ReactionRule. Coreactants must be defined by their ID as in the Coreactants file. The compound to match must be identified by the string 'Any'. The format must be:coreactant1_id;Any;coreactant_id
. The order in which the reactants and the compound to match are defined is relevant and specific to the Reaction Rule. If the Reaction Rules are mono-component (i.e. they do not contain any additional coreactant) the format must be:Any
.SMARTS
- The SMARTS representation of the Reaction Rule.EC_Numbers
- The EC Numbers associated with the Reaction Rule.Organisms
- The Organisms associated with the Reaction Rule.
By default our set of reaction rules is used.
All organisms' identifiers are defined in: https://www.genome.jp/kegg/catalog/org_list.html are allowed.
Example:
hsa is for Homo sapiens (human).
eco is for Escherichia coli K-12 MG1655.
sce is for Saccharomyces cerevisiae (budding yeast).
If you want to use your own organisms see organisms.csv3 for an example.
The file must be tab-separated and contain a column named org_id
with the organisms' identifiers (KEGG identifiers).
Alternatively, the organisms can be passed as ;-separated string with the organisms identifiers.
If you want to use your own patterns to remove see patterns.tsv4 for an example.
The file must be tab-separated and contain a column named smarts
with the SMARTS representation of the patterns to remove.
If you want to use your own molecules to remove see byproducts.tsv5 for an example.
The file must be tab-separated and contain a column named smiles
with the SMILES representation of the molecules to remove.
If set to True
, the predicted products will be matched to the MS data.
In this case the ms_data_path
must be set.
See ms_data.tsv6 for an example.
The file must be tab-separated and contain the following columns:
ParentCompound
- the parent/original compound identifiers.ParentCompoundSmiles
- the SMILES representation of the compounds (optional).Mass
- the mass of the molecule.
The mass tolerance (float
) to use when matching masses. Masses between mass - mass_tolerance
and mass + mass_tolerance
will be considered as a match.
The number of jobs to run in parallel. If -1
is passed, all available cores will be used.
biocatalyzer_cli file.tsv output_dir/ --neutralize=True --reaction_rules=reaction_rules.tsv --organisms="hsa;eco;sce" --patterns_to_remove=patterns.tsv --molecules_to_remove=byproducts.tsv --match_ms_data=True --ms_data_path=ms_data.tsv --mass_tolerance=0.1 --n_jobs=-1
For predicting compound metabolism only:
biocatalyzer_cli file.tsv output_dir/ --neutralize=True --reaction_rules=reaction_rules.tsv --organisms="hsa;eco;sce" --patterns_to_remove=patterns.tsv --molecules_to_remove=byproducts.tsv --n_jobs=-1
Both parts of this CLI (the generation of new compounds (bioreactor_cli
) and the matching with the MS data
(matcher_cli
)) can be run individually.
For the bioreactor_cli
see readme_bioreactor_cli.md.
For the matcher_cli
see readme_matcher_cli.md.
Manuscript under preparation!
Developed at Centre of Biological Engineering, University of Minho and EMBL Heidelberg (Zimmermann-Kogadeeva Group).
This project has received funding from the Portuguese FCT and EMBL CPP Scientific Visitors Fellowships.
Released under an MIT License.