Lantibiotic Structure Annotation

lantibiotic-structure-annotation

Summary

Annotate lantibiotic structures in RiPP biosynthetic gene clusters by matching tandem mass spectra against computationally constructed post-translationally modified peptide databases using MetaMiner. This skill integrates genomic (FASTA nucleotide sequences) and metabolomic (centroided MS/MS spectra in MGF or mzML/mzData format) data to identify lanthipeptides with characteristic dehydrobutyrine and dehydroalanine modifications.

When to use

Apply this skill when you have (1) genomic data from a Streptomyces or other RiPP-producing organism in raw FASTA format or annotated GenBank format, (2) high-resolution LC-MS/MS spectra in centroided MGF, mzML, mzXML, or mzData format, and (3) a known or predicted lantibiotic core peptide sequence you wish to detect and characterize. Use it to confirm the presence of lantibiotics by detecting their characteristic mass signatures (dehydration shifts of −18 Da on Ser/Thr residues) and verify that genome mining predictions have corresponding metabolite evidence.

When NOT to use

• Input genome is in antiSMASH .final.gbk or .gbk format—MetaMiner fails to detect lantibiotics from GenBank-formatted input; use raw contigs.fasta instead.
• Spectra are not centroided or are in non-standard formats without ProteoWizard-compatible conversion options.
• You are searching for non-RiPP natural products (e.g., polyketides, non-ribosomal peptides not post-translationally modified); use Dereplicator+ or database search pipelines instead.

Inputs

• nucleotide sequence (FASTA format, raw contigs.fasta, not antiSMASH .gbk output)
• centroided LC-MS/MS spectra (MGF, mzML, mzXML, or mzData format)
• lantibiotic core peptide sequence (expected/reference sequence, e.g. TGSQVSLLVCEYSSLSVVLCTP)

Outputs

• significant_matches.tsv (tab-separated file with FragmentSeq, ModifiedSeq, score, and spectral metadata)
• lantibiotic structure annotations with post-translational modifications (dehydrobutyrine/dehydroalanine positions and mass shifts)
• compound identifications linked to genomic loci

How to apply

Download and extract NPDtools 2.5.0 binaries for your platform (Linux or macOS), ensuring Python 2.6–3.3+ and joblib are available. Prepare input files: (1) obtain or assemble your organism's genomic sequence as a raw nucleotide FASTA file (avoid antiSMASH .gbk output, which has been shown to fail lantibiotic detection); (2) download or generate centroided MS/MS spectra in MGF format (use ProteoWizard's msconvert if spectra are in other formats). Run MetaMiner via command line with the lantibiotic RiPP class flag and default scoring. Parse the tab-separated significant_matches.tsv output file by searching the FragmentSeq column for your target lantibiotic core sequence (e.g., 'TGSQVSLLVCEYSSLSVVLCTP' for AmfS) and verifying that the ModifiedSeq column displays characteristic dehydration mass shifts (T−18 or S−18, indicating dehydrobutyrine or dehydroalanine residues). Filter results to include only hits with the expected modification pattern and spectral scoring above default thresholds.

Related tools

• MetaMiner (Core metabologenomic pipeline that integrates genome assembly and tandem MS data to identify and annotate RiPP structures with post-translational modifications) — https://github.com/mohimanilab/MetaMiner
• NPDtools 2.5.0 (Container toolkit providing MetaMiner executable, dependency management, and Dereplicator for RiPP structure database matching) — https://github.com/ablab/npdtools
• Dereplicator (Matches tandem mass spectra against post-translationally modified RiPP structure databases constructed by MetaMiner)
• ProteoWizard (Provides msconvert utility to convert non-native spectrum formats (mzML, mzData) to MGF for MetaMiner input)
• SPAdes (Assembles raw DNA short reads (.fastq) into longer contigs (FASTA) for input to MetaMiner when genome assembly is required)
• antiSMASH (Alternative genome mining tool whose output format (.gbk) is NOT recommended for lantibiotic detection; use raw FASTA instead)

Examples

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

Evaluation signals

• Verify that FragmentSeq column in significant_matches.tsv contains exact or near-exact match to your target lantibiotic core peptide (e.g., TGSQVSLLVCEYSSLSVVLCTP for AmfS).
• Confirm ModifiedSeq shows characteristic dehydration shifts: mass −18 assigned to Ser (S−18) or Thr (T−18) residues, indicating dehydrobutyrine/dehydroalanine post-translational modifications.
• Check that spectral match score meets or exceeds pipeline defaults and that the compound is ranked above noise/decoy hits in the significant_matches.tsv.
• Cross-reference genomic coordinates of the identified precursor peptide (PrecursorSeq and BGC location) to confirm it lies within a predicted biosynthetic gene cluster containing lantibiotic modification and transport genes.
• Validate that raw contigs.fasta input (not antiSMASH .gbk) was used and that input spectra were centroided (non-centroided spectra will yield low or missing hits).

Limitations

• MetaMiner successfully detects lantibiotics only from raw nucleotide sequence FASTA files; it fails when using antiSMASH-formatted GenBank output (.final.gbk or .gbk), restricting input flexibility.
• Requires centroided mass spectra; non-centroided spectra will not be matched correctly and must be pre-processed with external tools.
• Detection accuracy depends on the quality and completeness of the genome assembly and the presence of detectable post-translational modifications in the mass spectrum; heavily modified or truncated lantibiotics may be missed.
• MetaMiner requires Python 2.6–3.3+ and joblib for parallel processing; older or newer Python versions are not supported.
• No changelog is provided, limiting visibility into which specific modifications or RiPP classes are handled in version 2.5.0 relative to earlier releases.

Evidence

• [results] MetaMiner successfully detects AmfS using contigs.fasta genome files, though it fails when using antiSMASH results as input: "MetaMiner successfully detects AmfS using contigs.fasta genome files, though it fails when using antiSMASH results as input, indicating that raw nucleotide sequence format supports lantibiotic"
• [readme] MetaMiner is a metabologenomic pipeline which integrates metabolomic (tandem mass spectra) and genomic data to identify novel RiPPs: "MetaMiner is a metabologenomic pipeline which integrates metabolomic (tandem mass spectra) and genomic data to identify novel RiPPs"
• [readme] 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] parse the tab-separated significant_matches.tsv output file to extract compound identifications, verifying presence of AmfS by FragmentSeq column matching 'TGSQVSLLVCEYSSLSVVLCTP' and ModifiedSeq showing dehydrobutyrine/dehydroalanine modifications: "parse the tab-separated significant_matches.tsv output file to extract compound identifications, verifying presence of AmfS by FragmentSeq column matching 'TGSQVSLLVCEYSSLSVVLCTP' and ModifiedSeq"
• [readme] MetaMiner uses either raw nucleotide sequences or specific genome mining tools' output: raw nucleotide sequences .fasta format or antiSMASH's .final.gbk or .gbk file: "MetaMiner uses either raw nucleotide sequences or specific genome mining tools' output: raw nucleotide sequences .fasta format or antiSMASH's .final.gbk or .gbk file"
• [readme] NPDtools natively supports MGF, mzXML, mzData and uses msconvert utility from the ProteoWizard package to convert spectra in other formats to MGF: "NPDtools natively supports MGF, mzXML, mzData and uses msconvert utility from the ProteoWizard package to convert spectra in other formats to MGF"
• [readme] NPDtools requires a 64-bit Linux system or macOS and Python (versions 2.6-2.7, 3.3 and higher are supported): "NPDtools requires a 64-bit Linux system or macOS and Python (versions 2.6-2.7, 3.3 and higher are supported)"
• [readme] For parallel processing of multiple spectra or/and sequence files, MetaMiner requires joblib Python library: "For parallel processing of multiple spectra or/and sequence files, MetaMiner requires joblib Python library"