Metabolites prediction for toxicology

Overview

In silico prediction of molecule metabolites based on their SMILES using 5 software programs:

• BioTransformer3
• SyGMa
• GLORYx
• MetaTrans
• Meta-Predictor

BioTransformer3, Sygma, MetaTrans and GloryX (API) are used via singularity.
Meta-Predictor needs to clone its github and to create a conda environment.
Singularity image downloads and conda environment creations are automated (First use may take a long time).

As this project was designed for non-bioinformaticians, a graphical interface via zenity was included (optional).

This project has been tested and run on linux and windows-WSL2.

Due to hardware limitations, Meta-Predictor (which requires cuda drivers) may not function correctly. Its use is therefore disabled by default. You can try running it and seeing the error logs to solve potential problems.

Quick start

Required packages:

• Singularity (https://sylabs.io/docs/)

• Conda (Optional: need for MetaPredictor only) (https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html):
  wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh; chmod +x Miniconda3-latest-Linux-x86_64.sh; ./Miniconda3-latest-Linux-x86_64.sh

• APT packages (zenity is optional, gawk and dos2unix are often already installed by default):
  sudo apt install zenity gawk dos2unix

Download MetaTox

• git clone https://github.com/alexisbourdais/MetaTox; chmod +x MetaTox/Metatox.sh

Add the sylabs library to use singularity images stored there

• singularity remote add --no-login SylabsCloud cloud.sycloud.io

Download MetaPredictor and configure it

• cd MetaTox; git clone https://github.com/zhukeyun/Meta-Predictor; mkdir Meta-Predictor/prediction; mv Meta-Predictor/model/SoM\ identifier/ Meta-Predictor/model/SoM_identifier; mv Meta-Predictor/model/metabolite\ predictor/ Meta-Predictor/model/metabolite_predictor; chmod +x Meta-Predictor/predict-top15.sh

Run

• Input : Text file with the molecule ID/name in the 1st column and the smile code in the 2nd column, separated by a comma.
• ./Metatox.sh to activate zenity
• ./MetaTox.sh --input ExempleInput.txt (--option) to skip zenity

Parameters

• ./Metatox.sh -h to see available parameters when zenity was skipped

  REQUIRED parameter

    -i|--input   
  

  OPTIONAL parameter

    -o|--outdir     Name of the output directory [Results_prediction]
  
    -p|--predictor  To activate meta-Predictor [No]
  
    -b|--biotrans   Type of biotransformation to use with BioTransformer3:
                        [allHuman] : Predicts all possible metabolites from any applicable reaction(Oxidation, reduction, (de-)conjugation) at each step 
                        ecbased    : Prediction of promiscuous metabolism (e.g. glycerolipid metabolism). EC-based metabolism is also called Enzyme Commission based metabolism
                        cyp450     : CYP450 metabolism prediction 
                        phaseII    : Prediction of major conjugative reactions, including glucuronidation, sulfation, glycine transfer, N-acetyl transfer, and glutathione transfer, among others 
                        hgut       : Human gut microbial
                        superbio   : Runs a set number of transformation steps in a pre-defined order (e.g. deconjugation first, then Oxidation/reduction, etc.)
                        envimicro  : Environmental microbial
  
    -n|--nstep      The number of steps for the prediction by BioTransformer3 [default=1]
  
    -c|--cmode      CYP450 prediction Mode uses by BioTransformer3: 
                        1  = CypReact+BioTransformer rules
                        2  = CyProduct only
                       [3] = CypReact+BioTransformer rules+CyProducts
                
    -1|--phase1     Number of reaction cycles Phase 1 by SygMa [defaut=1]
    -2|--phase2     Number of reaction cycles Phase 2 by SygMa [defaut=1]
  
    -m|--metabo     Metabolism phase for GloryX : 
                      [phase_1_and_2]
                      phase_1
                      phase_2
  

Documentation

BioTransformer3 : https://bitbucket.org/wishartlab/biotransformer3.0jar/src/master/

SyGMa : https://github.com/3D-e-Chem/sygma

GLORYx : https://nerdd.univie.ac.at/gloryx/

MetaTrans : https://github.com/KavrakiLab/MetaTrans

Meta-Predictor : https://github.com/zhukeyun/Meta-Predictor/tree/main

Citation

BioTransformer : Djoumbou-Feunang, Y. et al. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform 11, 2 (2019)

SyGMa : Ridder, L. & Wagener, M. SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of Metabolites. ChemMedChem 3, 821–832 (2008).

GLORYx : Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics. (Chem. Res. Toxicol. 2020). Christina de Bruyn Kops, Martin Šícho, Angelica Mazzolari, Johannes Kirchmair

MetaTrans : Litsa, E. E., Das, P. & Kavraki, L. E. Prediction of drug metabolites using neural machine translation. Chem. Sci. 11, 12777–12788 (2020).

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering

Publication

Romain Pelletier; Dina Nahle; Mareme Sarr; Alexis Bourdais; Isabelle Morel; Brendan Le Daré; Thomas Gicquel. Identifying metabolites of new psychoactive substances using in silico prediction tools. Arch Toxicol (2025). https://doi.org/10.1007/s00204-025-04049-5

Pelletier R, Bourdais A, Fabresse N, Ferron PJ, Morel I, Gicquel T, Le Daré B. In silico and in vitro metabolism studies of the new synthetic opiate AP-237 (bucinnazine) using bioinformatics tools. Arch Toxicol. 2024 Jan;98(1):165-179. doi: 10.1007/s00204-023-03617-x. Epub 2023 Oct 15. PMID: 37839054.

Pelletier R, Le Daré B, Le Bouëdec D, Bourdais A, Ferron PJ, Morel I, Porée FH, Gicquel T. Identification, synthesis and quantification of eutylone consumption markers in a chemsex context. Arch Toxicol. 2024 Jan;98(1):151-158. doi: 10.1007/s00204-023-03615-z. Epub 2023 Oct 13. PMID: 37833490.