Rippp structure database matching

RiPP Structure Database Matching

Summary

Match tandem mass spectra against a constructed database of post-translationally modified RiPP structures to identify novel ribosomally synthesized and post-translationally modified peptides. This is a core step in the MetaMiner metabologenomic pipeline that bridges genomic predictions with mass spectrometry evidence.

When to use

Apply this skill when you have: (1) tandem MS/MS spectra in MGF, mzXML, mzML, or mzData format from LC-MS/MS analysis; (2) a set of predicted RiPP precursor peptides derived from genomic biosynthetic gene cluster mining (via antiSMASH, BOA, or raw FASTA); and (3) the goal is to identify which genomically predicted RiPPs are actually expressed and detectable in the sample.

When NOT to use

• Input spectra are not centroided (profile mode) — preprocessing to centroid is required before matching.
• RiPP precursor sequences are unavailable or not provided — matching requires known or predicted peptide sequences.
• The goal is to identify small-molecule metabolites or non-peptidic natural products — use Dereplicator+ or VarQuest instead for broader chemical structure databases.

Inputs

• Tandem MS/MS spectra (MGF, mzXML, mzML, or mzData format)
• RiPP precursor peptide sequences (FASTA file)
• Genomic context (antiSMASH .final.gbk/.gbk files or raw contigs.fasta, optional but recommended)

Outputs

• Dereplicator match results with PSM scores and p-values
• List of identified RiPPs with matched spectra and modification assignments
• Detailed match report showing theoretical vs. observed fragmentation patterns

How to apply

The matching process uses Dereplicator, which scores PSMs (peptide-spectrum matches) between experimental MS/MS spectra and in silico fragmentation patterns of post-translationally modified RiPP structures constructed from genomic precursor sequences. Run MetaMiner with the --blind flag for open modification search if testing novel RiPPs, or without it for modification-restricted search. The workflow reads the RiPP FASTA file (or antiSMASH .final.gbk / .gbk output), generates candidate PTM-modified structures, and scores matches against the input spectra. Evaluate matches using the scoring metrics reported in the output; higher PSM scores and lower p-values indicate stronger matches. The detailed output includes matched spectral peaks, theoretical fragmentation patterns, and per-spectrum match confidence.

Related tools

• Dereplicator (Core scoring and matching engine that performs database search of MS/MS spectra against RiPP structure database) — https://github.com/ablab/npdtools
• MetaMiner (Wrapper pipeline that orchestrates RiPP structure database construction from genomic sequences and invokes Dereplicator for matching) — https://github.com/mohimanilab/MetaMiner
• antiSMASH (Genome mining tool to identify biosynthetic gene clusters and predict RiPP precursor peptides from genomic DNA)
• BOA (Alternative genome mining tool for BGC identification and precursor peptide prediction) — https://github.com/idoerg/BOA
• ProteoWizard (Spectrum format conversion utility (msconvert) to convert non-native MS formats to MGF before matching)

Examples

python metaminer.py test_data/metaminer/msms/ -s test_data/metaminer/fasta/ -o metaminer_outdir

Evaluation signals

• PSM p-value < 0.05 (or equivalent significance threshold reported in results) indicates a confident match.
• Matched spectra show substantial overlap between observed and theoretical fragment peaks, with peak intensity ranking preserved.
• Multiple spectra from the same RiPP or related cluster match, increasing confidence in the identification.
• Identified RiPPs correspond to BGCs detected in genomic data, confirming biosynthetic plausibility.
• Output files (match scores, matched fragments, modification assignments) are non-empty and properly formatted.

Limitations

• Database-dependent: matching relies on accurate RiPP precursor sequence prediction; errors in genomic mining (e.g., antiSMASH missing AmfS) will reduce sensitivity.
• PTM diversity: the tool can only identify RiPPs with PTMs in its modification grammar; novel or unexpected modifications may be missed.
• Spectral quality: low-resolution or noisy spectra reduce match confidence; centroided data is required.
• No changelog available for version 2.5.0, limiting clarity on algorithm changes or bug fixes from prior releases.

Evidence

• [methods] matches tandem mass spectra against the constructed post-translationally modified RiPPs structure database using Dereplicator: "matches tandem mass spectra against the constructed post-translationally modified RiPPs structure database using Dereplicator"
• [methods] MetaMiner successfully detect AmfS using the contigs.fasta file, it fails with antiSMASH result as input: "While MetaMiner successfully detect AmfS using the contigs.fasta file, it fails with antiSMASH result as input"
• [methods] MetaMiner natively supports MGF, mzXML, mzData and uses msconvert utility from the ProteoWizard package to convert spectra in other formats to MGF: "MetaMiner natively supports MGF, mzXML, mzData and uses msconvert utility from the ProteoWizard package to convert spectra in other formats to MGF"
• [other] python metaminer.py <spectra_files> -s test_data/metaminer/molnet/example_RiPP.fasta --blind --spec-network test_data/metaminer/molnet/ProteoSAFe-METABOLOMICS-SNETS-V2-unpacked -o metaminer_outdir: "python metaminer.py <spectra_files> -s test_data/metaminer/molnet/example_RiPP.fasta --blind --spec-network test_data/metaminer/molnet/ProteoSAFe-METABOLOMICS-SNETS-V2-unpacked -o metaminer_outdir"
• [readme] Spectra files must be centroided and be in an open spectrum format (MGF, mzXML, mzML or mzData): "Spectra files must be centroided and be in an open spectrum format (MGF, mzXML, mzML or mzData)."