In-Silico Biotransformation / Metabolite Prediction (parent structure -> matched biotransformation products)
Summary
Parent SMILES + mzML in, a ranked biotransformation-product annotation table out: rule-based metabolite prediction (BioTransformer / EnviPath / Pickaxe), mass-based candidate filtering, MS/MS-based candidate screening and ranking.
When to use
Use when you have a parent structure (drug, natural product, xenobiotic) and untargeted LC-MS/MS data and want to find its biotransformation products — predict plausible metabolites in-silico by rule-based expansion (BioTransformer mammalian/gut-microbial/ environmental rules, EnviPath microbial degradation, or MINE/Pickaxe combinatorial reaction-network generation), filter candidates to those detectable in the experimental mass range, then screen and rank them against the observed LC-MS/MS features to annotate which predicted biotransformation products were actually seen.
When NOT to use
- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — preprocess
Goal: raw mzML -> feature table + MS2 export
EDAM operation: operation_3215
Inputs: mzML · Outputs: feature-table, mgf/gnps-fbmn
Candidate leaf skills: peak-detection-and-mass-alignment (primary), mass-spectrometry-feature-table-construction, spectral-feature-table-generation, lcms-feature-detection-and-quantification, feature-alignment-metabolomics
Tools (primary): MZmine2, Optimus, OpenMS
Other candidate tools: Python, pyOpenMS, MSConvert, PFΔScreen, JPA, R, XCMS, MS-Convert, MetaboAnalystR, openNAU, MetaQC
Grounding: 5 KB(s); DOIs: 10.1007/s00216-023-05070-2, 10.1021/acs.jnatprod.7b00737, 10.1038/s41467-024-48009-6, 10.21147/j.issn.1000-9604.2023.05.11 …
Stage 2 — biotransformation_prediction
Goal: parent structure -> predicted metabolite/transformation-product structures (rule-based expansion)
EDAM operation: operation_3802
Inputs: smiles · Outputs: tsv
Candidate leaf skills: biotransformation-rule-application (primary), biotransformation-prediction-across-microbiota-contexts, microbial-biotransformation-prediction, small-molecule-structure-input-preparation, metabolite-structure-prediction
Tools (primary): BioTransformer, EAWAG Biodegradation and Biocatalysis Database, EnviPath, MINE-Database Pickaxe, RDKit
Grounding: 2 KB(s); DOIs: 10.1093/nar/gkac408, 10.1186/s13321-019-0375-2
Stage 3 — candidate_filtering
Goal: predicted candidates -> mass-plausible candidates (filter against the experimental peak list)
EDAM operation: operation_3801
Inputs: tsv, feature-table · Outputs: tsv
Candidate leaf skills: adduct-mass-adjustment-calculation (primary), exact-mass-database-matching, adduct-mass-shift-calculation
Tools (primary): RDKit, mordred, pytest, MINE-Database Filter base class
Other candidate tools: tidyverse, CluMSID, CluMSIDdata, grid, OrgMassSpecR, pheatmap, reshape2, MSMSsim, msentropy, readxl, MSDial, Biotransformer, geoRge, R, basepeak_finder, XCMS, MetaboShiny
Grounding: 4 KB(s); DOIs: 10.1007/s11306-020-01717-8, 10.1021/acs.analchem.5b03628, 10.1021/acs.est.5c08558, 10.1186/s12859-023-05149-8
Stage 4 — ms_matching
Goal: mass-plausible candidates -> MS/MS-matched and ranked biotransformation products
EDAM operation: operation_3802
Inputs: tsv, mgf/gnps-fbmn · Outputs: tsv
Candidate leaf skills: in-silico-fragmentation-prediction (primary), candidate-metabolite-ranking, transformation-product-prediction, fragment-ion-scoring-and-ranking, structural-similarity-scoring-metabolites
Tools (primary): MAGMa, PubChem, BioTransformer, MetFrag
Other candidate tools: patRoon, CTS, PubChemLite, MetaboAnnotatoR, R (version or higher), R, xcms, RamClustR, DeepMASS, Keras, RDKit, IsoSpecPy
Grounding: 5 KB(s); DOIs: 10.1021/acs.analchem.1c03032, 10.1021/acs.analchem.8b05405, 10.1186/s13321-019-0375-2, 10.1186/s13321-020-00477-w …
Stage 5 — report
Goal: consolidate parent structure, predicted candidates, and MS/MS-matched hits into a biotransformation-product annotation table
EDAM operation: operation_3434
Inputs: tsv, feature-table · Outputs: tsv
Candidate leaf skills: biotransformation-candidate-integration-with-networking (primary), mass-spectrometry-compound-annotation-database-generation, transformation-product-parent-linkage, metabolite-structure-annotation-integration, parent-product-relationship-tracking
Tools (primary): BAM, PROXIMAL2, GNN-SOM
Other candidate tools: patRoon, MetFrag, BioTransformer, CTS, screenSuspects, convertToSuspects, RDKit, KEGG or RetroRules
Grounding: 2 KB(s); DOIs: 10.1021/acs.analchem.4c01565, 10.1186/s13321-020-00477-w
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.