bio-proteomics-protein-inference

name	bio-proteomics-protein-inference
description	Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
tool_type	mixed
primary_tool	pyOpenMS

Version Compatibility

Reference examples tested with: pyOpenMS 3.1+

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.

Protein Inference

"Resolve protein groups from my peptide identifications" → Group peptide-spectrum matches into protein groups, resolving shared-peptide ambiguity using parsimony or probabilistic methods, then apply protein-level FDR.

• Python: pyopenms.ProteinInference() for parsimony-based grouping
• R: Bioconductor protein inference workflows

The Protein Inference Problem

Peptides can map to multiple proteins (shared peptides), making protein identification ambiguous.

# Example: Peptide mapping
peptide_to_proteins = {
    'PEPTIDEK': ['P12345', 'P67890'],      # Shared between paralogs
    'UNIQUER': ['P12345'],                  # Unique to P12345
    'ANOTHERONE': ['P12345'],               # Unique to P12345
    'SHAREDK': ['P67890', 'P11111'],        # Shared
}

# P12345 has 2 unique peptides -> confident identification
# P67890 has 0 unique peptides -> subset, may be grouped with P12345

Parsimony Principle

Goal: Resolve protein identification ambiguity from shared peptides by finding the minimal protein set explaining all observed peptides.

Approach: Build a peptide-to-protein mapping, then greedily select proteins that cover the most unassigned peptides until all peptides are accounted for, producing a minimal explanatory protein list.

def apply_parsimony(peptide_protein_map):
    '''Find minimal set of proteins explaining all peptides'''
    proteins = set()
    for prots in peptide_protein_map.values():
        proteins.update(prots)

    protein_peptides = {p: set() for p in proteins}
    for pep, prots in peptide_protein_map.items():
        for p in prots:
            protein_peptides[p].add(pep)

    covered_peptides = set()
    selected_proteins = []

    # Greedy: select protein covering most uncovered peptides
    while covered_peptides != set(peptide_protein_map.keys()):
        best_protein = max(protein_peptides.keys(),
                          key=lambda p: len(protein_peptides[p] - covered_peptides))
        new_coverage = protein_peptides[best_protein] - covered_peptides
        if not new_coverage:
            break
        selected_proteins.append(best_protein)
        covered_peptides.update(new_coverage)

    return selected_proteins

Protein Groups

def create_protein_groups(peptide_protein_map):
    '''Group proteins with identical peptide evidence'''
    protein_peptides = {}
    for pep, prots in peptide_protein_map.items():
        for p in prots:
            protein_peptides.setdefault(p, set()).add(pep)

    # Group by peptide set
    peptide_set_to_proteins = {}
    for protein, peptides in protein_peptides.items():
        key = frozenset(peptides)
        peptide_set_to_proteins.setdefault(key, []).append(protein)

    groups = []
    for peptides, proteins in peptide_set_to_proteins.items():
        groups.append({
            'proteins': proteins,
            'peptides': list(peptides),
            'n_peptides': len(peptides),
            'is_group': len(proteins) > 1
        })

    return groups

pyOpenMS Protein Inference

from pyopenms import ProteinIdentification, PeptideIdentification
from pyopenms import BasicProteinInferenceAlgorithm

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

# Run inference
inference = BasicProteinInferenceAlgorithm()
inference.run(peptide_ids, protein_ids)

# Results include protein groups and scores
for protein_id in protein_ids:
    for hit in protein_id.getHits():
        accession = hit.getAccession()
        score = hit.getScore()

R: Protein Inference with ProteinInference

library(ProteinInference)

# From peptide-protein mapping
protein_groups <- infer_proteins(
    peptides = psm_data$peptide,
    proteins = psm_data$protein,
    method = 'parsimony'
)

# Count unique peptides per group
protein_groups$n_unique <- sapply(protein_groups$peptides, function(p) {
    sum(sapply(p, function(pep) length(peptide_to_protein[[pep]]) == 1))
})

Protein-Level FDR

def protein_fdr(protein_groups, target_fdr=0.01):
    '''Calculate protein-level FDR from group scores'''
    sorted_groups = sorted(protein_groups, key=lambda x: x['score'], reverse=True)

    target_count = 0
    decoy_count = 0

    for group in sorted_groups:
        if group['is_decoy']:
            decoy_count += 1
        else:
            target_count += 1
        group['fdr'] = decoy_count / target_count if target_count > 0 else 1.0

    # Q-value
    min_fdr = 1.0
    for group in reversed(sorted_groups):
        min_fdr = min(min_fdr, group['fdr'])
        group['qvalue'] = min_fdr

    return [g for g in sorted_groups if g['qvalue'] <= target_fdr and not g['is_decoy']]

Related Skills

• peptide-identification - Input for protein inference
• quantification - Quantify inferred proteins
• database-access/uniprot-access - Protein annotations