---
name: 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.
```python
# 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.
```python
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
```python
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
```python
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
```r
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
```python
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