filterzyme 0.0.6

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Filterzyme

Structural filtering pipeline using docking and active site heuristics to prioritze ML-predicted enzyme variants for experimental validation. This tool processes superimposed ligand poses and filters them using geometric criteria such as distances, angles, and optionally, esterase-specific filters or nucleophilic proximity.

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Features

• Analysis of enzyme-ligand docking using multiple docking tools (ML- and physics-based).
• Optional catalytic nucleophile-focused analysis for esterases or other enzymes with nucleophilic catalytic residues.
• User-friendly pipeline only using a .pkl file as input and ligand smile strings.

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Installation

Environment Setup

Using conda

conda env create -f environment.yml
conda activate filterpipeline

Clone the repository

git clone https://github.com/HelenSchmid/EnzymeStructuralFiltering.git
cd EnzymeStructuralFiltering
pip install .

Coming soon: Install via pip

pip install enzyme-filtering-pipline

Usage Example

The input pandas DataFrame must include:

• Entry – unique identifier for each enzyme
• Sequence – amino acid sequence of the enzyme
• substrate_name – name of the substrate
• substrate_smiles – SMILES string of substrate e.g. MEHP "CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)O"
• substrate_moiety – SMARTS pattern to define chemical moiety of interest within substrate e.g. general ester SMARTS "C(O)(O)"

If cofactors are included, add:

• cofactor_name – name of the cofactor
• cofactor_smiles – SMILES string of cofactor e.g. PLP "CC1=NC=C(C(=C1O)C=O)COP(=O)(O)O"
• cofactor_moiety – SMARTS pattern to define chemical moiety of interest within the cofactor

from filterzyme.pipeline import Pipeline
import pandas as pd

df = pd.read_pickle("example_df.pkl")

pipeline = Pipeline(
        df = df,
        ligand_name="TPP",
        max_matches=1000,                # number of matches during substructure SEARCH
        esterase=0,                      # 1 if interested specifically in esterases
        find_closest_nuc=1,
        num_threads=1,
        skip_catalytic_residue_prediction = False,
        alternative_structure_for_vina = 'Chai', 
        squidly_dir='/nvme2/helen/EnzymeStructuralFiltering/filterzyme/squidly_final_models/',
        base_output_dir="pipeline_output"
    )

pipeline.run()