Short, practical pipeline (what to do, in order)

1) Annotate the genome well (functional annotation)

Goal: convert raw gene calls into ECs / KO terms / protein families so you can search for candidates. How: run a prokaryotic annotation + orthology mapping (e.g., Prokka or similar) then run eggNOG-mapper or GhostKOALA to transfer KO/EC assignments and functional descriptions. This gives you KEGG, EC, COG/ortholog info to search for likely enzymes.

Practical:

Prokka to produce .faa (proteins).

eggNOG-mapper on the .faa to get KO/EC/domain assignments and GO terms.

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2) From chemistry → shortlist enzyme classes

Goal: translate the structural changes you observed into likely reaction types and therefore enzyme classes.

Common mappings:

Hydrolysis (cleavage of esters/amides) → hydrolases (esterases, amidases, lipases, carboxylesterases).

Oxidation / hydroxylation / dealkylation → monooxygenases (FAD-dependent monooxygenases, cytochrome P450s), peroxidases.

Aromatic ring mono-/dioxygenation / ring-cleavage → ring-hydroxylating dioxygenases, intradiol/extradiol dioxygenases.

Dehalogenation → dehalogenases (haloacid dehalogenase, reductive dehalogenases).

Reductions (nitro → amino) → nitroreductases / flavin reductases. Use these mappings to focus on families in the annotation results (EC numbers, KO ids, Pfam domains).

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3) Use pathway & reaction-prediction tools (match substrate → reactions)

Goal: predict which enzyme activities can produce your observed product(s) and get candidate reaction steps.

Recommended tools:

EAWAG-BBD / PPS (pathway prediction rules for biodegradation). Great for xenobiotic biodegradation rule-based predictions.

PathPred (KEGG) — predicts plausible enzyme-catalyzed reaction steps and suggests EC/KO matches. Useful to get candidate EC numbers to search against your genome.

BioTransformer (microbial/soil mode) — predicts microbial metabolites and can propose transformations. Useful when your product set is complex.

Workflow:

1. Submit your parent compound SMILES (or structure) to EAWAG-PPS / PathPred / BioTransformer.

2. Collect predicted intermediate(s) and the EC classes they imply.

3. Use the predicted ECs/KOs as search keys against your genome annotation.

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4) Genome-level searches for candidate enzymes

Goal: find gene(s) in your bacterium that match the enzyme classes predicted above.

Strategies & tools:

BLASTp / DIAMOND against curated enzyme sets (e.g., sequences from KEGG, UniProt with EC annotations).

HMMER searches against Pfam / TIGRFAM / custom HMMs for the enzyme families (e.g., Rieske dioxygenase alpha subunit Pfam, FAD-binding Pfam).

Use eggNOG/KEGG outputs to list genes already annotated with relevant ECs or KOs.

Search specific biodegradation databases (AromaDeg for aromatic-degrading enzymes if your compound is aromatic).

Tip: produce a short candidate table with gene id, annotation, EC/KO, Pfam domains, percent identity to characterized enzyme, predicted localization (signal peptide), and whether the gene is adjacent to transporter/regulatory genes (operon context).

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5) Prioritize candidates using additional evidence

Order genes by likelihood using:

Genomic context: catabolic operons often cluster genes (e.g., oxygenase + ferredoxin + dehydrogenase).

Sequence similarity to experimentally characterized enzymes (higher identity + conserved catalytic residues → higher priority).

Domain architecture (e.g., multi-domain monooxygenases vs small oxidoreductases).

Expression data: if possible, RNA-seq or RT-qPCR of cells grown with vs without the compound (see experimental section).

Proteomics: proteins enriched when cells grow on the compound strongly implicate enzymes.

For a computational boost, there are new ML tools that predict pollutant-degrading enzymes from sequence and substrate features (see XenoBug and recent literature). These can refine a ranked candidate list.

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6) In silico structural checks (optional but powerful)

Use AlphaFold (or ColabFold) to model top candidates, then dock the substrate with a docking tool to see if the active site geometry fits the predicted chemistry. Structural conservation of catalytic residues supports the assignment.

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7) Experimental validation (must-have)

Computational prediction is hypothesis generation — validate experimentally:

Quick validation experiments (in order of increasing effort):

1. Differential expression (RNA-seq or RT-qPCR): compare cells grown on the compound vs a control. Genes strongly upregulated by substrate are prime suspects.

2. Proteomics: detect enzymes induced during growth on the compound.

3. Gene knockout / CRISPRi or transposon mutant: loss of biodegradation upon gene disruption is strong evidence.

4. Heterologous expression + purified enzyme assays: clone candidate gene into E. coli, express and purify, run enzyme assays with substrate and monitor product formation (LC-MS, GC-MS).

5. Inhibitor profiling / cofactors: e.g., if you suspect a monooxygenase, test oxygenase inhibitors or measure dependence on FAD/NADH.

6. Isotope labeling: position-specific labeled substrate can show mechanistic details (where cleavage occurs).

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Example step-by-step commands / pipelines (conceptual)

Annotate proteins:

prokka --outdir prokka_out genome.fasta

Functional annotation:

emapper.py -i prokka_out/*.faa --output eggnog --cpu 8 (eggNOG-mapper)

Search for dioxygenases (example):

hmmsearch --tblout hits.txt Pfam_Rieske.hmm prokka_out/*.faa

BLAST top hits against KEGG/UniProt to find characterized homologs.

(Adjust commands to your local installations or use online services when possible.)

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Useful resources / databases & why they matter

EAWAG-BBD / Pathway Prediction System — rule-based biodegradation pathway predictions. Great first pass to map substrate→reaction types.

PathPred (KEGG GenomeNet) — suggests enzyme-catalyzed pathways and EC/KOs using KEGG reaction data. Useful to get EC or KO IDs to search your genome.

BioTransformer — predicts microbial/soil biotransformations and metabolites. Good when products are many/complex.

eggNOG-mapper / KEGG / UniProt — for fast genome-wide functional annotation and EC/KO mapping.

AromaDeg / HADEG / specialized curated enzyme DBs — for aromatic degradation and other niche enzyme families; very useful when working with aromatics.

New ML tools (e.g., XenoBug / MicrobeRX and recent literature) — can predict enzyme classes and give ranked candidates using sequence + substrate features. Use as complement to homology methods.

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Practical prioritization checklist (short)

1. Identify reaction type(s) from product structures.

2. Run pathway prediction tools to get candidate EC/KOs.

3. Annotate genome with eggNOG/KEGG to find matching genes.

4. HMMER / BLAST against curated enzyme families (AromaDeg, etc.).

5. Use expression/proteomics data or ML tools to rank candidates.

6. Validate experimentally (knockouts / heterologous expression + assays).

Start by annotating the genome fast—upload FASTA to Prokka (free, quick for bacteria) or RAST server to get EC numbers and metabolic hits. That'll flag potential enzymes.

Convert your compounds to SMILES (use RDKit in Python or PubChem online)—super easy for reaction matching.

Then, hit PathPred server: Plug in SMILES for target/products, and it'll spit out likely ECs and pathways for microbial breakdown. Cross-check with EAWAG-BBD database for known degraders.

For genome-specific ties, build a metabolic model with CarveMe, then run MicrobeRX to link reactions to your enzymes.

Validate top candidates: BLAST them against your proteome for strong hits (e-value

If you have the necessary equipment in your laboratory, proteomics analyses would be one possible approach. I have been working on the same task in recent years with regard to the breakdown of acesulfame. Article Protein fractionation and shotgun proteomics analysis of enr...