biocatalyzer 0.1.1b0

BioCatalyzer: a rule-based tool to predict compound metabolism

BioCatalyzer

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.

Installation

Installing from Pypi package repository:

pip install biocatalyzer

Installing from GitHub:

1. clone the repository: git clone https://github.com/jcorreia11/BioCatalyzer.git

2. run: python setup.py install

Command Line Interface

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>] [--mode=<STR>] [--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 this 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
mode	mass or mass_diff	The mode to use when matching the predicted products to the MS data.	mass
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

Compounds

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.

Output directory

The output path must be a directory. The results will be saved in the following files:

• new_compouds.tsv - the predicted products;
• matches.tsv (if match_ms_data is set to True) - the matches between the predicted products and the MS data;

Neutralize

If set to True, the compounds will be neutralized before predicting the products. In this case the new products will also be neutralized.

Reaction Rules

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 column
• Reactants - The Reactants of the ReactionRule. Coreactants must be defined by their ID as in the Coreactants file. The compound to match must be identifyed 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.

To use our complete set of Reaction Rules please download the following file and provide its path in the --reaction_rules argument.

You can directly use this file by providing the path to it as the value of the reaction_rules parameter.

Organisms

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.

Patterns to remove

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.

Molecules 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.

Match MS data

If set to True, the predicted products will be matched to the MS data.

In this case the ms_data_path must be set.

MS data path

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 or MassDiff - depending on the selected mode, the mass or mass difference of the molecule.

Mode

The mode to use when matching the predicted products to the MS data.

If set to mass, the Mass column will be used. This will match the predicted products exact mass to the MS data provided in the Mass column.

If set to mass_diff, the MassDiff column will be used. This will match the predicted products mass difference to the ParentDrug as provided in the MS data MassDiff column.

Mass Tolerance

The mass tolerance (float) to use when matching masses. Masses between mass - mass_tolerance and mass + mass_tolerance will be considered as a match.

Number of jobs

The number of jobs to run in parallel. If -1 is passed, all available cores will be used.

Usage example

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 --mode=mass --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

Individual CLIs

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.

Cite

Manuscript under preparation!

Credits and License

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.