bio-proteomics-peptide-identification

name	bio-proteomics-peptide-identification
description	Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.
tool_type	mixed
primary_tool	pyOpenMS

Version Compatibility

Reference examples tested with: MSnbase 2.28+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Peptide Identification

"Identify peptides from my MS/MS spectra" → Match tandem mass spectra against a protein database to identify peptide sequences, then control false discovery rate using target-decoy competition.

• Python: pyopenms for in-memory database search and PSM handling
• CLI: comet, MSFragger, X!Tandem for high-throughput database searching
• R: MSnbase::readMSData() for importing search results

Database Search with pyOpenMS

Goal: Identify peptide sequences from tandem mass spectra by matching against a protein database.

Approach: Load a FASTA database, perform in-silico tryptic digestion to generate theoretical peptides, then match experimental spectra against theoretical fragment ion patterns to identify peptide-spectrum matches (PSMs).

from pyopenms import MSExperiment, MzMLFile, FASTAFile, ProteaseDigestion
from pyopenms import ModificationsDB, AASequence

# Load FASTA database
fasta_entries = []
FASTAFile().load('uniprot_human.fasta', fasta_entries)

# In-silico digestion
digestion = ProteaseDigestion()
digestion.setEnzyme('Trypsin')
digestion.setMissedCleavages(2)

peptides = []
for entry in fasta_entries:
    seq = AASequence.fromString(entry.sequence)
    result = []
    digestion.digest(seq, result)
    peptides.extend([(entry.identifier, str(p)) for p in result])

Working with Search Results (idXML)

from pyopenms import IdXMLFile, ProteinIdentification, PeptideIdentification

protein_ids = []
peptide_ids = []
IdXMLFile().load('search_results.idXML', protein_ids, peptide_ids)

for pep_id in peptide_ids:
    rt = pep_id.getRT()
    mz = pep_id.getMZ()
    for hit in pep_id.getHits():
        sequence = hit.getSequence()
        score = hit.getScore()
        charge = hit.getCharge()

FDR Estimation (Target-Decoy)

def calculate_fdr(scores, is_decoy, score_threshold):
    above_threshold = scores >= score_threshold
    n_target = ((~is_decoy) & above_threshold).sum()
    n_decoy = (is_decoy & above_threshold).sum()
    fdr = n_decoy / n_target if n_target > 0 else 1.0
    return fdr

def find_score_at_fdr(scores, is_decoy, target_fdr=0.01):
    sorted_scores = np.sort(scores)[::-1]
    for threshold in sorted_scores:
        fdr = calculate_fdr(scores, is_decoy, threshold)
        if fdr <= target_fdr:
            return threshold
    return sorted_scores[-1]

R: Search Result Processing

library(MSnbase)

# Read mzIdentML results
psms <- readMzIdData('results.mzid')

# Filter to 1% FDR
psms_filtered <- psms[psms$qvalue <= 0.01, ]

# Unique peptides per protein
peptide_counts <- table(psms_filtered$accession)

Spectral Library Search

from pyopenms import SpectraSTSearchAlgorithm, MSExperiment

# Load spectral library
library = MSExperiment()
MzMLFile().load('spectral_library.mzML', library)

# Match query spectra against library
# Returns similarity scores and library matches

Related Skills

• data-import - Load raw MS data before identification
• protein-inference - Group peptides to proteins
• ptm-analysis - Identify modified peptides